diff --git a/.github/workflows/ci-on-pullreq.yaml b/.github/workflows/ci-on-pullreq.yaml
index 0c4b413..3f27d11 100644
--- a/.github/workflows/ci-on-pullreq.yaml
+++ b/.github/workflows/ci-on-pullreq.yaml
@@ -5,6 +5,8 @@ on: [pull_request]
 jobs:
   test:
     runs-on: ${{ matrix.os }}
+    env:
+      MPLBACKEND: Agg
     strategy:
       fail-fast: false
       matrix:
@@ -23,8 +25,9 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           architecture: x64
+      
 
-      - name: Install
+      - name: Install poetry
         run: |
           pip install poetry
           poetry install --with dev,test
diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py
index ade0632..54315ef 100644
--- a/benchmark/benchmark.py
+++ b/benchmark/benchmark.py
@@ -65,7 +65,7 @@ def sizeof(o):
 
 def benchmark_pfline(df):
     for le, ru in tqdm.tqdm(zip(LENGTHS, RUNS)):
-        i = pd.date_range("2020", periods=le, freq="15T")
+        i = pd.date_range("2020", periods=le, freq="15min")
         for cols in ["q", "p", "qr", "wp"]:
             print(f"{le=} {cols=}")
             data = pd.DataFrame({c: np.linspace(1, 1000, le) for c in cols}, i)
@@ -99,7 +99,7 @@ def benchmark_pfline(df):
 def benchmark_pfstate(df):
     for le, ru in tqdm.tqdm(zip(LENGTHS, RUNS)):
         print(f"{le=}")
-        i = pd.date_range("2020", periods=le, freq="15T")
+        i = pd.date_range("2020", periods=le, freq="15min")
         w_offtake = pf.dev.w_offtake(i)
         offtake = pf.PfLine(w_offtake)
         unsourced = pf.PfLine(pf.dev.p_marketprices(i))
diff --git a/docs/core/interoperability.rst b/docs/core/interoperability.rst
index dc9e9bd..dc1e47a 100644
--- a/docs/core/interoperability.rst
+++ b/docs/core/interoperability.rst
@@ -19,7 +19,7 @@ In the code examples below, the following imports are assumed and variables are
    
    import portfolyo as pf
    import pandas as pd
-   idx = pd.date_range("2023", freq="AS", periods=2)
+   idx = pd.date_range("2023", freq="YS", periods=2)
 
 ---------
 One value
@@ -82,7 +82,7 @@ For timeseries, ``pandas.Series`` are used. These can be "unit-agnostic" (i.e.,
    # --- hide: start ---
    import portfolyo as pf 
    import pandas as pd
-   idx = pd.date_range("2023", freq="AS", periods=2)
+   idx = pd.date_range("2023", freq="YS", periods=2)
    # --- hide: stop ---
    pd.Series([50, 56.0], idx, dtype="pint[Eur/MWh]")  # unit-aware
    # --- hide: start ---
@@ -103,7 +103,7 @@ To pass several timeseries, we can use:
      # --- hide: start ---
      import portfolyo as pf 
      import pandas as pd
-     idx = pd.date_range("2023", freq="AS", periods=2)
+     idx = pd.date_range("2023", freq="YS", periods=2)
      # --- hide: stop ---
      {"p": pd.Series([50, 56], idx), "w": pd.Series([120, 125], idx, dtype="pint[MW]")}
      # --- hide: start ---
@@ -118,7 +118,7 @@ To pass several timeseries, we can use:
      # --- hide: start ---
      import portfolyo as pf 
      import pandas as pd
-     idx = pd.date_range("2023", freq="AS", periods=2)
+     idx = pd.date_range("2023", freq="YS", periods=2)
      # --- hide: stop ---
      pd.DataFrame({"p": [50, 56], "w": [120, 125]}, idx)
      # --- hide: start ---
@@ -139,7 +139,7 @@ Dictionaries are the most versatily of these objects. They can be used to pass a
    # --- hide: start ---
    import portfolyo as pf 
    import pandas as pd
-   idx = pd.date_range("2023", freq="AS", periods=2)
+   idx = pd.date_range("2023", freq="YS", periods=2)
    # --- hide: stop ---
    d1 = {"p": 50}
    d2 = {"p": 50, "w": 120}
@@ -194,7 +194,7 @@ Footnotes
    .. code-block:: python 
        :emphasize-lines: 3,4
 
-       >>> idx = pandas.date_range("2023", freq="AS", periods=2)
+       >>> idx = pandas.date_range("2023", freq="YS", periods=2)
        >>> s_agn = pandas.Series([50, 56], idx)  # unit-agnostic
        >>> s1 = s_agn.astype("pint[Eur/MWh]")  # unit-aware
 
@@ -212,7 +212,7 @@ Footnotes
    .. code-block:: python
       :emphasize-lines: 4, 7
 
-      >>> idx = pandas.date_range("2023", freq="AS", periods=2)
+      >>> idx = pandas.date_range("2023", freq="YS", periods=2)
       >>> s_price = pandas.Series([50, 56], idx, dtype="pint[Eur/MWh]")
       >>> s_volume = pandas.Series([120, 125], idx, dtype="pint[MW]")
       >>> df1 = pandas.DataFrame({"p": s_price, "w": s_volume})
diff --git a/docs/core/pfline.rst b/docs/core/pfline.rst
index 3cb34a9..fdfbff0 100644
--- a/docs/core/pfline.rst
+++ b/docs/core/pfline.rst
@@ -80,7 +80,7 @@ The keys (or dataframe column names) must each be one of the following: ``w`` (p
    
    import portfolyo as pf 
    import pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    input_dict = {'w': pd.Series([200, 220, 300.0], index)}
    pf.PfLine(input_dict)
    # --- hide: start ---
@@ -95,7 +95,7 @@ Under the condition that a valid ``pint`` unit is present, we may also provide a
 
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    # --- hide: stop ---
    # using the imports and index from the previous example
    input_series = pd.Series([10, 11.5, 10.8], index, dtype='pint[ctEur/kWh]')
@@ -115,7 +115,7 @@ The keys are used as the children names:
 
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    # --- hide: stop ---
    pfl1 = pf.PfLine(pd.Series([0.2, 0.22, 0.3], index, dtype='pint[GW]')) 
    pfl2 = pf.PfLine(pd.Series([100, 150, 200.0], index, dtype='pint[MW]')) 
@@ -177,7 +177,7 @@ The properties ``PfLine.w``, ``.q``, ``.p`` and ``.r`` always return the informa
    # --- hide: start --- 
    # --- hide: stop ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    input_df = pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index)
    pfl = pf.PfLine(input_df)
    
@@ -198,7 +198,7 @@ If we want to extract more than one timeseries, we can use the ``.df`` attribute
 
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    input_df = pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index)
    pfl = pf.PfLine(input_df)
    # --- hide: stop ---
@@ -221,7 +221,7 @@ The ``PfLine.index`` property returns the ``pandas.DatetimeIndex`` that applies
 
    # --- hide: start --- 
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    input_df = pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index)
    pfl = pf.PfLine(input_df)
    # --- hide: stop ---
@@ -236,7 +236,7 @@ For convenience, ``portfolyo`` adds a ``.duration`` and a ``right`` property to
 
    # --- hide: start --- 
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    input_df = pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index)
    pfl = pf.PfLine(input_df)
    # --- hide: stop ---
@@ -258,7 +258,7 @@ Another slicing method is implemented with the ``.slice[]`` property. The improv
 
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    input_df = pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index)
    pfl = pf.PfLine(input_df)
    # --- hide: stop ---
@@ -280,12 +280,12 @@ Portfolio lines can be concatenated with the ``portfolio.concat()`` function. Th
 
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    input_df = pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index)
    pfl = pf.PfLine(input_df)
    # --- hide: stop ---
    # continuation of previous code example
-   index2 = pd.date_range('2025', freq='AS', periods=3)  # 2 years' overlap with pfl
+   index2 = pd.date_range('2025', freq='YS', periods=3)  # 2 years' overlap with pfl
    pfl2 = pf.PfLine(pd.DataFrame({'w':[22, 30, 40], 'p': [15, 20, 21]}, index))
    # first two datapoints (until/excl 2026) from pfl, last two datapoints (from/incl 2026) from pfl2 
    pf.concat([pfl.slice[:'2026'], pfl2.slice['2026':]]) 
@@ -316,7 +316,7 @@ The data can be shown graphically with the ``.plot()`` method:
 
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    pfl = pf.PfLine(pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index))
    # --- hide: stop ---
    # continuation of previous code example
@@ -352,7 +352,7 @@ Using the ``.asfreq()`` method, we can quickly and correctly downsample our data
 
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024', freq='AS', periods=3)
+   index = pd.date_range('2024', freq='YS', periods=3)
    pfl = pf.PfLine(pd.DataFrame({'w':[200, 220, 300], 'p': [100, 150, 200]}, index))
    # --- hide: stop ---
    # continuation of previous code example
@@ -380,7 +380,7 @@ General remarks:
   .. exec_code::
 
      import portfolyo as pf, pandas as pd
-     index = pd.date_range('2024', freq='AS', periods=3)
+     index = pd.date_range('2024', freq='YS', periods=3)
      pfl = pf.PfLine(pd.Series([2, 2.2, 3], index, dtype='pint[MW]')) 
      pfl_1 = pfl + {'q': 50.0}  # standard unit (here: MWh) is assumed
      pfl_2 = pfl + pf.Q_(50000.0, 'kWh')
@@ -435,7 +435,7 @@ Here are several examples.
   .. exec_code::
        
      import portfolyo as pf, pandas as pd
-     index = pd.date_range('2024', freq='AS', periods=3)
+     index = pd.date_range('2024', freq='YS', periods=3)
      vol = pf.PfLine(pd.Series([4, 4.6, 3], index, dtype='pint[MW]'))
      vol + pf.Q_(10.0, 'GWh')
      # --- hide: start ---
@@ -447,7 +447,7 @@ Here are several examples.
        
      # --- hide: start ---
      import portfolyo as pf, pandas as pd
-     index = pd.date_range('2024', freq='AS', periods=3)
+     index = pd.date_range('2024', freq='YS', periods=3)
      # --- hide: stop ---
      # continuation of previous code example
      vol_2 = pf.PfLine({
@@ -474,7 +474,7 @@ Here are several examples.
        
      # --- hide: start ---
      import portfolyo as pf, pandas as pd
-     index = pd.date_range('2024', freq='AS', periods=3)
+     index = pd.date_range('2024', freq='YS', periods=3)
      vol = pf.PfLine(pd.Series([4, 4.6, 3], index, dtype='pint[MW]'))
      vol_2 = pf.PfLine({
          'A': pd.Series([4, 4.6, 3], index, dtype='pint[MW]'), 
@@ -492,7 +492,7 @@ Here are several examples.
        
      # --- hide: start ---
      import portfolyo as pf, pandas as pd
-     index = pd.date_range('2024', freq='AS', periods=3)
+     index = pd.date_range('2024', freq='YS', periods=3)
      vol = pf.PfLine(pd.Series([4, 4.6, 3], index, dtype='pint[MW]'))
      vol_2 = pf.PfLine({
          'A': pd.Series([4, 4.6, 3], index, dtype='pint[MW]'), 
@@ -737,7 +737,7 @@ Using the ``.hedge_with()`` method, the volume timeseries in a portfolio line is
 .. exec_code:: 
 
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024-04-01', '2024-06-01', freq='H', inclusive='left')
+   index = pd.date_range('2024-04-01', '2024-06-01', freq='h', inclusive='left')
    offtake = pf.PfLine(pf.dev.w_offtake(index))  # mock offtake volumes
    prices = pf.PfLine(pf.dev.p_marketprices(index)) # mock market prices
    # Create hedge
@@ -764,7 +764,7 @@ For markets that have a concept of "peak" and "offpeak" periods, the ``.po()`` m
    
    # --- hide: start ---
    import portfolyo as pf, pandas as pd
-   index = pd.date_range('2024-04-01', '2024-06-01', freq='H', inclusive='left')
+   index = pd.date_range('2024-04-01', '2024-06-01', freq='h', inclusive='left')
    offtake = pf.PfLine(pf.dev.w_offtake(index))  # mock offtake volumes
    # --- hide: stop ---
    # continuation of previous code example
diff --git a/docs/savefig/fig_hedge.png b/docs/savefig/fig_hedge.png
index 8efdcad..6d19ad7 100644
Binary files a/docs/savefig/fig_hedge.png and b/docs/savefig/fig_hedge.png differ
diff --git a/docs/savefig/fig_offtake.png b/docs/savefig/fig_offtake.png
index 820ee35..6d8c20c 100644
Binary files a/docs/savefig/fig_offtake.png and b/docs/savefig/fig_offtake.png differ
diff --git a/docs/savefig/fig_plot_pfl.png b/docs/savefig/fig_plot_pfl.png
index c870ace..dfaa25f 100644
Binary files a/docs/savefig/fig_plot_pfl.png and b/docs/savefig/fig_plot_pfl.png differ
diff --git a/docs/savefig/fig_plot_pfs.png b/docs/savefig/fig_plot_pfs.png
index a3022e3..a5a191d 100644
Binary files a/docs/savefig/fig_plot_pfs.png and b/docs/savefig/fig_plot_pfs.png differ
diff --git a/docs/specialized_topics/dataprep.rst b/docs/specialized_topics/dataprep.rst
index 21912e3..908e597 100644
--- a/docs/specialized_topics/dataprep.rst
+++ b/docs/specialized_topics/dataprep.rst
@@ -71,12 +71,12 @@ Frequency
 
 The index must have a frequency (``fr.index.freq``) and it must be a valid frequency. To check validity of the frequency, one may use ``portfolyo.assert_freq_valid()``. The following abbreviations are used by ``pandas`` and throughout this package:
 
-* ``15T``: quarterhourly; 
-* ``H``: hourly;
+* ``15min``: quarterhourly; 
+* ``h``: hourly;
 * ``D``: daily;
 * ``MS``: monthly;
 * ``QS``: quarterly. Also allowed ``QS-FEB``, ``QS-MAR``, etc.;
-* ``AS``: yearly. Also allowed ``AS-FEB``, ``AS-MAR``, etc.
+* ``YS``: yearly. Also allowed ``YS-FEB``, ``YS-MAR``, etc.
 
 If the frequency is not set, we can try to make pandas infer it:
 
diff --git a/docs/specialized_topics/indices.rst b/docs/specialized_topics/indices.rst
index 78da2bd..6e863dc 100644
--- a/docs/specialized_topics/indices.rst
+++ b/docs/specialized_topics/indices.rst
@@ -62,7 +62,7 @@ However, when handling timeseries with shorter-than-daily indices, the offset ca
 
    # --- hide: start ---
    import pandas as pd
-   i = pd.date_range('2024-01-05 06:00', freq= 'H', periods = 40)
+   i = pd.date_range('2024-01-05 06:00', freq= 'h', periods = 40)
    print(repr(i))
 
 It contains 40 hourly values, i.e., it does not contain an integer number of days. We cannot infer, how a day is defined, and in these situations it is therefore assumed, that it is the offset of the first timestamp - '06:00' in this case.
diff --git a/docs/tutorial/part1.ipynb b/docs/tutorial/part1.ipynb
index 64c4dbb..c8bde76 100644
--- a/docs/tutorial/part1.ipynb
+++ b/docs/tutorial/part1.ipynb
@@ -240,7 +240,7 @@
    "source": [
     "(``portfolyo`` ensures that the values are aggregated correctly. In this case, the price (``p``) values are weighted-averaged (weighted with the duration of each datapoint - in this case a uniform 24h). See [Resampling](../specialized_topics/resampling.rst) for more information.)\n",
     "\n",
-    "The argument ``\"QS\"`` specifies that we want quarterly values starting from January (same as ``\"QS-JAN\"``). The allowed values, in increasing duration, are following: ``\"15T\"`` (=quarterhourly), ``\"H\"`` (=hourly), ``\"D\"`` (=daily), ``\"MS\"`` (=monthly), ``\"QS\"`` (=quarterly, or ``\"QS-FEB\"``, ``\"QS-MAR\"``, etc.), or ``\"AS\"`` (=yearly, or ``\"AS-FEB\"``, ``\"AS-MAR\"``, etc.).\n"
+    "The argument ``\"QS\"`` specifies that we want quarterly values starting from January (same as ``\"QS-JAN\"``). The allowed values, in increasing duration, are following: ``\"15min\"`` (=quarterhourly), ``\"h\"`` (=hourly), ``\"D\"`` (=daily), ``\"MS\"`` (=monthly), ``\"QS\"`` (=quarterly, or ``\"QS-FEB\"``, ``\"QS-MAR\"``, etc.), or ``\"YS\"`` (=yearly, or ``\"YS-FEB\"``, ``\"YS-MAR\"``, etc.).\n"
    ]
   },
   {
diff --git a/docs/tutorial/part3.ipynb b/docs/tutorial/part3.ipynb
index 1abbd36..2f139e5 100644
--- a/docs/tutorial/part3.ipynb
+++ b/docs/tutorial/part3.ipynb
@@ -1,968 +1,968 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial part 3\n",
-    "\n",
-    "In [part 1](part1.ipynb) and [part 2](part2.ipynb) we have learnt about portfolio lines. These are timeseries, or collections of timeseries, describing the volumes and/or prices during various delivery periods.\n",
-    "\n",
-    "In this part, we'll combine portfolio lines into a \"portfolio state\" (``PfState``) object. As we'll see, some of the methods and properties we know from the ``PfLine`` class also apply here.\n",
-    "\n",
-    "\n",
-    "## Example data\n",
-    "\n",
-    "Let's again use the mock functions to get some portfolio lines. (The parameter details here are not important, we just want some more-or-less realistic data). To change things up a bit from the previous tutorial parts, we'll look at about 80 days in the autumn of 2024, in quarterhourly (``\"15T\"``) resolution. And let's localize the data to a specific timezone:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import portfolyo as pf\n",
-    "import pandas as pd\n",
-    "\n",
-    "index = pd.date_range(\n",
-    "    \"2024-09-20\", \"2024-12-10\", freq=\"15T\", inclusive=\"left\", tz=\"Europe/Berlin\"\n",
-    ")\n",
-    "# Creating offtake portfolio line.\n",
-    "ts_offtake = -1 * pf.dev.w_offtake(index, avg=50)\n",
-    "offtake = pf.PfLine({\"w\": ts_offtake})\n",
-    "# Creating portfolio line with market prices (here: price-forward curve).\n",
-    "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
-    "prices = pf.PfLine({\"p\": ts_prices})\n",
-    "\n",
-    "# Creating portfolio line with sourced volume.\n",
-    "ts_sourced_power1, ts_sourced_price1 = pf.dev.wp_sourced(\n",
-    "    ts_offtake, \"QS\", 0.3, p_avg=120\n",
-    ")\n",
-    "sourced_quarters = pf.PfLine({\"w\": ts_sourced_power1, \"p\": ts_sourced_price1})\n",
-    "ts_sourced_power2, ts_sourced_price2 = pf.dev.wp_sourced(\n",
-    "    ts_offtake, \"MS\", 0.2, p_avg=150\n",
-    ")\n",
-    "sourced_months = pf.PfLine({\"w\": ts_sourced_power2, \"p\": ts_sourced_price2})\n",
-    "sourced = pf.PfLine(\n",
-    "    {\"quarter_products\": sourced_quarters, \"month_products\": sourced_months}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now use these portfolio lines to create a portfolio state.\n",
-    "\n",
-    "## Portfolio State\n",
-    "\n",
-    "The ``PfState`` class is used to hold information about offtake, market prices, and sourcing. Let's create one from the portfolio lines we just created:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PfState object.\n",
-       ". Start: 2024-09-20 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-       ". End  : 2024-12-10 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-       ". Freq : <15 * Minutes> (7780 datapoints)\n",
-       "                                                w           q           p             r\n",
-       "                                               MW         MWh     Eur/MWh           Eur\n",
-       "──────── offtake\n",
-       "           2024-09-20 00:00:00 +0200        -45.8         -11                          \n",
-       "           2024-09-20 00:15:00 +0200        -44.4         -11                          \n",
-       "           ..                                  ..          ..          ..            ..\n",
-       "           2024-12-09 23:30:00 +0100        -60.5         -15                          \n",
-       "           2024-12-09 23:45:00 +0100        -58.3         -15                          \n",
-       "─●────── pnl_cost\n",
-       " │         2024-09-20 00:00:00 +0200         45.8          11      136.54         1 564\n",
-       " │         2024-09-20 00:15:00 +0200         44.4          11      135.21         1 500\n",
-       " │         ..                                  ..          ..          ..            ..\n",
-       " │         2024-12-09 23:30:00 +0100         60.5          15      167.89         2 540\n",
-       " │         2024-12-09 23:45:00 +0100         58.3          15      167.21         2 436\n",
-       " ├●───── sourced\n",
-       " ││        2024-09-20 00:00:00 +0200         31.3           8      135.03         1 058\n",
-       " ││        2024-09-20 00:15:00 +0200         31.3           8      135.03         1 058\n",
-       " ││        ..                                  ..          ..          ..            ..\n",
-       " ││        2024-12-09 23:30:00 +0100         35.7           9      126.09         1 124\n",
-       " ││        2024-12-09 23:45:00 +0100         35.7           9      126.09         1 124\n",
-       " │├───── quarter_products\n",
-       " ││        2024-09-20 00:00:00 +0200         16.4           4      102.48           421\n",
-       " ││        2024-09-20 00:15:00 +0200         16.4           4      102.48           421\n",
-       " ││        ..                                  ..          ..          ..            ..\n",
-       " ││        2024-12-09 23:30:00 +0100         13.4           3      111.14           372\n",
-       " ││        2024-12-09 23:45:00 +0100         13.4           3      111.14           372\n",
-       " │└───── month_products\n",
-       " │         2024-09-20 00:00:00 +0200         14.9           4      170.85           637\n",
-       " │         2024-09-20 00:15:00 +0200         14.9           4      170.85           637\n",
-       " │         ..                                  ..          ..          ..            ..\n",
-       " │         2024-12-09 23:30:00 +0100         22.3           6      135.07           752\n",
-       " │         2024-12-09 23:45:00 +0100         22.3           6      135.07           752\n",
-       " └────── unsourced\n",
-       "           2024-09-20 00:00:00 +0200         14.5           4      139.81           506\n",
-       "           2024-09-20 00:15:00 +0200         13.0           3      135.64           442\n",
-       "           ..                                  ..          ..          ..            ..\n",
-       "           2024-12-09 23:30:00 +0100         24.8           6      227.88         1 416\n",
-       "           2024-12-09 23:45:00 +0100         22.6           6      232.05         1 312"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pfs = pf.PfState(offtake, prices, sourced)\n",
-    "\n",
-    "pfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note from how these portfolio lines were created, that ``offtake`` has negative values. The sign conventions are discussed [here](../core/pfstate.rst#sign-conventions).\n",
-    "\n",
-    "This portfolio state contains values for every quarterhour in the specified time period. Let's see what features this class has, starting with two methods we already met when discussing the ``PfLine`` class.\n",
-    "\n",
-    "### Plotting\n",
-    "\n",
-    "Just as when working with portfolio lines, we can get a quick overview of the portfolio state with the ``.plot()`` method..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 720x432 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pfs.plot();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "...and we can copy its data to the clipboard, or save it as an Excel workbook:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pfs.to_clipboard()\n",
-    "pfs.to_excel(\"portfolio_state.xlsx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looking at the offtake, we can see the daily and weekly cycles, as well as a slow increase over the entire time period.\n",
-    "\n",
-    "This graph is a bit too detailed for most purposes, so let's look at the second method we know from ``PfLine``: resampling.\n",
-    "\n",
-    "### Resampling\n",
-    "\n",
-    "We might prefer to see hourly, daily, or monthly values instead of the quarterhourly values that are in ``pfs``. For this, we can resample the object with the ``.asfreq()`` method. In this code example, we also use the ``.print()`` method, which adds some helpful coloring to the output:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PfState object.\n",
-      ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-      ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-      ". Freq : <MonthBegin> (2 datapoints)\n",
-      "                                                w           q           p             r\n",
-      "                                               MW         MWh     Eur/MWh           Eur\n",
-      "\u001b[1m\u001b[37m──────── offtake\n",
-      "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        -54.7     -40 744                          \n",
-      "  \u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100        -59.4     -42 732                          \n",
-      "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452\n",
-      " \u001b[1m\u001b[33m├\u001b[1m\u001b[36m●\u001b[1m\u001b[33m───── sourced\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         31.2      23 221      132.58     3 078 642\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         25.9      18 652      133.27     2 485 849\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m├───── quarter_products\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         13.8      10 256      106.54     1 092 646\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         13.7       9 897      106.78     1 056 810\n",
-      " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m└───── month_products\n",
-      " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         17.4      12 964      153.19     1 985 996\n",
-      " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         12.2       8 755      163.22     1 429 039\n",
-      " \u001b[1m\u001b[33m└────── unsourced\n",
-      "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         23.5      17 523      197.44     3 459 829\n",
-      "   \u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         33.4      24 080      221.41     5 331 603\n"
-     ]
-    }
-   ],
-   "source": [
-    "pfs_monthly = pfs.asfreq(\"MS\")\n",
-    "pfs_monthly.print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note how the resampled output only contains those months, that are *entirely* included in the original data. ``pfl`` has data in September and December, but as these are not present in their entirety, they are dropped from ``pfl_monthly``.\n",
-    "\n",
-    "### On frequencies and unsourced prices\n",
-    "\n",
-    "There is one other important consequence of resampling: the unsourced prices are now *specific for this portfolio*. We can demonstrate this by creating a second portfolio state, using the *same price-forward curve* (but different offtake and sourced volume):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pfs2 = pf.PfState(offtake * 1.5, prices, sourced * 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this shortest frequency, the unsourced prices are identical (namely, the price-forward curve). Let's create a dataframe with the prices to verify this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pfs</th>\n",
-       "      <th>pfs2</th>\n",
-       "      <th>hpfc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2024-09-20 00:00:00+02:00</th>\n",
-       "      <td>139.80568207211454</td>\n",
-       "      <td>139.80568207211454</td>\n",
-       "      <td>139.80568207211454</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-09-20 00:15:00+02:00</th>\n",
-       "      <td>135.63901540544785</td>\n",
-       "      <td>135.63901540544785</td>\n",
-       "      <td>135.63901540544785</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-09-20 00:30:00+02:00</th>\n",
-       "      <td>131.47234873878116</td>\n",
-       "      <td>131.47234873878116</td>\n",
-       "      <td>131.47234873878116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-09-20 00:45:00+02:00</th>\n",
-       "      <td>128.13901540544785</td>\n",
-       "      <td>128.13901540544785</td>\n",
-       "      <td>128.13901540544785</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-09-20 01:00:00+02:00</th>\n",
-       "      <td>124.80568207211451</td>\n",
-       "      <td>124.80568207211451</td>\n",
-       "      <td>124.80568207211451</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-12-09 22:45:00+01:00</th>\n",
-       "      <td>217.88078474854294</td>\n",
-       "      <td>217.88078474854294</td>\n",
-       "      <td>217.88078474854294</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-12-09 23:00:00+01:00</th>\n",
-       "      <td>221.2141180818763</td>\n",
-       "      <td>221.2141180818763</td>\n",
-       "      <td>221.2141180818763</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-12-09 23:15:00+01:00</th>\n",
-       "      <td>224.54745141520962</td>\n",
-       "      <td>224.54745141520962</td>\n",
-       "      <td>224.54745141520962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-12-09 23:30:00+01:00</th>\n",
-       "      <td>227.88078474854294</td>\n",
-       "      <td>227.88078474854294</td>\n",
-       "      <td>227.88078474854294</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-12-09 23:45:00+01:00</th>\n",
-       "      <td>232.0474514152096</td>\n",
-       "      <td>232.0474514152096</td>\n",
-       "      <td>232.0474514152096</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>7780 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                          pfs                pfs2  \\\n",
-       "2024-09-20 00:00:00+02:00  139.80568207211454  139.80568207211454   \n",
-       "2024-09-20 00:15:00+02:00  135.63901540544785  135.63901540544785   \n",
-       "2024-09-20 00:30:00+02:00  131.47234873878116  131.47234873878116   \n",
-       "2024-09-20 00:45:00+02:00  128.13901540544785  128.13901540544785   \n",
-       "2024-09-20 01:00:00+02:00  124.80568207211451  124.80568207211451   \n",
-       "...                                       ...                 ...   \n",
-       "2024-12-09 22:45:00+01:00  217.88078474854294  217.88078474854294   \n",
-       "2024-12-09 23:00:00+01:00   221.2141180818763   221.2141180818763   \n",
-       "2024-12-09 23:15:00+01:00  224.54745141520962  224.54745141520962   \n",
-       "2024-12-09 23:30:00+01:00  227.88078474854294  227.88078474854294   \n",
-       "2024-12-09 23:45:00+01:00   232.0474514152096   232.0474514152096   \n",
-       "\n",
-       "                                         hpfc  \n",
-       "2024-09-20 00:00:00+02:00  139.80568207211454  \n",
-       "2024-09-20 00:15:00+02:00  135.63901540544785  \n",
-       "2024-09-20 00:30:00+02:00  131.47234873878116  \n",
-       "2024-09-20 00:45:00+02:00  128.13901540544785  \n",
-       "2024-09-20 01:00:00+02:00  124.80568207211451  \n",
-       "...                                       ...  \n",
-       "2024-12-09 22:45:00+01:00  217.88078474854294  \n",
-       "2024-12-09 23:00:00+01:00   221.2141180818763  \n",
-       "2024-12-09 23:15:00+01:00  224.54745141520962  \n",
-       "2024-12-09 23:30:00+01:00  227.88078474854294  \n",
-       "2024-12-09 23:45:00+01:00   232.0474514152096  \n",
-       "\n",
-       "[7780 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(\n",
-    "    {\"pfs\": pfs.unsourcedprice.p, \"pfs2\": pfs2.unsourcedprice.p, \"hpfc\": prices.p}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, at every other frequency, they are not equal. When changing the frequency, a *volume-weighted* average is calculated for the unsourced prices - just like with every other price-and-volume timeseries. This makes that the unsourced prices apply only to the unsourced volume profile *of that portfolio*:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pfs</th>\n",
-       "      <th>pfs2</th>\n",
-       "      <th>hpfc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2024-10-01 00:00:00+02:00</th>\n",
-       "      <td>197.4437803760265</td>\n",
-       "      <td>192.11463549822653</td>\n",
-       "      <td>194.78888729464586</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-11-01 00:00:00+01:00</th>\n",
-       "      <td>221.40989836363784</td>\n",
-       "      <td>217.79829542112196</td>\n",
-       "      <td>220.07479863376392</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                          pfs                pfs2  \\\n",
-       "2024-10-01 00:00:00+02:00   197.4437803760265  192.11463549822653   \n",
-       "2024-11-01 00:00:00+01:00  221.40989836363784  217.79829542112196   \n",
-       "\n",
-       "                                         hpfc  \n",
-       "2024-10-01 00:00:00+02:00  194.78888729464586  \n",
-       "2024-11-01 00:00:00+01:00  220.07479863376392  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(\n",
-    "    {\n",
-    "        \"pfs\": pfs.asfreq(\"MS\").unsourcedprice.p,\n",
-    "        \"pfs2\": pfs2.asfreq(\"MS\").unsourcedprice.p,\n",
-    "        \"hpfc\": prices.asfreq(\"MS\").p,\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This has important consequences. \n",
-    "\n",
-    "For example, when doing a scenario analysis in which the unsourced volume is changed (e.g. \"what happens if the offtake increases by 50%?\"), we cannot expect the results to be correct *unless we are working at the original frequency*. In situations where it is clear that this error looms, a ``UserWarning`` is shown to alert the user (e.g. in the examples further below). For more information on unsourced volume, [see this section](../core/pfstate.rst#Unsourced-price) in the documentation on the `PfState` class.\n",
-    "\n",
-    "For this reason, we commonly work with our porfolio states at the frequency of the price-forward curve. Downsampling is only done to see the aggregated values for verification or reporting."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Components\n",
-    "\n",
-    "Now, let's look at the portfolio state ``pfs_monthly`` in a bit more detail, to learn more about the ``PfState`` class.\n",
-    "\n",
-    "The portfolio state is presented to us as a tree structure, with several branches. Each branch is a portfolio line. E.g, ``offtake`` and ``sourced`` are the portfolio lines we specified when creating the object. Also, the branch ``pnl_cost`` is the sum of ``sourced`` and ``unsourced``, with ``sourced`` being the sum of ``quarter_products`` and ``month_products``. \n",
-    "\n",
-    "The unsourced volume is found by comparing the offtake to what is already sourced. This volume is valued at the market prices in the forward curve.\n",
-    "\n",
-    "These portfolio lines can be obtained from the portfolio state by accessing them as attributes. E.g. ``.offtakevolume``, ``.sourced``, ``.unsourced``, or ``.pnl_cost``. The latter is the best estimate for what it will cost to procure the offtake:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PfLine object with price and volume information.\n",
-       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-       ". Freq : <MonthBegin> (2 datapoints)\n",
-       ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
-       "                                     w           q           p             r\n",
-       "                                    MW         MWh     Eur/MWh           Eur\n",
-       "\n",
-       "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
-       "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pfs_monthly.pnl_cost"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that this portfolio line has children, and as a reminder, we can \"drill into\" the object to get these nested portfolio line, e.g. with ``pfl_monthly.pnl_cost[\"sourced\"]``.\n",
-    "\n",
-    "There are some other components that are not explicitly shown:\n",
-    "\n",
-    "* We may be interested in how much of the offtake has already been sourced or unsourced. These fractions are available at the ``.sourcedfraction`` and ``.unsourcedfraction`` properties.\n",
-    "\n",
-    "* You may have noticed that ``unsourced`` is the inverse from what traders would call the \"open positions\" or \"portfolio positions\": if our portfolio is short, the unsourced volume is positive. For those that prefer this other perspective, it is available at ``.netposition``.\n",
-    "\n",
-    "### Export\n",
-    "\n",
-    "Just as with portfolio lines, we can create an excel file that contains all the information in a portfolio state with its ``.to_excel()`` method, and we can copy it to the clipboard with the ``.to_clipboard()`` method.\n",
-    "\n",
-    "### MtM\n",
-    "\n",
-    "We can evaluate the value of our sourcing contracts against the current forward curve (\"mark-to-market\") with the ``.mtm_of_sourced()`` method."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Analyses with portfolio states\n",
-    "\n",
-    "We'll now look at how we can do \"what-if\" analyses with portfolio state. The original portfolio state we will consider as the reference and store it in an appropriately named variable:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ref = pfs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The monthly procurement prices of this portfolio are what interest us the most. As a reminder, we can find the procurement volumes and costs with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PfLine object with price and volume information.\n",
-       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-       ". Freq : <MonthBegin> (2 datapoints)\n",
-       ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
-       "                                     w           q           p             r\n",
-       "                                    MW         MWh     Eur/MWh           Eur\n",
-       "\n",
-       "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
-       "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cost_ref = ref.asfreq(\"MS\").pnl_cost\n",
-    "cost_ref"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(Or we could go one step further and focus on only the prices with ``ref.asfreq(\"MS\").pnl_cost.p``.)\n",
-    "\n",
-    "### Change in offtake\n",
-    "\n",
-    "Now, what would happen if the offtake were to increase by 25%? Qualitatively, this is not hard. An increase in the offtake increases the unsourced volume. And because the market prices are higher than what we pay for the sourced volume, this means that the procurement price will go up. \n",
-    "\n",
-    "How much? Let's see. First, we create a new portfolio state, from the reference, by setting the offtake to the new value. We can do this with the ``.set_offtake()`` method. After that, we can again see what the procurement volumes and costs are. (Note the ``UserWarning`` which was mentioned [above](#on-frequencies-and-unsourced-pricesE).)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
-      "  warnings.warn(\n"
-     ]
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Tutorial part 3\n",
+                "\n",
+                "In [part 1](part1.ipynb) and [part 2](part2.ipynb) we have learnt about portfolio lines. These are timeseries, or collections of timeseries, describing the volumes and/or prices during various delivery periods.\n",
+                "\n",
+                "In this part, we'll combine portfolio lines into a \"portfolio state\" (``PfState``) object. As we'll see, some of the methods and properties we know from the ``PfLine`` class also apply here.\n",
+                "\n",
+                "\n",
+                "## Example data\n",
+                "\n",
+                "Let's again use the mock functions to get some portfolio lines. (The parameter details here are not important, we just want some more-or-less realistic data). To change things up a bit from the previous tutorial parts, we'll look at about 80 days in the autumn of 2024, in quarterhourly (``\"15min\"``) resolution. And let's localize the data to a specific timezone:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import portfolyo as pf\n",
+                "import pandas as pd\n",
+                "\n",
+                "index = pd.date_range(\n",
+                "    \"2024-09-20\", \"2024-12-10\", freq=\"15min\", inclusive=\"left\", tz=\"Europe/Berlin\"\n",
+                ")\n",
+                "# Creating offtake portfolio line.\n",
+                "ts_offtake = -1 * pf.dev.w_offtake(index, avg=50)\n",
+                "offtake = pf.PfLine({\"w\": ts_offtake})\n",
+                "# Creating portfolio line with market prices (here: price-forward curve).\n",
+                "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
+                "prices = pf.PfLine({\"p\": ts_prices})\n",
+                "\n",
+                "# Creating portfolio line with sourced volume.\n",
+                "ts_sourced_power1, ts_sourced_price1 = pf.dev.wp_sourced(\n",
+                "    ts_offtake, \"QS\", 0.3, p_avg=120\n",
+                ")\n",
+                "sourced_quarters = pf.PfLine({\"w\": ts_sourced_power1, \"p\": ts_sourced_price1})\n",
+                "ts_sourced_power2, ts_sourced_price2 = pf.dev.wp_sourced(\n",
+                "    ts_offtake, \"MS\", 0.2, p_avg=150\n",
+                ")\n",
+                "sourced_months = pf.PfLine({\"w\": ts_sourced_power2, \"p\": ts_sourced_price2})\n",
+                "sourced = pf.PfLine(\n",
+                "    {\"quarter_products\": sourced_quarters, \"month_products\": sourced_months}\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We can now use these portfolio lines to create a portfolio state.\n",
+                "\n",
+                "## Portfolio State\n",
+                "\n",
+                "The ``PfState`` class is used to hold information about offtake, market prices, and sourcing. Let's create one from the portfolio lines we just created:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "PfState object.\n",
+                            ". Start: 2024-09-20 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+                            ". End  : 2024-12-10 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+                            ". Freq : <15 * Minutes> (7780 datapoints)\n",
+                            "                                                w           q           p             r\n",
+                            "                                               MW         MWh     Eur/MWh           Eur\n",
+                            "──────── offtake\n",
+                            "           2024-09-20 00:00:00 +0200        -45.8         -11                          \n",
+                            "           2024-09-20 00:15:00 +0200        -44.4         -11                          \n",
+                            "           ..                                  ..          ..          ..            ..\n",
+                            "           2024-12-09 23:30:00 +0100        -60.5         -15                          \n",
+                            "           2024-12-09 23:45:00 +0100        -58.3         -15                          \n",
+                            "─●────── pnl_cost\n",
+                            " │         2024-09-20 00:00:00 +0200         45.8          11      136.54         1 564\n",
+                            " │         2024-09-20 00:15:00 +0200         44.4          11      135.21         1 500\n",
+                            " │         ..                                  ..          ..          ..            ..\n",
+                            " │         2024-12-09 23:30:00 +0100         60.5          15      167.89         2 540\n",
+                            " │         2024-12-09 23:45:00 +0100         58.3          15      167.21         2 436\n",
+                            " ├●───── sourced\n",
+                            " ││        2024-09-20 00:00:00 +0200         31.3           8      135.03         1 058\n",
+                            " ││        2024-09-20 00:15:00 +0200         31.3           8      135.03         1 058\n",
+                            " ││        ..                                  ..          ..          ..            ..\n",
+                            " ││        2024-12-09 23:30:00 +0100         35.7           9      126.09         1 124\n",
+                            " ││        2024-12-09 23:45:00 +0100         35.7           9      126.09         1 124\n",
+                            " │├───── quarter_products\n",
+                            " ││        2024-09-20 00:00:00 +0200         16.4           4      102.48           421\n",
+                            " ││        2024-09-20 00:15:00 +0200         16.4           4      102.48           421\n",
+                            " ││        ..                                  ..          ..          ..            ..\n",
+                            " ││        2024-12-09 23:30:00 +0100         13.4           3      111.14           372\n",
+                            " ││        2024-12-09 23:45:00 +0100         13.4           3      111.14           372\n",
+                            " │└───── month_products\n",
+                            " │         2024-09-20 00:00:00 +0200         14.9           4      170.85           637\n",
+                            " │         2024-09-20 00:15:00 +0200         14.9           4      170.85           637\n",
+                            " │         ..                                  ..          ..          ..            ..\n",
+                            " │         2024-12-09 23:30:00 +0100         22.3           6      135.07           752\n",
+                            " │         2024-12-09 23:45:00 +0100         22.3           6      135.07           752\n",
+                            " └────── unsourced\n",
+                            "           2024-09-20 00:00:00 +0200         14.5           4      139.81           506\n",
+                            "           2024-09-20 00:15:00 +0200         13.0           3      135.64           442\n",
+                            "           ..                                  ..          ..          ..            ..\n",
+                            "           2024-12-09 23:30:00 +0100         24.8           6      227.88         1 416\n",
+                            "           2024-12-09 23:45:00 +0100         22.6           6      232.05         1 312"
+                        ]
+                    },
+                    "execution_count": 2,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "pfs = pf.PfState(offtake, prices, sourced)\n",
+                "\n",
+                "pfs"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Note from how these portfolio lines were created, that ``offtake`` has negative values. The sign conventions are discussed [here](../core/pfstate.rst#sign-conventions).\n",
+                "\n",
+                "This portfolio state contains values for every quarterhour in the specified time period. Let's see what features this class has, starting with two methods we already met when discussing the ``PfLine`` class.\n",
+                "\n",
+                "### Plotting\n",
+                "\n",
+                "Just as when working with portfolio lines, we can get a quick overview of the portfolio state with the ``.plot()`` method..."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 720x432 with 4 Axes>"
+                        ]
+                    },
+                    "metadata": {
+                        "needs_background": "light"
+                    },
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "pfs.plot();"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "...and we can copy its data to the clipboard, or save it as an Excel workbook:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "pfs.to_clipboard()\n",
+                "pfs.to_excel(\"portfolio_state.xlsx\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Looking at the offtake, we can see the daily and weekly cycles, as well as a slow increase over the entire time period.\n",
+                "\n",
+                "This graph is a bit too detailed for most purposes, so let's look at the second method we know from ``PfLine``: resampling.\n",
+                "\n",
+                "### Resampling\n",
+                "\n",
+                "We might prefer to see hourly, daily, or monthly values instead of the quarterhourly values that are in ``pfs``. For this, we can resample the object with the ``.asfreq()`` method. In this code example, we also use the ``.print()`` method, which adds some helpful coloring to the output:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "PfState object.\n",
+                        ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+                        ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+                        ". Freq : <MonthBegin> (2 datapoints)\n",
+                        "                                                w           q           p             r\n",
+                        "                                               MW         MWh     Eur/MWh           Eur\n",
+                        "\u001b[1m\u001b[37m──────── offtake\n",
+                        "  \u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200        -54.7     -40 744                          \n",
+                        "  \u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100        -59.4     -42 732                          \n",
+                        "\u001b[1m\u001b[37m─\u001b[1m\u001b[33m●\u001b[1m\u001b[37m────── pnl_cost\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[37m         \u001b[0m2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452\n",
+                        " \u001b[1m\u001b[33m├\u001b[1m\u001b[36m●\u001b[1m\u001b[33m───── sourced\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         31.2      23 221      132.58     3 078 642\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│\u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         25.9      18 652      133.27     2 485 849\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m├───── quarter_products\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         13.8      10 256      106.54     1 092 646\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m│ \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         13.7       9 897      106.78     1 056 810\n",
+                        " \u001b[1m\u001b[33m│\u001b[1m\u001b[36m└───── month_products\n",
+                        " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-10-01 00:00:00 +0200         17.4      12 964      153.19     1 985 996\n",
+                        " \u001b[1m\u001b[33m│  \u001b[1m\u001b[36m       \u001b[0m2024-11-01 00:00:00 +0100         12.2       8 755      163.22     1 429 039\n",
+                        " \u001b[1m\u001b[33m└────── unsourced\n",
+                        "   \u001b[1m\u001b[33m        \u001b[0m2024-10-01 00:00:00 +0200         23.5      17 523      197.44     3 459 829\n",
+                        "   \u001b[1m\u001b[33m        \u001b[0m2024-11-01 00:00:00 +0100         33.4      24 080      221.41     5 331 603\n"
+                    ]
+                }
+            ],
+            "source": [
+                "pfs_monthly = pfs.asfreq(\"MS\")\n",
+                "pfs_monthly.print()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Note how the resampled output only contains those months, that are *entirely* included in the original data. ``pfl`` has data in September and December, but as these are not present in their entirety, they are dropped from ``pfl_monthly``.\n",
+                "\n",
+                "### On frequencies and unsourced prices\n",
+                "\n",
+                "There is one other important consequence of resampling: the unsourced prices are now *specific for this portfolio*. We can demonstrate this by creating a second portfolio state, using the *same price-forward curve* (but different offtake and sourced volume):"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "pfs2 = pf.PfState(offtake * 1.5, prices, sourced * 2)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "At this shortest frequency, the unsourced prices are identical (namely, the price-forward curve). Let's create a dataframe with the prices to verify this:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>pfs</th>\n",
+                            "      <th>pfs2</th>\n",
+                            "      <th>hpfc</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>2024-09-20 00:00:00+02:00</th>\n",
+                            "      <td>139.80568207211454</td>\n",
+                            "      <td>139.80568207211454</td>\n",
+                            "      <td>139.80568207211454</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-09-20 00:15:00+02:00</th>\n",
+                            "      <td>135.63901540544785</td>\n",
+                            "      <td>135.63901540544785</td>\n",
+                            "      <td>135.63901540544785</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-09-20 00:30:00+02:00</th>\n",
+                            "      <td>131.47234873878116</td>\n",
+                            "      <td>131.47234873878116</td>\n",
+                            "      <td>131.47234873878116</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-09-20 00:45:00+02:00</th>\n",
+                            "      <td>128.13901540544785</td>\n",
+                            "      <td>128.13901540544785</td>\n",
+                            "      <td>128.13901540544785</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-09-20 01:00:00+02:00</th>\n",
+                            "      <td>124.80568207211451</td>\n",
+                            "      <td>124.80568207211451</td>\n",
+                            "      <td>124.80568207211451</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>...</th>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "      <td>...</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-12-09 22:45:00+01:00</th>\n",
+                            "      <td>217.88078474854294</td>\n",
+                            "      <td>217.88078474854294</td>\n",
+                            "      <td>217.88078474854294</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-12-09 23:00:00+01:00</th>\n",
+                            "      <td>221.2141180818763</td>\n",
+                            "      <td>221.2141180818763</td>\n",
+                            "      <td>221.2141180818763</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-12-09 23:15:00+01:00</th>\n",
+                            "      <td>224.54745141520962</td>\n",
+                            "      <td>224.54745141520962</td>\n",
+                            "      <td>224.54745141520962</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-12-09 23:30:00+01:00</th>\n",
+                            "      <td>227.88078474854294</td>\n",
+                            "      <td>227.88078474854294</td>\n",
+                            "      <td>227.88078474854294</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-12-09 23:45:00+01:00</th>\n",
+                            "      <td>232.0474514152096</td>\n",
+                            "      <td>232.0474514152096</td>\n",
+                            "      <td>232.0474514152096</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "<p>7780 rows × 3 columns</p>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                          pfs                pfs2  \\\n",
+                            "2024-09-20 00:00:00+02:00  139.80568207211454  139.80568207211454   \n",
+                            "2024-09-20 00:15:00+02:00  135.63901540544785  135.63901540544785   \n",
+                            "2024-09-20 00:30:00+02:00  131.47234873878116  131.47234873878116   \n",
+                            "2024-09-20 00:45:00+02:00  128.13901540544785  128.13901540544785   \n",
+                            "2024-09-20 01:00:00+02:00  124.80568207211451  124.80568207211451   \n",
+                            "...                                       ...                 ...   \n",
+                            "2024-12-09 22:45:00+01:00  217.88078474854294  217.88078474854294   \n",
+                            "2024-12-09 23:00:00+01:00   221.2141180818763   221.2141180818763   \n",
+                            "2024-12-09 23:15:00+01:00  224.54745141520962  224.54745141520962   \n",
+                            "2024-12-09 23:30:00+01:00  227.88078474854294  227.88078474854294   \n",
+                            "2024-12-09 23:45:00+01:00   232.0474514152096   232.0474514152096   \n",
+                            "\n",
+                            "                                         hpfc  \n",
+                            "2024-09-20 00:00:00+02:00  139.80568207211454  \n",
+                            "2024-09-20 00:15:00+02:00  135.63901540544785  \n",
+                            "2024-09-20 00:30:00+02:00  131.47234873878116  \n",
+                            "2024-09-20 00:45:00+02:00  128.13901540544785  \n",
+                            "2024-09-20 01:00:00+02:00  124.80568207211451  \n",
+                            "...                                       ...  \n",
+                            "2024-12-09 22:45:00+01:00  217.88078474854294  \n",
+                            "2024-12-09 23:00:00+01:00   221.2141180818763  \n",
+                            "2024-12-09 23:15:00+01:00  224.54745141520962  \n",
+                            "2024-12-09 23:30:00+01:00  227.88078474854294  \n",
+                            "2024-12-09 23:45:00+01:00   232.0474514152096  \n",
+                            "\n",
+                            "[7780 rows x 3 columns]"
+                        ]
+                    },
+                    "execution_count": 7,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "pd.DataFrame(\n",
+                "    {\"pfs\": pfs.unsourcedprice.p, \"pfs2\": pfs2.unsourcedprice.p, \"hpfc\": prices.p}\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "However, at every other frequency, they are not equal. When changing the frequency, a *volume-weighted* average is calculated for the unsourced prices - just like with every other price-and-volume timeseries. This makes that the unsourced prices apply only to the unsourced volume profile *of that portfolio*:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead th {\n",
+                            "        text-align: right;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr style=\"text-align: right;\">\n",
+                            "      <th></th>\n",
+                            "      <th>pfs</th>\n",
+                            "      <th>pfs2</th>\n",
+                            "      <th>hpfc</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>2024-10-01 00:00:00+02:00</th>\n",
+                            "      <td>197.4437803760265</td>\n",
+                            "      <td>192.11463549822653</td>\n",
+                            "      <td>194.78888729464586</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-11-01 00:00:00+01:00</th>\n",
+                            "      <td>221.40989836363784</td>\n",
+                            "      <td>217.79829542112196</td>\n",
+                            "      <td>220.07479863376392</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                          pfs                pfs2  \\\n",
+                            "2024-10-01 00:00:00+02:00   197.4437803760265  192.11463549822653   \n",
+                            "2024-11-01 00:00:00+01:00  221.40989836363784  217.79829542112196   \n",
+                            "\n",
+                            "                                         hpfc  \n",
+                            "2024-10-01 00:00:00+02:00  194.78888729464586  \n",
+                            "2024-11-01 00:00:00+01:00  220.07479863376392  "
+                        ]
+                    },
+                    "execution_count": 8,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "pd.DataFrame(\n",
+                "    {\n",
+                "        \"pfs\": pfs.asfreq(\"MS\").unsourcedprice.p,\n",
+                "        \"pfs2\": pfs2.asfreq(\"MS\").unsourcedprice.p,\n",
+                "        \"hpfc\": prices.asfreq(\"MS\").p,\n",
+                "    }\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "This has important consequences. \n",
+                "\n",
+                "For example, when doing a scenario analysis in which the unsourced volume is changed (e.g. \"what happens if the offtake increases by 50%?\"), we cannot expect the results to be correct *unless we are working at the original frequency*. In situations where it is clear that this error looms, a ``UserWarning`` is shown to alert the user (e.g. in the examples further below). For more information on unsourced volume, [see this section](../core/pfstate.rst#Unsourced-price) in the documentation on the `PfState` class.\n",
+                "\n",
+                "For this reason, we commonly work with our porfolio states at the frequency of the price-forward curve. Downsampling is only done to see the aggregated values for verification or reporting."
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Components\n",
+                "\n",
+                "Now, let's look at the portfolio state ``pfs_monthly`` in a bit more detail, to learn more about the ``PfState`` class.\n",
+                "\n",
+                "The portfolio state is presented to us as a tree structure, with several branches. Each branch is a portfolio line. E.g, ``offtake`` and ``sourced`` are the portfolio lines we specified when creating the object. Also, the branch ``pnl_cost`` is the sum of ``sourced`` and ``unsourced``, with ``sourced`` being the sum of ``quarter_products`` and ``month_products``. \n",
+                "\n",
+                "The unsourced volume is found by comparing the offtake to what is already sourced. This volume is valued at the market prices in the forward curve.\n",
+                "\n",
+                "These portfolio lines can be obtained from the portfolio state by accessing them as attributes. E.g. ``.offtakevolume``, ``.sourced``, ``.unsourced``, or ``.pnl_cost``. The latter is the best estimate for what it will cost to procure the offtake:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "PfLine object with price and volume information.\n",
+                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+                            ". Freq : <MonthBegin> (2 datapoints)\n",
+                            ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
+                            "                                     w           q           p             r\n",
+                            "                                    MW         MWh     Eur/MWh           Eur\n",
+                            "\n",
+                            "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
+                            "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
+                        ]
+                    },
+                    "execution_count": 9,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "pfs_monthly.pnl_cost"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Notice that this portfolio line has children, and as a reminder, we can \"drill into\" the object to get these nested portfolio line, e.g. with ``pfl_monthly.pnl_cost[\"sourced\"]``.\n",
+                "\n",
+                "There are some other components that are not explicitly shown:\n",
+                "\n",
+                "* We may be interested in how much of the offtake has already been sourced or unsourced. These fractions are available at the ``.sourcedfraction`` and ``.unsourcedfraction`` properties.\n",
+                "\n",
+                "* You may have noticed that ``unsourced`` is the inverse from what traders would call the \"open positions\" or \"portfolio positions\": if our portfolio is short, the unsourced volume is positive. For those that prefer this other perspective, it is available at ``.netposition``.\n",
+                "\n",
+                "### Export\n",
+                "\n",
+                "Just as with portfolio lines, we can create an excel file that contains all the information in a portfolio state with its ``.to_excel()`` method, and we can copy it to the clipboard with the ``.to_clipboard()`` method.\n",
+                "\n",
+                "### MtM\n",
+                "\n",
+                "We can evaluate the value of our sourcing contracts against the current forward curve (\"mark-to-market\") with the ``.mtm_of_sourced()`` method."
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Analyses with portfolio states\n",
+                "\n",
+                "We'll now look at how we can do \"what-if\" analyses with portfolio state. The original portfolio state we will consider as the reference and store it in an appropriately named variable:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "ref = pfs"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "The monthly procurement prices of this portfolio are what interest us the most. As a reminder, we can find the procurement volumes and costs with:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "PfLine object with price and volume information.\n",
+                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+                            ". Freq : <MonthBegin> (2 datapoints)\n",
+                            ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
+                            "                                     w           q           p             r\n",
+                            "                                    MW         MWh     Eur/MWh           Eur\n",
+                            "\n",
+                            "2024-10-01 00:00:00 +0200         54.7      40 744      160.48     6 538 471\n",
+                            "2024-11-01 00:00:00 +0100         59.4      42 732      182.94     7 817 452"
+                        ]
+                    },
+                    "execution_count": 11,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "cost_ref = ref.asfreq(\"MS\").pnl_cost\n",
+                "cost_ref"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "(Or we could go one step further and focus on only the prices with ``ref.asfreq(\"MS\").pnl_cost.p``.)\n",
+                "\n",
+                "### Change in offtake\n",
+                "\n",
+                "Now, what would happen if the offtake were to increase by 25%? Qualitatively, this is not hard. An increase in the offtake increases the unsourced volume. And because the market prices are higher than what we pay for the sourced volume, this means that the procurement price will go up. \n",
+                "\n",
+                "How much? Let's see. First, we create a new portfolio state, from the reference, by setting the offtake to the new value. We can do this with the ``.set_offtake()`` method. After that, we can again see what the procurement volumes and costs are. (Note the ``UserWarning`` which was mentioned [above](#on-frequencies-and-unsourced-pricesE).)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
+                        "  warnings.warn(\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "PfLine object with price and volume information.\n",
+                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+                            ". Freq : <MonthBegin> (2 datapoints)\n",
+                            ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
+                            "                                     w           q           p             r\n",
+                            "                                    MW         MWh     Eur/MWh           Eur\n",
+                            "\n",
+                            "2024-10-01 00:00:00 +0200         68.4      50 930      168.64     8 588 719\n",
+                            "2024-11-01 00:00:00 +0100         74.2      53 416      191.54    10 231 182"
+                        ]
+                    },
+                    "execution_count": 12,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "higherofftake = ref.offtakevolume * 1.25\n",
+                "pfs_higherofftake = ref.set_offtakevolume(higherofftake)\n",
+                "cost_higherofftake = pfs_higherofftake.asfreq(\"MS\").pnl_cost\n",
+                "cost_higherofftake"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Comparing these two ``cost`` portfolio lines, we see that indeed the values for ``w`` and ``q`` have increased to 125% of the original values. Also, the procurement prices have increased. We can quickly calculate by how much:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "2024-10-01 00:00:00+02:00    8.160879013401626\n",
+                            "2024-11-01 00:00:00+01:00    8.599884206454362\n",
+                            "Freq: MS, Name: p, dtype: pint[Eur/MWh]"
+                        ]
+                    },
+                    "execution_count": 13,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "cost_higherofftake.p - cost_ref.p"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We could similarly create a portfolio states for situations with a market price drop of 40%. Or one which combines both effects:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 14,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
+                        "  warnings.warn(\n"
+                    ]
+                }
+            ],
+            "source": [
+                "lowerprices = ref.unsourcedprice * 0.6\n",
+                "pfs_lowerprices = ref.set_unsourcedprice(lowerprices)\n",
+                "pfs_lowerprices_higherofftake = ref.set_offtakevolume(higherofftake).set_unsourcedprice(\n",
+                "    lowerprices\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Hedging\n",
+                "\n",
+                "Hedging can reduce the sensitivity of our portfolio to changes in the market price. Given the current market price curve, we can calculate how much we'd need to source to obtain a fully hedged portfolio:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 15,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "needed = ref.hedge_of_unsourced(\"val\", \"MS\")  # value hedge with month products"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Let's say we procure exactly that volume. We can add it to the sourced volume in our portfolio state:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 16,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:209: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
+                        "  warnings.warn(\n"
+                    ]
+                }
+            ],
+            "source": [
+                "hedged = ref.add_sourced(pf.PfLine({\"newvolume\": needed}))"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "(We could have obtained the same result with the ``ref.source_unsourced()`` method.)\n",
+                "\n",
+                "The portfolio is now hedged at the month level. We can verify this by looking at the unsourced volume. In case of a volume hedge, the unsourced volume (``q`` and ``w``) is 0, even if its monetary value (``r``) is not; in case of a value hedge, it is the reverse:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 17,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "PfLine object with price and volume information.\n",
+                            ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
+                            ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
+                            ". Freq : <MonthBegin> (2 datapoints)\n",
+                            "                                     w           q           p             r\n",
+                            "                                    MW         MWh     Eur/MWh           Eur\n",
+                            "\n",
+                            "2024-10-01 00:00:00 +0200         -0.2        -173        0.00            -0\n",
+                            "2024-11-01 00:00:00 +0100         -0.2        -134        0.00            -0"
+                        ]
+                    },
+                    "execution_count": 17,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "hedged.asfreq(\"MS\").unsourced"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Because the market prices have not changed, the best-estimate procurement prices (at month level and longer) are also unchanged from before. (This is verified in the \"before\" columns of the dataframe further below.)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Market price change\n",
+                "\n",
+                "A hedged profile is less impacted by market price changes. To see that this is indeed the case, let's look at a scenario with an increase in the forward price curve by 40 Eur/MWh, for both portfolio states:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 18,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "newprices = prices + pf.Q_(40.0, \"Eur/MWh\")\n",
+                "ref_higherprices = ref.set_unsourcedprice(newprices)\n",
+                "hedged_higherprices = hedged.set_unsourcedprice(newprices)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "The reference portfolio has gotten a lot more expensive, whereas the procurement price for the hedged portfolio has not moved significantly:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 19,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<div>\n",
+                            "<style scoped>\n",
+                            "    .dataframe tbody tr th:only-of-type {\n",
+                            "        vertical-align: middle;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe tbody tr th {\n",
+                            "        vertical-align: top;\n",
+                            "    }\n",
+                            "\n",
+                            "    .dataframe thead tr th {\n",
+                            "        text-align: left;\n",
+                            "    }\n",
+                            "</style>\n",
+                            "<table border=\"1\" class=\"dataframe\">\n",
+                            "  <thead>\n",
+                            "    <tr>\n",
+                            "      <th></th>\n",
+                            "      <th colspan=\"2\" halign=\"left\">ref</th>\n",
+                            "      <th colspan=\"2\" halign=\"left\">hedged</th>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th></th>\n",
+                            "      <th>before</th>\n",
+                            "      <th>after</th>\n",
+                            "      <th>before</th>\n",
+                            "      <th>after</th>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>unit</th>\n",
+                            "      <th>Eur/MWh</th>\n",
+                            "      <th>Eur/MWh</th>\n",
+                            "      <th>Eur/MWh</th>\n",
+                            "      <th>Eur/MWh</th>\n",
+                            "    </tr>\n",
+                            "  </thead>\n",
+                            "  <tbody>\n",
+                            "    <tr>\n",
+                            "      <th>2024-10-01 00:00:00+02:00</th>\n",
+                            "      <td>160.478106</td>\n",
+                            "      <td>177.681366</td>\n",
+                            "      <td>160.478106</td>\n",
+                            "      <td>160.307936</td>\n",
+                            "    </tr>\n",
+                            "    <tr>\n",
+                            "      <th>2024-11-01 00:00:00+01:00</th>\n",
+                            "      <td>182.939602</td>\n",
+                            "      <td>205.480086</td>\n",
+                            "      <td>182.939602</td>\n",
+                            "      <td>182.814260</td>\n",
+                            "    </tr>\n",
+                            "  </tbody>\n",
+                            "</table>\n",
+                            "</div>"
+                        ],
+                        "text/plain": [
+                            "                                  ref                  hedged            \n",
+                            "                               before       after      before       after\n",
+                            "unit                          Eur/MWh     Eur/MWh     Eur/MWh     Eur/MWh\n",
+                            "2024-10-01 00:00:00+02:00  160.478106  177.681366  160.478106  160.307936\n",
+                            "2024-11-01 00:00:00+01:00  182.939602  205.480086  182.939602  182.814260"
+                        ]
+                    },
+                    "execution_count": 19,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "pd.DataFrame(\n",
+                "    {\n",
+                "        (\"ref\", \"before\"): ref.pnl_cost.asfreq(\"MS\").p,\n",
+                "        (\"ref\", \"after\"): ref_higherprices.pnl_cost.asfreq(\"MS\").p,\n",
+                "        (\"hedged\", \"before\"): hedged.pnl_cost.asfreq(\"MS\").p,\n",
+                "        (\"hedged\", \"after\"): hedged_higherprices.pnl_cost.asfreq(\"MS\").p,\n",
+                "    }\n",
+                ").pint.dequantify()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "For the observant reader: it may seem that the portfolio was not fully hedged after all, as a small change in the procurement price is seen. The reason is that each strategy (i.e., volume or value hedge) fully protects only against a specific price change (i.e., absolute or relative). A volume hedge does not *fully* hedge against an absolute price change such as the one we see here."
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "This tutorial is continued [in part 4](part4.ipynb)."
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "Python 3.8.13 ('pf38')",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        },
+        "orig_nbformat": 4,
+        "vscode": {
+            "interpreter": {
+                "hash": "642a4be8010ca5d45039b988c1d8379a91572488c4d23a0b88e966c6713c7e45"
+            }
+        }
     },
-    {
-     "data": {
-      "text/plain": [
-       "PfLine object with price and volume information.\n",
-       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-       ". Freq : <MonthBegin> (2 datapoints)\n",
-       ". Children: 'sourced' (price and volume), 'unsourced' (price and volume)\n",
-       "                                     w           q           p             r\n",
-       "                                    MW         MWh     Eur/MWh           Eur\n",
-       "\n",
-       "2024-10-01 00:00:00 +0200         68.4      50 930      168.64     8 588 719\n",
-       "2024-11-01 00:00:00 +0100         74.2      53 416      191.54    10 231 182"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "higherofftake = ref.offtakevolume * 1.25\n",
-    "pfs_higherofftake = ref.set_offtakevolume(higherofftake)\n",
-    "cost_higherofftake = pfs_higherofftake.asfreq(\"MS\").pnl_cost\n",
-    "cost_higherofftake"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Comparing these two ``cost`` portfolio lines, we see that indeed the values for ``w`` and ``q`` have increased to 125% of the original values. Also, the procurement prices have increased. We can quickly calculate by how much:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2024-10-01 00:00:00+02:00    8.160879013401626\n",
-       "2024-11-01 00:00:00+01:00    8.599884206454362\n",
-       "Freq: MS, Name: p, dtype: pint[Eur/MWh]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cost_higherofftake.p - cost_ref.p"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We could similarly create a portfolio states for situations with a market price drop of 40%. Or one which combines both effects:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:199: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "lowerprices = ref.unsourcedprice * 0.6\n",
-    "pfs_lowerprices = ref.set_unsourcedprice(lowerprices)\n",
-    "pfs_lowerprices_higherofftake = ref.set_offtakevolume(higherofftake).set_unsourcedprice(\n",
-    "    lowerprices\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hedging\n",
-    "\n",
-    "Hedging can reduce the sensitivity of our portfolio to changes in the market price. Given the current market price curve, we can calculate how much we'd need to source to obtain a fully hedged portfolio:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "needed = ref.hedge_of_unsourced(\"val\", \"MS\")  # value hedge with month products"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's say we procure exactly that volume. We can add it to the sourced volume in our portfolio state:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\ruud.wijtvliet\\ruud\\python\\dev\\portfolyo\\portfolyo\\core\\pfstate\\pfstate.py:209: UserWarning: This operation changes the unsourced volume. This causes inaccuracies in its price if the portfolio state has a frequency that is longer than the spot market.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "hedged = ref.add_sourced(pf.PfLine({\"newvolume\": needed}))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(We could have obtained the same result with the ``ref.source_unsourced()`` method.)\n",
-    "\n",
-    "The portfolio is now hedged at the month level. We can verify this by looking at the unsourced volume. In case of a volume hedge, the unsourced volume (``q`` and ``w``) is 0, even if its monetary value (``r``) is not; in case of a value hedge, it is the reverse:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PfLine object with price and volume information.\n",
-       ". Start: 2024-10-01 00:00:00+02:00 (incl)    . Timezone    : Europe/Berlin  \n",
-       ". End  : 2024-12-01 00:00:00+01:00 (excl)    . Start-of-day: 00:00:00  \n",
-       ". Freq : <MonthBegin> (2 datapoints)\n",
-       "                                     w           q           p             r\n",
-       "                                    MW         MWh     Eur/MWh           Eur\n",
-       "\n",
-       "2024-10-01 00:00:00 +0200         -0.2        -173        0.00            -0\n",
-       "2024-11-01 00:00:00 +0100         -0.2        -134        0.00            -0"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hedged.asfreq(\"MS\").unsourced"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because the market prices have not changed, the best-estimate procurement prices (at month level and longer) are also unchanged from before. (This is verified in the \"before\" columns of the dataframe further below.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Market price change\n",
-    "\n",
-    "A hedged profile is less impacted by market price changes. To see that this is indeed the case, let's look at a scenario with an increase in the forward price curve by 40 Eur/MWh, for both portfolio states:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "newprices = prices + pf.Q_(40.0, \"Eur/MWh\")\n",
-    "ref_higherprices = ref.set_unsourcedprice(newprices)\n",
-    "hedged_higherprices = hedged.set_unsourcedprice(newprices)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The reference portfolio has gotten a lot more expensive, whereas the procurement price for the hedged portfolio has not moved significantly:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead tr th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">ref</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">hedged</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>before</th>\n",
-       "      <th>after</th>\n",
-       "      <th>before</th>\n",
-       "      <th>after</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>unit</th>\n",
-       "      <th>Eur/MWh</th>\n",
-       "      <th>Eur/MWh</th>\n",
-       "      <th>Eur/MWh</th>\n",
-       "      <th>Eur/MWh</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2024-10-01 00:00:00+02:00</th>\n",
-       "      <td>160.478106</td>\n",
-       "      <td>177.681366</td>\n",
-       "      <td>160.478106</td>\n",
-       "      <td>160.307936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2024-11-01 00:00:00+01:00</th>\n",
-       "      <td>182.939602</td>\n",
-       "      <td>205.480086</td>\n",
-       "      <td>182.939602</td>\n",
-       "      <td>182.814260</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  ref                  hedged            \n",
-       "                               before       after      before       after\n",
-       "unit                          Eur/MWh     Eur/MWh     Eur/MWh     Eur/MWh\n",
-       "2024-10-01 00:00:00+02:00  160.478106  177.681366  160.478106  160.307936\n",
-       "2024-11-01 00:00:00+01:00  182.939602  205.480086  182.939602  182.814260"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame(\n",
-    "    {\n",
-    "        (\"ref\", \"before\"): ref.pnl_cost.asfreq(\"MS\").p,\n",
-    "        (\"ref\", \"after\"): ref_higherprices.pnl_cost.asfreq(\"MS\").p,\n",
-    "        (\"hedged\", \"before\"): hedged.pnl_cost.asfreq(\"MS\").p,\n",
-    "        (\"hedged\", \"after\"): hedged_higherprices.pnl_cost.asfreq(\"MS\").p,\n",
-    "    }\n",
-    ").pint.dequantify()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the observant reader: it may seem that the portfolio was not fully hedged after all, as a small change in the procurement price is seen. The reason is that each strategy (i.e., volume or value hedge) fully protects only against a specific price change (i.e., absolute or relative). A volume hedge does not *fully* hedge against an absolute price change such as the one we see here."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This tutorial is continued [in part 4](part4.ipynb)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.13 ('pf38')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "642a4be8010ca5d45039b988c1d8379a91572488c4d23a0b88e966c6713c7e45"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+    "nbformat": 4,
+    "nbformat_minor": 2
 }
diff --git a/docs/tutorial/part4.ipynb b/docs/tutorial/part4.ipynb
index 0a323da..5fb4570 100644
--- a/docs/tutorial/part4.ipynb
+++ b/docs/tutorial/part4.ipynb
@@ -54,7 +54,7 @@
     "import pandas as pd\n",
     "\n",
     "index = pd.date_range(\n",
-    "    \"2024-09-01\", \"2024-11-01\", freq=\"15T\", inclusive=\"left\", tz=\"Europe/Berlin\"\n",
+    "    \"2024-09-01\", \"2024-11-01\", freq=\"15min\", inclusive=\"left\", tz=\"Europe/Berlin\"\n",
     ")\n",
     "# Creating portfolio line with market prices (here: price-forward curve).\n",
     "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
@@ -279,4 +279,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/docs/tutorial/quickstart.ipynb b/docs/tutorial/quickstart.ipynb
index bf0bda8..9061f14 100644
--- a/docs/tutorial/quickstart.ipynb
+++ b/docs/tutorial/quickstart.ipynb
@@ -22,7 +22,7 @@
     "import portfolyo as pf\n",
     "import pandas as pd\n",
     "\n",
-    "index = pd.date_range(\"2024\", freq=\"H\", periods=8784, tz=\"Europe/Berlin\")\n",
+    "index = pd.date_range(\"2024\", freq=\"h\", periods=8784, tz=\"Europe/Berlin\")\n",
     "\n",
     "# Creating market prices (here: forward price curve) timeseries.\n",
     "ts_prices = pf.dev.p_marketprices(index, avg=200)\n",
diff --git a/poetry.lock b/poetry.lock
index 1cfe5b4..bf057a3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -53,32 +53,32 @@ test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "24.2.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
+    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
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 [package.extras]
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-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
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+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
-version = "2.15.0"
+version = "2.16.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"},
+    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
+    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
 ]
 
 [package.extras]
@@ -207,63 +207,78 @@ files = [
 
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 [package.dependencies]
@@ -501,63 +516,83 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
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 [package.dependencies]
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 [package.extras]
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 [[package]]
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@@ -2454,126 +2524,131 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+    {file = "rpds_py-0.20.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:fdfc3a892927458d98f3d55428ae46b921d1f7543b89382fdb483f5640daaec8"},
+    {file = "rpds_py-0.20.0.tar.gz", hash = "sha256:d72a210824facfdaf8768cf2d7ca25a042c30320b3020de2fa04640920d4e121"},
 ]
 
 [[package]]
 name = "setuptools"
-version = "70.3.0"
+version = "72.1.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "setuptools-70.3.0-py3-none-any.whl", hash = "sha256:fe384da74336c398e0d956d1cae0669bc02eed936cdb1d49b57de1990dc11ffc"},
-    {file = "setuptools-70.3.0.tar.gz", hash = "sha256:f171bab1dfbc86b132997f26a119f6056a57950d058587841a0082e8830f9dc5"},
+    {file = "setuptools-72.1.0-py3-none-any.whl", hash = "sha256:5a03e1860cf56bb6ef48ce186b0e557fdba433237481a9a625176c2831be15d1"},
+    {file = "setuptools-72.1.0.tar.gz", hash = "sha256:8d243eff56d095e5817f796ede6ae32941278f542e0f941867cc05ae52b162ec"},
 ]
 
 [package.extras]
+core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "ordered-set (>=3.1.1)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.11.*)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
@@ -2712,49 +2787,49 @@ dev = ["bump2version", "sphinxcontrib-httpdomain", "transifex-client", "wheel"]
 
 [[package]]
 name = "sphinxcontrib-applehelp"
-version = "1.0.8"
+version = "2.0.0"
 description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple help books"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_applehelp-1.0.8-py3-none-any.whl", hash = "sha256:cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4"},
-    {file = "sphinxcontrib_applehelp-1.0.8.tar.gz", hash = "sha256:c40a4f96f3776c4393d933412053962fac2b84f4c99a7982ba42e09576a70619"},
+    {file = "sphinxcontrib_applehelp-2.0.0-py3-none-any.whl", hash = "sha256:4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5"},
+    {file = "sphinxcontrib_applehelp-2.0.0.tar.gz", hash = "sha256:2f29ef331735ce958efa4734873f084941970894c6090408b079c61b2e1c06d1"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-devhelp"
-version = "1.0.6"
+version = "2.0.0"
 description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp documents"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_devhelp-1.0.6-py3-none-any.whl", hash = "sha256:6485d09629944511c893fa11355bda18b742b83a2b181f9a009f7e500595c90f"},
-    {file = "sphinxcontrib_devhelp-1.0.6.tar.gz", hash = "sha256:9893fd3f90506bc4b97bdb977ceb8fbd823989f4316b28c3841ec128544372d3"},
+    {file = "sphinxcontrib_devhelp-2.0.0-py3-none-any.whl", hash = "sha256:aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2"},
+    {file = "sphinxcontrib_devhelp-2.0.0.tar.gz", hash = "sha256:411f5d96d445d1d73bb5d52133377b4248ec79db5c793ce7dbe59e074b4dd1ad"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
 [[package]]
 name = "sphinxcontrib-htmlhelp"
-version = "2.0.5"
+version = "2.1.0"
 description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML help files"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_htmlhelp-2.0.5-py3-none-any.whl", hash = "sha256:393f04f112b4d2f53d93448d4bce35842f62b307ccdc549ec1585e950bc35e04"},
-    {file = "sphinxcontrib_htmlhelp-2.0.5.tar.gz", hash = "sha256:0dc87637d5de53dd5eec3a6a01753b1ccf99494bd756aafecd74b4fa9e729015"},
+    {file = "sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl", hash = "sha256:166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8"},
+    {file = "sphinxcontrib_htmlhelp-2.1.0.tar.gz", hash = "sha256:c9e2916ace8aad64cc13a0d233ee22317f2b9025b9cf3295249fa985cc7082e9"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["html5lib", "pytest"]
 
@@ -2788,33 +2863,33 @@ test = ["flake8", "mypy", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-qthelp"
-version = "1.0.7"
+version = "2.0.0"
 description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp documents"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_qthelp-1.0.7-py3-none-any.whl", hash = "sha256:e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182"},
-    {file = "sphinxcontrib_qthelp-1.0.7.tar.gz", hash = "sha256:053dedc38823a80a7209a80860b16b722e9e0209e32fea98c90e4e6624588ed6"},
+    {file = "sphinxcontrib_qthelp-2.0.0-py3-none-any.whl", hash = "sha256:b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb"},
+    {file = "sphinxcontrib_qthelp-2.0.0.tar.gz", hash = "sha256:4fe7d0ac8fc171045be623aba3e2a8f613f8682731f9153bb2e40ece16b9bbab"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
-test = ["pytest"]
+test = ["defusedxml (>=0.7.1)", "pytest"]
 
 [[package]]
 name = "sphinxcontrib-serializinghtml"
-version = "1.1.10"
+version = "2.0.0"
 description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs \"serialized\" HTML files (json and pickle)"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl", hash = "sha256:326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7"},
-    {file = "sphinxcontrib_serializinghtml-1.1.10.tar.gz", hash = "sha256:93f3f5dc458b91b192fe10c397e324f262cf163d79f3282c158e8436a2c4511f"},
+    {file = "sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331"},
+    {file = "sphinxcontrib_serializinghtml-2.0.0.tar.gz", hash = "sha256:e9d912827f872c029017a53f0ef2180b327c3f7fd23c87229f7a8e8b70031d4d"},
 ]
 
 [package.extras]
-lint = ["docutils-stubs", "flake8", "mypy"]
+lint = ["mypy", "ruff (==0.5.5)", "types-docutils"]
 standalone = ["Sphinx (>=5)"]
 test = ["pytest"]
 
@@ -2998,13 +3073,13 @@ files = [
 
 [[package]]
 name = "wheel"
-version = "0.43.0"
+version = "0.44.0"
 description = "A built-package format for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "wheel-0.43.0-py3-none-any.whl", hash = "sha256:55c570405f142630c6b9f72fe09d9b67cf1477fcf543ae5b8dcb1f5b7377da81"},
-    {file = "wheel-0.43.0.tar.gz", hash = "sha256:465ef92c69fa5c5da2d1cf8ac40559a8c940886afcef87dcf14b9470862f1d85"},
+    {file = "wheel-0.44.0-py3-none-any.whl", hash = "sha256:2376a90c98cc337d18623527a97c31797bd02bad0033d41547043a1cbfbe448f"},
+    {file = "wheel-0.44.0.tar.gz", hash = "sha256:a29c3f2817e95ab89aa4660681ad547c0e9547f20e75b0562fe7723c9a2a9d49"},
 ]
 
 [package.extras]
@@ -3012,13 +3087,13 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 
 [[package]]
 name = "zipp"
-version = "3.19.2"
+version = "3.20.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
-    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+    {file = "zipp-3.20.0-py3-none-any.whl", hash = "sha256:58da6168be89f0be59beb194da1250516fdaa062ccebd30127ac65d30045e10d"},
+    {file = "zipp-3.20.0.tar.gz", hash = "sha256:0145e43d89664cfe1a2e533adc75adafed82fe2da404b4bbb6b026c0157bdb31"},
 ]
 
 [package.extras]
diff --git a/portfolyo/_version.py b/portfolyo/_version.py
index 597108b..97491ce 100644
--- a/portfolyo/_version.py
+++ b/portfolyo/_version.py
@@ -191,7 +191,7 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose):
         # it's been around since git-1.5.3, and it's too difficult to
         # discover which version we're using, or to work around using an
         # older one.
-        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+        date = date.strip().replace(" ", "min", 1).replace(" ", "", 1)
     refnames = keywords["refnames"].strip()
     if refnames.startswith("$Format"):
         if verbose:
@@ -375,7 +375,7 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
     # Use only the last line.  Previous lines may contain GPG signature
     # information.
     date = date.splitlines()[-1]
-    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    pieces["date"] = date.strip().replace(" ", "min", 1).replace(" ", "", 1)
 
     return pieces
 
diff --git a/portfolyo/core/commodity/commodity.py b/portfolyo/core/commodity/commodity.py
index 168f886..fafa035 100644
--- a/portfolyo/core/commodity/commodity.py
+++ b/portfolyo/core/commodity/commodity.py
@@ -21,7 +21,7 @@ def __post_init__(self):
 
 
 power = Commodity(
-    "15T",
+    "15min",
     tools.peakperiod.factory(dt.time(hour=8), dt.time(hour=20), [1, 2, 3, 4, 5]),
     0,
 )
diff --git a/portfolyo/core/pfline/classes.py b/portfolyo/core/pfline/classes.py
index 7e0b2bc..290e9e2 100644
--- a/portfolyo/core/pfline/classes.py
+++ b/portfolyo/core/pfline/classes.py
@@ -153,7 +153,7 @@ def po(
         ----------
         peak_fn : PeakFunction
             Function that returns boolean Series indicating if timestamps in index lie in peak period.
-        freq : {'MS' (months, default), 'QS' (quarters), 'AS' (years)}
+        freq : {'MS' (months, default), 'QS' (quarters), 'YS' (years)}
             Frequency of resulting dataframe.
 
         Returns
@@ -187,7 +187,7 @@ def hedge_with(
             To hedge with peak and offpeak products: function that returns boolean
             Series indicating if timestamps in index lie in peak period.
             If None, hedge with base products.
-        freq : {'D' (days), 'MS' (months, default), 'QS' (quarters), 'AS' (years)}
+        freq : {'D' (days), 'MS' (months, default), 'QS' (quarters), 'YS' (years)}
             Frequency of hedging products. E.g. 'QS' to hedge with quarter products.
 
         See also
diff --git a/portfolyo/core/pfline/flat_methods.py b/portfolyo/core/pfline/flat_methods.py
index ed8a117..ebceb0b 100644
--- a/portfolyo/core/pfline/flat_methods.py
+++ b/portfolyo/core/pfline/flat_methods.py
@@ -56,7 +56,7 @@ def hedge_with(
         raise ValueError(
             "Cannot hedge a PfLine that does not contain volume information."
         )
-    if self.index.freq not in ["15T", "H", "D"]:
+    if self.index.freq not in ["15min", "h", "D"]:
         raise ValueError(
             "Can only hedge a PfLine with daily or (quarter)hourly information."
         )
diff --git a/portfolyo/core/pfstate/pfstate.py b/portfolyo/core/pfstate/pfstate.py
index 0a0407a..4d0e578 100644
--- a/portfolyo/core/pfstate/pfstate.py
+++ b/portfolyo/core/pfstate/pfstate.py
@@ -209,8 +209,8 @@ def asfreq(self, freq: str = "MS") -> PfState:  # from ABC
         Parameters
         ----------
         freq : str, optional
-            The frequency at which to resample. 'AS' for year, 'QS' for quarter, 'MS'
-            (default) for month, 'D for day', 'H' for hour, '15T' for quarterhour.
+            The frequency at which to resample. 'YS' for year, 'QS' for quarter, 'MS'
+            (default) for month, 'D for day', 'h' for hour, '15min' for quarterhour.
 
         Returns
         -------
@@ -239,7 +239,7 @@ def hedge_of_unsourced(
             To hedge with peak and offpeak products: function that returns boolean
             Series indicating if timestamps in index lie in peak period.
             If None, hedge with base products.
-        freq : {'D' (days), 'MS' (months, default), 'QS' (quarters), 'AS' (years)}
+        freq : {'D' (days), 'MS' (months, default), 'QS' (quarters), 'YS' (years)}
             Frequency of hedging products. E.g. 'QS' to hedge with quarter products.
 
         See also
@@ -271,7 +271,7 @@ def source_unsourced(
             To hedge with peak and offpeak products: function that returns boolean
             Series indicating if timestamps in index lie in peak period.
             If None, hedge with base products.
-        freq : {'D' (days), 'MS' (months, default), 'QS' (quarters), 'AS' (years)}
+        freq : {'D' (days), 'MS' (months, default), 'QS' (quarters), 'YS' (years)}
             Frequency of hedging products. E.g. 'QS' to hedge with quarter products.
 
         See also
diff --git a/portfolyo/core/shared/ndframelike.py b/portfolyo/core/shared/ndframelike.py
index ad9e512..96de82b 100644
--- a/portfolyo/core/shared/ndframelike.py
+++ b/portfolyo/core/shared/ndframelike.py
@@ -27,8 +27,8 @@ def asfreq(self, freq: str = "MS") -> NDFrameLike:
         Parameters
         ----------
         freq : str, optional
-            The frequency at which to resample. 'AS' for year, 'QS' for quarter, 'MS'
-            (default) for month, 'D for day', 'H' for hour, '15T' for quarterhour.
+            The frequency at which to resample. 'YS' for year, 'QS' for quarter, 'MS'
+            (default) for month, 'D for day', 'h' for hour, '15min' for quarterhour.
 
         Returns
         -------
diff --git a/portfolyo/dev/develop.py b/portfolyo/dev/develop.py
index 9a2e6b0..efe7af0 100644
--- a/portfolyo/dev/develop.py
+++ b/portfolyo/dev/develop.py
@@ -15,7 +15,7 @@
 
 OK_COL_COMBOS = ["w", "q", "p", "pr", "qr", "qp", "wp", "wr"]
 
-INDEX_LEN = {"AS": 4, "QS": 5, "MS": 14, "D": 400, "H": 10_000, "15T": 50_000}
+INDEX_LEN = {"YS": 4, "QS": 5, "MS": 14, "D": 400, "h": 10_000, "15min": 50_000}
 
 
 def get_index(
@@ -48,7 +48,7 @@ def get_index(
     start = tools.stamp.create(startdate, tz, start_of_day)
     i = pd.date_range(start, periods=periods, freq=freq)  # tz included in start
     # Some checks.
-    if tools.freq.up_or_down(freq, "H") <= 0:
+    if tools.freq.up_or_down(freq, "h") <= 0:
         i = _shorten_index_if_necessary(i, start_of_day)
     return i
 
diff --git a/portfolyo/dev/mockup.py b/portfolyo/dev/mockup.py
index 210e69f..096d2bb 100644
--- a/portfolyo/dev/mockup.py
+++ b/portfolyo/dev/mockup.py
@@ -102,9 +102,9 @@ def p_marketprices(
     # week angle: Sun0:00..Sun0:00 -> 0..2pi. But: uniform within day.
     wa = i.map(lambda ts: ts.weekday() + 1) / 7 * np.pi * 2
     # peak fraction: -1 (middle of offpeak hours) .. 1 (middle of peak hours)
-    if i.freq in ["H", "15T"]:
+    if i.freq in ["h", "15min"]:
         b = np.array([0.5, 0.8, 1, 0.8, 0.5])
-        if i.freq == "15T":  # repeat every value 4 times
+        if i.freq == "15min":  # repeat every value 4 times
             b = np.array([[bb, bb, bb, bb] for bb in b]).flatten()
         b = b[: len(i)]  # slice in case i is very short
         pa = np.convolve(-1 + 2 * germanpower_peakfn(i), b / sum(b), mode="same")
@@ -167,7 +167,7 @@ def calc_wp(sub_s):
     def group_and_calc(s):
         return s.resample(freq, group_keys=False).apply(calc_wp)
 
-    if sin.index.freq in ["15T", "H"]:
+    if sin.index.freq in ["15min", "h"]:
         is_peak = germanpower_peakfn(sin.index)  # avoid running on each ts individually
         df = sin.groupby(is_peak, group_keys=False).apply(group_and_calc)
     else:
diff --git a/portfolyo/tools/ceil.py b/portfolyo/tools/ceil.py
index a1cb631..e0eaea4 100644
--- a/portfolyo/tools/ceil.py
+++ b/portfolyo/tools/ceil.py
@@ -40,11 +40,11 @@ def stamp(
 
     Examples
     --------
-    >>> ceil.stamp(pd.Timestamp('2020-04-21 15:42'), 'AS')
+    >>> ceil.stamp(pd.Timestamp('2020-04-21 15:42'), 'YS')
     Timestamp('2021-01-01 00:00:00')
     >>> ceil.stamp(pd.Timestamp('2020-04-21 15:42'), 'MS')
     Timestamp('2020-05-01 00:00:00')
-    >>> ceil.stamp(pd.Timestamp('2020-04-21 15:42'), '15T')
+    >>> ceil.stamp(pd.Timestamp('2020-04-21 15:42'), '15min')
     Timestamp('2020-04-21 15:45:00')
     >>> ceil.stamp(pd.Timestamp('2020-04-21 15:42', tz='Europe/Berlin'), 'MS')
     Timestamp('2020-05-01 00:00:00+0200', tz='Europe/Berlin')
diff --git a/portfolyo/tools/duration.py b/portfolyo/tools/duration.py
index 6d773c1..fd02fe6 100644
--- a/portfolyo/tools/duration.py
+++ b/portfolyo/tools/duration.py
@@ -30,8 +30,8 @@ def stamp(ts: pd.Timestamp, freq: str) -> tools_unit.Q_:
     >>> duration.stamp(pd.Timestamp('2020-03-29', tz='Europe/Berlin'), 'D')
     23.0 h
     """
-    if freq in ["15T", "H"]:
-        h = 1.0 if freq == "H" else 0.25
+    if freq in ["15min", "h"]:
+        h = 1.0 if freq == "h" else 0.25
     else:
         h = (tools_right.stamp(ts, freq) - ts).total_seconds() / 3600
     return tools_unit.Q_(h, "h")
@@ -50,9 +50,9 @@ def index(i: pd.DatetimeIndex) -> pd.Series:
     pint-Series
         With ``i`` as its index, and the corresponding duration as the values.
     """
-    if i.freq in ["15T", "H"]:
+    if i.freq in ["15min", "h"]:
         # Speed-up things for fixed-duration frequencies.
-        h = 1.0 if i.freq == "H" else 0.25
+        h = 1.0 if i.freq == "h" else 0.25
     else:
         # Individual calculations for non-fixed-duration frequencies.
         h = (tools_right.index(i) - i).map(lambda td: td.total_seconds() / 3600)
diff --git a/portfolyo/tools/floor.py b/portfolyo/tools/floor.py
index 837f64a..2ce3e26 100644
--- a/portfolyo/tools/floor.py
+++ b/portfolyo/tools/floor.py
@@ -40,11 +40,11 @@ def stamp(
 
     Examples
     --------
-    >>> floor.stamp(pd.Timestamp('2020-04-21 15:42'), 'AS')
+    >>> floor.stamp(pd.Timestamp('2020-04-21 15:42'), 'YS')
     Timestamp('2020-01-01 00:00:00')
     >>> floor.stamp(pd.Timestamp('2020-04-21 15:42'), 'MS')
     Timestamp('2020-04-01 00:00:00')
-    >>> floor.stamp(pd.Timestamp('2020-04-21 15:42'), '15T')
+    >>> floor.stamp(pd.Timestamp('2020-04-21 15:42'), '15min')
     Timestamp('2020-04-21 15:30:00')
     >>> floor.stamp(pd.Timestamp('2020-04-21 15:42', tz='Europe/Berlin'), 'MS')
     Timestamp('2020-04-01 00:00:00+0200', tz='Europe/Berlin')
diff --git a/portfolyo/tools/freq.py b/portfolyo/tools/freq.py
index 679cb2b..5dd1f66 100644
--- a/portfolyo/tools/freq.py
+++ b/portfolyo/tools/freq.py
@@ -9,7 +9,7 @@
 
 
 # Allowed frequencies.
-ALLOWED_FREQUENCIES_DOCS = "'15T' (=quarterhour), 'H', 'D', 'MS', 'QS' (or 'QS-FEB', 'QS-MAR', etc.), or 'AS' (or 'AS-FEB', 'AS-MAR', etc.)"
+ALLOWED_FREQUENCIES_DOCS = "'15min' (=quarterhour), 'h', 'D', 'MS', 'QS' (or 'QS-FEB', 'QS-MAR', etc.), or 'YS' (or 'YS-FEB', 'YS-MAR', etc.)"
 ALLOWED_CLASSES = [
     pd.tseries.offsets.YearBegin,
     pd.tseries.offsets.QuarterBegin,
@@ -20,7 +20,7 @@
 ]
 TO_OFFSET = pd.tseries.frequencies.to_offset
 SHORTEST_TO_LONGEST = [
-    type(TO_OFFSET(freq)) for freq in ["15T", "H", "D", "MS", "QS", "AS"]
+    type(TO_OFFSET(freq)) for freq in ["15min", "h", "D", "MS", "QS", "YS"]
 ]
 
 quarter_matrix = [
@@ -35,7 +35,7 @@ def assert_freq_valid(freq: str) -> None:
     Validate if the given frequency string is allowed based on pandas offset objects.
 
     Parameters:
-    freq (str): A string representing a frequency alias (e.g., "AS", "QS", "MS").
+    freq (str): A string representing a frequency alias (e.g., "YS", "QS", "MS").
 
     Raises:
     ValueError: If the frequency is not allowed.
@@ -231,8 +231,8 @@ def shortest(*freqs: str) -> str:
 
     Examples
     --------
-    >>> freq.shortest('MS', 'H', 'AS', 'D')
-    'H'
+    >>> freq.shortest('MS', 'h', 'YS', 'D')
+    'h'
     """
     return _longestshortest(True, *freqs)
 
@@ -251,8 +251,8 @@ def longest(*freqs: str) -> str:
 
     Examples
     --------
-    >>> freq.longest('MS', 'H', 'AS', 'D')
-    'AS'
+    >>> freq.longest('MS', 'h', 'YS', 'D')
+    'YS'
     """
     return _longestshortest(False, *freqs)
 
@@ -271,7 +271,7 @@ def to_offset(freq: str) -> pd.Timedelta | pd.DateOffset:
 
     Examples
     --------
-    >>> freq.to_offset("H")
+    >>> freq.to_offset("h")
     Timedelta('0 days 01:00:00')
     >>> freq.to_offset("MS")
     <DateOffset: months=1>
@@ -312,9 +312,9 @@ def from_tdelta(tdelta: pd.Timedelta) -> str:
         One of {ALLOWED_FREQUENCIES_DOCS}.
     """
     if tdelta == pd.Timedelta(minutes=15):
-        return "15T"
+        return "15min"
     elif tdelta == pd.Timedelta(hours=1):
-        return "H"
+        return "h"
     elif pd.Timedelta(hours=23) <= tdelta <= pd.Timedelta(hours=25):
         return "D"
     elif pd.Timedelta(days=27) <= tdelta <= pd.Timedelta(days=32):
@@ -322,7 +322,7 @@ def from_tdelta(tdelta: pd.Timedelta) -> str:
     elif pd.Timedelta(days=89) <= tdelta <= pd.Timedelta(days=93):
         return "QS"
     elif pd.Timedelta(days=364) <= tdelta <= pd.Timedelta(days=367):
-        return "AS"
+        return "YS"
     else:
         raise ValueError(
             f"The timedelta ({tdelta}) doesn't seem to be fit to any of the allowed "
diff --git a/portfolyo/tools/hedge.py b/portfolyo/tools/hedge.py
index b728bbc..c49dbf0 100644
--- a/portfolyo/tools/hedge.py
+++ b/portfolyo/tools/hedge.py
@@ -52,7 +52,7 @@ def hedge(
     peak_fn : PeakFunction, optional (default: None)
         Function that returns boolean Series indicating if timestamps in index lie in peak period.
         If None, hedge with base products.
-    freq : {'D' (days), 'MS' (months), 'QS' (quarters), 'AS' (years)}, optional (default: 'MS')
+    freq : {'D' (days), 'MS' (months), 'QS' (quarters), 'YS' (years)}, optional (default: 'MS')
         Frequency of hedging products. E.g. 'QS' to hedge with quarter products.
 
     Returns
@@ -68,13 +68,13 @@ def hedge(
         raise ValueError(
             f"Parameters ``w`` and ``p`` must have same frequency; got {w.index.freq} and {p.index.freq}."
         )
-    if w.index.freq not in ["15T", "H", "D"]:
+    if w.index.freq not in ["15min", "h", "D"]:
         raise ValueError("Can only hedge a timeseries with daily (or shorter) values.")
-    if freq not in ["D", "MS", "QS", "AS"]:
+    if freq not in ["D", "MS", "QS", "YS"]:
         raise ValueError(
-            f"Parameter ``freq`` must be one of 'D', 'MS', 'QS', 'AS'; got '{freq}'."
+            f"Parameter ``freq`` must be one of 'D', 'MS', 'QS', 'YS'; got '{freq}'."
         )
-    if peak_fn is not None and not (w.index.freq in ["15T", "H"] and freq != "D"):
+    if peak_fn is not None and not (w.index.freq in ["15min", "h"] and freq != "D"):
         raise ValueError(
             "Split into peak and offpeak only possible when (a) hedging with monthly (or "
             "longer) products, and (b) if timeseries have hourly (or shorter) values."
diff --git a/portfolyo/tools/isboundary.py b/portfolyo/tools/isboundary.py
index 1fe464a..a84525e 100644
--- a/portfolyo/tools/isboundary.py
+++ b/portfolyo/tools/isboundary.py
@@ -74,7 +74,7 @@ def is_X_start(i: pd.Timestamp | pd.DatetimeIndex, freq: str) -> bool | np.ndarr
         return is_month_start(i)
     elif freq == "QS":
         return is_quarter_start(i)
-    elif freq == "AS":
+    elif freq == "YS":
         return is_year_start(i)
     else:
         raise ValueError(f"Unexpected frequency ``freq``; got {freq}.")
@@ -99,9 +99,9 @@ def stamp(ts: pd.Timestamp, freq: str, start_of_day: dt.time = None) -> bool:
     bool
     """
     start_of_day = start_of_day or dt.time(0, 0)
-    if freq == "15T":
+    if freq == "15min":
         return ts.minute % 15 == 0
-    elif freq == "H":
+    elif freq == "h":
         return ts.minute == 0
     elif freq == "D":
         return ts.time() == start_of_day
@@ -109,7 +109,7 @@ def stamp(ts: pd.Timestamp, freq: str, start_of_day: dt.time = None) -> bool:
         return (ts.time() == start_of_day) & is_month_start(ts)
     elif freq == "QS":
         return (ts.time() == start_of_day) & is_quarter_start(ts)
-    elif freq == "AS":
+    elif freq == "YS":
         return (ts.time() == start_of_day) & is_year_start(ts)
     else:
         raise ValueError(f"Unexpected frequency ``freq``; got {freq}.")
@@ -148,9 +148,9 @@ def index(i: pd.DatetimeIndex, freq: str) -> pd.Series:
         values = is_X_start(i, freq)
 
     # Comparing shorter-than-daily index to other shorter-than-daily frequency X,
-    # (i.e., '15T' with 'H')
-    elif tools_freq.up_or_down(freq, "H") <= 0:
-        if i.freq == "15T" and freq == "H":
+    # (i.e., '15min' with 'h')
+    elif tools_freq.up_or_down(freq, "h") <= 0:
+        if i.freq == "15min" and freq == "h":
             values = i.minute == 0
         else:
             raise ValueError(
diff --git a/portfolyo/tools/leftandright.py b/portfolyo/tools/leftandright.py
index 2049cee..d89e3d3 100644
--- a/portfolyo/tools/leftandright.py
+++ b/portfolyo/tools/leftandright.py
@@ -53,17 +53,17 @@ def stamps(
 
     if right is pd.NaT:
         if left is pd.NaT:
-            left = tools_floor.stamp(pd.Timestamp.now(tz=tz), "AS", 1, start_of_day)
-        right = tools_floor.stamp(left, "AS", 1, left.time())
+            left = tools_floor.stamp(pd.Timestamp.now(tz=tz), "YS", 1, start_of_day)
+        right = tools_floor.stamp(left, "YS", 1, left.time())
 
     # if we land here, we at least know right.
     if left is pd.NaT:
         start_of_day = right.time()
-        if tools_isboundary.stamp(right, "AS", start_of_day):
+        if tools_isboundary.stamp(right, "YS", start_of_day):
             back = -1
         else:
             back = 0
-        left = tools_floor.stamp(right, "AS", back, start_of_day)
+        left = tools_floor.stamp(right, "YS", back, start_of_day)
 
     # if we land here, we know left and right.
     zones = [None if ts.tz is None else ts.tz.zone for ts in [left, right]]
diff --git a/portfolyo/tools/peakconvert.py b/portfolyo/tools/peakconvert.py
index 7729f64..67f32fa 100644
--- a/portfolyo/tools/peakconvert.py
+++ b/portfolyo/tools/peakconvert.py
@@ -24,11 +24,11 @@ def group_index(
         groups.append(i.month)
     elif freq == "QS":
         groups.append(i.quarter)
-    elif freq == "AS":
+    elif freq == "YS":
         pass
     else:
         raise ValueError(
-            f"Parameter ``freq`` must be one of 'MS', 'QS', 'AS'; got '{freq}'."
+            f"Parameter ``freq`` must be one of 'MS', 'QS', 'YS'; got '{freq}'."
         )
 
     # Add grouping due to peak.
@@ -48,7 +48,7 @@ def complete_bpoframe(
     df : DataFrame
         With at least 2 of following columns: {'base', 'peak', 'offpeak'}. If all 3 are
         present, no verification is done. Additional columns are dropped. DatetimeIndex
-        with freq in {'MS', 'QS', 'AS'}.
+        with freq in {'MS', 'QS', 'YS'}.
     peak_fn : PeakFunction
         Function that returns boolean Series indicating if timestamps in index lie in peak period.
     is_summable : bool, optional (default: False)
@@ -233,7 +233,7 @@ def tseries2poframe(
 def poframe2tseries(
     df: pd.DataFrame,
     peak_fn: tools_peakfn.PeakFunction,
-    freq: str = "H",
+    freq: str = "h",
     is_summable: bool = False,
 ) -> pd.Series:
     """
@@ -310,7 +310,7 @@ def poframe2tseries(
 #         Timeseries with hourly or quarterhourly frequency.
 #     peak_fn : PeakFunction, optional (default: None)
 #         Function that returns boolean Series indicating if timestamps in index lie in peak period.
-#     freq : {'MS' (month, default) 'QS' (quarter), 'AS' (year)}
+#     freq : {'MS' (month, default) 'QS' (quarter), 'YS' (year)}
 #         Target frequency within which peak and offpeak values will be uniform.
 #     is_summable : bool, optional (default: False)
 #         True if data is summable, False if it is averagable.
@@ -367,7 +367,7 @@ def poframe2tseries(
 def poframe2poframe(
     df: pd.DataFrame,
     peak_fn: tools_peakfn.PeakFunction,
-    freq: str = "AS",
+    freq: str = "YS",
     is_summable: bool = False,
 ) -> pd.DataFrame:
     """
@@ -381,7 +381,7 @@ def poframe2poframe(
         or-longer frequency.
     peak_fn : PeakFunction
         Function that returns boolean Series indicating if timestamps in index lie in peak period.
-    freq : str, optional (default: 'AS')
+    freq : str, optional (default: 'YS')
         Target frequency; monthly-or-longer.
     is_summable : bool, optional (default: False)
         True if data is summable, False if it is averagable.
@@ -421,6 +421,6 @@ def poframe2poframe(
             " The result will be uniform at the frequency of the original frame ``df``."
         )
 
-    upsampled = poframe2tseries(df, peak_fn, "H", is_summable)
+    upsampled = poframe2tseries(df, peak_fn, "h", is_summable)
     downsampled = tseries2poframe(upsampled, peak_fn, freq, is_summable)
     return downsampled
diff --git a/portfolyo/tools/peakfn.py b/portfolyo/tools/peakfn.py
index 7e933d7..2b327ac 100644
--- a/portfolyo/tools/peakfn.py
+++ b/portfolyo/tools/peakfn.py
@@ -72,9 +72,9 @@ def factory(
     if not must_check_time:
         longest_freq = "D"
     elif peak_left.minute == 0 and peak_right.minute == 0:
-        longest_freq = "H"
+        longest_freq = "h"
     elif peak_left.minute % 15 == 0 and peak_right.minute % 15 == 0:
-        longest_freq = "15T"
+        longest_freq = "15min"
     else:
         raise ValueError(
             f"Input specifies times that are not 'round' quarter-hours; got {peak_left} and {peak_right}."
@@ -144,7 +144,7 @@ def peak_duration(i: pd.DatetimeIndex, peak_fn: PeakFunction) -> pd.Series:
     Series has the original index.
     """
     eval_i = i  # index to evaluate if peak or offpeak
-    for eval_freq in ("D", "H", "15T"):
+    for eval_freq in ("D", "h", "15min"):
         if tools_freq.up_or_down(eval_i.freq, eval_freq) > 0:  # upsampling necessary
             eval_i = tools_changefreq.index(eval_i, eval_freq)
         try:
diff --git a/portfolyo/tools/product.py b/portfolyo/tools/product.py
index c9fbb0e..ac6597a 100644
--- a/portfolyo/tools/product.py
+++ b/portfolyo/tools/product.py
@@ -46,7 +46,7 @@ def delivery_period(
         Left (inclusive) and right (exclusive) timestamp of delivery period.
     """
     ts_trade = ts_trade.replace(hour=23, minute=59)  # ensure after start_of_day
-    if period_type in ["m", "q", "a"]:
+    if period_type in ["m", "q", "y"]:
         freq = period_type.upper() + "S"
         ts_left = tools_floor.stamp(ts_trade, freq, front_count, start_of_day)
         ts_right = tools_right.stamp(ts_left, freq)
@@ -62,6 +62,6 @@ def delivery_period(
             ts_right = tools_right.stamp(ts_right, "QS")
     else:
         raise ValueError(
-            f"Parameter ``period_type`` must be one of 'd', 'm', 'q', 's', 'a'; got '{period_type}'."
+            f"Parameter ``period_type`` must be one of 'd', 'm', 'q', 's', 'y'; got '{period_type}'."
         )
     return ts_left, ts_right
diff --git a/portfolyo/tools/round.py b/portfolyo/tools/round.py
index 64b59d4..49329a4 100644
--- a/portfolyo/tools/round.py
+++ b/portfolyo/tools/round.py
@@ -81,7 +81,7 @@ def stamp_current(
     else:
         part_of_prevday = ts.time() < start_of_day
         rounded = ts.replace(hour=start_of_day.hour, minute=start_of_day.minute).floor(
-            "15T"
+            "15min"
         )
         if part_of_prevday and fn == "floor":
             rounded -= tools_freq.to_offset("D")
@@ -155,9 +155,9 @@ def stamp_current(
 
 
 def _offset(freq: str, future: int):
-    if freq == "15T":
+    if freq == "15min":
         return pd.Timedelta(minutes=future * 15)
-    elif freq == "H":
+    elif freq == "h":
         return pd.Timedelta(hours=future)
     elif freq == "D":
         return pd.Timedelta(days=future)
@@ -165,7 +165,7 @@ def _offset(freq: str, future: int):
         return pd.offsets.MonthBegin(future)
     elif freq == "QS":
         return pd.offsets.QuarterBegin(future, startingMonth=1)
-    elif freq == "AS":
+    elif freq == "YS":
         return pd.offsets.YearBegin(future)
     else:
         raise ValueError(
diff --git a/portfolyo/tools/standardize.py b/portfolyo/tools/standardize.py
index bc5ea1c..487fcc8 100644
--- a/portfolyo/tools/standardize.py
+++ b/portfolyo/tools/standardize.py
@@ -182,7 +182,7 @@ def assert_index_standardized(i: pd.DatetimeIndex, __right: bool = False):
         raise AssertionError("Index must have values; got empty index.")
 
     # Check hour and minute.
-    if tools_freq.up_or_down(freq, "15T") <= 0:  # quarterhour
+    if tools_freq.up_or_down(freq, "15min") <= 0:  # quarterhour
         startminute = 15 if __right else 0
         if i[0].minute != startminute:
             err = ("right-bound", "15 min past the") if __right else ("", "at a full")
@@ -203,7 +203,7 @@ def assert_index_standardized(i: pd.DatetimeIndex, __right: bool = False):
             )
 
     # Check time-of-day.
-    if tools_freq.up_or_down(freq, "H") <= 0:  # hour or shorter
+    if tools_freq.up_or_down(freq, "h") <= 0:  # hour or shorter
         if not __right:
             start = i[0]
             end = tools_right.stamp(i[-1], i.freq)
@@ -229,7 +229,7 @@ def assert_index_standardized(i: pd.DatetimeIndex, __right: bool = False):
             period, not_ok = "month", ~i.is_month_start
         elif freq == "QS":
             period, not_ok = "quarter", ~i.is_quarter_start
-        elif freq == "AS":
+        elif freq == "YS":
             period, not_ok = "year", ~i.is_year_start
         if any(not_ok):
             raise AssertionError(
diff --git a/portfolyo/tools/testing.py b/portfolyo/tools/testing.py
index b3a9933..ed5c241 100644
--- a/portfolyo/tools/testing.py
+++ b/portfolyo/tools/testing.py
@@ -64,9 +64,9 @@ def assert_indices_compatible(left: pd.DatetimeIndex, right: pd.DatetimeIndex):
 
 def assert_w_q_compatible(freq: str, w: pd.Series, q: pd.Series):
     """Assert if timeseries with power- and energy-values are consistent."""
-    if freq == "15T":
+    if freq == "15min":
         assert_series_equal(q, w * tools_unit.Q_(0.25, "h"), check_names=False)
-    elif freq == "H":
+    elif freq == "h":
         assert_series_equal(q, w * tools_unit.Q_(1.0, "h"), check_names=False)
     elif freq == "D":
         assert (q >= w * tools_unit.Q_(22.99, "h")).all()
@@ -77,7 +77,7 @@ def assert_w_q_compatible(freq: str, w: pd.Series, q: pd.Series):
     elif freq == "QS":
         assert (q >= w * 89 * tools_unit.Q_(24.0, "h")).all()
         assert (q <= w * 93 * tools_unit.Q_(24.0, "h")).all()
-    elif freq == "AS":
+    elif freq == "YS":
         assert (q >= w * tools_unit.Q_(8759.9, "h")).all()
         assert (q <= w * tools_unit.Q_(8784.1, "h")).all()
     else:
diff --git a/portfolyo/tools/unit.py b/portfolyo/tools/unit.py
index 18c259c..717faba 100644
--- a/portfolyo/tools/unit.py
+++ b/portfolyo/tools/unit.py
@@ -4,11 +4,13 @@
 
 from pathlib import Path
 from typing import Tuple, overload
-from .types import Series_or_DataFrame
+
 import pandas as pd
 import pint
 import pint_pandas
 
+from .types import Series_or_DataFrame
+
 path = Path(__file__).parent / "unitdefinitions.txt"
 
 
@@ -18,7 +20,7 @@
     auto_reduce_dimensions=True,
     case_sensitive=False,
 )
-ureg.default_format = "~P"  # short by default
+ureg.formatter.default_format = "~P"  # short by default
 ureg.setup_matplotlib()
 
 # Set for export.
@@ -185,23 +187,21 @@ def avoid_frame_of_objects(fr: Series_or_DataFrame) -> Series_or_DataFrame:
 
     # fr is now a Series.
 
-    if fr.dtype == int:
+    if pd.api.types.is_integer_dtype(fr):
         return fr.astype(float)
-    if fr.dtype == float:
+    if pd.api.types.is_float_dtype(fr):
         return fr
     if hasattr(fr, "pint"):
         if isinstance(fr.dtype, pint_pandas.PintType):
             return fr
         # We may have a series of pint quantities. Convert to pint-series, if possible.
-        dimensions = {v.dimensionality for v in fr.values}
-        if len(dimensions) != 1:
-            raise ValueError(
+        try:
+            return fr.astype(f"pint[{fr.iloc[0].units}]")
+        except pint.DimensionalityError as e:
+            dimensions = {v.dimensionality for v in fr.values}
+            raise pint.DimensionalityError(
                 f"Expected a Series with quantities of the same dimension; got {dimensions}."
-            )
-        # Convert all values to same unit.
-        units = fr.values[0].units
-        magnitudes = [v.to(units).magnitude for v in fr.values]
-        return pd.Series(magnitudes, fr.index, dtype=f"pint[{units}]")
+            ) from e
     raise TypeError(
         "Expected int-Series, float-Series, pint-Series, or Series of pint quantities (of equal dimensionality)."
     )
diff --git a/portfolyo/tools/visualize/categories.py b/portfolyo/tools/visualize/categories.py
index 0136710..f31e2c4 100644
--- a/portfolyo/tools/visualize/categories.py
+++ b/portfolyo/tools/visualize/categories.py
@@ -58,7 +58,7 @@ def short_labels(self, max_count: int = None) -> Iterable[str]:
 
 
 def create_categories(s: pd.Series) -> Iterable[Category]:
-    if s.index.freq == "AS":
+    if s.index.freq == "YS":
         prios = priolist(len(s))
 
         def category(i, ts, y):
diff --git a/pyproject.toml b/pyproject.toml
index 5642af4..2df8986 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "portfolyo"
-version = "0.6.0"
+version = "0.6.1"
 description = "Analyse and manipulate timeseries related to power and gas offtake portfolios"
 authors = [
     "Ruud Wijtvliet <rwijtvliet@gmail.com>",
diff --git a/tests/core/pfline/test_flat.py b/tests/core/pfline/test_flat.py
index 5da45e0..f57b1ed 100644
--- a/tests/core/pfline/test_flat.py
+++ b/tests/core/pfline/test_flat.py
@@ -77,7 +77,7 @@ def test_flatpfline_access(columns: str, available: str, constructor: type):
 
 
 series = {}
-for freq in ["MS", "D", "15T"]:
+for freq in ["MS", "D", "15min"]:
     idx = pd.date_range(
         "2020", "2020-04", freq=freq, inclusive="left", tz="Europe/Berlin"
     )
@@ -87,8 +87,8 @@ def test_flatpfline_access(columns: str, available: str, constructor: type):
     series[freq] = {"i": idx, "w": w, "q": q, "p": p, "r": q * p}
 
 
-@pytest.mark.parametrize("freq_in", ["MS", "D", "15T"])
-@pytest.mark.parametrize("freq_out", ["MS", "D", "15T"])
+@pytest.mark.parametrize("freq_in", ["MS", "D", "15min"])
+@pytest.mark.parametrize("freq_out", ["MS", "D", "15min"])
 @pytest.mark.parametrize("columns", ["w", "q", "p", "pw", "wr"])
 def test_flatpfline_asfreqcorrect1(freq_in: str, freq_out: str, columns: str):
     """Test if changing frequency is done correctly (when it's possible), for uniform pflines."""
@@ -101,8 +101,8 @@ def test_flatpfline_asfreqcorrect1(freq_in: str, freq_out: str, columns: str):
 # . check correct working of attributes .asfreq().
 @pytest.mark.only_on_pr
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
-@pytest.mark.parametrize("freq", ["H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("newfreq", ["H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq", ["h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("newfreq", ["h", "D", "MS", "QS", "YS"])
 @pytest.mark.parametrize("columns", ["pr", "qr", "pq", "wp", "wr"])
 def test_flatpfline_asfreqcorrect2(freq, newfreq, columns, tz):
     """Test if changing frequency is done correctly (when it's possible)."""
@@ -137,8 +137,8 @@ def test_flatpfline_asfreqcorrect2(freq, newfreq, columns, tz):
     testing.assert_series_equal(df1.apply(np.sum), df2.apply(np.sum))
 
 
-@pytest.mark.parametrize("freq", ["15T", "H", "D"])
-@pytest.mark.parametrize("newfreq", ["MS", "QS", "AS"])
+@pytest.mark.parametrize("freq", ["15min", "h", "D"])
+@pytest.mark.parametrize("newfreq", ["MS", "QS", "YS"])
 @pytest.mark.parametrize("kind", [Kind.COMPLETE, Kind.VOLUME, Kind.PRICE])
 def test_flatpfline_asfreqimpossible(freq, newfreq, kind):
     """Test if changing frequency raises error if it's impossible."""
diff --git a/tests/core/pfline/test_flat_helper.py b/tests/core/pfline/test_flat_helper.py
index 1f80b62..0de8865 100644
--- a/tests/core/pfline/test_flat_helper.py
+++ b/tests/core/pfline/test_flat_helper.py
@@ -7,16 +7,16 @@
 from portfolyo.core.pfline import flat_helper
 
 TEST_FREQUENCIES = [
-    "AS",
-    "AS-FEB",
-    "AS-APR",
+    "YS",
+    "YS-FEB",
+    "YS-APR",
     "QS",
     "QS-FEB",
     "QS-APR",
     "MS",
     "D",
-    "H",
-    "15T",
+    "h",
+    "15min",
 ]
 
 
@@ -160,8 +160,8 @@ def test_makedataframe_consistency(tz, freq, columns, inputtype):
     testing.assert_frame_equal(result, expected)
 
 
-@pytest.mark.parametrize("freq1", ["15T", "MS", "AS"])  # don't do all - many!
-@pytest.mark.parametrize("freq2", ["H", "D", "QS"])
+@pytest.mark.parametrize("freq1", ["15min", "MS", "YS"])  # don't do all - many!
+@pytest.mark.parametrize("freq2", ["h", "D", "QS"])
 @pytest.mark.parametrize("columns", ["rp", "wp", "pq", "qr", "wr"])
 def test_makedataframe_unequalfrequencies(freq1, freq2, columns):
     """Test if error is raised when creating a dataframe from series with unequal frequencies."""
@@ -187,7 +187,7 @@ def test_makedataframe_unequalfrequencies(freq1, freq2, columns):
 
 
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
-@pytest.mark.parametrize("freq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("freq", ["15min", "h", "D", "MS"])
 def test_makedataframe_unequalstartofday(freq: str, tz: str):
     """Test if error is raised for series with unequal starttimes."""
 
@@ -203,7 +203,7 @@ def test_makedataframe_unequalstartofday(freq: str, tz: str):
 
 
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
-@pytest.mark.parametrize("freq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("freq", ["15min", "h", "D", "MS"])
 @pytest.mark.parametrize("overlap", [True, False])
 def test_makedataframe_unequaltimeperiods(freq: str, overlap: bool, tz: str):
     """Test if only intersection is kept for overlapping series, and error is raised
diff --git a/tests/core/pfline/test_nested_helper.py b/tests/core/pfline/test_nested_helper.py
index 3ccf994..ee99d63 100644
--- a/tests/core/pfline/test_nested_helper.py
+++ b/tests/core/pfline/test_nested_helper.py
@@ -42,8 +42,8 @@ def test_verifydict_kindconsistency(freq, kind1, kind2, kind3):
 
 
 @pytest.mark.only_on_pr
-@pytest.mark.parametrize("freq1", ["15T", "D", "MS", "QS"])  # don't do all - many!
-@pytest.mark.parametrize("freq2", ["15T", "H", "D", "MS", "QS"])
+@pytest.mark.parametrize("freq1", ["15min", "D", "MS", "QS"])  # don't do all - many!
+@pytest.mark.parametrize("freq2", ["15min", "h", "D", "MS", "QS"])
 def test_verifydict_frequencyconsistency(freq1, freq2):
     """Test if error is raised when creating a dictionary from pflines with unequal frequencies."""
 
@@ -72,7 +72,7 @@ def test_verifydict_frequencyconsistency(freq1, freq2):
 
 
 @pytest.mark.only_on_pr
-@pytest.mark.parametrize("freq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("freq", ["15min", "h", "D", "MS"])
 @pytest.mark.parametrize("overlap", [True, False])
 def test_verifydict_unequaltimeperiods(freq, overlap):
     """Test if only intersection is kept for overlapping pflines, and error is raised
diff --git a/tests/core/pfline/test_pfline_init.py b/tests/core/pfline/test_pfline_init.py
index fdea8a6..68f590f 100644
--- a/tests/core/pfline/test_pfline_init.py
+++ b/tests/core/pfline/test_pfline_init.py
@@ -13,16 +13,16 @@
 from portfolyo.core.pfline import classes
 
 TEST_FREQUENCIES = [
-    "15T",
-    "H",
+    "15min",
+    "h",
     "D",
     "MS",
     "QS",
     "QS-FEB",
     "QS-APR",
-    "AS",
-    "AS-FEB",
-    "AS-APR",
+    "YS",
+    "YS-FEB",
+    "YS-APR",
 ]
 
 
@@ -269,7 +269,7 @@ def test_init_with_integers(col: str):
 
 
 @pytest.mark.parametrize("inclusive", ["left", "both"])
-@pytest.mark.parametrize("freq", ["15T", "H"])
+@pytest.mark.parametrize("freq", ["15min", "h"])
 def test_contain_whole_day(inclusive: str, freq: str):
     """An index must contain full days.
     For hourly-or-shorter values, this means that the start time of the first period () must equal the end time of the
@@ -287,7 +287,7 @@ def test_contain_whole_day(inclusive: str, freq: str):
             pfl = dev.get_flatpfline(index)
 
 
-@pytest.mark.parametrize("freq", ["D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq", ["D", "MS", "QS", "YS"])
 def test_equal_sod(freq: str):
     """In an index with daily-or-longer values, all timestamps (all periods) should start at the same time ."""
     i = pd.date_range("2024-03-28", freq=freq, periods=10, tz="Europe/Berlin")
diff --git a/tests/core/pfline/test_slice.py b/tests/core/pfline/test_slice.py
index 87b8f6e..0f5ca1e 100644
--- a/tests/core/pfline/test_slice.py
+++ b/tests/core/pfline/test_slice.py
@@ -23,7 +23,7 @@ def get_idx(
     return pd.date_range(ts_start, ts_end, freq=freq, inclusive=inclusive)
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize(
     "slice_start", ["2021", "2022", "2022-01-02", "2022-05-23 14:34"]
 )
@@ -41,7 +41,7 @@ def test_flat_slice_start(
 
 
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize(
     "slice_end",
     [
@@ -62,7 +62,7 @@ def test_flat_slice_end(slice_end: str, freq: str, tz: str, sod: str, inclusive:
     assert pfl1.slice[: slice_end[0]] == pfl1.loc[: slice_end[1]]
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize(
     "where",
     ["2022", "2022-03", "2022-04-21", "2022-05-23 14:34"],
@@ -85,7 +85,7 @@ def test_flat_slice_whole(where: str, freq: str, tz: str, sod: str, inclusive: s
     assert len(left.index.intersection(right.index)) == 0
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize(
     "slice_start",
     [
@@ -108,7 +108,7 @@ def test_nested_slice_start(
     assert pfl1.slice[slice_start:] == pfl1.loc[slice_start:]
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 @pytest.mark.parametrize(
     "slice_end",
@@ -130,7 +130,7 @@ def test_nested_slice_end(slice_end: str, freq: str, tz: str, sod: str, inclusiv
     assert pfl1.slice[: slice_end[0]] == pfl1.loc[: slice_end[1]]
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 @pytest.mark.parametrize(
     "where",
@@ -153,7 +153,7 @@ def test_nested_slice_whole(where: str, freq: str, tz: str, sod: str, inclusive:
     assert len(left.index.intersection(right.index)) == 0
 
 
-@pytest.mark.parametrize("freq", ["H", "15T"])
+@pytest.mark.parametrize("freq", ["h", "15min"])
 @pytest.mark.parametrize("sod", ["00:00", "06:00"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 @pytest.mark.parametrize("startdate", ["2021", "2022", "2022-01-02"])
@@ -179,7 +179,7 @@ def test__start_less_than_daily(startdate: str, freq: str, tz: str, sod: str):
 )
 def test__end_less_than_daily(enddate: str, tz: str):
     index = get_idx(
-        "2020", starttime="00:00", enddate="2024", freq="15T", inclusive="left", tz=tz
+        "2020", starttime="00:00", enddate="2024", freq="15min", inclusive="left", tz=tz
     )
     pfl1 = dev.get_flatpfline(index)
     slice_end = f"{enddate[0]} 00:00"
diff --git a/tests/core/pfstate/test_pfstate_init.py b/tests/core/pfstate/test_pfstate_init.py
index fa59ac5..931b261 100644
--- a/tests/core/pfstate/test_pfstate_init.py
+++ b/tests/core/pfstate/test_pfstate_init.py
@@ -18,7 +18,7 @@
 s_less = dev.get_series(i_less, "")
 i_more = pd.date_range("2019-12-15", freq="D", periods=100)
 s_more = dev.get_series(i_more, "")
-i_difffreq = pd.date_range("2020", freq="H", periods=24 * 5)
+i_difffreq = pd.date_range("2020", freq="h", periods=24 * 5)
 s_difffreq = dev.get_series(i_difffreq, "")
 
 
@@ -509,7 +509,7 @@ def test_pfstate_consistency_nosourcing():
 
 
 @pytest.mark.parametrize("inclusive", ["left", "both"])
-@pytest.mark.parametrize("freq", ["15T", "H"])
+@pytest.mark.parametrize("freq", ["15min", "h"])
 def test_contain_whole_day(inclusive: str, freq: str):
     """An index must contain full days.
     For hourly-or-shorter values, this means that the start time of the first period () must equal the end time of the
diff --git a/tests/core/pfstate/test_slice_state.py b/tests/core/pfstate/test_slice_state.py
index a3e0e0c..0e9835a 100644
--- a/tests/core/pfstate/test_slice_state.py
+++ b/tests/core/pfstate/test_slice_state.py
@@ -23,7 +23,7 @@ def get_idx(
     return pd.date_range(ts_start, ts_end, freq=freq, inclusive=inclusive)
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize("slice_start", ["2021", "2022", "2022-01-02"])
 @pytest.mark.parametrize(
     "slice_end",
@@ -55,7 +55,7 @@ def test_slice_state(
     assert pfs_to_concat == pfs_to_concat2
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize(
     "slice_start",
     [
@@ -82,7 +82,7 @@ def test_state_slice_start(
     assert pfs.slice[slice_start:] == pfs.loc[slice_start:]
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize(
     "slice_end",
     [
@@ -104,7 +104,7 @@ def test_state_slice_end(slice_end: str, freq: str, sod: str, inclusive: str, tz
     assert pfs.slice[: slice_end[0]] == pfs.loc[: slice_end[1]]
 
 
-@pytest.mark.parametrize("freq", ["MS", "AS", "QS", "D"])
+@pytest.mark.parametrize("freq", ["MS", "YS", "QS", "D"])
 @pytest.mark.parametrize(
     "where",
     ["2022", "2022-03", "2022-04-21", "2022-05-23 14:34"],
@@ -127,7 +127,7 @@ def test_state_slice_whole(where: str, freq: str, sod: str, inclusive: str, tz:
     assert len(left.index.intersection(right.index)) == 0
 
 
-@pytest.mark.parametrize("freq", ["H", "15T"])
+@pytest.mark.parametrize("freq", ["h", "15min"])
 @pytest.mark.parametrize("sod", ["00:00", "06:00"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 @pytest.mark.parametrize("startdate", ["2021", "2022", "2022-01-02"])
@@ -154,7 +154,7 @@ def test__start_less_than_daily(startdate: str, freq: str, tz: str, sod: str):
 # @pytest.mark.parametrize("sod", ["00:00", "06:00"])
 def test__end_less_than_daily(enddate: str, tz: str):
     index = get_idx(
-        "2020", starttime="00:00", enddate="2024", freq="15T", inclusive="left", tz=tz
+        "2020", starttime="00:00", enddate="2024", freq="15min", inclusive="left", tz=tz
     )
     pfl1 = dev.get_pfstate(index)
     slice_end = f"{enddate[0]} 00:00"
diff --git a/tests/dev/test_develop.py b/tests/dev/test_develop.py
index 8e1cd51..51be1b2 100644
--- a/tests/dev/test_develop.py
+++ b/tests/dev/test_develop.py
@@ -13,12 +13,12 @@
     ("freq", "startdate", "start_of_day"),
     [
         (None, None, None),
-        ("H", "2020", None),
+        ("h", "2020", None),
         ("D", "2020", None),
         ("MS", "2020", None),
         ("QS", "2020", None),
-        ("AS", "2020", None),
-        ("H", "2020-04-21", dt.time(hour=15)),
+        ("YS", "2020", None),
+        ("h", "2020-04-21", dt.time(hour=15)),
         ("D", "2020-04-21", None),
         ("MS", "2020-04", None),
         ("QS", "2020-04", None),
@@ -39,11 +39,11 @@ def test_index(freq, tz, startdate, start_of_day):
     ("freq", "startdate", "start_of_day"),
     [
         (None, None, None),
-        ("H", "2020-04-21", dt.time(hour=15)),
+        ("h", "2020-04-21", dt.time(hour=15)),
         ("D", "2020-04-21", None),
         ("MS", "2020-04", None),
         ("QS", "2020-04", None),
-        ("AS", "2020", None),
+        ("YS", "2020", None),
     ],
 )
 def test_series(freq, tz, startdate, name, name_has_unit, request_unit, start_of_day):
@@ -79,11 +79,11 @@ def test_series(freq, tz, startdate, name, name_has_unit, request_unit, start_of
     ("freq", "startdate", "start_of_day"),
     [
         (None, None, None),
-        ("H", "2020-04-21", dt.time(hour=15)),
+        ("h", "2020-04-21", dt.time(hour=15)),
         ("D", "2020-04-21", None),
         ("MS", "2020-04", None),
         ("QS", "2020-04", None),
-        ("AS", "2020", None),
+        ("YS", "2020", None),
     ],
 )
 def test_dataframe(
@@ -109,11 +109,11 @@ def test_dataframe(
     ("freq", "startdate", "start_of_day"),
     [
         (None, None, None),
-        ("H", "2020-04-21", dt.time(hour=15)),
+        ("h", "2020-04-21", dt.time(hour=15)),
         ("D", "2020-04-21", None),
         ("MS", "2020-04", None),
         ("QS", "2020-04", None),
-        ("AS", "2020", None),
+        ("YS", "2020", None),
     ],
 )
 def test_flatnestedpfline(freq, tz, startdate, kind, start_of_day):
@@ -134,11 +134,11 @@ def test_flatnestedpfline(freq, tz, startdate, kind, start_of_day):
     ("freq", "startdate", "start_of_day"),
     [
         (None, None, None),
-        ("H", "2020-04-21", dt.time(hour=15)),
+        ("h", "2020-04-21", dt.time(hour=15)),
         ("D", "2020-04-21", None),
         ("MS", "2020-04", None),
         ("QS", "2020-04", None),
-        ("AS", "2020", None),
+        ("YS", "2020", None),
     ],
 )
 def test_pfline(freq, tz, startdate, kind, max_nlevels, start_of_day):
diff --git a/tests/tools/prices/test_convert.py.bak b/tests/tools/prices/test_convert.py.bak
index 77ad906..520df45 100644
--- a/tests/tools/prices/test_convert.py.bak
+++ b/tests/tools/prices/test_convert.py.bak
@@ -21,7 +21,7 @@ def get_df_fromexcel(tz, aggfreq) -> pd.DataFrame:
 
 
 @pytest.mark.parametrize("withunit", [True, False])
-@pytest.mark.parametrize("aggfreq", ["MS", "QS", "AS"])
+@pytest.mark.parametrize("aggfreq", ["MS", "QS", "YS"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 def test_pbaseppeakpoffpeak_fromexcel(tz: str, aggfreq: str, withunit: bool):
     """Test if base, peak and offpeak value for a period can be calculated from the other two."""
@@ -40,7 +40,7 @@ def test_pbaseppeakpoffpeak_fromexcel(tz: str, aggfreq: str, withunit: bool):
     assert np.isclose(convert.base(p, o, ts_left, aggfreq), b)
 
 
-@pytest.mark.parametrize("aggfreq", ["MS", "QS", "AS"])
+@pytest.mark.parametrize("aggfreq", ["MS", "QS", "YS"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 def test_completebpoframe_fromexcel(tz: str, aggfreq: str):
     """Test if a partial bpoframe can be completed to a full bpoframe."""
@@ -52,8 +52,8 @@ def test_completebpoframe_fromexcel(tz: str, aggfreq: str):
         testing.assert_frame_equal(bpoframe_result, bpoframe)
 
 
-@pytest.mark.parametrize("aggfreq", ["MS", "QS", "AS"])
-@pytest.mark.parametrize("freq", ["15T", "H"])
+@pytest.mark.parametrize("aggfreq", ["MS", "QS", "YS"])
+@pytest.mark.parametrize("freq", ["15min", "h"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 def test_tseries2singlebpo_fromexcel(tz, freq, aggfreq):
     """Test if a timeseries can be converted into single base, peak and offpeak values."""
@@ -70,8 +70,8 @@ def test_tseries2singlebpo_fromexcel(tz, freq, aggfreq):
         assert np.isclose(values_result[key], values_expected[key])
 
 
-@pytest.mark.parametrize("aggfreq", ["MS", "QS", "AS"])
-@pytest.mark.parametrize("freq", ["15T", "H"])
+@pytest.mark.parametrize("aggfreq", ["MS", "QS", "YS"])
+@pytest.mark.parametrize("freq", ["15min", "h"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 def test_tseries2bpoframe_fromexcel(tz, freq, aggfreq):
     """Test if a timeseries (i.e., long) can be converted into a bpo-frame (i.e., wide)."""
@@ -83,8 +83,8 @@ def test_tseries2bpoframe_fromexcel(tz, freq, aggfreq):
     testing.assert_frame_equal(bpoframe_result, bpoframe_expected)
 
 
-@pytest.mark.parametrize("aggfreq", ["MS", "QS", "AS"])
-@pytest.mark.parametrize("freq", ["15T", "H"])
+@pytest.mark.parametrize("aggfreq", ["MS", "QS", "YS"])
+@pytest.mark.parametrize("freq", ["15min", "h"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 def test_bpoframe2timeseries_fromexcel(tz, freq, aggfreq, drop):
     """Test if a bpoframe (i.e., wide) can be converted into a timeseries (i.e., long)."""
@@ -100,10 +100,10 @@ def test_bpoframe2timeseries_fromexcel(tz, freq, aggfreq, drop):
     [
         ("MS", "MS"),
         ("MS", "QS"),
-        ("MS", "AS"),
+        ("MS", "YS"),
         ("QS", "QS"),
-        ("QS", "AS"),
-        ("AS", "AS"),
+        ("QS", "YS"),
+        ("YS", "YS"),
     ],
 )
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
@@ -124,13 +124,13 @@ def test_bpoframe2bpoframe_fromexcel(tz, short_aggfreq, long_aggfreq):
     [
         ("MS", "MS"),
         ("MS", "QS"),
-        ("MS", "AS"),
+        ("MS", "YS"),
         ("QS", "QS"),
-        ("QS", "AS"),
-        ("AS", "AS"),
+        ("QS", "YS"),
+        ("YS", "YS"),
     ],
 )
-@pytest.mark.parametrize("freq", ["15T", "H"])
+@pytest.mark.parametrize("freq", ["15min", "h"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 def test_tseries2tseries_fromexcel(tz, freq, short_aggfreq, long_aggfreq, po):
     """Test if a (e.g., hourly) timeseries with a short aggregation frequency (e.g., months)
diff --git a/tests/tools/test_changefreq.py b/tests/tools/test_changefreq.py
index c9c3ff9..c249ede 100644
--- a/tests/tools/test_changefreq.py
+++ b/tests/tools/test_changefreq.py
@@ -18,9 +18,9 @@ def utc(i):
         else:
             return [ts.utcoffset().total_seconds() for ts in i]
 
-    if longestfreq == "15T":
+    if longestfreq == "15min":
         return lambda i: pd.Index(zip(i.year, i.month, i.day, i.hour, i.minute, utc(i)))
-    if longestfreq == "H":
+    if longestfreq == "h":
         return lambda i: pd.Index(zip(i.year, i.month, i.day, i.hour, utc(i)))
     if longestfreq == "D":
         return lambda i: pd.Index(zip(i.year, i.month, i.day))
@@ -28,7 +28,7 @@ def utc(i):
         return lambda i: pd.Index(zip(i.year, i.month))
     if longestfreq == "QS":
         return lambda i: pd.Index(zip(i.year, i.quarter))
-    if longestfreq == "AS":
+    if longestfreq == "YS":
         return lambda i: i.year
 
 
@@ -61,9 +61,9 @@ def day(i):
         mask = d == 0
         return d + mask * numofdaysinprevmonth(i)
 
-    if longestfreq == "15T":
+    if longestfreq == "15min":
         return lambda i: pd.Index(zip(i.year, i.month, i.day, i.hour, i.minute, utc(i)))
-    if longestfreq == "H":
+    if longestfreq == "h":
         return lambda i: pd.Index(zip(i.year, i.month, i.day, i.hour, utc(i)))
     if longestfreq == "D":
         return lambda i: pd.Index(zip(year(i), month(i), day(i)))
@@ -71,7 +71,7 @@ def day(i):
         return lambda i: pd.Index(zip(year(i), month(i)))
     if longestfreq == "QS":
         return lambda i: pd.Index(zip(year(i), quarter(i)))
-    if longestfreq == "AS":
+    if longestfreq == "YS":
         return lambda i: year(i)
 
 
@@ -152,20 +152,20 @@ def idxs_and_mapping(
 
 
 def startdate(freq):
-    if freq == "15T" or freq == "H" or freq == "D":
+    if freq == "15min" or freq == "h" or freq == "D":
         return "2019-12-15"
     if freq == "MS":
         return "2019-12-01"
     if freq == "QS":
         return "2020-04-01"
-    if freq == "AS":
+    if freq == "YS":
         return "2020-01-01"
 
 
 @pytest.mark.parametrize("starttime", ["00:00", "06:00"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin", "Asia/Kolkata"])
-@pytest.mark.parametrize("freq_long", ["15T", "H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("freq_shrt", ["15T", "H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq_long", ["15min", "h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("freq_shrt", ["15min", "h", "D", "MS", "QS", "YS"])
 def test_downsample_index(freq_shrt: str, tz: str, freq_long: str, starttime: str):
     """Test downsampling of indices."""
 
@@ -183,8 +183,8 @@ def test_downsample_index(freq_shrt: str, tz: str, freq_long: str, starttime: st
 
 @pytest.mark.parametrize("starttime", ["00:00", "06:00"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin", "Asia/Kolkata"])
-@pytest.mark.parametrize("freq_long", ["15T", "H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("freq_shrt", ["15T", "H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq_long", ["15min", "h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("freq_shrt", ["15min", "h", "D", "MS", "QS", "YS"])
 def test_upsample_index(freq_shrt: str, tz: str, freq_long: str, starttime: str):
     """Test upsampling of indices."""
 
@@ -208,8 +208,8 @@ def test_upsample_index(freq_shrt: str, tz: str, freq_long: str, starttime: str)
 @pytest.mark.parametrize("seriesordf", ["s", "s_unit", "df", "df_unit"])
 @pytest.mark.parametrize("complexity", ["ones", "allnumbers"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin", "Asia/Kolkata"])
-@pytest.mark.parametrize("freq_long", ["15T", "H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("freq_shrt", ["15T", "H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq_long", ["15min", "h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("freq_shrt", ["15min", "h", "D", "MS", "QS", "YS"])
 def test_downsample_summable(
     seriesordf: str,
     freq_shrt: str,
@@ -243,8 +243,8 @@ def test_downsample_summable(
 @pytest.mark.parametrize("seriesordf", ["s", "s_unit", "df", "df_unit"])
 @pytest.mark.parametrize("complexity", ["ones", "allnumbers"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin", "Asia/Kolkata"])
-@pytest.mark.parametrize("freq_long", ["15T", "H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("freq_shrt", ["15T", "H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq_long", ["15min", "h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("freq_shrt", ["15min", "h", "D", "MS", "QS", "YS"])
 def test_downsample_avgable(
     seriesordf: str,
     freq_shrt: str,
@@ -278,8 +278,8 @@ def test_downsample_avgable(
 @pytest.mark.parametrize("seriesordf", ["s", "s_unit", "df", "df_unit"])
 @pytest.mark.parametrize("complexity", ["ones", "allnumbers"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin", "Asia/Kolkata"])
-@pytest.mark.parametrize("freq_shrt", ["15T", "H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("freq_long", ["15T", "H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq_shrt", ["15min", "h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("freq_long", ["15min", "h", "D", "MS", "QS", "YS"])
 def test_upsample_avgable(
     seriesordf: str,
     freq_long: str,
@@ -315,8 +315,8 @@ def test_upsample_avgable(
 @pytest.mark.parametrize("seriesordf", ["s", "s_unit", "df", "df_unit"])
 @pytest.mark.parametrize("complexity", ["ones", "allnumbers"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin", "Asia/Kolkata"])
-@pytest.mark.parametrize("freq_shrt", ["15T", "H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("freq_long", ["15T", "H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq_shrt", ["15min", "h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("freq_long", ["15min", "h", "D", "MS", "QS", "YS"])
 def test_upsample_summable(
     seriesordf: str,
     freq_long: str,
diff --git a/tests/tools/test_changefreq_fromexcel.py.bak b/tests/tools/test_changefreq_fromexcel.py.bak
index 1d7d70b..8edc822 100644
--- a/tests/tools/test_changefreq_fromexcel.py.bak
+++ b/tests/tools/test_changefreq_fromexcel.py.bak
@@ -7,20 +7,20 @@ import pytest
 
 from portfolyo import testing, tools
 
-freqs_small_to_large = ["T", "5T", "15T", "30T", "H", "2H", "D", "MS", "QS", "AS"]
+freqs_small_to_large = ["min", "5min", "15min", "30min", "h", "2h", "D", "MS", "QS", "YS"]
 
 # No timezone.
 
-i1 = pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="H", inclusive="left")
+i1 = pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="h", inclusive="left")
 s1 = pd.Series(range(len(i1)), i1)
 
 s1_15t_sum = pd.Series(
     [v2 for v in range(len(i1)) for v2 in [v / 4.0] * 4],
-    pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="15T", inclusive="left"),
+    pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="15min", inclusive="left"),
 )
 s1_15t_avg = pd.Series(
     [v2 for v in range(len(i1)) for v2 in [v * 1.0] * 4],
-    pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="15T", inclusive="left"),
+    pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="15min", inclusive="left"),
 )
 
 s1_d_sum = pd.Series([564.0, 1140], pd.date_range("2020-01-16", freq="D", periods=2))
@@ -33,7 +33,7 @@ s1_q_sum = s1_q_avg = pd.Series(
     [], pd.date_range("2020", periods=0, freq="QS"), dtype=float
 )
 s1_a_sum = s1_a_avg = pd.Series(
-    [], pd.date_range("2020", periods=0, freq="AS"), dtype=float
+    [], pd.date_range("2020", periods=0, freq="YS"), dtype=float
 )
 
 # Timezone, no DST.
@@ -78,16 +78,16 @@ def withtz(sin):
 
 i3_start, i3_end = "2020-03-28 12:00", "2020-03-31 12:00"
 tz = "Europe/Berlin"
-i3 = pd.date_range(i3_start, i3_end, freq="H", inclusive="left", tz=tz)
+i3 = pd.date_range(i3_start, i3_end, freq="h", inclusive="left", tz=tz)
 s3 = pd.Series(range(len(i3)), i3)
 
 s3_15t_sum = pd.Series(
     [v2 for v in range(len(i3)) for v2 in [v / 4.0] * 4],
-    pd.date_range(i3_start, i3_end, freq="15T", inclusive="left", tz=tz),
+    pd.date_range(i3_start, i3_end, freq="15min", inclusive="left", tz=tz),
 )
 s3_15t_avg = pd.Series(
     [v2 for v in range(len(i3)) for v2 in [v * 1.0] * 4],
-    pd.date_range(i3_start, i3_end, freq="15T", inclusive="left", tz=tz),
+    pd.date_range(i3_start, i3_end, freq="15min", inclusive="left", tz=tz),
 )
 
 s3_d_sum = pd.Series(
@@ -104,22 +104,22 @@ s3_q_sum = s3_q_avg = pd.Series(
     [], pd.date_range("2020", periods=0, freq="QS", tz=tz), dtype=float
 )
 s3_a_sum = s3_a_avg = pd.Series(
-    [], pd.date_range("2020", periods=0, freq="AS", tz=tz), dtype=float
+    [], pd.date_range("2020", periods=0, freq="YS", tz=tz), dtype=float
 )
 
 # Timezone, end of DST.
 
 i4_start, i4_end = "2020-10-24 12:00", "2020-10-27 12:00"
-i4 = pd.date_range(i4_start, i4_end, freq="H", inclusive="left", tz=tz)
+i4 = pd.date_range(i4_start, i4_end, freq="h", inclusive="left", tz=tz)
 s4 = pd.Series(range(len(i4)), i4)
 
 s4_15t_sum = pd.Series(
     [v2 for v in range(len(i4)) for v2 in [v / 4.0] * 4],
-    pd.date_range(i4_start, i4_end, freq="15T", inclusive="left", tz=tz),
+    pd.date_range(i4_start, i4_end, freq="15min", inclusive="left", tz=tz),
 )
 s4_15t_avg = pd.Series(
     [v2 for v in range(len(i4)) for v2 in [v * 1.0] * 4],
-    pd.date_range(i4_start, i4_end, freq="15T", inclusive="left", tz=tz),
+    pd.date_range(i4_start, i4_end, freq="15min", inclusive="left", tz=tz),
 )
 
 s4_d_sum = pd.Series(
@@ -136,7 +136,7 @@ s4_q_sum = s4_q_avg = pd.Series(
     [], pd.date_range("2020", periods=0, freq="QS", tz=tz), dtype=float
 )
 s4_a_sum = s4_a_avg = pd.Series(
-    [], pd.date_range("2020", periods=0, freq="AS", tz=tz), dtype=float
+    [], pd.date_range("2020", periods=0, freq="YS", tz=tz), dtype=float
 )
 
 # Months (= unequal lengths) as starting point.
@@ -166,7 +166,7 @@ s5_q_avg = pd.Series(
 )
 
 s5_a_sum = s5_a_avg = pd.Series(
-    [], pd.date_range("2020", periods=0, freq="AS"), dtype=float
+    [], pd.date_range("2020", periods=0, freq="YS"), dtype=float
 )
 
 # Months (= unequal lengths) as starting point, with DST
@@ -206,39 +206,39 @@ s6_q_avg = pd.Series(
 )
 
 s6_a_sum = s6_a_avg = pd.Series(
-    [], pd.date_range("2020", periods=0, freq="AS", tz=tz), dtype=float
+    [], pd.date_range("2020", periods=0, freq="YS", tz=tz), dtype=float
 )
 
 
 @pytest.mark.parametrize(
     ("s", "freq", "expected"),
     [
-        (s1, "15T", s1_15t_sum),
+        (s1, "15min", s1_15t_sum),
         (s1, "D", s1_d_sum),
         (s1, "MS", s1_m_sum),
         (s1, "QS", s1_q_sum),
-        (s1, "AS", s1_a_sum),
-        (s2, "15T", s2_15t_sum),
+        (s1, "YS", s1_a_sum),
+        (s2, "15min", s2_15t_sum),
         (s2, "D", s2_d_sum),
         (s2, "MS", s2_m_sum),
         (s2, "QS", s2_q_sum),
-        (s2, "AS", s2_a_sum),
-        (s3, "15T", s3_15t_sum),
+        (s2, "YS", s2_a_sum),
+        (s3, "15min", s3_15t_sum),
         (s3, "D", s3_d_sum),
         (s3, "MS", s3_m_sum),
         (s3, "QS", s3_q_sum),
-        (s3, "AS", s3_a_sum),
-        (s4, "15T", s4_15t_sum),
+        (s3, "YS", s3_a_sum),
+        (s4, "15min", s4_15t_sum),
         (s4, "D", s4_d_sum),
         (s4, "MS", s4_m_sum),
         (s4, "QS", s4_q_sum),
-        (s4, "AS", s4_a_sum),
+        (s4, "YS", s4_a_sum),
         (s5, "D", s5_d_sum),
         (s5, "QS", s5_q_sum),
-        (s5, "AS", s5_a_sum),
+        (s5, "YS", s5_a_sum),
         (s6, "D", s6_d_sum),
         (s6, "QS", s6_q_sum),
-        (s6, "AS", s6_a_sum),
+        (s6, "YS", s6_a_sum),
     ],
 )
 def test_resample_summable(s, freq, expected):
@@ -250,32 +250,32 @@ def test_resample_summable(s, freq, expected):
 @pytest.mark.parametrize(
     ("s", "freq", "expected"),
     [
-        (s1, "15T", s1_15t_avg),
+        (s1, "15min", s1_15t_avg),
         (s1, "D", s1_d_avg),
         (s1, "MS", s1_m_avg),
         (s1, "QS", s1_q_avg),
-        (s1, "AS", s1_a_avg),
-        (s2, "15T", s2_15t_avg),
+        (s1, "YS", s1_a_avg),
+        (s2, "15min", s2_15t_avg),
         (s2, "D", s2_d_avg),
         (s2, "MS", s2_m_avg),
         (s2, "QS", s2_q_avg),
-        (s2, "AS", s2_a_avg),
-        (s3, "15T", s3_15t_avg),
+        (s2, "YS", s2_a_avg),
+        (s3, "15min", s3_15t_avg),
         (s3, "D", s3_d_avg),
         (s3, "MS", s3_m_avg),
         (s3, "QS", s3_q_avg),
-        (s3, "AS", s3_a_avg),
-        (s4, "15T", s4_15t_avg),
+        (s3, "YS", s3_a_avg),
+        (s4, "15min", s4_15t_avg),
         (s4, "D", s4_d_avg),
         (s4, "MS", s4_m_avg),
         (s4, "QS", s4_q_avg),
-        (s4, "AS", s4_a_avg),
+        (s4, "YS", s4_a_avg),
         (s5, "D", s5_d_avg),
         (s5, "QS", s5_q_avg),
-        (s5, "AS", s5_a_avg),
+        (s5, "YS", s5_a_avg),
         (s6, "D", s6_d_avg),
         (s6, "QS", s6_q_avg),
-        (s6, "AS", s6_a_avg),
+        (s6, "YS", s6_a_avg),
     ],
 )
 def test_resample_avgable(s, freq, expected):
@@ -287,14 +287,14 @@ def test_resample_avgable(s, freq, expected):
 # Using gas days instead of calendar days.
 
 # . no tz
-i7 = pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="H", inclusive="left")
+i7 = pd.date_range("2020-01-15 12:00", "2020-01-18 12:00", freq="h", inclusive="left")
 s7 = pd.Series(range(len(i7)), i7)
 s7_d_sum = pd.Series([708.0, 1284], pd.date_range("2020-01-16", freq="D", periods=2))
 s7_d_avg = pd.Series([29.5, 53.5], pd.date_range("2020-01-16", freq="D", periods=2))
 
 # . start of dst
 i8 = pd.date_range(
-    "2020-03-26 12:00", "2020-03-30 12:00", freq="H", inclusive="left", tz=tz
+    "2020-03-26 12:00", "2020-03-30 12:00", freq="h", inclusive="left", tz=tz
 )
 s8 = pd.Series(range(len(i8)), i8)
 s8_d_sum = pd.Series(
@@ -306,7 +306,7 @@ s8_d_avg = pd.Series(
 
 # . end of dst
 i9 = pd.date_range(
-    "2020-10-22 12:00", "2020-10-26 12:00", freq="H", inclusive="left", tz=tz
+    "2020-10-22 12:00", "2020-10-26 12:00", freq="h", inclusive="left", tz=tz
 )
 s9 = pd.Series(range(len(i9)), i9)
 s9_d_sum = pd.Series(
@@ -357,7 +357,7 @@ s9_d_avg = pd.Series(
 # def aggdata():
 #     # Sample data
 #     i_15T = pd.date_range(
-#         "2020", "2022", freq="15T", tz="Europe/Berlin", inclusive="left"
+#         "2020", "2022", freq="15min", tz="Europe/Berlin", inclusive="left"
 #     )
 
 #     def value_func(mean, ampl_a, ampl_m, ampl_d):
@@ -381,7 +381,7 @@ s9_d_avg = pd.Series(
 
 #     # Seperate the values in bins for later aggregation.
 #     def isstart_f(freq):
-#         if freq == "AS":
+#         if freq == "YS":
 #             return lambda ts: ts.floor("D") == ts and ts.is_year_start
 #         if freq == "QS":
 #             return lambda ts: ts.floor("D") == ts and ts.is_quarter_start
@@ -389,7 +389,7 @@ s9_d_avg = pd.Series(
 #             return lambda ts: ts.floor("D") == ts and ts.is_month_start
 #         if freq == "D":
 #             return lambda ts: ts.floor("D") == ts
-#         if freq == "H":
+#         if freq == "h":
 #             return lambda ts: ts.minute == 0
 #         raise ValueError("Invalid value for `freq`.")
 
@@ -401,7 +401,7 @@ s9_d_avg = pd.Series(
 #             "hours": [],
 #             "new": isstart_f(freq),
 #         }
-#         for freq in ["H", "D", "MS", "QS", "AS"]
+#         for freq in ["h", "D", "MS", "QS", "YS"]
 #     }
 #     for ts, val, dur in zip(source.index, source.values, source.index.duration):
 #         for freq, dic in agg_data.items():
@@ -413,7 +413,7 @@ s9_d_avg = pd.Series(
 #             dic["values"][-1].append(val)
 #             dic["durations"][-1].append(dur)
 #             dic["hours"][-1].append(dur.magnitude)
-#     agg_data["15T"] = {
+#     agg_data["15min"] = {
 #         "values": [[v] for v in source.values],
 #         "durations": [[d] for d in source.index.duration],
 #         "hours": [[d.magnitude] for d in source.index.duration],
diff --git a/tests/tools/test_changeyear.py b/tests/tools/test_changeyear.py
index 1be874b..3bafda6 100644
--- a/tests/tools/test_changeyear.py
+++ b/tests/tools/test_changeyear.py
@@ -83,7 +83,9 @@ class TCase:  # testcase
     idx_source: pd.DatetimeIndex  # source index
     idx_target: pd.DatetimeIndex  # target index
     holiday_country: str
-    expected_mapping: pd.Series  # for every day in the target index, get position in source
+    expected_mapping: (
+        pd.Series
+    )  # for every day in the target index, get position in source
 
 
 def create_testcase_factory():
@@ -110,10 +112,10 @@ def add_testcase(tc: dict, several_years: bool):
 
         # Testcase with hourly frequency.
         idx_s2 = pd.date_range(
-            str(year_s_start), str(year_s_end), freq="H", inclusive="left", tz=tz
+            str(year_s_start), str(year_s_end), freq="h", inclusive="left", tz=tz
         )
         idx_t2 = pd.date_range(
-            str(year_t_start), str(year_t_end), freq="H", inclusive="left", tz=tz
+            str(year_t_start), str(year_t_end), freq="h", inclusive="left", tz=tz
         )
         count_s = pd.Series(0, idx_s2).resample("D").count()
         count_t = pd.Series(0, idx_t2).resample("D").count()
@@ -121,7 +123,7 @@ def add_testcase(tc: dict, several_years: bool):
         for count_t_here, m in zip(count_t, mapping):
             count_s_start = count_s[:m].sum()
             mapping_hourly.extend(range(count_s_start, count_s_start + count_t_here))
-        key = (year_s_start, year_t_start, tz, country, "H", several_years)
+        key = (year_s_start, year_t_start, tz, country, "h", several_years)
 
         testcases[key] = TCase(idx_s2, idx_t2, country, mapping_hourly)
         # Testcase for identical mapping (days).
@@ -129,7 +131,7 @@ def add_testcase(tc: dict, several_years: bool):
         testcases[key] = TCase(idx_s, idx_s, country, range(len(idx_s)))
 
         # Testcase for identical mapping (hours).
-        key = (year_s_start, year_s_start, tz, country, "H", several_years)
+        key = (year_s_start, year_s_start, tz, country, "h", several_years)
         testcases[key] = TCase(idx_s2, idx_s2, country, range(len(idx_s2)))
 
     testcases: Dict[Tuple, TCase] = {}
@@ -158,7 +160,7 @@ def testcase(
 # ---
 
 
-@pytest.mark.parametrize("freq", ["15T", "H", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq", ["15min", "h", "MS", "QS", "YS"])
 @pytest.mark.parametrize("tz", ["Europe/Berlin", None])
 def test_characterizeindex_error(freq: str, tz: str):
     """Test if characterization is only possible for daily indices."""
@@ -260,10 +262,10 @@ def test_characterizeindex_holidays(
         # Distinct frequencies
         (
             pd.date_range("2020", "2021", freq="D", inclusive="left", tz=None),
-            pd.date_range("2021", "2022", freq="15T", inclusive="left", tz=None),
+            pd.date_range("2021", "2022", freq="15min", inclusive="left", tz=None),
         ),
         (
-            pd.date_range("2020", "2021", freq="15T", inclusive="left", tz=None),
+            pd.date_range("2020", "2021", freq="15min", inclusive="left", tz=None),
             pd.date_range("2021", "2022", freq="D", inclusive="left", tz=None),
         ),
         (
@@ -271,7 +273,7 @@ def test_characterizeindex_holidays(
                 "2020", "2021", freq="D", inclusive="left", tz="Europe/Berlin"
             ),
             pd.date_range(
-                "2021", "2022", freq="H", inclusive="left", tz="Europe/Berlin"
+                "2021", "2022", freq="h", inclusive="left", tz="Europe/Berlin"
             ),
         ),
         # Too few source data
@@ -295,7 +297,7 @@ def test_mapindextoindex_error(
 @pytest.mark.parametrize("tz", ["Europe/Berlin", None])
 @pytest.mark.parametrize("holiday_country", ["DE", None])
 @pytest.mark.parametrize("numyears", [1, 3])
-@pytest.mark.parametrize("freq", ["MS", "D", "H"])
+@pytest.mark.parametrize("freq", ["MS", "D", "h"])
 @pytest.mark.parametrize("partial", ["partial", "full"])
 def test_mapindextoindex_identical(
     tz: str, holiday_country: str, numyears: int, partial: str, freq: str
@@ -327,10 +329,10 @@ def test_mapindextoindex_identical(
         ("QS", "2020", "2021", 7, 7, [4, 5, 6, 3, 0, 1, 2]),
         ("QS", "2020", "2021", 4, 7, [0, 1, 2, 3, 0, 1, 2]),
         ("QS", "2020", "2021", 7, 4, [4, 5, 6, 3]),
-        ("AS", "2020", "2024", 4, 4, range(4)),
-        ("AS", "2020", "2024", 7, 7, [4, 5, 6, 0, 1, 2, 3]),
-        ("AS", "2020", "2024", 4, 7, [0, 1, 2, 3, 0, 1, 2]),
-        ("AS", "2020", "2024", 7, 4, [4, 5, 6, 0]),
+        ("YS", "2020", "2024", 4, 4, range(4)),
+        ("YS", "2020", "2024", 7, 7, [4, 5, 6, 0, 1, 2, 3]),
+        ("YS", "2020", "2024", 4, 7, [0, 1, 2, 3, 0, 1, 2]),
+        ("YS", "2020", "2024", 7, 4, [4, 5, 6, 0]),
     ],
 )
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
@@ -365,7 +367,7 @@ def test_mapindextoindex_monthlyandlonger(
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 @pytest.mark.parametrize("holiday_country", [None, "DE"])
 @pytest.mark.parametrize("partial", ["partial", "full"])
-@pytest.mark.parametrize("freq", ["D", "H"])
+@pytest.mark.parametrize("freq", ["D", "h"])
 @pytest.mark.parametrize("several_years", [True, False])
 def test_mapindextoindex_daysandhours(
     year_s: int,
@@ -399,7 +401,7 @@ def test_mapindextoindex_daysandhours(
 @pytest.mark.parametrize("holiday_country", [None, "DE"])
 @pytest.mark.parametrize("partial", ["partial", "full"])
 @pytest.mark.parametrize("series_or_df", ["series", "df"])
-@pytest.mark.parametrize("freq", ["D", "H"])
+@pytest.mark.parametrize("freq", ["D", "h"])
 @pytest.mark.parametrize("several_years", [True, False])
 def test_mapframetoindex(
     year_s: int,
@@ -450,7 +452,7 @@ def test_mapframetoindex(
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 @pytest.mark.parametrize("holiday_country", [None, "DE"])
 @pytest.mark.parametrize("series_or_df", ["series", "df"])
-@pytest.mark.parametrize("freq", ["D", "H"])
+@pytest.mark.parametrize("freq", ["D", "h"])
 @pytest.mark.parametrize("several_years_source", [True, False])
 def test_mapframetoyear_oneyear(
     year_s: int,
diff --git a/tests/tools/test_duration.py b/tests/tools/test_duration.py
index 43eef6e..7a5d26c 100644
--- a/tests/tools/test_duration.py
+++ b/tests/tools/test_duration.py
@@ -5,41 +5,41 @@
 
 TESTCASES = [  # ts, freq, expected_hours
     # First day of X and start of day.
-    ("2020-01-01", "15T", (0.25, 0.25)),
-    ("2020-01-01", "H", (1, 1)),
+    ("2020-01-01", "15min", (0.25, 0.25)),
+    ("2020-01-01", "h", (1, 1)),
     ("2020-01-01", "D", (24, 24)),
     ("2020-01-01", "MS", (744, 696)),
-    ("2020-01-01", "AS", (8784, 8760)),
+    ("2020-01-01", "YS", (8784, 8760)),
     # First day of X but not start of day.
-    ("2020-01-01 06:00", "15T", (0.25, 0.25)),
-    ("2020-01-01 06:00", "H", (1, 1)),
+    ("2020-01-01 06:00", "15min", (0.25, 0.25)),
+    ("2020-01-01 06:00", "h", (1, 1)),
     ("2020-01-01 06:00", "D", (24, 24)),
     ("2020-01-01 06:00", "MS", (744, 696)),
-    ("2020-01-01 06:00", "AS", (8784, 8760)),
+    ("2020-01-01 06:00", "YS", (8784, 8760)),
     # Not first day of X but start of day.
-    ("2020-04-01", "15T", (0.25, 0.25)),
-    ("2020-04-01", "H", (1, 1)),
+    ("2020-04-01", "15min", (0.25, 0.25)),
+    ("2020-04-01", "h", (1, 1)),
     ("2020-04-01", "D", (24, 24)),
     ("2020-04-01", "MS", (720, 744)),
     ("2020-04-01", "QS", (2184, 2208)),
-    ("2020-04-21", "15T", (0.25, 0.25)),
-    ("2020-04-21", "H", (1, 1)),
+    ("2020-04-21", "15min", (0.25, 0.25)),
+    ("2020-04-21", "h", (1, 1)),
     ("2020-04-21", "D", (24, 24)),
     # Not first day of X and not start of day.
-    ("2020-04-01 06:00", "15T", (0.25, 0.25)),
-    ("2020-04-01 06:00", "H", (1, 1)),
+    ("2020-04-01 06:00", "15min", (0.25, 0.25)),
+    ("2020-04-01 06:00", "h", (1, 1)),
     ("2020-04-01 06:00", "D", (24, 24)),
     ("2020-04-01 06:00", "MS", (720, 744)),
     ("2020-04-01 06:00", "QS", (2184, 2208)),
-    ("2020-04-21 06:00", "15T", (0.25, 0.25)),
-    ("2020-04-21 06:00", "H", (1, 1)),
+    ("2020-04-21 06:00", "15min", (0.25, 0.25)),
+    ("2020-04-21 06:00", "h", (1, 1)),
     ("2020-04-21 06:00", "D", (24, 24)),
 ]
 
 TESTCASES_DST = [  # ts, freq, expected_hours
     # Start of DST.
-    ("2020-03-29 01:00", "15T", (0.25, 0.25)),
-    ("2020-03-29 01:00", "H", (1, 1)),
+    ("2020-03-29 01:00", "15min", (0.25, 0.25)),
+    ("2020-03-29 01:00", "h", (1, 1)),
     ("2020-03-29 00:00", "D", (23, 24)),
     ("2020-03-29 01:00", "D", (23, 24)),
     ("2020-03-29 03:00", "D", (24, 24)),
@@ -52,14 +52,14 @@
     ("2020-01-01 00:00", "QS", (2183, 2184)),
     ("2020-01-01 06:00", "QS", (2183, 2184)),
     # End of DST.
-    ("2020-10-25 01:00", "15T", (0.25, 0.25)),
-    ("2020-10-25 02:00+0200", "15T", (0.25, 0.25)),
-    ("2020-10-25 02:00+0100", "15T", (0.25, 0.25)),
-    ("2020-10-25 03:00", "15T", (0.25, 0.25)),
-    ("2020-10-25 01:00", "H", (1, 1)),
-    ("2020-10-25 02:00+0200", "H", (1, 1)),
-    ("2020-10-25 02:00+0100", "H", (1, 1)),
-    ("2020-10-25 03:00", "H", (1, 1)),
+    ("2020-10-25 01:00", "15min", (0.25, 0.25)),
+    ("2020-10-25 02:00+0200", "15min", (0.25, 0.25)),
+    ("2020-10-25 02:00+0100", "15min", (0.25, 0.25)),
+    ("2020-10-25 03:00", "15min", (0.25, 0.25)),
+    ("2020-10-25 01:00", "h", (1, 1)),
+    ("2020-10-25 02:00+0200", "h", (1, 1)),
+    ("2020-10-25 02:00+0100", "h", (1, 1)),
+    ("2020-10-25 03:00", "h", (1, 1)),
     ("2020-10-25 00:00", "D", (25, 24)),
     ("2020-10-25 01:00", "D", (25, 24)),
     ("2020-10-25 03:00", "D", (24, 24)),
diff --git a/tests/tools/test_floorceil.py b/tests/tools/test_floorceil.py
index 3a925f8..3b311b3 100644
--- a/tests/tools/test_floorceil.py
+++ b/tests/tools/test_floorceil.py
@@ -7,40 +7,40 @@
 from portfolyo import tools
 
 # TESTCASES_ALL_ON_BOUNDARY = [  # date, freq, periods, freqs
-#     ("2020-01-01", "15T", 80, ("D", "MS", "QS", "AS")),
-#     ("2020-01-01", "H", 20, ("D", "MS", "QS", "AS")),
-#     ("2020-01-01", "D", 25, ("MS", "QS", "AS")),
-#     ("2020-01-01", "MS", 3, ("QS", "AS")),
-#     ("2020-01-01", "QS", 3, ("AS",)),
-#     ("2020-04-01", "15T", 80, ("D", "MS", "QS")),
-#     ("2020-04-01", "H", 20, ("D", "MS", "QS")),
+#     ("2020-01-01", "15min", 80, ("D", "MS", "QS", "YS")),
+#     ("2020-01-01", "h", 20, ("D", "MS", "QS", "YS")),
+#     ("2020-01-01", "D", 25, ("MS", "QS", "YS")),
+#     ("2020-01-01", "MS", 3, ("QS", "YS")),
+#     ("2020-01-01", "QS", 3, ("YS",)),
+#     ("2020-04-01", "15min", 80, ("D", "MS", "QS")),
+#     ("2020-04-01", "h", 20, ("D", "MS", "QS")),
 #     ("2020-04-01", "D", 25, ("MS", "QS")),
 #     ("2020-04-01", "MS", 3, ("QS",)),
-#     ("2020-02-01", "15T", 80, ("D", "MS")),
-#     ("2020-02-01", "H", 20, ("D", "MS")),
+#     ("2020-02-01", "15min", 80, ("D", "MS")),
+#     ("2020-02-01", "h", 20, ("D", "MS")),
 #     ("2020-02-01", "D", 25, ("MS",)),
-#     ("2020-04-21", "15T", 80, ("D",)),
-#     ("2020-04-21", "H", 20, ("D",)),
+#     ("2020-04-21", "15min", 80, ("D",)),
+#     ("2020-04-21", "h", 20, ("D",)),
 # ]
 
 # TESTCASES_STAMPS_ONBOUNDARY = [  # date, freq
-#     ("2020-01-01", "15T"),
-#     ("2020-01-01", "H"),
+#     ("2020-01-01", "15min"),
+#     ("2020-01-01", "h"),
 #     ("2020-01-01", "D"),
 #     ("2020-01-01", "MS"),
 #     ("2020-01-01", "QS"),
-#     ("2020-01-01", "AS"),
-#     ("2020-04-01", "15T"),
-#     ("2020-04-01", "H"),
+#     ("2020-01-01", "YS"),
+#     ("2020-04-01", "15min"),
+#     ("2020-04-01", "h"),
 #     ("2020-04-01", "D"),
 #     ("2020-04-01", "MS"),
 #     ("2020-04-01", "QS"),
-#     ("2020-02-01", "15T"),
-#     ("2020-02-01", "H"),
+#     ("2020-02-01", "15min"),
+#     ("2020-02-01", "h"),
 #     ("2020-02-01", "D"),
 #     ("2020-02-01", "MS"),
-#     ("2020-04-21", "15T"),
-#     ("2020-04-21", "H"),
+#     ("2020-04-21", "15min"),
+#     ("2020-04-21", "h"),
 #     ("2020-04-21", "D"),
 # ]
 
@@ -48,74 +48,74 @@
 #     ("2020-01-01", 0, "D", "2020-01-01", "2020-01-01"),
 #     ("2020-01-01", 0, "MS", "2020-01-01", "2020-01-01"),
 #     ("2020-01-01", 0, "QS", "2020-01-01", "2020-01-01"),
-#     ("2020-01-01", 0, "AS", "2020-01-01", "2020-01-01"),
+#     ("2020-01-01", 0, "YS", "2020-01-01", "2020-01-01"),
 #     ("2020-01-01", 1, "D", "2020-01-02", "2020-01-02"),
 #     ("2020-01-01", 1, "MS", "2020-02-01", "2020-02-01"),
 #     ("2020-01-01", 1, "QS", "2020-04-01", "2020-04-01"),
-#     ("2020-01-01", 1, "AS", "2021-01-01", "2021-01-01"),
+#     ("2020-01-01", 1, "YS", "2021-01-01", "2021-01-01"),
 #     ("2020-01-01", -1, "D", "2019-12-31", "2019-12-31"),
 #     ("2020-01-01", -1, "MS", "2019-12-01", "2019-12-01"),
 #     ("2020-01-01", -1, "QS", "2019-10-01", "2019-10-01"),
-#     ("2020-01-01", -1, "AS", "2019-01-01", "2019-01-01"),
+#     ("2020-01-01", -1, "YS", "2019-01-01", "2019-01-01"),
 # ]
 # TESTCASES_STAMPS_DATES = [  # ts, fut, freq, floored, ceiled
 #     ("2020-12-31 12:00", 0, "D", "2020-12-31", "2021-01-01"),
 #     ("2020-12-31 12:00", 0, "MS", "2020-12-01", "2021-01-01"),
 #     ("2020-12-31 12:00", 0, "QS", "2020-10-01", "2021-01-01"),
-#     ("2020-12-31 12:00", 0, "AS", "2020-01-01", "2021-01-01"),
+#     ("2020-12-31 12:00", 0, "YS", "2020-01-01", "2021-01-01"),
 #     ("2020-02-01", 0, "D", "2020-02-01", "2020-02-01"),
 #     ("2020-02-01", 0, "MS", "2020-02-01", "2020-02-01"),
 #     ("2020-02-01", 0, "QS", "2020-01-01", "2020-04-01"),
-#     ("2020-02-01", 0, "AS", "2020", "2021"),
+#     ("2020-02-01", 0, "YS", "2020", "2021"),
 #     ("2020-01-01 23:55", 0, "D", "2020", "2020-01-02"),
 #     ("2020-01-24 1:32", 0, "MS", "2020", "2020-02"),
 #     ("2020-03-03 3:33", 0, "QS", "2020", "2020-04"),
-#     ("2020-10-11 12:34", 0, "AS", "2020", "2021"),
+#     ("2020-10-11 12:34", 0, "YS", "2020", "2021"),
 #     ("2020-01-01 23:55", 1, "D", "2020-01-02", "2020-01-03"),
 #     ("2020-01-24 1:32", 1, "MS", "2020-02", "2020-03"),
 #     ("2020-03-03 3:33", 1, "QS", "2020-04", "2020-07"),
-#     ("2020-10-11 12:34", 1, "AS", "2021", "2022"),
+#     ("2020-10-11 12:34", 1, "YS", "2021", "2022"),
 #     ("2020-01-01 23:55", -1, "D", "2019-12-31", "2020-01-01"),
 #     ("2020-01-24 1:32", -1, "MS", "2019-12", "2020"),
 #     ("2020-03-03 3:33", -1, "QS", "2019-10", "2020"),
-#     ("2020-10-11 12:34", -1, "AS", "2019", "2020"),
-#     ("2020-03-29 00:00", 0, "H", "2020-03-29 00:00", "2020-03-29 00:00"),
-#     ("2020-10-25 00:00", 0, "H", "2020-10-25 00:00", "2020-10-25 00:00"),
+#     ("2020-10-11 12:34", -1, "YS", "2019", "2020"),
+#     ("2020-03-29 00:00", 0, "h", "2020-03-29 00:00", "2020-03-29 00:00"),
+#     ("2020-10-25 00:00", 0, "h", "2020-10-25 00:00", "2020-10-25 00:00"),
 # ]
 
 
 # TESTCASES = [  # ts, freq, floored,
 #     # First day of X and start of day.
-#     ("2020-01-01", "15T", ["2020-01-01 00:15"),
-#     ("2020-01-01", "H", "2020-01-01 01:00"),
+#     ("2020-01-01", "15min", ["2020-01-01 00:15"),
+#     ("2020-01-01", "h", "2020-01-01 01:00"),
 #     ("2020-01-01", "D", "2020-01-02"),
 #     ("2020-01-01", "MS", "2020-02-01"),
 #     ("2020-01-01", "QS", "2020-04-01"),
-#     ("2020-01-01", "AS", "2021-01-01"),
+#     ("2020-01-01", "YS", "2021-01-01"),
 #     # First day of X but not start of day.
-#     ("2020-01-01 06:00",0,  "15T", "2020-01-01 06:15"),
-#     ("2020-01-01 06:00",0,  "H", "2020-01-01 07:00"),
+#     ("2020-01-01 06:00",0,  "15min", "2020-01-01 06:15"),
+#     ("2020-01-01 06:00",0,  "h", "2020-01-01 07:00"),
 #     ("2020-01-01 06:00",0,  "D", "2020-01-02 06:00"),
 #     ("2020-01-01 06:00",0,  "MS", "2020-02-01 06:00"),
 #     ("2020-01-01 06:00",0,  "QS", "2020-04-01 06:00"),
-#     ("2020-01-01 06:00",0,  "AS", "2021-01-01 06:00"),
+#     ("2020-01-01 06:00",0,  "YS", "2021-01-01 06:00"),
 #     # Not first day of X but start of day.
-#     ("2020-04-01",0,  "15T", "2020-04-01 00:15"),
-#     ("2020-04-01",0,  "H", "2020-04-01 01:00"),
+#     ("2020-04-01",0,  "15min", "2020-04-01 00:15"),
+#     ("2020-04-01",0,  "h", "2020-04-01 01:00"),
 #     ("2020-04-01",0,  "D", "2020-04-02"),
 #     ("2020-04-01",0,  "MS", "2020-05-01"),
 #     ("2020-04-01",0,  "QS", "2020-07-01"),
-#     ("2020-04-21",0,  "15T", "2020-04-21 00:15"),
-#     ("2020-04-21",0,  "H", "2020-04-21 01:00"),
+#     ("2020-04-21",0,  "15min", "2020-04-21 00:15"),
+#     ("2020-04-21",0,  "h", "2020-04-21 01:00"),
 #     ("2020-04-21",0,  "D", "2020-04-22"),
 #     # Not first day of X and not start of day.
-#     ("2020-04-01 06:00", 0, "15T", "2020-04-01 06:15"),
-#     ("2020-04-01 06:00", 0, "H", "2020-04-01 07:00"),
+#     ("2020-04-01 06:00", 0, "15min", "2020-04-01 06:15"),
+#     ("2020-04-01 06:00", 0, "h", "2020-04-01 07:00"),
 #     ("2020-04-01 06:00", 0, "D", "2020-04-02 06:00"),
 #     ("2020-04-01 06:00", 0, "MS", "2020-05-01 06:00"),
 #     ("2020-04-01 06:00", 0, "QS", "2020-07-01 06:00"),
-#     ("2020-04-21 06:00", 0, "15T", "2020-04-21 06:15"),
-#     ("2020-04-21 06:00", 0, "H", "2020-04-21 07:00"),
+#     ("2020-04-21 06:00", 0, "15min", "2020-04-21 06:15"),
+#     ("2020-04-21 06:00", 0, "h", "2020-04-21 07:00"),
 #     ("2020-04-21 06:00", 0, "D", "2020-04-22 06:00"),
 # ]
 
@@ -194,158 +194,158 @@
 
 
 TESTCASES = [  # ts, fut, freq, floored, ceiled
-    ("2020-04-21 12:34:56", 0, "15T", "2020-04-21 12:30", "2020-04-21 12:45"),
-    ("2020-04-21 12:34:56", 0, "H", "2020-04-21 12:00", "2020-04-21 13:00"),
+    ("2020-04-21 12:34:56", 0, "15min", "2020-04-21 12:30", "2020-04-21 12:45"),
+    ("2020-04-21 12:34:56", 0, "h", "2020-04-21 12:00", "2020-04-21 13:00"),
     ("2020-04-21 12:34:56", 0, "D", "2020-04-21", "2020-04-22"),
     ("2020-04-21 12:34:56", 0, "MS", "2020-04-01", "2020-05-01"),
     ("2020-04-21 12:34:56", 0, "QS", "2020-04-01", "2020-07-01"),
-    ("2020-04-21 12:34:56", 0, "AS", "2020-01-01", "2021-01-01"),
-    ("2020-04-21 23:54:16", 0, "15T", "2020-04-21 23:45", "2020-04-22"),
-    ("2020-04-21 23:54:16", 0, "H", "2020-04-21 23:00", "2020-04-22"),
+    ("2020-04-21 12:34:56", 0, "YS", "2020-01-01", "2021-01-01"),
+    ("2020-04-21 23:54:16", 0, "15min", "2020-04-21 23:45", "2020-04-22"),
+    ("2020-04-21 23:54:16", 0, "h", "2020-04-21 23:00", "2020-04-22"),
     ("2020-04-21 23:54:16", 0, "D", "2020-04-21", "2020-04-22"),
     ("2020-04-21 23:54:16", 0, "MS", "2020-04-01", "2020-05-01"),
     ("2020-04-21 23:54:16", 0, "QS", "2020-04-01", "2020-07-01"),
-    ("2020-04-21 23:54:16", 0, "AS", "2020-01-01", "2021-01-01"),
+    ("2020-04-21 23:54:16", 0, "YS", "2020-01-01", "2021-01-01"),
     ("2020", 0, "D", "2020", "2020"),
     ("2020", 0, "MS", "2020", "2020"),
     ("2020", 0, "QS", "2020", "2020"),
-    ("2020", 0, "AS", "2020", "2020"),
+    ("2020", 0, "YS", "2020", "2020"),
     ("2020", 1, "D", "2020-01-02", "2020-01-02"),
     ("2020", 1, "MS", "2020-02", "2020-02"),
     ("2020", 1, "QS", "2020-04", "2020-04"),
-    ("2020", 1, "AS", "2021", "2021"),
+    ("2020", 1, "YS", "2021", "2021"),
     ("2020", -1, "D", "2019-12-31", "2019-12-31"),
     ("2020", -1, "MS", "2019-12", "2019-12"),
     ("2020", -1, "QS", "2019-10", "2019-10"),
-    ("2020", -1, "AS", "2019", "2019"),
+    ("2020", -1, "YS", "2019", "2019"),
     ("2020-12-31 12:00", 0, "D", "2020-12-31", "2021-01-01"),
     ("2020-12-31 12:00", 0, "MS", "2020-12-01", "2021-01-01"),
     ("2020-12-31 12:00", 0, "QS", "2020-10-01", "2021-01-01"),
-    ("2020-12-31 12:00", 0, "AS", "2020-01-01", "2021-01-01"),
+    ("2020-12-31 12:00", 0, "YS", "2020-01-01", "2021-01-01"),
     ("2020-02-01", 0, "D", "2020-02-01", "2020-02-01"),
     ("2020-02-01", 0, "MS", "2020-02-01", "2020-02-01"),
     ("2020-02-01", 0, "QS", "2020-01-01", "2020-04-01"),
-    ("2020-02-01", 0, "AS", "2020", "2021"),
+    ("2020-02-01", 0, "YS", "2020", "2021"),
     ("2020-01-01 23:55", 0, "D", "2020", "2020-01-02"),
     ("2020-01-24 1:32", 0, "MS", "2020", "2020-02"),
     ("2020-03-03 3:33", 0, "QS", "2020", "2020-04"),
-    ("2020-10-11 12:34:56", 0, "AS", "2020", "2021"),
+    ("2020-10-11 12:34:56", 0, "YS", "2020", "2021"),
     ("2020-01-01 23:55", 1, "D", "2020-01-02", "2020-01-03"),
     ("2020-01-24 1:32", 1, "MS", "2020-02", "2020-03"),
     ("2020-03-03 3:33", 1, "QS", "2020-04", "2020-07"),
-    ("2020-10-11 12:34:56", 1, "AS", "2021", "2022"),
+    ("2020-10-11 12:34:56", 1, "YS", "2021", "2022"),
     ("2020-01-01 23:55", -1, "D", "2019-12-31", "2020-01-01"),
     ("2020-01-24 1:32", -1, "MS", "2019-12", "2020"),
     ("2020-03-03 3:33", -1, "QS", "2019-10", "2020"),
-    ("2020-10-11 12:34:56", -1, "AS", "2019", "2020"),
-    ("2020-03-29 00:00", 0, "H", "2020-03-29 00:00", "2020-03-29 00:00"),
-    ("2020-10-25 00:00", 0, "H", "2020-10-25 00:00", "2020-10-25 00:00"),
+    ("2020-10-11 12:34:56", -1, "YS", "2019", "2020"),
+    ("2020-03-29 00:00", 0, "h", "2020-03-29 00:00", "2020-03-29 00:00"),
+    ("2020-10-25 00:00", 0, "h", "2020-10-25 00:00", "2020-10-25 00:00"),
 ]
 
 TESTCASES_DST = [  # ts, tz, freq, floored, ceiled
-    ("2020-04-21 15:25", None, "H", "2020-04-21 15:00", "2020-04-21 16:00"),
-    ("2020-04-21 15:25", "Europe/Berlin", "H", "2020-04-21 15:00", "2020-04-21 16:00"),
+    ("2020-04-21 15:25", None, "h", "2020-04-21 15:00", "2020-04-21 16:00"),
+    ("2020-04-21 15:25", "Europe/Berlin", "h", "2020-04-21 15:00", "2020-04-21 16:00"),
     (
         "2020-04-21 15:25+0200",
         "Europe/Berlin",
-        "H",
+        "h",
         "2020-04-21 15:00+0200",
         "2020-04-21 16:00+0200",
     ),
-    ("2020-04-21 15:25", "Asia/Kolkata", "H", "2020-04-21 15:00", "2020-04-21 16:00"),
-    ("2020-03-29 01:50", None, "15T", "2020-03-29 01:45", "2020-03-29 02:00"),
-    ("2020-03-29 03:05", None, "15T", "2020-03-29 03:00", "2020-03-29 03:15"),
+    ("2020-04-21 15:25", "Asia/Kolkata", "h", "2020-04-21 15:00", "2020-04-21 16:00"),
+    ("2020-03-29 01:50", None, "15min", "2020-03-29 01:45", "2020-03-29 02:00"),
+    ("2020-03-29 03:05", None, "15min", "2020-03-29 03:00", "2020-03-29 03:15"),
     (
         "2020-03-29 01:50+0100",
         "Europe/Berlin",
-        "15T",
+        "15min",
         "2020-03-29 01:45+0100",
         "2020-03-29 03:00+0200",
     ),
     (
         "2020-03-29 03:05+0200",
         "Europe/Berlin",
-        "15T",
+        "15min",
         "2020-03-29 03:00+0200",
         "2020-03-29 03:15+0200",
     ),
     (
         "2020-03-29 01:50",
         "Europe/Berlin",
-        "15T",
+        "15min",
         "2020-03-29 01:45",
         "2020-03-29 03:00",
     ),
-    ("2020-03-29 03:05", None, "15T", "2020-03-29 03:00", "2020-03-29 03:15"),
-    ("2020-10-25 02:50", None, "15T", "2020-10-25 02:45", "2020-10-25 03:00"),
-    ("2020-10-25 02:05", None, "15T", "2020-10-25 02:00", "2020-10-25 02:15"),
+    ("2020-03-29 03:05", None, "15min", "2020-03-29 03:00", "2020-03-29 03:15"),
+    ("2020-10-25 02:50", None, "15min", "2020-10-25 02:45", "2020-10-25 03:00"),
+    ("2020-10-25 02:05", None, "15min", "2020-10-25 02:00", "2020-10-25 02:15"),
     (
         "2020-10-25 02:50+0200",
         "Europe/Berlin",
-        "15T",
+        "15min",
         "2020-10-25 02:45+0200",
         "2020-10-25 02:00+0100",
     ),
     (
         "2020-10-25 02:05+0200",
         "Europe/Berlin",
-        "15T",
+        "15min",
         "2020-10-25 02:00+0200",
         "2020-10-25 02:15+0200",
     ),
     (
         "2020-10-25 02:50+0100",
         "Europe/Berlin",
-        "15T",
+        "15min",
         "2020-10-25 02:45+0100",
         "2020-10-25 03:00+0100",
     ),
     (
         "2020-10-25 02:05+0100",
         "Europe/Berlin",
-        "15T",
+        "15min",
         "2020-10-25 02:00+0100",
         "2020-10-25 02:15+0100",
     ),
     (
         "2020-10-25 02:30+0200",
         "Europe/Berlin",
-        "H",
+        "h",
         "2020-10-25 02:00+0200",
         "2020-10-25 02:00+0100",
     ),
     (
         "2020-10-25 02:30+0100",
         "Europe/Berlin",
-        "H",
+        "h",
         "2020-10-25 02:00+0100",
         "2020-10-25 03:00+0100",
     ),
 ]
 
 TESTCASES_NONNATURAL = [  # ts, freq, offset_hours, floored, ceiled
-    ("2020-01-01 00:00", "15T", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
-    ("2020-01-01 00:00", "H", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
+    ("2020-01-01 00:00", "15min", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
+    ("2020-01-01 00:00", "h", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
     ("2020-01-01 00:00", "D", 6, "2019-12-31 06:00", "2020-01-01 06:00"),
     ("2020-01-01 00:00", "MS", 6, "2019-12-01 06:00", "2020-01-01 06:00"),
     ("2020-01-01 00:00", "QS", 6, "2019-10-01 06:00", "2020-01-01 06:00"),
-    ("2020-01-01 00:00", "AS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
-    ("2020-01-01 04:00", "15T", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
-    ("2020-01-01 04:00", "H", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
+    ("2020-01-01 00:00", "YS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
+    ("2020-01-01 04:00", "15min", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
+    ("2020-01-01 04:00", "h", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
     ("2020-01-01 04:00", "D", 6, "2019-12-31 06:00", "2020-01-01 06:00"),
     ("2020-01-01 04:00", "MS", 6, "2019-12-01 06:00", "2020-01-01 06:00"),
     ("2020-01-01 04:00", "QS", 6, "2019-10-01 06:00", "2020-01-01 06:00"),
-    ("2020-01-01 04:00", "AS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
-    ("2019-12-31 12:00", "15T", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
-    ("2019-12-31 12:00", "H", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
+    ("2020-01-01 04:00", "YS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
+    ("2019-12-31 12:00", "15min", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
+    ("2019-12-31 12:00", "h", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
     ("2019-12-31 12:00", "D", 6, "2019-12-31 06:00", "2020-01-01 06:00"),
     ("2019-12-31 12:00", "MS", 6, "2019-12-01 06:00", "2020-01-01 06:00"),
     ("2019-12-31 12:00", "QS", 6, "2019-10-01 06:00", "2020-01-01 06:00"),
-    ("2019-12-31 12:00", "AS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
-    ("2020-04-21 15:25", "15T", 6, "2020-04-21 15:15", "2020-04-21 15:30"),
-    ("2020-04-21 15:25", "H", 6, "2020-04-21 15:00", "2020-04-21 16:00"),
-    ("2020-03-29 01:40", "15T", 6, "2020-03-29 01:30", "2020-03-29 01:45"),
-    ("2020-03-29 03:05", "15T", 6, "2020-03-29 03:00", "2020-03-29 03:15"),
+    ("2019-12-31 12:00", "YS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
+    ("2020-04-21 15:25", "15min", 6, "2020-04-21 15:15", "2020-04-21 15:30"),
+    ("2020-04-21 15:25", "h", 6, "2020-04-21 15:00", "2020-04-21 16:00"),
+    ("2020-03-29 01:40", "15min", 6, "2020-03-29 01:30", "2020-03-29 01:45"),
+    ("2020-03-29 03:05", "15min", 6, "2020-03-29 03:00", "2020-03-29 03:15"),
     ("2020-04-21 15:25", "D", 6, "2020-04-21 06:00", "2020-04-22 06:00"),
     ("2020-03-28 01:40", "D", 6, "2020-03-27 06:00", "2020-03-28 06:00"),
     ("2020-03-29 01:40", "D", 6, "2020-03-28 06:00", "2020-03-29 06:00"),
@@ -378,28 +378,28 @@
 ]
 
 # TESTCASES_NONNATURAL_INDEX = [  # ts, i_freq, freq, floored, ceiled
-#     ("2020-01-01 00:00", "15T", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
-#     ("2020-01-01 00:00", "H", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
+#     ("2020-01-01 00:00", "15min", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
+#     ("2020-01-01 00:00", "h", 6, "2020-01-01 00:00", "2020-01-01 00:00"),
 #     ("2020-01-01 00:00", "D", 6, "2019-12-31 06:00", "2020-01-01 06:00"),
 #     ("2020-01-01 00:00", "MS", 6, "2019-12-01 06:00", "2020-01-01 06:00"),
 #     ("2020-01-01 00:00", "QS", 6, "2019-10-01 06:00", "2020-01-01 06:00"),
-#     ("2020-01-01 00:00", "AS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
-#     ("2020-01-01 04:00", "15T", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
-#     ("2020-01-01 04:00", "H", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
+#     ("2020-01-01 00:00", "YS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
+#     ("2020-01-01 04:00", "15min", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
+#     ("2020-01-01 04:00", "h", 6, "2020-01-01 04:00", "2020-01-01 04:00"),
 #     ("2020-01-01 04:00", "D", 6, "2019-12-31 06:00", "2020-01-01 06:00"),
 #     ("2020-01-01 04:00", "MS", 6, "2019-12-01 06:00", "2020-01-01 06:00"),
 #     ("2020-01-01 04:00", "QS", 6, "2019-10-01 06:00", "2020-01-01 06:00"),
-#     ("2020-01-01 04:00", "AS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
-#     ("2019-12-31 12:00", "15T", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
-#     ("2019-12-31 12:00", "H", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
+#     ("2020-01-01 04:00", "YS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
+#     ("2019-12-31 12:00", "15min", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
+#     ("2019-12-31 12:00", "h", 6, "2019-12-31 12:00", "2019-12-31 12:00"),
 #     ("2019-12-31 12:00", "D", 6, "2019-12-31 06:00", "2020-01-01 06:00"),
 #     ("2019-12-31 12:00", "MS", 6, "2019-12-01 06:00", "2020-01-01 06:00"),
 #     ("2019-12-31 12:00", "QS", 6, "2019-10-01 06:00", "2020-01-01 06:00"),
-#     ("2019-12-31 12:00", "AS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
-#     ("2020-04-21 15:25", "15T", 6, "2020-04-21 15:15", "2020-04-21 15:30"),
-#     ("2020-04-21 15:25", "H", 6, "2020-04-21 15:00", "2020-04-21 16:00"),
-#     ("2020-03-29 01:40", "15T", 6, "2020-03-29 01:30", "2020-03-29 01:45"),
-#     ("2020-03-29 03:05", "15T", 6, "2020-03-29 03:00", "2020-03-29 03:15"),
+#     ("2019-12-31 12:00", "YS", 6, "2019-01-01 06:00", "2020-01-01 06:00"),
+#     ("2020-04-21 15:25", "15min", 6, "2020-04-21 15:15", "2020-04-21 15:30"),
+#     ("2020-04-21 15:25", "h", 6, "2020-04-21 15:00", "2020-04-21 16:00"),
+#     ("2020-03-29 01:40", "15min", 6, "2020-03-29 01:30", "2020-03-29 01:45"),
+#     ("2020-03-29 03:05", "15min", 6, "2020-03-29 03:00", "2020-03-29 03:15"),
 #     ("2020-04-21 15:25", "D", 6, "2020-04-21 06:00", "2020-04-22 06:00"),
 #     ("2020-03-28 01:40", "D", 6, "2020-03-27 06:00", "2020-03-28 06:00"),
 #     ("2020-03-29 01:40", "D", 6, "2020-03-28 06:00", "2020-03-29 06:00"),
diff --git a/tests/tools/test_frames.py b/tests/tools/test_frames.py
index 77e8caa..f65a30f 100644
--- a/tests/tools/test_frames.py
+++ b/tests/tools/test_frames.py
@@ -26,7 +26,7 @@
         (range(7), 0),
         (range(-3, 4), 0),
         (pd.date_range("2020", periods=7, freq="D"), 0),
-        (pd.date_range("2020", periods=7, freq="M", tz="Europe/Berlin"), 0.04),
+        (pd.date_range("2020", periods=7, freq="ME", tz="Europe/Berlin"), 0.04),
     ],
 )
 def test_fill_gaps(values, index, maxgap, gapvalues, tol):
@@ -72,7 +72,7 @@ def test_addheader_tocolumns(df_columns, header, expected_columns):
 # TODO: put in ... fixture (?)
 test_index_D = dev.get_index("D")
 test_index_D_deconstructed = test_index_D.map(lambda ts: (ts.year, ts.month, ts.day))
-test_index_H = dev.get_index("H")
+test_index_H = dev.get_index("h")
 test_index_H_deconstructed = test_index_H.map(lambda ts: (ts.year, ts.month, ts.day))
 
 
diff --git a/tests/tools/test_freq.py b/tests/tools/test_freq.py
index 88e38fb..ac9b275 100644
--- a/tests/tools/test_freq.py
+++ b/tests/tools/test_freq.py
@@ -4,18 +4,29 @@
 
 from portfolyo import tools
 
-freqs_small_to_large = ["T", "5T", "15T", "30T", "H", "2H", "D", "MS", "QS", "AS"]
+freqs_small_to_large = [
+    "min",
+    "5min",
+    "15min",
+    "30min",
+    "h",
+    "2h",
+    "D",
+    "MS",
+    "QS",
+    "YS",
+]
 freqs_small_to_large_valid = [
-    "15T",
-    "H",
+    "15min",
+    "h",
     "D",
     "MS",
     "QS",
     "QS-FEB",
-    "AS",
-    "AS-APR",
+    "YS",
+    "YS-APR",
 ]
-invalid_freq = ["T", "5T", "2H", "5D", "3MS"]
+invalid_freq = ["min", "5min", "2h", "5D", "3MS"]
 
 
 @pytest.mark.parametrize("count", range(1, 30))
@@ -30,12 +41,12 @@ def test_longestshortestfreq(count):
 @pytest.mark.parametrize(
     ("start", "end", "expected"),
     [
-        ("2020", "2021", "AS"),
+        ("2020", "2021", "YS"),
         ("2020", "2020-04", "QS"),
         ("2020", "2020-02", "MS"),
         ("2020", "2020-01-02", "D"),
-        ("2020", "2020-01-01 01:00", "H"),
-        ("2020", "2020-01-01 00:15", "15T"),
+        ("2020", "2020-01-01 01:00", "h"),
+        ("2020", "2020-01-01 00:15", "15min"),
         ("2020-03-29", "2020-03-30", "D"),
         ("2020-03-01", "2020-04-01", "MS"),
         ("2020-10-25", "2020-10-26", "D"),
@@ -57,19 +68,19 @@ def test_fromtdelta(start, end, expected, tz):
     ("start", "end", "expected"),
     [
         # Hourly.
-        ("2020-03-29 01:00", "2020-03-29 03:00", "H"),
-        ("2020-10-25 01:00", "2020-10-25 02:00+0200", "H"),
+        ("2020-03-29 01:00", "2020-03-29 03:00", "h"),
+        ("2020-10-25 01:00", "2020-10-25 02:00+0200", "h"),
         ("2020-10-25 01:00", "2020-10-25 02:00+0100", None),
-        ("2020-10-25 02:00+0200", "2020-10-25 02:00+0100", "H"),
+        ("2020-10-25 02:00+0200", "2020-10-25 02:00+0100", "h"),
         ("2020-10-25 02:00+0200", "2020-10-25 03:00", None),
-        ("2020-10-25 02:00+0100", "2020-10-25 03:00", "H"),
+        ("2020-10-25 02:00+0100", "2020-10-25 03:00", "h"),
         # Quarterhourly.
-        ("2020-03-29 01:45", "2020-03-29 03:00", "15T"),
-        ("2020-10-25 01:45", "2020-10-25 02:00+0200", "15T"),
+        ("2020-03-29 01:45", "2020-03-29 03:00", "15min"),
+        ("2020-10-25 01:45", "2020-10-25 02:00+0200", "15min"),
         ("2020-10-25 01:45", "2020-10-25 02:00+0100", None),
-        ("2020-10-25 02:45+0200", "2020-10-25 02:00+0100", "15T"),
+        ("2020-10-25 02:45+0200", "2020-10-25 02:00+0100", "15min"),
         ("2020-10-25 02:45+0200", "2020-10-25 03:00", None),
-        ("2020-10-25 02:45+0100", "2020-10-25 03:00", "15T"),
+        ("2020-10-25 02:45+0100", "2020-10-25 03:00", "15min"),
     ],
 )
 def test_fromtdelta_dst(start, end, expected):
@@ -94,9 +105,9 @@ def test_fromtdelta_dst(start, end, expected):
         # . too few
         ("D", 2, "MS", ValueError),
         ("D", 2, "D", "D"),
-        # 15T, too few
-        ("15T", 2, "MS", ValueError),
-        ("15T", 2, "15T", "15T"),
+        # 15min, too few
+        ("15min", 2, "MS", ValueError),
+        ("15min", 2, "15min", "15min"),
         # invalid freq
         # . enough
         ("2D", 10, "MS", ValueError),
@@ -148,8 +159,8 @@ def test_setfreq(
         ("D", 10, "D"),
         # . too few
         ("D", 2, None),
-        # 15T, too few
-        ("15T", 2, None),
+        # 15min, too few
+        ("15min", 2, None),
         # invalid freq
         # . enough
         ("2D", 10, "2D"),
@@ -164,7 +175,7 @@ def test_setfreq(
         # . too few
         ("QS-APR", 2, None),
         ("QS", 2, None),
-        ("AS-FEB", 10, "AS-FEB"),
+        ("YS-FEB", 10, "YS-FEB"),
     ],
 )
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
@@ -191,18 +202,18 @@ def test_guessfreq(
 
 # Define your frequencies and their validity
 freqs_with_validity = [
-    ("15T", True),
-    ("30T", False),
+    ("15min", True),
+    ("30min", False),
     ("D", True),
-    ("H", True),
+    ("h", True),
     ("MS", True),
     ("QS", True),
-    ("AS", True),
-    ("AS-APR", True),
+    ("YS", True),
+    ("YS-APR", True),
     ("QS-FEB", True),
-    ("T", False),
-    ("5T", False),
-    ("2H", False),
+    ("min", False),
+    ("5min", False),
+    ("2h", False),
     ("5D", False),
     ("3MS", False),
 ]
@@ -222,20 +233,20 @@ def test_freq_validity(freq: str, is_valid: bool):
 @pytest.mark.parametrize(
     ("freq1", "freq2", "strict", "is_supposed_to_fail"),
     [
-        ("15T", "15T", False, False),
-        ("15T", "15T", True, True),
-        ("H", "15T", True, False),
-        ("15T", "H", True, True),
-        ("15T", "H", False, True),
+        ("15min", "15min", False, False),
+        ("15min", "15min", True, True),
+        ("h", "15min", True, False),
+        ("15min", "h", True, True),
+        ("15min", "h", False, True),
         ("MS", "MS", True, True),
         ("MS", "MS", False, False),
         ("MS", "QS-APR", False, True),
-        ("QS", "AS", True, True),
+        ("QS", "YS", True, True),
         ("QS", "QS-APR", False, False),
         ("QS-FEB", "QS-APR", True, True),
         ("QS-FEB", "QS-APR", False, False),
-        ("AS", "QS", False, False),
-        ("QS-APR", "AS-APR", False, True),
+        ("YS", "QS", False, False),
+        ("QS-APR", "YS-APR", False, True),
     ],
 )
 def test_freq_sufficiently_long(
@@ -251,17 +262,17 @@ def test_freq_sufficiently_long(
 @pytest.mark.parametrize(
     ("freq1", "freq2", "is_supposed_to_fail"),
     [
-        ("15T", "15T", False),
-        ("H", "15T", True),
-        ("15T", "H", True),
+        ("15min", "15min", False),
+        ("h", "15min", True),
+        ("15min", "h", True),
         ("MS", "MS", False),
         ("MS", "QS-APR", True),
-        ("QS", "AS", True),
+        ("QS", "YS", True),
         ("QS", "QS-APR", False),
         ("QS-FEB", "QS-APR", False),
-        ("AS", "QS", True),
-        ("QS-APR", "AS-APR", True),
-        ("AS-APR", "AS-FEB", False),
+        ("YS", "QS", True),
+        ("QS-APR", "YS-APR", True),
+        ("YS-APR", "YS-FEB", False),
     ],
 )
 def test_freq_equally_long(freq1: str, freq2: str, is_supposed_to_fail: bool):
@@ -275,20 +286,20 @@ def test_freq_equally_long(freq1: str, freq2: str, is_supposed_to_fail: bool):
 @pytest.mark.parametrize(
     ("freq1", "freq2", "strict", "is_supposed_to_fail"),
     [
-        ("15T", "15T", False, False),
-        ("15T", "15T", True, True),
-        ("H", "15T", True, True),
-        ("15T", "H", True, False),
-        ("15T", "H", False, False),
+        ("15min", "15min", False, False),
+        ("15min", "15min", True, True),
+        ("h", "15min", True, True),
+        ("15min", "h", True, False),
+        ("15min", "h", False, False),
         ("MS", "MS", True, True),
         ("MS", "MS", False, False),
         ("MS", "QS-APR", False, False),
-        ("QS", "AS", True, False),
+        ("QS", "YS", True, False),
         ("QS", "QS-APR", False, False),
         ("QS-FEB", "QS-APR", True, True),
         ("QS-FEB", "QS-APR", False, False),
-        ("AS", "QS", False, True),
-        ("QS-APR", "AS-APR", False, False),
+        ("YS", "QS", False, True),
+        ("QS-APR", "YS-APR", False, False),
     ],
 )
 def test_freq_sufficiently_short(
@@ -308,11 +319,11 @@ def test_freq_sufficiently_short(
         ("D", "MS", -1),
         ("MS", "QS", -1),
         ("MS", "QS-APR", -1),
-        ("QS", "AS-APR", -1),
-        ("QS", "AS", -1),
+        ("QS", "YS-APR", -1),
+        ("QS", "YS", -1),
         # upsampling
         ("QS", "D", 1),
-        ("AS-APR", "QS", 1),
+        ("YS-APR", "QS", 1),
         # the same
         ("MS", "MS", 0),
         ("QS", "QS", 0),
@@ -320,9 +331,9 @@ def test_freq_sufficiently_short(
         ("QS", "QS-JAN", 0),
         # ValueError
         ("QS", "QS-FEB", ValueError),
-        ("QS", "AS-FEB", ValueError),
-        ("AS-APR", "AS", ValueError),
-        ("AS-FEB", "QS", ValueError),
+        ("QS", "YS-FEB", ValueError),
+        ("YS-APR", "YS", ValueError),
+        ("YS-FEB", "QS", ValueError),
     ],
 )
 def test_up_pr_down2(source_freq: str, ref_freq: str, expected: int | Exception):
diff --git a/tests/tools/test_hedge.py b/tests/tools/test_hedge.py
index 38b9dba..8f403ef 100644
--- a/tests/tools/test_hedge.py
+++ b/tests/tools/test_hedge.py
@@ -19,7 +19,7 @@
 )
 def test_onehedge_uniformpricesandduration(w_vals, start, w_expected, how):
     """Test hedge with uniform prices and durations."""
-    i = pd.date_range(start, freq="H", periods=len(w_vals), tz="Europe/Berlin")
+    i = pd.date_range(start, freq="h", periods=len(w_vals), tz="Europe/Berlin")
     df = pd.DataFrame({"w": w_vals, "p": 100.0}, i)
     df["duration"] = tools.duration.index(i)
 
@@ -108,8 +108,8 @@ def test_onehedge(
 @pytest.mark.parametrize("withunits", ["units", "nounits"])
 @pytest.mark.parametrize("how", ["vol", "val"])
 @pytest.mark.parametrize("bpo", ["b", "po"])
-@pytest.mark.parametrize("aggfreq", ["MS", "QS", "AS"])
-@pytest.mark.parametrize("freq", ["H", "D"])
+@pytest.mark.parametrize("aggfreq", ["MS", "QS", "YS"])
+@pytest.mark.parametrize("freq", ["h", "D"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin"])
 def test_hedge_fromexcel(tz, freq, aggfreq, bpo, how, withunits):
     """Test if hedge results are correctly calculated, by comparing against previously calculated results."""
diff --git a/tests/tools/test_hedge1.py b/tests/tools/test_hedge1.py
index e3d1008..ac277c8 100644
--- a/tests/tools/test_hedge1.py
+++ b/tests/tools/test_hedge1.py
@@ -19,7 +19,7 @@
 )
 def test_onehedge_uniformpricesandduration(w_vals, start, w_expected, how):
     """Test hedge with uniform prices and durations."""
-    i = pd.date_range(start, freq="H", periods=len(w_vals), tz="Europe/Berlin")
+    i = pd.date_range(start, freq="h", periods=len(w_vals), tz="Europe/Berlin")
     df = pd.DataFrame({"w": w_vals, "p": 100.0}, i)
     df["duration"] = tools.duration.index(i)
 
diff --git a/tests/tools/test_hedge2.py b/tests/tools/test_hedge2.py
index bc82b59..d034934 100644
--- a/tests/tools/test_hedge2.py
+++ b/tests/tools/test_hedge2.py
@@ -22,7 +22,7 @@ def has_units(request):
     return request.param
 
 
-@pytest.fixture(scope="session", params=["H", "D"])
+@pytest.fixture(scope="session", params=["h", "D"])
 def freq(request):
     return request.param
 
@@ -48,7 +48,7 @@ def peak_fn(request):
     return request.param
 
 
-@pytest.fixture(scope="session", params=["MS", "QS", "AS"])
+@pytest.fixture(scope="session", params=["MS", "QS", "YS"])
 def aggfreq(request):
     return request.param
 
diff --git a/tests/tools/test_hedge_data.xlsx b/tests/tools/test_hedge_data.xlsx
index 463bb26..386d8d0 100644
Binary files a/tests/tools/test_hedge_data.xlsx and b/tests/tools/test_hedge_data.xlsx differ
diff --git a/tests/tools/test_intersect.py b/tests/tools/test_intersect.py
index 18c1770..b425c18 100644
--- a/tests/tools/test_intersect.py
+++ b/tests/tools/test_intersect.py
@@ -6,39 +6,39 @@
 from portfolyo import testing, tools
 
 TEST_FREQUENCIES = [
-    "AS",
-    "AS-FEB",
-    "AS-APR",
+    "YS",
+    "YS-FEB",
+    "YS-APR",
     "QS",
     "QS-FEB",
     "QS-APR",
     "MS",
     "D",
-    "H",
-    "15T",
+    "h",
+    "15min",
 ]
 
 COMMON_END = "2022-02-02"
 
 TESTCASES = [  # startdates, freq, expected_startdate
     # One starts at first day of year.
-    (("2020-01-01", "2020-01-20"), "15T", "2020-01-20"),
-    (("2020-01-01", "2020-01-20"), "15T", "2020-01-20"),
-    (("2020-01-01", "2020-01-20"), "H", "2020-01-20"),
-    (("2020-01-01", "2020-01-20"), "H", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "15min", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "15min", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "h", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "h", "2020-01-20"),
     (("2020-01-01", "2020-01-20"), "D", "2020-01-20"),
     (("2020-01-01", "2020-01-20"), "D", "2020-01-20"),
     (("2020-01-01", "2020-03-01"), "MS", "2020-03-01"),
     (("2020-01-01", "2020-03-01"), "MS", "2020-03-01"),
     (("2020-01-01", "2020-04-01"), "QS", "2020-04-01"),
     (("2020-01-01", "2020-04-01"), "QS", "2020-04-01"),
-    (("2020-01-01", "2021-01-01"), "AS", "2021-01-01"),
-    (("2020-01-01", "2021-01-01"), "AS", "2021-01-01"),
+    (("2020-01-01", "2021-01-01"), "YS", "2021-01-01"),
+    (("2020-01-01", "2021-01-01"), "YS", "2021-01-01"),
     # Both start in middle of year.
-    (("2020-04-21", "2020-06-20"), "15T", "2020-06-20"),
-    (("2020-04-21", "2020-06-20"), "15T", "2020-06-20"),
-    (("2020-04-21", "2020-06-20"), "H", "2020-06-20"),
-    (("2020-04-21", "2020-06-20"), "H", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "15min", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "15min", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "h", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "h", "2020-06-20"),
     (("2020-04-21", "2020-06-20"), "D", "2020-06-20"),
     (("2020-04-21", "2020-06-20"), "D", "2020-06-20"),
 ]
@@ -107,7 +107,7 @@ def test_intersect_distinctfreq(
     expected_startdate: str,
 ):
     """Test if intersection of indices with distinct frequencies gives correct result."""
-    otherfreq = "H" if freq == "D" else "D"
+    otherfreq = "h" if freq == "D" else "D"
     idxs = [
         get_idx(startdates[0], starttime, tz, freq),
         get_idx(startdates[1], starttime, tz, otherfreq),
diff --git a/tests/tools/test_intersect_flex.py b/tests/tools/test_intersect_flex.py
index fab118a..fcd9d19 100644
--- a/tests/tools/test_intersect_flex.py
+++ b/tests/tools/test_intersect_flex.py
@@ -9,23 +9,23 @@
 
 TESTCASES = [  # startdates, freq, expected_startdate
     # One starts at first day of year.
-    (("2020-01-01", "2020-01-20"), "15T", "2020-01-20"),
-    (("2020-01-01", "2020-01-20"), "15T", "2020-01-20"),
-    (("2020-01-01", "2020-01-20"), "H", "2020-01-20"),
-    (("2020-01-01", "2020-01-20"), "H", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "15min", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "15min", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "h", "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), "h", "2020-01-20"),
     (("2020-01-01", "2020-01-20"), "D", "2020-01-20"),
     (("2020-01-01", "2020-01-20"), "D", "2020-01-20"),
     (("2020-01-01", "2020-03-01"), "MS", "2020-03-01"),
     (("2020-01-01", "2020-03-01"), "MS", "2020-03-01"),
     (("2020-01-01", "2020-04-01"), "QS", "2020-04-01"),
     (("2020-01-01", "2020-04-01"), "QS", "2020-04-01"),
-    (("2020-01-01", "2021-01-01"), "AS", "2021-01-01"),
-    (("2020-01-01", "2021-01-01"), "AS", "2021-01-01"),
+    (("2020-01-01", "2021-01-01"), "YS", "2021-01-01"),
+    (("2020-01-01", "2021-01-01"), "YS", "2021-01-01"),
     # Both start in middle of year.
-    (("2020-04-21", "2020-06-20"), "15T", "2020-06-20"),
-    (("2020-04-21", "2020-06-20"), "15T", "2020-06-20"),
-    (("2020-04-21", "2020-06-20"), "H", "2020-06-20"),
-    (("2020-04-21", "2020-06-20"), "H", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "15min", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "15min", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "h", "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), "h", "2020-06-20"),
     (("2020-04-21", "2020-06-20"), "D", "2020-06-20"),
     (("2020-04-21", "2020-06-20"), "D", "2020-06-20"),
 ]
@@ -33,17 +33,17 @@
 COMMON_END_2 = "2023-01-01"
 TESTCASES_2 = [  # startdates, freq, expected_dates
     # One starts at first day of year.
-    (("2020-01-01", "2020-01-20"), ("15T", "H"), "2020-01-20"),
-    (("2020-01-01", "2020-01-20"), ("15T", "D"), "2020-01-20"),
-    (("2022-04-01", "2021-02-01"), ("H", "MS"), "2022-04-01"),
-    (("2020-01-01", "2020-04-01"), ("H", "QS"), "2020-04-01"),
-    (("2020-01-01", "2021-01-01"), ("D", "AS"), "2021-01-01"),
+    (("2020-01-01", "2020-01-20"), ("15min", "h"), "2020-01-20"),
+    (("2020-01-01", "2020-01-20"), ("15min", "D"), "2020-01-20"),
+    (("2022-04-01", "2021-02-01"), ("h", "MS"), "2022-04-01"),
+    (("2020-01-01", "2020-04-01"), ("h", "QS"), "2020-04-01"),
+    (("2020-01-01", "2021-01-01"), ("D", "YS"), "2021-01-01"),
     # Both start in middle of year.
-    (("2020-04-21", "2020-06-20"), ("15T", "H"), "2020-06-20"),
-    (("2020-04-21", "2020-06-20"), ("15T", "D"), "2020-06-20"),
-    (("2020-04-21", "2020-07-01"), ("H", "MS"), "2020-07-01"),
-    (("2020-04-21", "2020-07-01"), ("H", "QS"), "2020-07-01"),
-    (("2020-04-21", "2021-01-01"), ("D", "AS"), "2021-01-01"),
+    (("2020-04-21", "2020-06-20"), ("15min", "h"), "2020-06-20"),
+    (("2020-04-21", "2020-06-20"), ("15min", "D"), "2020-06-20"),
+    (("2020-04-21", "2020-07-01"), ("h", "MS"), "2020-07-01"),
+    (("2020-04-21", "2020-07-01"), ("h", "QS"), "2020-07-01"),
+    (("2020-04-21", "2021-01-01"), ("D", "YS"), "2021-01-01"),
 ]
 
 
@@ -95,7 +95,7 @@ def test_intersect_flex_ignore_start_of_day(
     do_test_intersect(
         "idx",
         idxs,
-        ValueError if freq == "15T" or freq == "H" else expected_startdate,
+        ValueError if freq == "15min" or freq == "h" else expected_startdate,
         expected_tz=tz,
         expected_freq=freq,
         expected_starttime=starttime,
@@ -192,7 +192,10 @@ def test_ignore_all(  # indexorframe: str,
         idxs,
         (
             ValueError
-            if freq[0] == "15T" or freq[0] == "H" or freq[1] == "15T" or freq[1] == "H"
+            if freq[0] == "15min"
+            or freq[0] == "h"
+            or freq[1] == "15min"
+            or freq[1] == "h"
             else expected_startdate
         ),
         expected_tz=tz,
diff --git a/tests/tools/test_intersect_flex_frame.py b/tests/tools/test_intersect_flex_frame.py
index 5c42b05..9f0d5b6 100644
--- a/tests/tools/test_intersect_flex_frame.py
+++ b/tests/tools/test_intersect_flex_frame.py
@@ -91,11 +91,11 @@ def test_frames_ignore_freq(types: str, ignore_freq: bool):
     idx_a = pd.date_range("2022-04-01", "2024-07-01", freq="QS", inclusive="left")
     a = pd.Series(range(0, 9), idx_a)
 
-    idx_b = pd.date_range("2021-01-01", "2024-01-01", freq="AS", inclusive="left")
+    idx_b = pd.date_range("2021-01-01", "2024-01-01", freq="YS", inclusive="left")
     b = pd.Series(range(0, 3), idx_b)
 
     exp_idx_a = pd.date_range("2023-01-01", "2024-01-01", freq="QS", inclusive="left")
-    exp_idx_b = pd.date_range("2023-01-01", "2024-01-01", freq="AS", inclusive="left")
+    exp_idx_b = pd.date_range("2023-01-01", "2024-01-01", freq="YS", inclusive="left")
     exp_a = pd.Series(range(3, 7), exp_idx_a)
     exp_b = pd.Series(range(2, 3), exp_idx_b)
     if types == "series":
@@ -131,7 +131,7 @@ def test_frames_ignore_all(types: str, ignore_all: bool):
     a = pd.Series(range(0, 9), idx_a)
 
     idx_b = pd.date_range(
-        "2021-01-01 06:00", "2024-01-01 06:00", freq="AS", inclusive="left"
+        "2021-01-01 06:00", "2024-01-01 06:00", freq="YS", inclusive="left"
     )
     b = pd.Series(range(0, 3), idx_b)
 
@@ -143,7 +143,7 @@ def test_frames_ignore_all(types: str, ignore_all: bool):
         inclusive="left",
     )
     exp_idx_b = pd.date_range(
-        "2023-01-01 06:00", "2024-01-01 06:00", freq="AS", inclusive="left"
+        "2023-01-01 06:00", "2024-01-01 06:00", freq="YS", inclusive="left"
     )
     exp_a = pd.Series(range(3, 7), exp_idx_a)
     exp_b = pd.Series(range(2, 3), exp_idx_b)
diff --git a/tests/tools/test_isboundary.py b/tests/tools/test_isboundary.py
index ce1f505..2cc81b2 100644
--- a/tests/tools/test_isboundary.py
+++ b/tests/tools/test_isboundary.py
@@ -7,239 +7,245 @@
 from portfolyo import testing, tools
 
 TESTCASES = [  # date, i_freq, periods, freq, expected_repeat
-    ("2020-01-01", "15T", 2 * 366 * 24 * 4, "15T", 1),
-    ("2020-01-01", "15T", 2 * 366 * 24 * 4, "H", 4),
-    ("2020-01-01", "15T", 2 * 31 * 24 * 4, "D", 24 * 4),  # >2MS
-    ("2020-01-01", "15T", 2 * 31 * 24 * 4, "MS", (31 * 24 * 4, 29 * 24 * 4)),  # >2MS
-    ("2020-01-01", "15T", 2 * 366 * 24 * 4, "AS", (366 * 24 * 4, 365 * 24 * 4)),  # >2AS
-    ("2020-01-01", "H", 2 * 366 * 24, "15T", 1),
-    ("2020-01-01", "H", 2 * 366 * 24, "H", 1),
-    ("2020-01-01", "H", 2 * 31 * 24, "D", 24),  # >2MS
-    ("2020-01-01", "H", 2 * 31 * 24, "MS", (31 * 24, 29 * 24)),  # >2MS
-    ("2020-01-01", "H", 2 * 366 * 24, "AS", (366 * 24, 365 * 24)),  # >2AS
-    ("2020-01-01", "D", 2 * 366, "15T", 1),
-    ("2020-01-01", "D", 2 * 366, "H", 1),
+    ("2020-01-01", "15min", 2 * 366 * 24 * 4, "15min", 1),
+    ("2020-01-01", "15min", 2 * 366 * 24 * 4, "h", 4),
+    ("2020-01-01", "15min", 2 * 31 * 24 * 4, "D", 24 * 4),  # >2MS
+    ("2020-01-01", "15min", 2 * 31 * 24 * 4, "MS", (31 * 24 * 4, 29 * 24 * 4)),  # >2MS
+    (
+        "2020-01-01",
+        "15min",
+        2 * 366 * 24 * 4,
+        "YS",
+        (366 * 24 * 4, 365 * 24 * 4),
+    ),  # >2AS
+    ("2020-01-01", "h", 2 * 366 * 24, "15min", 1),
+    ("2020-01-01", "h", 2 * 366 * 24, "h", 1),
+    ("2020-01-01", "h", 2 * 31 * 24, "D", 24),  # >2MS
+    ("2020-01-01", "h", 2 * 31 * 24, "MS", (31 * 24, 29 * 24)),  # >2MS
+    ("2020-01-01", "h", 2 * 366 * 24, "YS", (366 * 24, 365 * 24)),  # >2AS
+    ("2020-01-01", "D", 2 * 366, "15min", 1),
+    ("2020-01-01", "D", 2 * 366, "h", 1),
     ("2020-01-01", "D", 2 * 366, "D", 1),
     ("2020-01-01", "D", 2 * 31, "MS", (31, 29)),  # >2MS
     ("2020-01-01", "D", 6 * 31, "QS", (91, 91)),  # >2QS
-    ("2020-01-01", "D", 2 * 366, "AS", (366, 365)),  # >2AS
-    ("2020-01-01", "MS", 2 * 12, "15T", 1),
-    ("2020-01-01", "MS", 2 * 12, "H", 1),
+    ("2020-01-01", "D", 2 * 366, "YS", (366, 365)),  # >2AS
+    ("2020-01-01", "MS", 2 * 12, "15min", 1),
+    ("2020-01-01", "MS", 2 * 12, "h", 1),
     ("2020-01-01", "MS", 2 * 12, "D", 1),
     ("2020-01-01", "MS", 2 * 12, "MS", 1),
     ("2020-01-01", "MS", 2 * 3, "QS", 3),  # >2QS
-    ("2020-01-01", "MS", 2 * 12, "AS", 12),  # >2AS
-    ("2020-01-01", "QS", 2 * 4, "15T", 1),
-    ("2020-01-01", "QS", 2 * 4, "H", 1),
+    ("2020-01-01", "MS", 2 * 12, "YS", 12),  # >2AS
+    ("2020-01-01", "QS", 2 * 4, "15min", 1),
+    ("2020-01-01", "QS", 2 * 4, "h", 1),
     ("2020-01-01", "QS", 2 * 4, "D", 1),
     ("2020-01-01", "QS", 2 * 4, "MS", 1),
     ("2020-01-01", "QS", 2 * 4, "QS", 1),
-    ("2020-01-01", "QS", 2 * 4, "AS", 4),  # >2AS
-    ("2020-01-01", "AS", 2, "15T", 1),
-    ("2020-01-01", "AS", 2, "H", 1),
-    ("2020-01-01", "AS", 2, "D", 1),
-    ("2020-01-01", "AS", 2, "MS", 1),
-    ("2020-01-01", "AS", 2, "QS", 1),
-    ("2020-01-01", "AS", 2, "AS", 1),
+    ("2020-01-01", "QS", 2 * 4, "YS", 4),  # >2AS
+    ("2020-01-01", "YS", 2, "15min", 1),
+    ("2020-01-01", "YS", 2, "h", 1),
+    ("2020-01-01", "YS", 2, "D", 1),
+    ("2020-01-01", "YS", 2, "MS", 1),
+    ("2020-01-01", "YS", 2, "QS", 1),
+    ("2020-01-01", "YS", 2, "YS", 1),
 ]
 
 TESTCASES_DST = [  # ts, i_freq, periods, freq, expected_repeat
     # Start of DST, 2 days.
-    ("2020-03-28 00:00", "15T", 2 * 24 * 4 - 4, "15T", 1),
-    ("2020-03-28 00:00", "15T", 2 * 24 * 4 - 4, "H", 4),
-    ("2020-03-28 00:00", "15T", 2 * 24 * 4 - 4, "D", (24 * 4, 23 * 4)),
-    ("2020-03-28 00:00", "15T", 2 * 24 * 4 - 4, "MS", None),  # None at month start
-    ("2020-03-28 00:00", "15T", 2 * 24 * 4 - 4, "QS", None),  # None at quarter start
-    ("2020-03-28 00:00", "15T", 2 * 24 * 4 - 4, "AS", None),  # None at year start
-    ("2020-03-28 00:00", "H", 2 * 24 - 1, "15T", 1),
-    ("2020-03-28 00:00", "H", 2 * 24 - 1, "H", 1),
-    ("2020-03-28 00:00", "H", 2 * 24 - 1, "D", (24, 23)),
-    ("2020-03-28 00:00", "H", 2 * 24 - 1, "MS", None),  # None at month start
-    ("2020-03-28 00:00", "H", 2 * 24 - 1, "QS", None),  # None at quarter start
-    ("2020-03-28 00:00", "H", 2 * 24 - 1, "AS", None),  # None at year start
-    ("2020-03-28 00:00", "D", 2, "15T", 1),
-    ("2020-03-28 00:00", "D", 2, "H", 1),
+    ("2020-03-28 00:00", "15min", 2 * 24 * 4 - 4, "15min", 1),
+    ("2020-03-28 00:00", "15min", 2 * 24 * 4 - 4, "h", 4),
+    ("2020-03-28 00:00", "15min", 2 * 24 * 4 - 4, "D", (24 * 4, 23 * 4)),
+    ("2020-03-28 00:00", "15min", 2 * 24 * 4 - 4, "MS", None),  # None at month start
+    ("2020-03-28 00:00", "15min", 2 * 24 * 4 - 4, "QS", None),  # None at quarter start
+    ("2020-03-28 00:00", "15min", 2 * 24 * 4 - 4, "YS", None),  # None at year start
+    ("2020-03-28 00:00", "h", 2 * 24 - 1, "15min", 1),
+    ("2020-03-28 00:00", "h", 2 * 24 - 1, "h", 1),
+    ("2020-03-28 00:00", "h", 2 * 24 - 1, "D", (24, 23)),
+    ("2020-03-28 00:00", "h", 2 * 24 - 1, "MS", None),  # None at month start
+    ("2020-03-28 00:00", "h", 2 * 24 - 1, "QS", None),  # None at quarter start
+    ("2020-03-28 00:00", "h", 2 * 24 - 1, "YS", None),  # None at year start
+    ("2020-03-28 00:00", "D", 2, "15min", 1),
+    ("2020-03-28 00:00", "D", 2, "h", 1),
     ("2020-03-28 00:00", "D", 2, "D", 1),
-    ("2020-03-28 06:00", "15T", 2 * 24 * 4 - 4, "15T", 1),
-    ("2020-03-28 06:00", "15T", 2 * 24 * 4 - 4, "H", 4),
-    ("2020-03-28 06:00", "15T", 2 * 24 * 4 - 4, "D", (23 * 4, 24 * 4)),
-    ("2020-03-28 06:00", "15T", 2 * 24 * 4 - 4, "MS", None),  # None at month start
-    ("2020-03-28 06:00", "15T", 2 * 24 * 4 - 4, "QS", None),  # None at quarter start.
-    ("2020-03-28 06:00", "15T", 2 * 24 * 4 - 4, "AS", None),  # None at year start
-    ("2020-03-28 06:00", "H", 2 * 24 - 1, "15T", 1),
-    ("2020-03-28 06:00", "H", 2 * 24 - 1, "H", 1),
-    ("2020-03-28 06:00", "H", 2 * 24 - 1, "D", (23, 24)),
-    ("2020-03-28 06:00", "H", 2 * 24 - 1, "MS", None),  # None at month start
-    ("2020-03-28 06:00", "H", 2 * 24 - 1, "QS", None),  # None at quarter start
-    ("2020-03-28 06:00", "H", 2 * 24 - 1, "AS", None),  # None at year start
-    ("2020-03-28 06:00", "D", 2, "15T", 1),
-    ("2020-03-28 06:00", "D", 2, "H", 1),
+    ("2020-03-28 06:00", "15min", 2 * 24 * 4 - 4, "15min", 1),
+    ("2020-03-28 06:00", "15min", 2 * 24 * 4 - 4, "h", 4),
+    ("2020-03-28 06:00", "15min", 2 * 24 * 4 - 4, "D", (23 * 4, 24 * 4)),
+    ("2020-03-28 06:00", "15min", 2 * 24 * 4 - 4, "MS", None),  # None at month start
+    ("2020-03-28 06:00", "15min", 2 * 24 * 4 - 4, "QS", None),  # None at quarter start.
+    ("2020-03-28 06:00", "15min", 2 * 24 * 4 - 4, "YS", None),  # None at year start
+    ("2020-03-28 06:00", "h", 2 * 24 - 1, "15min", 1),
+    ("2020-03-28 06:00", "h", 2 * 24 - 1, "h", 1),
+    ("2020-03-28 06:00", "h", 2 * 24 - 1, "D", (23, 24)),
+    ("2020-03-28 06:00", "h", 2 * 24 - 1, "MS", None),  # None at month start
+    ("2020-03-28 06:00", "h", 2 * 24 - 1, "QS", None),  # None at quarter start
+    ("2020-03-28 06:00", "h", 2 * 24 - 1, "YS", None),  # None at year start
+    ("2020-03-28 06:00", "D", 2, "15min", 1),
+    ("2020-03-28 06:00", "D", 2, "h", 1),
     ("2020-03-28 06:00", "D", 2, "D", 1),
     # Start of DST, entire month.
-    ("2020-03-01 00:00", "15T", 31 * 24 * 4 - 4, "15T", 1),
-    ("2020-03-01 00:00", "15T", 31 * 24 * 4 - 4, "H", 4),
-    ("2020-03-01 00:00", "15T", 31 * 24 * 4 - 4, "D", (*[96] * 28, 92, *[96] * 2)),
-    ("2020-03-01 00:00", "15T", 31 * 24 * 4 - 4, "MS", 31 * 24 * 4 - 4),
-    ("2020-03-01 00:00", "15T", 31 * 24 * 4 - 4, "AS", None),
-    ("2020-03-01 00:00", "H", 31 * 24 - 1, "15T", 1),
-    ("2020-03-01 00:00", "H", 31 * 24 - 1, "H", 1),
-    ("2020-03-01 00:00", "H", 31 * 24 - 1, "D", (*[24] * 28, 23, *[24] * 2)),
-    ("2020-03-01 00:00", "H", 31 * 24 - 1, "MS", 31 * 24 - 1),
-    ("2020-03-01 00:00", "H", 31 * 24 - 1, "AS", None),
-    ("2020-03-01 00:00", "D", 31, "15T", 1),
-    ("2020-03-01 00:00", "D", 31, "H", 1),
+    ("2020-03-01 00:00", "15min", 31 * 24 * 4 - 4, "15min", 1),
+    ("2020-03-01 00:00", "15min", 31 * 24 * 4 - 4, "h", 4),
+    ("2020-03-01 00:00", "15min", 31 * 24 * 4 - 4, "D", (*[96] * 28, 92, *[96] * 2)),
+    ("2020-03-01 00:00", "15min", 31 * 24 * 4 - 4, "MS", 31 * 24 * 4 - 4),
+    ("2020-03-01 00:00", "15min", 31 * 24 * 4 - 4, "YS", None),
+    ("2020-03-01 00:00", "h", 31 * 24 - 1, "15min", 1),
+    ("2020-03-01 00:00", "h", 31 * 24 - 1, "h", 1),
+    ("2020-03-01 00:00", "h", 31 * 24 - 1, "D", (*[24] * 28, 23, *[24] * 2)),
+    ("2020-03-01 00:00", "h", 31 * 24 - 1, "MS", 31 * 24 - 1),
+    ("2020-03-01 00:00", "h", 31 * 24 - 1, "YS", None),
+    ("2020-03-01 00:00", "D", 31, "15min", 1),
+    ("2020-03-01 00:00", "D", 31, "h", 1),
     ("2020-03-01 00:00", "D", 31, "D", 1),
     ("2020-03-01 00:00", "D", 31, "MS", 31),
-    ("2020-03-01 00:00", "D", 31, "AS", None),
-    ("2020-03-01 00:00", "MS", 1, "15T", 1),
-    ("2020-03-01 00:00", "MS", 1, "H", 1),
+    ("2020-03-01 00:00", "D", 31, "YS", None),
+    ("2020-03-01 00:00", "MS", 1, "15min", 1),
+    ("2020-03-01 00:00", "MS", 1, "h", 1),
     ("2020-03-01 00:00", "MS", 1, "D", 1),
     ("2020-03-01 00:00", "MS", 1, "MS", 1),
-    ("2020-03-01 00:00", "MS", 1, "AS", None),
-    ("2020-03-01 06:00", "15T", 31 * 24 * 4 - 4, "15T", 1),
-    ("2020-03-01 06:00", "15T", 31 * 24 * 4 - 4, "H", 4),
-    ("2020-03-01 06:00", "15T", 31 * 24 * 4 - 4, "D", (*[96] * 27, 92, *[96] * 3)),
-    ("2020-03-01 06:00", "15T", 31 * 24 * 4 - 4, "MS", 31 * 24 * 4 - 4),
-    ("2020-03-01 06:00", "15T", 31 * 24 * 4 - 4, "AS", None),
-    ("2020-03-01 06:00", "H", 31 * 24 - 1, "15T", 1),
-    ("2020-03-01 06:00", "H", 31 * 24 - 1, "H", 1),
-    ("2020-03-01 06:00", "H", 31 * 24 - 1, "D", (*[24] * 27, 23, *[24] * 3)),
-    ("2020-03-01 06:00", "H", 31 * 24 - 1, "MS", 31 * 24 - 1),
-    ("2020-03-01 06:00", "H", 31 * 24 - 1, "AS", None),
-    ("2020-03-01 06:00", "D", 31, "15T", 1),
-    ("2020-03-01 06:00", "D", 31, "H", 1),
+    ("2020-03-01 00:00", "MS", 1, "YS", None),
+    ("2020-03-01 06:00", "15min", 31 * 24 * 4 - 4, "15min", 1),
+    ("2020-03-01 06:00", "15min", 31 * 24 * 4 - 4, "h", 4),
+    ("2020-03-01 06:00", "15min", 31 * 24 * 4 - 4, "D", (*[96] * 27, 92, *[96] * 3)),
+    ("2020-03-01 06:00", "15min", 31 * 24 * 4 - 4, "MS", 31 * 24 * 4 - 4),
+    ("2020-03-01 06:00", "15min", 31 * 24 * 4 - 4, "YS", None),
+    ("2020-03-01 06:00", "h", 31 * 24 - 1, "15min", 1),
+    ("2020-03-01 06:00", "h", 31 * 24 - 1, "h", 1),
+    ("2020-03-01 06:00", "h", 31 * 24 - 1, "D", (*[24] * 27, 23, *[24] * 3)),
+    ("2020-03-01 06:00", "h", 31 * 24 - 1, "MS", 31 * 24 - 1),
+    ("2020-03-01 06:00", "h", 31 * 24 - 1, "YS", None),
+    ("2020-03-01 06:00", "D", 31, "15min", 1),
+    ("2020-03-01 06:00", "D", 31, "h", 1),
     ("2020-03-01 06:00", "D", 31, "D", 1),
     ("2020-03-01 06:00", "D", 31, "MS", 31),
-    ("2020-03-01 06:00", "D", 31, "AS", None),
-    ("2020-03-01 06:00", "MS", 1, "15T", 1),
-    ("2020-03-01 06:00", "MS", 1, "H", 1),
+    ("2020-03-01 06:00", "D", 31, "YS", None),
+    ("2020-03-01 06:00", "MS", 1, "15min", 1),
+    ("2020-03-01 06:00", "MS", 1, "h", 1),
     ("2020-03-01 06:00", "MS", 1, "D", 1),
     ("2020-03-01 06:00", "MS", 1, "MS", 1),
-    ("2020-03-01 06:00", "MS", 1, "AS", None),
+    ("2020-03-01 06:00", "MS", 1, "YS", None),
     # End of DST, 2 days.
-    ("2020-10-24 00:00", "15T", 2 * 24 * 4 + 4, "15T", 1),
-    ("2020-10-24 00:00", "15T", 2 * 24 * 4 + 4, "H", 4),
-    ("2020-10-24 00:00", "15T", 2 * 24 * 4 + 4, "D", (24 * 4, 25 * 4)),
-    ("2020-10-24 00:00", "15T", 2 * 24 * 4 + 4, "MS", None),  # None at month start
-    ("2020-10-24 00:00", "15T", 2 * 24 * 4 + 4, "QS", None),  # None at quarter start
-    ("2020-10-24 00:00", "15T", 2 * 24 * 4 + 4, "AS", None),  # None at year start
-    ("2020-10-24 00:00", "H", 2 * 24 + 1, "15T", 1),
-    ("2020-10-24 00:00", "H", 2 * 24 + 1, "H", 1),
-    ("2020-10-24 00:00", "H", 2 * 24 + 1, "D", (24, 25)),
-    ("2020-10-24 00:00", "H", 2 * 24 + 1, "MS", None),  # None at month start
-    ("2020-10-24 00:00", "H", 2 * 24 + 1, "QS", None),  # None at quarter start
-    ("2020-10-24 00:00", "H", 2 * 24 + 1, "AS", None),  # None at year start
-    ("2020-10-24 00:00", "D", 2 * 24 + 1, "15T", 1),
-    ("2020-10-24 00:00", "D", 2 * 24 + 1, "H", 1),
+    ("2020-10-24 00:00", "15min", 2 * 24 * 4 + 4, "15min", 1),
+    ("2020-10-24 00:00", "15min", 2 * 24 * 4 + 4, "h", 4),
+    ("2020-10-24 00:00", "15min", 2 * 24 * 4 + 4, "D", (24 * 4, 25 * 4)),
+    ("2020-10-24 00:00", "15min", 2 * 24 * 4 + 4, "MS", None),  # None at month start
+    ("2020-10-24 00:00", "15min", 2 * 24 * 4 + 4, "QS", None),  # None at quarter start
+    ("2020-10-24 00:00", "15min", 2 * 24 * 4 + 4, "YS", None),  # None at year start
+    ("2020-10-24 00:00", "h", 2 * 24 + 1, "15min", 1),
+    ("2020-10-24 00:00", "h", 2 * 24 + 1, "h", 1),
+    ("2020-10-24 00:00", "h", 2 * 24 + 1, "D", (24, 25)),
+    ("2020-10-24 00:00", "h", 2 * 24 + 1, "MS", None),  # None at month start
+    ("2020-10-24 00:00", "h", 2 * 24 + 1, "QS", None),  # None at quarter start
+    ("2020-10-24 00:00", "h", 2 * 24 + 1, "YS", None),  # None at year start
+    ("2020-10-24 00:00", "D", 2 * 24 + 1, "15min", 1),
+    ("2020-10-24 00:00", "D", 2 * 24 + 1, "h", 1),
     ("2020-10-24 00:00", "D", 2 * 24 + 1, "D", 1),
-    ("2020-10-24 06:00", "15T", 2 * 24 * 4 + 4, "15T", 1),
-    ("2020-10-24 06:00", "15T", 2 * 24 * 4 + 4, "H", 4),
-    ("2020-10-24 06:00", "15T", 2 * 24 * 4 + 4, "D", (25 * 4, 24 * 4)),
-    ("2020-10-24 06:00", "15T", 2 * 24 * 4 + 4, "MS", None),  # None at month start
-    ("2020-10-24 06:00", "15T", 2 * 24 * 4 + 4, "QS", None),  # None at quarter start
-    ("2020-10-24 06:00", "15T", 2 * 24 * 4 + 4, "AS", None),  # None at year start
-    ("2020-10-24 06:00", "H", 2 * 24 + 1, "15T", 1),
-    ("2020-10-24 06:00", "H", 2 * 24 + 1, "H", 1),
-    ("2020-10-24 06:00", "H", 2 * 24 + 1, "D", (25, 24)),
-    ("2020-10-24 06:00", "H", 2 * 24 + 1, "MS", None),  # None at month start
-    ("2020-10-24 06:00", "H", 2 * 24 + 1, "QS", None),  # None at quarter start
-    ("2020-10-24 06:00", "H", 2 * 24 + 1, "AS", None),  # None at year start
-    ("2020-10-24 06:00", "D", 2, "15T", 1),
-    ("2020-10-24 06:00", "D", 2, "H", 1),
+    ("2020-10-24 06:00", "15min", 2 * 24 * 4 + 4, "15min", 1),
+    ("2020-10-24 06:00", "15min", 2 * 24 * 4 + 4, "h", 4),
+    ("2020-10-24 06:00", "15min", 2 * 24 * 4 + 4, "D", (25 * 4, 24 * 4)),
+    ("2020-10-24 06:00", "15min", 2 * 24 * 4 + 4, "MS", None),  # None at month start
+    ("2020-10-24 06:00", "15min", 2 * 24 * 4 + 4, "QS", None),  # None at quarter start
+    ("2020-10-24 06:00", "15min", 2 * 24 * 4 + 4, "YS", None),  # None at year start
+    ("2020-10-24 06:00", "h", 2 * 24 + 1, "15min", 1),
+    ("2020-10-24 06:00", "h", 2 * 24 + 1, "h", 1),
+    ("2020-10-24 06:00", "h", 2 * 24 + 1, "D", (25, 24)),
+    ("2020-10-24 06:00", "h", 2 * 24 + 1, "MS", None),  # None at month start
+    ("2020-10-24 06:00", "h", 2 * 24 + 1, "QS", None),  # None at quarter start
+    ("2020-10-24 06:00", "h", 2 * 24 + 1, "YS", None),  # None at year start
+    ("2020-10-24 06:00", "D", 2, "15min", 1),
+    ("2020-10-24 06:00", "D", 2, "h", 1),
     ("2020-10-24 06:00", "D", 2, "D", 1),
     # End of DST, entire month.
-    ("2020-10-01 00:00", "15T", 31 * 24 * 4 + 4, "15T", 1),
-    ("2020-10-01 00:00", "15T", 31 * 24 * 4 + 4, "H", 4),
-    ("2020-10-01 00:00", "15T", 31 * 24 * 4 + 4, "D", (*[96] * 24, 100, *[96] * 6)),
-    ("2020-10-01 00:00", "15T", 31 * 24 * 4 + 4, "MS", 31 * 24 * 4 + 4),
-    ("2020-10-01 00:00", "15T", 31 * 24 * 4 + 4, "AS", None),
-    ("2020-10-01 00:00", "H", 31 * 24 + 1, "15T", 1),
-    ("2020-10-01 00:00", "H", 31 * 24 + 1, "H", 1),
-    ("2020-10-01 00:00", "H", 31 * 24 + 1, "D", (*[24] * 24, 25, *[24] * 6)),
-    ("2020-10-01 00:00", "H", 31 * 24 + 1, "MS", 31 * 24 + 1),
-    ("2020-10-01 00:00", "H", 31 * 24 + 1, "AS", None),
-    ("2020-10-01 00:00", "D", 31, "15T", 1),
-    ("2020-10-01 00:00", "D", 31, "H", 1),
+    ("2020-10-01 00:00", "15min", 31 * 24 * 4 + 4, "15min", 1),
+    ("2020-10-01 00:00", "15min", 31 * 24 * 4 + 4, "h", 4),
+    ("2020-10-01 00:00", "15min", 31 * 24 * 4 + 4, "D", (*[96] * 24, 100, *[96] * 6)),
+    ("2020-10-01 00:00", "15min", 31 * 24 * 4 + 4, "MS", 31 * 24 * 4 + 4),
+    ("2020-10-01 00:00", "15min", 31 * 24 * 4 + 4, "YS", None),
+    ("2020-10-01 00:00", "h", 31 * 24 + 1, "15min", 1),
+    ("2020-10-01 00:00", "h", 31 * 24 + 1, "h", 1),
+    ("2020-10-01 00:00", "h", 31 * 24 + 1, "D", (*[24] * 24, 25, *[24] * 6)),
+    ("2020-10-01 00:00", "h", 31 * 24 + 1, "MS", 31 * 24 + 1),
+    ("2020-10-01 00:00", "h", 31 * 24 + 1, "YS", None),
+    ("2020-10-01 00:00", "D", 31, "15min", 1),
+    ("2020-10-01 00:00", "D", 31, "h", 1),
     ("2020-10-01 00:00", "D", 31, "D", 1),
     ("2020-10-01 00:00", "D", 31, "MS", 31),
-    ("2020-10-01 00:00", "D", 31, "AS", None),
-    ("2020-10-01 00:00", "MS", 1, "15T", 1),
-    ("2020-10-01 00:00", "MS", 1, "H", 1),
+    ("2020-10-01 00:00", "D", 31, "YS", None),
+    ("2020-10-01 00:00", "MS", 1, "15min", 1),
+    ("2020-10-01 00:00", "MS", 1, "h", 1),
     ("2020-10-01 00:00", "MS", 1, "D", 1),
     ("2020-10-01 00:00", "MS", 1, "MS", 1),
-    ("2020-10-01 00:00", "MS", 1, "AS", None),
-    ("2020-10-01 06:00", "15T", 31 * 24 * 4 + 4, "15T", 1),
-    ("2020-10-01 06:00", "15T", 31 * 24 * 4 + 4, "H", 4),
-    ("2020-10-01 06:00", "15T", 31 * 24 * 4 + 4, "D", (*[96] * 23, 100, *[96] * 7)),
-    ("2020-10-01 06:00", "15T", 31 * 24 * 4 + 4, "MS", 31 * 24 * 4 + 4),
-    ("2020-10-01 06:00", "15T", 31 * 24 * 4 + 4, "AS", None),
-    ("2020-10-01 06:00", "H", 31 * 24 + 1, "15T", 1),
-    ("2020-10-01 06:00", "H", 31 * 24 + 1, "H", 1),
-    ("2020-10-01 06:00", "H", 31 * 24 + 1, "D", (*[24] * 23, 25, *[24] * 7)),
-    ("2020-10-01 06:00", "H", 31 * 24 + 1, "MS", 31 * 24 + 1),
-    ("2020-10-01 06:00", "H", 31 * 24 + 1, "AS", None),
-    ("2020-10-01 06:00", "D", 31, "15T", 1),
-    ("2020-10-01 06:00", "D", 31, "H", 1),
+    ("2020-10-01 00:00", "MS", 1, "YS", None),
+    ("2020-10-01 06:00", "15min", 31 * 24 * 4 + 4, "15min", 1),
+    ("2020-10-01 06:00", "15min", 31 * 24 * 4 + 4, "h", 4),
+    ("2020-10-01 06:00", "15min", 31 * 24 * 4 + 4, "D", (*[96] * 23, 100, *[96] * 7)),
+    ("2020-10-01 06:00", "15min", 31 * 24 * 4 + 4, "MS", 31 * 24 * 4 + 4),
+    ("2020-10-01 06:00", "15min", 31 * 24 * 4 + 4, "YS", None),
+    ("2020-10-01 06:00", "h", 31 * 24 + 1, "15min", 1),
+    ("2020-10-01 06:00", "h", 31 * 24 + 1, "h", 1),
+    ("2020-10-01 06:00", "h", 31 * 24 + 1, "D", (*[24] * 23, 25, *[24] * 7)),
+    ("2020-10-01 06:00", "h", 31 * 24 + 1, "MS", 31 * 24 + 1),
+    ("2020-10-01 06:00", "h", 31 * 24 + 1, "YS", None),
+    ("2020-10-01 06:00", "D", 31, "15min", 1),
+    ("2020-10-01 06:00", "D", 31, "h", 1),
     ("2020-10-01 06:00", "D", 31, "D", 1),
     ("2020-10-01 06:00", "D", 31, "MS", 31),
-    ("2020-10-01 06:00", "D", 31, "AS", None),
-    ("2020-10-01 06:00", "MS", 1, "15T", 1),
-    ("2020-10-01 06:00", "MS", 1, "H", 1),
+    ("2020-10-01 06:00", "D", 31, "YS", None),
+    ("2020-10-01 06:00", "MS", 1, "15min", 1),
+    ("2020-10-01 06:00", "MS", 1, "h", 1),
     ("2020-10-01 06:00", "MS", 1, "D", 1),
     ("2020-10-01 06:00", "MS", 1, "MS", 1),
-    ("2020-10-01 06:00", "MS", 1, "AS", None),
+    ("2020-10-01 06:00", "MS", 1, "YS", None),
 ]
 
 TESTCASES_MIDYEAR = [  # date, i_freq, periods, freq, expected_repeat,
-    ("2019-12-15", "15T", 2 * 366 * 24 * 4, "15T", 1),
-    ("2019-12-15", "15T", 2 * 366 * 24 * 4, "H", 4),
-    ("2019-12-15", "15T", 2 * 31 * 24 * 4, "D", 24 * 4),  # >2MS
-    ("2019-12-15", "H", 2 * 366 * 24, "15T", 1),
-    ("2019-12-15", "H", 2 * 366 * 24, "H", 1),
-    ("2019-12-15", "H", 2 * 31 * 24, "D", 24),  # >2MS
-    ("2019-12-15", "D", 2 * 366, "15T", 1),
-    ("2019-12-15", "D", 2 * 366, "H", 1),
+    ("2019-12-15", "15min", 2 * 366 * 24 * 4, "15min", 1),
+    ("2019-12-15", "15min", 2 * 366 * 24 * 4, "h", 4),
+    ("2019-12-15", "15min", 2 * 31 * 24 * 4, "D", 24 * 4),  # >2MS
+    ("2019-12-15", "h", 2 * 366 * 24, "15min", 1),
+    ("2019-12-15", "h", 2 * 366 * 24, "h", 1),
+    ("2019-12-15", "h", 2 * 31 * 24, "D", 24),  # >2MS
+    ("2019-12-15", "D", 2 * 366, "15min", 1),
+    ("2019-12-15", "D", 2 * 366, "h", 1),
     ("2019-12-15", "D", 2 * 366, "D", 1),
-    ("2019-12-01", "MS", 2 * 12, "15T", 1),
-    ("2019-12-01", "MS", 2 * 12, "H", 1),
+    ("2019-12-01", "MS", 2 * 12, "15min", 1),
+    ("2019-12-01", "MS", 2 * 12, "h", 1),
     ("2019-12-01", "MS", 2 * 12, "D", 1),
     ("2019-12-01", "MS", 2 * 12, "MS", 1),
-    ("2019-10-01", "QS", 2 * 4, "15T", 1),
-    ("2019-10-01", "QS", 2 * 4, "H", 1),
+    ("2019-10-01", "QS", 2 * 4, "15min", 1),
+    ("2019-10-01", "QS", 2 * 4, "h", 1),
     ("2019-10-01", "QS", 2 * 4, "D", 1),
     ("2019-10-01", "QS", 2 * 4, "MS", 1),
     ("2019-10-01", "QS", 2 * 4, "QS", 1),
 ]
 
 TESTCASES_LEADINGZERO = [  # date, i_freq, periods, freq, expected_repeat, leading_zeros
-    ("2019-12-15", "15T", 2 * 31 * 24 * 4, "MS", 31 * 24 * 4, 17 * 24 * 4),
-    ("2019-12-15", "15T", 2 * 366 * 24 * 4, "AS", 366 * 24 * 4, 17 * 24 * 4),
-    ("2019-12-15", "H", 2 * 31 * 24, "MS", 31 * 24, 17 * 24),
-    ("2019-12-15", "H", 2 * 366 * 24, "AS", 366 * 24, 17 * 24),
+    ("2019-12-15", "15min", 2 * 31 * 24 * 4, "MS", 31 * 24 * 4, 17 * 24 * 4),
+    ("2019-12-15", "15min", 2 * 366 * 24 * 4, "YS", 366 * 24 * 4, 17 * 24 * 4),
+    ("2019-12-15", "h", 2 * 31 * 24, "MS", 31 * 24, 17 * 24),
+    ("2019-12-15", "h", 2 * 366 * 24, "YS", 366 * 24, 17 * 24),
     ("2019-12-15", "D", 2 * 31, "MS", 31, 17),
     ("2019-12-15", "D", 6 * 31, "QS", 91, 17),
-    ("2019-12-15", "D", 2 * 366, "AS", 366, 17),  # >2AS
+    ("2019-12-15", "D", 2 * 366, "YS", 366, 17),  # >2AS
     ("2019-12-01", "MS", 2 * 3, "QS", 3, 1),  # >2QS
-    ("2019-12-01", "MS", 2 * 12, "AS", 12, 1),  # >2AS
-    ("2019-10-01", "QS", 2 * 4, "AS", 4, 1),  # >2AS
+    ("2019-12-01", "MS", 2 * 12, "YS", 12, 1),  # >2AS
+    ("2019-10-01", "QS", 2 * 4, "YS", 4, 1),  # >2AS
     # DST start.
     # ("2020-03-29 00:00", "D", 2*24, "MS", None), #None at month start
     # ("2020-03-29 00:00", "D", 2*24, "QS", None), #None at quarter start
-    # ("2020-03-29 00:00", "D", 2*24, "AS", None), #None at year start
-    # ("2020-03-01 00:00", "15T", 31*24*4-4, "QS", 31*24*4-4),
-    # ("2020-03-01 00:00", "H", 31*24-1, "QS", 31*24*4-4),
+    # ("2020-03-29 00:00", "D", 2*24, "YS", None), #None at year start
+    # ("2020-03-01 00:00", "15min", 31*24*4-4, "QS", 31*24*4-4),
+    # ("2020-03-01 00:00", "h", 31*24-1, "QS", 31*24*4-4),
     # ("2020-03-01 00:00", "D", 31, "QS", 31*24*4-4),
     # ("2020-03-01 00:00", "MS", 1, "QS", 31*24*4-4),
-    # ("2020-03-01 06:00", "15T", 31*24*4-4, "QS", 31*24*4-4),
-    # ("2020-03-01 06:00", "H", 31*24-1, "QS", 31*24*4-4),
+    # ("2020-03-01 06:00", "15min", 31*24*4-4, "QS", 31*24*4-4),
+    # ("2020-03-01 06:00", "h", 31*24-1, "QS", 31*24*4-4),
     # ("2020-03-01 06:00", "D", 31, "QS", 31*24*4-4),
     # ("2020-03-01 06:00", "MS", 1, "QS", 31*24*4-4),
-    # ("2020-10-01 00:00", "15T", 31*24*4+4, "QS", 31*24*4-4),
-    # ("2020-10-01 00:00", "H", 31*24+1, "QS", 31*24+1*4-4),
+    # ("2020-10-01 00:00", "15min", 31*24*4+4, "QS", 31*24*4-4),
+    # ("2020-10-01 00:00", "h", 31*24+1, "QS", 31*24+1*4-4),
     # ("2020-10-01 00:00", "D", 31, "QS", 31*24*4-4),
     # ("2020-10-01 00:00", "MS", 1, "QS", 31*24*4-4),
-    # ("2020-10-01 06:00", "15T", 31*24*4+4, "QS", 31*24*4+4-4),
-    # ("2020-10-01 06:00", "H", 31*24+1, "QS", 31*24+1*4-4),
+    # ("2020-10-01 06:00", "15min", 31*24*4+4, "QS", 31*24*4+4-4),
+    # ("2020-10-01 06:00", "h", 31*24+1, "QS", 31*24+1*4-4),
     # ("2020-10-01 06:00", "D", 31, "QS", 31*24+1*4-4),
     # ("2020-10-01 06:00", "MS", 1, "QS", 31*24*4-4),
 ]
@@ -340,66 +346,66 @@ def do_test_index(ts, i_freq, periods, tz, freq, expected_repeat, leading_zeros)
 
 
 TESTCASES_STAMP = [  # ts, freq, offset_hours, expected
-    ("2020", "15T", 0, True),
-    ("2020", "15T", 6, True),
-    ("2020", "H", 0, True),
-    ("2020", "H", 6, True),
+    ("2020", "15min", 0, True),
+    ("2020", "15min", 6, True),
+    ("2020", "h", 0, True),
+    ("2020", "h", 6, True),
     ("2020", "D", 0, True),
     ("2020", "D", 6, False),
     ("2020", "MS", 0, True),
     ("2020", "MS", 6, False),
-    ("2020", "AS", 0, True),
-    ("2020", "AS", 6, False),
-    ("2020-04-01", "15T", 0, True),
-    ("2020-04-01", "15T", 6, True),
-    ("2020-04-01", "H", 0, True),
-    ("2020-04-01", "H", 6, True),
+    ("2020", "YS", 0, True),
+    ("2020", "YS", 6, False),
+    ("2020-04-01", "15min", 0, True),
+    ("2020-04-01", "15min", 6, True),
+    ("2020-04-01", "h", 0, True),
+    ("2020-04-01", "h", 6, True),
     ("2020-04-01", "D", 0, True),
     ("2020-04-01", "D", 6, False),
     ("2020-04-01", "MS", 0, True),
     ("2020-04-01", "MS", 6, False),
-    ("2020-04-01", "AS", 0, False),
-    ("2020-04-01", "AS", 6, False),
-    ("2020-01-01 15:00", "15T", 0, True),
-    ("2020-01-01 15:00", "15T", 6, True),
-    ("2020-01-01 15:00", "H", 0, True),
-    ("2020-01-01 15:00", "H", 6, True),
+    ("2020-04-01", "YS", 0, False),
+    ("2020-04-01", "YS", 6, False),
+    ("2020-01-01 15:00", "15min", 0, True),
+    ("2020-01-01 15:00", "15min", 6, True),
+    ("2020-01-01 15:00", "h", 0, True),
+    ("2020-01-01 15:00", "h", 6, True),
     ("2020-01-01 15:00", "D", 0, False),
     ("2020-01-01 15:00", "D", 6, False),
     ("2020-01-01 15:00", "MS", 0, False),
     ("2020-01-01 15:00", "MS", 6, False),
-    ("2020-01-01 15:00", "AS", 0, False),
-    ("2020-01-01 15:00", "AS", 6, False),
-    ("2020-01-01 15:45", "15T", 0, True),
-    ("2020-01-01 15:45", "15T", 6, True),
-    ("2020-01-01 15:45", "H", 0, False),
-    ("2020-01-01 15:45", "H", 6, False),
+    ("2020-01-01 15:00", "YS", 0, False),
+    ("2020-01-01 15:00", "YS", 6, False),
+    ("2020-01-01 15:45", "15min", 0, True),
+    ("2020-01-01 15:45", "15min", 6, True),
+    ("2020-01-01 15:45", "h", 0, False),
+    ("2020-01-01 15:45", "h", 6, False),
     ("2020-01-01 15:45", "D", 0, False),
     ("2020-01-01 15:45", "D", 6, False),
     ("2020-01-01 15:45", "MS", 0, False),
     ("2020-01-01 15:45", "MS", 6, False),
-    ("2020-01-01 15:45", "AS", 0, False),
-    ("2020-01-01 15:45", "AS", 6, False),
-    ("2020-01-01 06:00", "15T", 0, True),
-    ("2020-01-01 06:00", "15T", 6, True),
-    ("2020-01-01 06:00", "H", 0, True),
-    ("2020-01-01 06:00", "H", 6, True),
+    ("2020-01-01 15:45", "YS", 0, False),
+    ("2020-01-01 15:45", "YS", 6, False),
+    ("2020-01-01 06:00", "15min", 0, True),
+    ("2020-01-01 06:00", "15min", 6, True),
+    ("2020-01-01 06:00", "h", 0, True),
+    ("2020-01-01 06:00", "h", 6, True),
     ("2020-01-01 06:00", "D", 0, False),
     ("2020-01-01 06:00", "D", 6, True),
     ("2020-01-01 06:00", "MS", 0, False),
     ("2020-01-01 06:00", "MS", 6, True),
-    ("2020-01-01 06:00", "AS", 0, False),
-    ("2020-01-01 06:00", "AS", 6, True),
-    ("2020-04-21 06:00", "15T", 0, True),
-    ("2020-04-21 06:00", "15T", 6, True),
-    ("2020-04-21 06:00", "H", 0, True),
-    ("2020-04-21 06:00", "H", 6, True),
+    ("2020-01-01 06:00", "YS", 0, False),
+    ("2020-01-01 06:00", "YS", 6, True),
+    ("2020-04-21 06:00", "15min", 0, True),
+    ("2020-04-21 06:00", "15min", 6, True),
+    ("2020-04-21 06:00", "h", 0, True),
+    ("2020-04-21 06:00", "h", 6, True),
     ("2020-04-21 06:00", "D", 0, False),
     ("2020-04-21 06:00", "D", 6, True),
     ("2020-04-21 06:00", "MS", 0, False),
     ("2020-04-21 06:00", "MS", 6, False),
-    ("2020-04-21 06:00", "AS", 0, False),
-    ("2020-04-21 06:00", "AS", 6, False),
+    ("2020-04-21 06:00", "YS", 0, False),
+    ("2020-04-21 06:00", "YS", 6, False),
 ]
 
 
diff --git a/tests/tools/test_peakconvert.py b/tests/tools/test_peakconvert.py
index 0b7eff9..c18e22d 100644
--- a/tests/tools/test_peakconvert.py
+++ b/tests/tools/test_peakconvert.py
@@ -27,7 +27,7 @@
                 "base": [80.0, 80, 80, 80],
                 "offpeak": [68.8510638, 68.8699360, 68.9361702, 68.9361702],
             },
-            pd.date_range("2020", periods=4, freq="AS", tz="Europe/Berlin"),
+            pd.date_range("2020", periods=4, freq="YS", tz="Europe/Berlin"),
         ),
     ],
 )
@@ -59,7 +59,7 @@ def test_completebpoframe_averagable(bpoframe, testcol: str, withunits: str):
                 "base": [280.0, 180, 80, 380],
                 "offpeak": [180, 80, -20, 280],
             },
-            pd.date_range("2020", periods=4, freq="AS", tz="Europe/Berlin"),
+            pd.date_range("2020", periods=4, freq="YS", tz="Europe/Berlin"),
         ),
     ],
 )
diff --git a/tests/tools/test_peakfn.py b/tests/tools/test_peakfn.py
index df8a9e4..1bc9215 100644
--- a/tests/tools/test_peakfn.py
+++ b/tests/tools/test_peakfn.py
@@ -17,56 +17,56 @@ def index(start: str, end: str, freq: str, tz: str) -> pd.DatetimeIndex:
 
 f_germanpower = tools.peakfn.factory(dt.time(hour=8), dt.time(hour=20))
 TESTCASES_GERMANPOWER = [  # end, freq, count, stretch
-    ("2020-01-08", "15T", 5 * 12 * 4, (32, 79)),
-    ("2020-01-08", "H", 5 * 12, (8, 19)),
-    ("2020-04-01", "15T", 65 * 12 * 4, (-64, -17)),
-    ("2020-04-01", "H", 65 * 12, (-16, -5)),
-    ("2021", "15T", 262 * 12 * 4, (-64, -17)),
-    ("2021", "H", 262 * 12, (-16, -5)),
+    ("2020-01-08", "15min", 5 * 12 * 4, (32, 79)),
+    ("2020-01-08", "h", 5 * 12, (8, 19)),
+    ("2020-04-01", "15min", 65 * 12 * 4, (-64, -17)),
+    ("2020-04-01", "h", 65 * 12, (-16, -5)),
+    ("2021", "15min", 262 * 12 * 4, (-64, -17)),
+    ("2021", "h", 262 * 12, (-16, -5)),
     ("2021", "D", ValueError, None),
     ("2021", "MS", ValueError, None),
     ("2021", "QS", ValueError, None),
-    ("2021", "AS", ValueError, None),
+    ("2021", "YS", ValueError, None),
 ]
 
 f_everyday_13half = tools.peakfn.factory(
     dt.time(hour=8), dt.time(hour=21, minute=30), [1, 2, 3, 4, 5, 6, 7]
 )
 TESTCASES_13HALF = [  # end, freq, count, stretch
-    ("2020-01-08", "15T", 7 * 13.5 * 4, (32, 85)),
-    ("2020-04-01", "15T", 91 * 13.5 * 4, (-64, -11)),
-    ("2021", "15T", 366 * 13.5 * 4, (-64, -11)),
-    ("2021", "H", ValueError, None),
+    ("2020-01-08", "15min", 7 * 13.5 * 4, (32, 85)),
+    ("2020-04-01", "15min", 91 * 13.5 * 4, (-64, -11)),
+    ("2021", "15min", 366 * 13.5 * 4, (-64, -11)),
+    ("2021", "h", ValueError, None),
     ("2021", "D", ValueError, None),
     ("2021", "MS", ValueError, None),
     ("2021", "QS", ValueError, None),
-    ("2021", "AS", ValueError, None),
+    ("2021", "YS", ValueError, None),
 ]
 
 f_workingdays_full = tools.peakfn.factory(None, None, [1, 2, 3, 4, 5])
 TESTCASES_WORKINGDAYS = [  # end, freq, count, stretch
-    ("2020-01-08", "15T", 5 * 24 * 4, (0, 72 * 4 - 1)),
-    ("2020-01-08", "H", 5 * 24, (0, 72 - 1)),
+    ("2020-01-08", "15min", 5 * 24 * 4, (0, 72 * 4 - 1)),
+    ("2020-01-08", "h", 5 * 24, (0, 72 - 1)),
     ("2020-01-08", "D", 5, (0, 3 - 1)),
-    ("2020-04-01", "15T", 65 * 24 * 4, (0, 72 * 4 - 1)),  # avoid DST transition
-    ("2020-04-01", "H", 65 * 24, (0, 72 - 1)),  # avoid DST transition
+    ("2020-04-01", "15min", 65 * 24 * 4, (0, 72 * 4 - 1)),  # avoid DST transition
+    ("2020-04-01", "h", 65 * 24, (0, 72 - 1)),  # avoid DST transition
     ("2020-04-01", "D", 65, (-9, -4 - 1)),
-    ("2021", "15T", 262 * 24 * 4, (-11 * 24 * 4, -6 * 24 * 4 - 1)),
-    ("2021", "H", 262 * 24, (-11 * 24, -6 * 24 - 1)),
+    ("2021", "15min", 262 * 24 * 4, (-11 * 24 * 4, -6 * 24 * 4 - 1)),
+    ("2021", "h", 262 * 24, (-11 * 24, -6 * 24 - 1)),
     ("2021", "D", 262, (-11, -6 - 1)),
     ("2021", "MS", ValueError, None),
     ("2021", "QS", ValueError, None),
-    ("2021", "AS", ValueError, None),
+    ("2021", "YS", ValueError, None),
 ]
 
 f_everyday_until6 = tools.peakfn.factory(None, dt.time(hour=6), [1, 2, 3, 4, 5, 6, 7])
 TESTCASES_EVERYDAY6 = [  # month, freq, tz, count, stretch
-    (1, "15T", None, 31 * 6 * 4, (24 * 4, 30 * 4 - 1)),
-    (1, "15T", "Europe/Berlin", 31 * 6 * 4, (24 * 4, 30 * 4 - 1)),
-    (1, "15T", "Asia/Kolkata", 31 * 6 * 4, (24 * 4, 30 * 4 - 1)),
-    (1, "H", None, 31 * 6, (24, 30 - 1)),
-    (1, "H", "Europe/Berlin", 31 * 6, (24, 30 - 1)),
-    (1, "H", "Asia/Kolkata", 31 * 6, (24, 30 - 1)),
+    (1, "15min", None, 31 * 6 * 4, (24 * 4, 30 * 4 - 1)),
+    (1, "15min", "Europe/Berlin", 31 * 6 * 4, (24 * 4, 30 * 4 - 1)),
+    (1, "15min", "Asia/Kolkata", 31 * 6 * 4, (24 * 4, 30 * 4 - 1)),
+    (1, "h", None, 31 * 6, (24, 30 - 1)),
+    (1, "h", "Europe/Berlin", 31 * 6, (24, 30 - 1)),
+    (1, "h", "Asia/Kolkata", 31 * 6, (24, 30 - 1)),
     (1, "D", None, ValueError, None),
     (1, "D", "Europe/Berlin", ValueError, None),
     (1, "D", "Asia/Kolkata", ValueError, None),
@@ -76,21 +76,21 @@ def index(start: str, end: str, freq: str, tz: str) -> pd.DatetimeIndex:
     (1, "QS", None, ValueError, None),
     (1, "QS", "Europe/Berlin", ValueError, None),
     (1, "QS", "Asia/Kolkata", ValueError, None),
-    (1, "AS", None, ValueError, None),
-    (1, "AS", "Europe/Berlin", ValueError, None),
-    (1, "AS", "Asia/Kolkata", ValueError, None),
-    (3, "15T", None, 31 * 6 * 4, (-72 * 4, -66 * 4 - 1)),
-    (3, "15T", "Europe/Berlin", (31 * 6 - 1) * 4, (-71 * 4, -66 * 4 - 1)),
-    (3, "15T", "Asia/Kolkata", 31 * 6 * 4, (-72 * 4, -66 * 4 - 1)),
-    (3, "H", None, 31 * 6, (-72, -66 - 1)),
-    (3, "H", "Europe/Berlin", 31 * 6 - 1, (-71, -66 - 1)),  # dst start; one hour less
-    (3, "H", "Asia/Kolkata", 31 * 6, (-72, -66 - 1)),
-    (10, "15T", None, 31 * 6 * 4, (-168 * 4, -162 * 4 - 1)),
-    (10, "15T", "Europe/Berlin", (31 * 6 + 1) * 4, (-169 * 4, -162 * 4 - 1)),
-    (10, "15T", "Asia/Kolkata", 31 * 6 * 4, (-168 * 4, -162 * 4 - 1)),
-    (10, "H", None, 31 * 6, (-168, -162 - 1)),
-    (10, "H", "Europe/Berlin", 31 * 6 + 1, (-169, -162 - 1)),  # dst end; one hour more
-    (10, "H", "Asia/Kolkata", 31 * 6, (-168, -162 - 1)),
+    (1, "YS", None, ValueError, None),
+    (1, "YS", "Europe/Berlin", ValueError, None),
+    (1, "YS", "Asia/Kolkata", ValueError, None),
+    (3, "15min", None, 31 * 6 * 4, (-72 * 4, -66 * 4 - 1)),
+    (3, "15min", "Europe/Berlin", (31 * 6 - 1) * 4, (-71 * 4, -66 * 4 - 1)),
+    (3, "15min", "Asia/Kolkata", 31 * 6 * 4, (-72 * 4, -66 * 4 - 1)),
+    (3, "h", None, 31 * 6, (-72, -66 - 1)),
+    (3, "h", "Europe/Berlin", 31 * 6 - 1, (-71, -66 - 1)),  # dst start; one hour less
+    (3, "h", "Asia/Kolkata", 31 * 6, (-72, -66 - 1)),
+    (10, "15min", None, 31 * 6 * 4, (-168 * 4, -162 * 4 - 1)),
+    (10, "15min", "Europe/Berlin", (31 * 6 + 1) * 4, (-169 * 4, -162 * 4 - 1)),
+    (10, "15min", "Asia/Kolkata", 31 * 6 * 4, (-168 * 4, -162 * 4 - 1)),
+    (10, "h", None, 31 * 6, (-168, -162 - 1)),
+    (10, "h", "Europe/Berlin", 31 * 6 + 1, (-169, -162 - 1)),  # dst end; one hour more
+    (10, "h", "Asia/Kolkata", 31 * 6, (-168, -162 - 1)),
 ]
 
 
@@ -184,7 +184,7 @@ def do_test(
 
 @pytest.mark.parametrize(("tz", "mar_b_corr"), [(None, 0), ("Europe/Berlin", -1)])
 @pytest.mark.parametrize("month", [1, 2, 3])
-@pytest.mark.parametrize("freq", ["D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq", ["D", "MS", "QS", "YS"])
 @pytest.mark.parametrize(
     ("year", "bp", "jan_1_weekday", "jan_p", "feb_p", "mar_p"),
     [
@@ -208,7 +208,7 @@ def test_peakduration_longfreqs(
     i = pd.date_range(start, freq=freq, periods=1)
 
     # Expected values.
-    if freq == "AS":
+    if freq == "YS":
         b = 24 * bp[0]
         p = 12 * bp[1]
     elif freq == "QS":
@@ -285,24 +285,24 @@ def test_peakduration_longfreqs(
         ),
         # Hours
         (
-            pd.date_range("2020", freq="H", periods=48, tz=None),
+            pd.date_range("2020", freq="h", periods=48, tz=None),
             [1] * 48,
             [*[0] * 8, *[1] * 12, *[0] * 12, *[1] * 12, *[0] * 4],
         ),
         # . End-of-March: DST (if observed in tz)
         (
-            pd.date_range("2020-03-29", freq="H", periods=48, tz=None),
+            pd.date_range("2020-03-29", freq="h", periods=48, tz=None),
             [1] * 48,
             [*[0] * 32, *[1] * 12, *[0] * 4],
         ),
         (
-            pd.date_range("2020-03-29", freq="H", periods=47, tz="Europe/Berlin"),
+            pd.date_range("2020-03-29", freq="h", periods=47, tz="Europe/Berlin"),
             [1] * 47,
             [*[0] * 31, *[1] * 12, *[0] * 4],
         ),
         # Quarterhours
         (
-            pd.date_range("2020", freq="15T", periods=192, tz=None),
+            pd.date_range("2020", freq="15min", periods=192, tz=None),
             [0.25] * 192,
             [*[0] * 32, *[0.25] * 48, *[0] * 48, *[0.25] * 48, *[0] * 16],
         ),
diff --git a/tests/tools/test_product.py b/tests/tools/test_product.py
index aabaa15..2a9b7ac 100644
--- a/tests/tools/test_product.py
+++ b/tests/tools/test_product.py
@@ -13,32 +13,32 @@
         ("2020-1-1", "m", 0, "2020-1-1"),
         ("2020-1-1", "q", 0, "2020-1-1"),
         ("2020-1-1", "s", 0, "2019-10-1"),
-        ("2020-1-1", "a", 0, "2020-1-1"),
+        ("2020-1-1", "y", 0, "2020-1-1"),
         ("2020-1-1", "d", 1, "2020-1-2"),
         ("2020-1-1", "m", 1, "2020-2-1"),
         ("2020-1-1", "q", 1, "2020-4-1"),
         ("2020-1-1", "s", 1, "2020-4-1"),
-        ("2020-1-1", "a", 1, "2021-1-1"),
+        ("2020-1-1", "y", 1, "2021-1-1"),
         ("2020-1-1", "d", 3, "2020-1-4"),
         ("2020-1-1", "m", 3, "2020-4-1"),
         ("2020-1-1", "q", 3, "2020-10-1"),
         ("2020-1-1", "s", 3, "2021-4-1"),
-        ("2020-1-1", "a", 3, "2023-1-1"),
+        ("2020-1-1", "y", 3, "2023-1-1"),
         ("2020-1-31", "d", 0, "2020-1-31"),
         ("2020-1-31", "m", 0, "2020-1-1"),
         ("2020-1-31", "q", 0, "2020-1-1"),
         ("2020-1-31", "s", 0, "2019-10-1"),
-        ("2020-1-31", "a", 0, "2020-1-1"),
+        ("2020-1-31", "y", 0, "2020-1-1"),
         ("2020-1-31", "d", 1, "2020-2-1"),
         ("2020-1-31", "m", 1, "2020-2-1"),
         ("2020-1-31", "q", 1, "2020-4-1"),
         ("2020-1-31", "s", 1, "2020-4-1"),
-        ("2020-1-31", "a", 1, "2021-1-1"),
+        ("2020-1-31", "y", 1, "2021-1-1"),
         ("2020-1-31", "d", 3, "2020-2-3"),
         ("2020-1-31", "m", 3, "2020-4-1"),
         ("2020-1-31", "q", 3, "2020-10-1"),
         ("2020-1-31", "s", 3, "2021-4-1"),
-        ("2020-1-31", "a", 3, "2023-1-1"),
+        ("2020-1-31", "y", 3, "2023-1-1"),
         ("2020-2-14", "s", 0, "2019-10-1"),
         ("2020-3-14", "s", 0, "2019-10-1"),
         ("2020-4-14", "s", 0, "2020-4-1"),
@@ -95,7 +95,7 @@ def test_deliveryperiod(
     )
     assert ts_deliv[0] == expected_left
     try:
-        add = {"m": 1, "q": 3, "s": 6, "a": 12}[period_type]
+        add = {"m": 1, "q": 3, "s": 6, "y": 12}[period_type]
         assert ts_deliv[1] == expected_left + pd.offsets.MonthBegin(add)
     except KeyError:
         assert ts_deliv[1] == expected_left + dt.timedelta(1)
diff --git a/tests/tools/test_right.py b/tests/tools/test_right.py
index b4db911..67a0d12 100644
--- a/tests/tools/test_right.py
+++ b/tests/tools/test_right.py
@@ -8,43 +8,43 @@
 
 TESTCASES = [  # ts, freq, expected_ts_right
     # First day of X and start of day.
-    ("2020-01-01", "15T", "2020-01-01 00:15"),
-    ("2020-01-01", "H", "2020-01-01 01:00"),
+    ("2020-01-01", "15min", "2020-01-01 00:15"),
+    ("2020-01-01", "h", "2020-01-01 01:00"),
     ("2020-01-01", "D", "2020-01-02"),
     ("2020-01-01", "MS", "2020-02-01"),
     ("2020-01-01", "QS", "2020-04-01"),
-    ("2020-01-01", "AS", "2021-01-01"),
+    ("2020-01-01", "YS", "2021-01-01"),
     # First day of X but not start of day.
-    ("2020-01-01 06:00", "15T", "2020-01-01 06:15"),
-    ("2020-01-01 06:00", "H", "2020-01-01 07:00"),
+    ("2020-01-01 06:00", "15min", "2020-01-01 06:15"),
+    ("2020-01-01 06:00", "h", "2020-01-01 07:00"),
     ("2020-01-01 06:00", "D", "2020-01-02 06:00"),
     ("2020-01-01 06:00", "MS", "2020-02-01 06:00"),
     ("2020-01-01 06:00", "QS", "2020-04-01 06:00"),
-    ("2020-01-01 06:00", "AS", "2021-01-01 06:00"),
+    ("2020-01-01 06:00", "YS", "2021-01-01 06:00"),
     # Not first day of X but start of day.
-    ("2020-04-01", "15T", "2020-04-01 00:15"),
-    ("2020-04-01", "H", "2020-04-01 01:00"),
+    ("2020-04-01", "15min", "2020-04-01 00:15"),
+    ("2020-04-01", "h", "2020-04-01 01:00"),
     ("2020-04-01", "D", "2020-04-02"),
     ("2020-04-01", "MS", "2020-05-01"),
     ("2020-04-01", "QS", "2020-07-01"),
-    ("2020-04-21", "15T", "2020-04-21 00:15"),
-    ("2020-04-21", "H", "2020-04-21 01:00"),
+    ("2020-04-21", "15min", "2020-04-21 00:15"),
+    ("2020-04-21", "h", "2020-04-21 01:00"),
     ("2020-04-21", "D", "2020-04-22"),
     # Not first day of X and not start of day.
-    ("2020-04-01 06:00", "15T", "2020-04-01 06:15"),
-    ("2020-04-01 06:00", "H", "2020-04-01 07:00"),
+    ("2020-04-01 06:00", "15min", "2020-04-01 06:15"),
+    ("2020-04-01 06:00", "h", "2020-04-01 07:00"),
     ("2020-04-01 06:00", "D", "2020-04-02 06:00"),
     ("2020-04-01 06:00", "MS", "2020-05-01 06:00"),
     ("2020-04-01 06:00", "QS", "2020-07-01 06:00"),
-    ("2020-04-21 06:00", "15T", "2020-04-21 06:15"),
-    ("2020-04-21 06:00", "H", "2020-04-21 07:00"),
+    ("2020-04-21 06:00", "15min", "2020-04-21 06:15"),
+    ("2020-04-21 06:00", "h", "2020-04-21 07:00"),
     ("2020-04-21 06:00", "D", "2020-04-22 06:00"),
 ]
 
 TESTCASES_DST = [  # ts, freq, expected_ts_right, periods
     # Start of DST.
-    ("2020-03-29 01:00", "15T", "2020-03-29 01:15", 92 + 9 * 96),
-    ("2020-03-29 01:00", "H", "2020-03-29 03:00", 23 + 9 * 24),
+    ("2020-03-29 01:00", "15min", "2020-03-29 01:15", 92 + 9 * 96),
+    ("2020-03-29 01:00", "h", "2020-03-29 03:00", 23 + 9 * 24),
     ("2020-03-29 00:00", "D", "2020-03-30 00:00", None),
     ("2020-03-29 01:00", "D", "2020-03-30 01:00", None),
     ("2020-03-29 03:00", "D", "2020-03-30 03:00", None),
@@ -54,14 +54,14 @@
     ("2020-01-01 00:00", "QS", "2020-04-01 00:00", None),
     ("2020-01-01 06:00", "QS", "2020-04-01 06:00", None),
     # End of DST.
-    ("2020-10-25 01:00", "15T", "2020-10-25 01:15+0200", 100 + 9 * 96),
-    ("2020-10-25 02:00+0200", "15T", "2020-10-25 02:15+0200", 100 + 9 * 96),
-    ("2020-10-25 02:00+0100", "15T", "2020-10-25 02:15+0100", 10 * 96),
-    ("2020-10-25 03:00", "15T", "2020-10-25 03:15", 10 * 96),
-    ("2020-10-25 01:00", "H", "2020-10-25 02:00+0200", 25 + 9 * 24),
-    ("2020-10-25 02:00+0200", "H", "2020-10-25 02:00+0100", 25 + 9 * 24),
-    ("2020-10-25 02:00+0100", "H", "2020-10-25 03:00", 10 * 24),
-    ("2020-10-25 03:00", "H", "2020-10-25 04:00", 10 * 24),
+    ("2020-10-25 01:00", "15min", "2020-10-25 01:15+0200", 100 + 9 * 96),
+    ("2020-10-25 02:00+0200", "15min", "2020-10-25 02:15+0200", 100 + 9 * 96),
+    ("2020-10-25 02:00+0100", "15min", "2020-10-25 02:15+0100", 10 * 96),
+    ("2020-10-25 03:00", "15min", "2020-10-25 03:15", 10 * 96),
+    ("2020-10-25 01:00", "h", "2020-10-25 02:00+0200", 25 + 9 * 24),
+    ("2020-10-25 02:00+0200", "h", "2020-10-25 02:00+0100", 25 + 9 * 24),
+    ("2020-10-25 02:00+0100", "h", "2020-10-25 03:00", 10 * 24),
+    ("2020-10-25 03:00", "h", "2020-10-25 04:00", 10 * 24),
     ("2020-10-25 00:00", "D", "2020-10-26 00:00", None),
     ("2020-10-25 01:00", "D", "2020-10-26 01:00", None),
     ("2020-10-25 03:00", "D", "2020-10-26 03:00", None),
@@ -72,7 +72,7 @@
     ("2020-10-01 06:00", "QS", "2021-01-01 06:00", None),
 ]
 
-TESTCASES_15T_STAMPONLY = [  # ts, expected_ts_right
+TESTCASES_15min_STAMPONLY = [  # ts, expected_ts_right
     # Start of DST.
     ("2020-03-29 00:45", "2020-03-29 01:00"),
     ("2020-03-29 01:45", "2020-03-29 03:00"),
@@ -112,10 +112,10 @@ def test_right_stamp_dst(ts: str, freq: str, expected_ts_right: str, periods: in
     do_test_stamp(ts, freq, "Europe/Berlin", expected_ts_right)
 
 
-@pytest.mark.parametrize(("ts", "expected_ts_right"), TESTCASES_15T_STAMPONLY)
-def test_right_stamp_15T(ts: str, expected_ts_right: str):
+@pytest.mark.parametrize(("ts", "expected_ts_right"), TESTCASES_15min_STAMPONLY)
+def test_right_stamp_15min(ts: str, expected_ts_right: str):
     """Test if right timestamp is correctly calculated for timestamp at non-round hour."""
-    do_test_stamp(ts, "15T", "Europe/Berlin", expected_ts_right)
+    do_test_stamp(ts, "15min", "Europe/Berlin", expected_ts_right)
 
 
 def do_test_index(ts, freq, tz, expected_ts_right, periods):
diff --git a/tests/tools/test_righttoleft.py b/tests/tools/test_righttoleft.py
index ae594d5..e0d3746 100644
--- a/tests/tools/test_righttoleft.py
+++ b/tests/tools/test_righttoleft.py
@@ -6,26 +6,26 @@
 TESTCASES = [  # start, periods, freq, expected_start
     # Natural-boundary.
     # Natural-boundary timestamps; without DST-transition.
-    ("2020-02-01 1:00", 24, "H", "2020-02-01"),
-    ("2020-02-01 0:15", 96, "15T", "2020-02-01"),
+    ("2020-02-01 1:00", 24, "h", "2020-02-01"),
+    ("2020-02-01 0:15", 96, "15min", "2020-02-01"),
     ("2020-02-02", 28, "D", "2020-02-01"),
     # Natural-boundary timestamps; with DST-start.
-    ("2020-03-29 1:00", 24, "H", "2020-03-29"),
-    ("2020-03-29 4:00", 24, "H", "2020-03-29 3:00"),
-    ("2020-03-29 0:15", 96, "15T", "2020-03-29"),
-    ("2020-03-29 0:30", 96, "15T", "2020-03-29 0:15"),
-    ("2020-03-29 1:30", 96, "15T", "2020-03-29 1:15"),
-    ("2020-03-29 3:30", 96, "15T", "2020-03-29 3:15"),
-    ("2020-03-29 3:15", 96, "15T", "2020-03-29 3:00"),
+    ("2020-03-29 1:00", 24, "h", "2020-03-29"),
+    ("2020-03-29 4:00", 24, "h", "2020-03-29 3:00"),
+    ("2020-03-29 0:15", 96, "15min", "2020-03-29"),
+    ("2020-03-29 0:30", 96, "15min", "2020-03-29 0:15"),
+    ("2020-03-29 1:30", 96, "15min", "2020-03-29 1:15"),
+    ("2020-03-29 3:30", 96, "15min", "2020-03-29 3:15"),
+    ("2020-03-29 3:15", 96, "15min", "2020-03-29 3:00"),
     ("2020-03-02", 31, "D", "2020-03-01"),
     # Natural-boundary timestamps; with DST-end.
-    ("2020-10-25 1:00", 24, "H", "2020-10-25"),
-    ("2020-10-25 4:00", 24, "H", "2020-10-25 3:00"),
-    ("2020-10-25 0:15", 96, "15T", "2020-10-25"),
-    ("2020-10-25 0:30", 96, "15T", "2020-10-25 0:15"),
-    ("2020-10-25 1:30", 96, "15T", "2020-10-25 1:15"),
-    ("2020-10-25 3:30", 96, "15T", "2020-10-25 3:15"),
-    ("2020-10-25 3:15", 96, "15T", "2020-10-25 3:00"),
+    ("2020-10-25 1:00", 24, "h", "2020-10-25"),
+    ("2020-10-25 4:00", 24, "h", "2020-10-25 3:00"),
+    ("2020-10-25 0:15", 96, "15min", "2020-10-25"),
+    ("2020-10-25 0:30", 96, "15min", "2020-10-25 0:15"),
+    ("2020-10-25 1:30", 96, "15min", "2020-10-25 1:15"),
+    ("2020-10-25 3:30", 96, "15min", "2020-10-25 3:15"),
+    ("2020-10-25 3:15", 96, "15min", "2020-10-25 3:00"),
     ("2020-10-02", 31, "D", "2020-10-01"),
     # Natural-boundary timestamps; with DST-start and DST-end.
     ("2020-04-01", 12, "MS", "2020-03-01"),
@@ -38,26 +38,26 @@
     ("2020-08-01", 4, "QS-FEB", "2020-05-01"),
     # Unnatural-boundary.
     # Unnatural-boundary timestamps; without DST-transition.
-    ("2020-02-01 01:30", 24, "H", "2020-02-01 00:30"),
-    ("2020-02-01 01:32", 96, "15T", "2020-02-01 01:17"),
+    ("2020-02-01 01:30", 24, "h", "2020-02-01 00:30"),
+    ("2020-02-01 01:32", 96, "15min", "2020-02-01 01:17"),
     ("2020-02-02 06:00", 5, "D", "2020-02-01 06:00"),
     # Unnatural-boundary timestamps; with DST-start.
-    ("2020-03-29 1:30", 24, "H", "2020-03-29 0:30"),
-    ("2020-03-29 4:30", 24, "H", "2020-03-29 3:30"),
-    ("2020-03-29 0:17", 96, "15T", "2020-03-29 0:02"),
-    ("2020-03-29 0:32", 96, "15T", "2020-03-29 0:17"),
-    ("2020-03-29 1:32", 96, "15T", "2020-03-29 1:17"),
-    ("2020-03-29 3:32", 96, "15T", "2020-03-29 3:17"),
-    ("2020-03-29 3:17", 96, "15T", "2020-03-29 3:02"),
+    ("2020-03-29 1:30", 24, "h", "2020-03-29 0:30"),
+    ("2020-03-29 4:30", 24, "h", "2020-03-29 3:30"),
+    ("2020-03-29 0:17", 96, "15min", "2020-03-29 0:02"),
+    ("2020-03-29 0:32", 96, "15min", "2020-03-29 0:17"),
+    ("2020-03-29 1:32", 96, "15min", "2020-03-29 1:17"),
+    ("2020-03-29 3:32", 96, "15min", "2020-03-29 3:17"),
+    ("2020-03-29 3:17", 96, "15min", "2020-03-29 3:02"),
     ("2020-03-02 06:00", 31, "D", "2020-03-01 06:00"),
     # Natural-boundary timestamps; with DST-end.
-    ("2020-10-25 1:30", 24, "H", "2020-10-25 0:30"),
-    ("2020-10-25 4:30", 24, "H", "2020-10-25 3:30"),
-    ("2020-10-25 0:17", 96, "15T", "2020-10-25 0:02"),
-    ("2020-10-25 0:32", 96, "15T", "2020-10-25 0:17"),
-    ("2020-10-25 1:32", 96, "15T", "2020-10-25 1:17"),
-    ("2020-10-25 3:32", 96, "15T", "2020-10-25 3:17"),
-    ("2020-10-25 3:17", 96, "15T", "2020-10-25 3:02"),
+    ("2020-10-25 1:30", 24, "h", "2020-10-25 0:30"),
+    ("2020-10-25 4:30", 24, "h", "2020-10-25 3:30"),
+    ("2020-10-25 0:17", 96, "15min", "2020-10-25 0:02"),
+    ("2020-10-25 0:32", 96, "15min", "2020-10-25 0:17"),
+    ("2020-10-25 1:32", 96, "15min", "2020-10-25 1:17"),
+    ("2020-10-25 3:32", 96, "15min", "2020-10-25 3:17"),
+    ("2020-10-25 3:17", 96, "15min", "2020-10-25 3:02"),
     ("2020-10-02 06:00", 31, "D", "2020-10-01 06:00"),
     # Natural-boundary timestamps; with DST-start and DST-end.
     ("2020-04-01 06:00", 12, "MS", "2020-03-01 06:00"),
@@ -68,31 +68,31 @@
 
 TESTCASES_DST_AWARE = [  # start, periods, freq, expected_start"
     # Index with DST-start.
-    ("2020-03-29 1:00", 24, "H", "2020-03-29"),
-    ("2020-03-29 3:00", 24, "H", "2020-03-29 1:00"),
-    ("2020-03-29 4:00", 24, "H", "2020-03-29 3:00"),
-    ("2020-03-29 0:15", 96, "15T", "2020-03-29"),
-    ("2020-03-29 1:30", 96, "15T", "2020-03-29 1:15"),
-    ("2020-03-29 1:45", 96, "15T", "2020-03-29 1:30"),
-    ("2020-03-29 3:00", 96, "15T", "2020-03-29 1:45"),
-    ("2020-03-29 3:15", 96, "15T", "2020-03-29 3:00"),
+    ("2020-03-29 1:00", 24, "h", "2020-03-29"),
+    ("2020-03-29 3:00", 24, "h", "2020-03-29 1:00"),
+    ("2020-03-29 4:00", 24, "h", "2020-03-29 3:00"),
+    ("2020-03-29 0:15", 96, "15min", "2020-03-29"),
+    ("2020-03-29 1:30", 96, "15min", "2020-03-29 1:15"),
+    ("2020-03-29 1:45", 96, "15min", "2020-03-29 1:30"),
+    ("2020-03-29 3:00", 96, "15min", "2020-03-29 1:45"),
+    ("2020-03-29 3:15", 96, "15min", "2020-03-29 3:00"),
     # Index with DST-end.
-    ("2020-10-25 1:00", 24, "H", "2020-10-25"),
-    ("2020-10-25 2:00+0200", 24, "H", "2020-10-25 1:00"),
-    ("2020-10-25 2:00+0100", 24, "H", "2020-10-25 2:00+0200"),
-    ("2020-10-25 3:00", 24, "H", "2020-10-25 2:00+0100"),
-    ("2020-10-25 4:00", 24, "H", "2020-10-25 3:00"),
-    ("2020-10-25 0:15", 96, "15T", "2020-10-25"),
-    ("2020-10-25 0:30", 96, "15T", "2020-10-25 0:15"),
-    ("2020-10-25 1:30", 96, "15T", "2020-10-25 1:15"),
-    ("2020-10-25 2:15+0200", 96, "15T", "2020-10-25 2:00+0200"),
-    ("2020-10-25 2:30+0200", 96, "15T", "2020-10-25 2:15+0200"),
-    ("2020-10-25 2:00+0100", 96, "15T", "2020-10-25 2:45+0200"),
-    ("2020-10-25 2:15+0100", 96, "15T", "2020-10-25 2:00+0100"),
-    ("2020-10-25 2:30+0100", 96, "15T", "2020-10-25 2:15+0100"),
-    ("2020-10-25 1:30", 96, "15T", "2020-10-25 1:15"),
-    ("2020-10-25 3:30", 96, "15T", "2020-10-25 3:15"),
-    ("2020-10-25 3:15", 96, "15T", "2020-10-25 3:00"),
+    ("2020-10-25 1:00", 24, "h", "2020-10-25"),
+    ("2020-10-25 2:00+0200", 24, "h", "2020-10-25 1:00"),
+    ("2020-10-25 2:00+0100", 24, "h", "2020-10-25 2:00+0200"),
+    ("2020-10-25 3:00", 24, "h", "2020-10-25 2:00+0100"),
+    ("2020-10-25 4:00", 24, "h", "2020-10-25 3:00"),
+    ("2020-10-25 0:15", 96, "15min", "2020-10-25"),
+    ("2020-10-25 0:30", 96, "15min", "2020-10-25 0:15"),
+    ("2020-10-25 1:30", 96, "15min", "2020-10-25 1:15"),
+    ("2020-10-25 2:15+0200", 96, "15min", "2020-10-25 2:00+0200"),
+    ("2020-10-25 2:30+0200", 96, "15min", "2020-10-25 2:15+0200"),
+    ("2020-10-25 2:00+0100", 96, "15min", "2020-10-25 2:45+0200"),
+    ("2020-10-25 2:15+0100", 96, "15min", "2020-10-25 2:00+0100"),
+    ("2020-10-25 2:30+0100", 96, "15min", "2020-10-25 2:15+0100"),
+    ("2020-10-25 1:30", 96, "15min", "2020-10-25 1:15"),
+    ("2020-10-25 3:30", 96, "15min", "2020-10-25 3:15"),
+    ("2020-10-25 3:15", 96, "15min", "2020-10-25 3:00"),
 ]
 
 TESTCASES_DST_NAIVE = [  # date, times, expected_times
diff --git a/tests/tools/test_standardize.py b/tests/tools/test_standardize.py
index 93ca893..d6751b1 100644
--- a/tests/tools/test_standardize.py
+++ b/tests/tools/test_standardize.py
@@ -5,16 +5,16 @@
 from portfolyo import dev, tools
 
 TEST_FREQUENCIES = [
-    "AS",
-    "AS-FEB",
-    "AS-APR",
+    "YS",
+    "YS-FEB",
+    "YS-APR",
     "QS",
     "QS-FEB",
     "QS-APR",
     "MS",
     "D",
-    "H",
-    "15T",
+    "h",
+    "15min",
 ]
 
 
@@ -27,7 +27,7 @@
 @pytest.mark.parametrize("in_aware", [True, False])
 @pytest.mark.parametrize("in_tz", ["Europe/Berlin", "Asia/Kolkata"])
 @pytest.mark.parametrize("force", ["agnostic", "aware"])
-@pytest.mark.parametrize("freq", ["15T", "D"])
+@pytest.mark.parametrize("freq", ["15min", "D"])
 def test_standardize_DST(
     in_vals_num_specialconditions: int,
     start: str,
@@ -115,9 +115,9 @@ def test_standardize_convert(freq, in_tz, floating, series_or_df, bound, out_tz)
 
     # Get index.
     i = dev.get_index(freq, in_tz, _seed=1)
-    if bound == "right" and freq == "15T":  # Ensure it's a correct full-hour index
+    if bound == "right" and freq == "15min":  # Ensure it's a correct full-hour index
         i += pd.Timedelta(minutes=15)
-    if freq == "15T" and in_tz == "Asia/Kolkata" and not floating and out_tz:
+    if freq == "15min" and in_tz == "Asia/Kolkata" and not floating and out_tz:
         i += pd.Timedelta(minutes=30)
 
     # If no timezone specified and below-daily values, the created index will have too few/many datapoints.
@@ -134,7 +134,7 @@ def test_standardize_convert(freq, in_tz, floating, series_or_df, bound, out_tz)
     if (
         in_tz == "Asia/Kolkata"
         and out_tz == "Europe/Berlin"
-        and tools.freq.shortest(freq, "H") == "H"
+        and tools.freq.shortest(freq, "h") == "h"
         and not floating
     ):
         # Kolkata and Berlin timezone only share 15T-boundaries. Therefore, any other
diff --git a/tests/tools/test_startofday.py b/tests/tools/test_startofday.py
index f8460ad..ea784a7 100644
--- a/tests/tools/test_startofday.py
+++ b/tests/tools/test_startofday.py
@@ -19,7 +19,7 @@
 
 
 STARTDATE_AND_FREQ = [
-    ("2020-01-01", "H"),
+    ("2020-01-01", "h"),
     ("2020-01-01", "D"),
     ("2020-01-01", "MS"),
     ("2020-03-28", "D"),
@@ -59,7 +59,7 @@ def create_start_of_day(hour, minute, returntype):
         ),
         (
             pd.date_range(
-                "2020-01-01", "2021-01-01", freq="H", inclusive="left", tz=None
+                "2020-01-01", "2021-01-01", freq="h", inclusive="left", tz=None
             ),
             0,
             0,
@@ -91,7 +91,7 @@ def create_start_of_day(hour, minute, returntype):
             pd.date_range(
                 "2020-01-01",
                 "2021-01-01",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz="Europe/Berlin",
             ),
@@ -126,7 +126,7 @@ def create_start_of_day(hour, minute, returntype):
             pd.date_range(
                 "2020-01-01 06:00",
                 "2021-01-01 06:00",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz=None,
             ),
@@ -160,7 +160,7 @@ def create_start_of_day(hour, minute, returntype):
             pd.date_range(
                 "2020-01-01 06:30",
                 "2021-01-01 06:30",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz="Europe/Berlin",
             ),
@@ -200,7 +200,7 @@ def test_get_startofday(i: pd.DatetimeIndex, hour: int, minute: int, returntype:
         ),
         (
             pd.date_range(
-                "2020-01-01", "2021-01-01", freq="H", inclusive="left", tz=None
+                "2020-01-01", "2021-01-01", freq="h", inclusive="left", tz=None
             ),
             0,
             0,
@@ -235,7 +235,7 @@ def test_get_startofday(i: pd.DatetimeIndex, hour: int, minute: int, returntype:
             pd.date_range(
                 "2020-01-01",
                 "2021-01-01",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz="Europe/Berlin",
             ),
@@ -275,14 +275,14 @@ def test_get_startofday(i: pd.DatetimeIndex, hour: int, minute: int, returntype:
         ),
         (
             pd.date_range(
-                "2020-01-01", "2021-01-01", freq="H", inclusive="left", tz=None
+                "2020-01-01", "2021-01-01", freq="h", inclusive="left", tz=None
             ),
             6,
             0,
             pd.date_range(
                 "2020-01-01 06:00",
                 "2020-12-31 06:00",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz=None,
             ),
@@ -328,7 +328,7 @@ def test_get_startofday(i: pd.DatetimeIndex, hour: int, minute: int, returntype:
             pd.date_range(
                 "2020-01-01",
                 "2021-01-01",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz="Europe/Berlin",
             ),
@@ -337,7 +337,7 @@ def test_get_startofday(i: pd.DatetimeIndex, hour: int, minute: int, returntype:
             pd.date_range(
                 "2020-01-01 06:00",
                 "2020-12-31 06:00",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz="Europe/Berlin",
             ),
@@ -362,7 +362,7 @@ def test_get_startofday(i: pd.DatetimeIndex, hour: int, minute: int, returntype:
         ),
         (
             pd.date_range(
-                "2020-01-01", "2021-01-01", freq="H", inclusive="left", tz=None
+                "2020-01-01", "2021-01-01", freq="h", inclusive="left", tz=None
             ),
             6,
             3,
@@ -397,7 +397,7 @@ def test_get_startofday(i: pd.DatetimeIndex, hour: int, minute: int, returntype:
             pd.date_range(
                 "2020-01-01",
                 "2021-01-01",
-                freq="H",
+                freq="h",
                 inclusive="left",
                 tz="Europe/Berlin",
             ),
diff --git a/tests/tools/test_trim.py b/tests/tools/test_trim.py
index 1a5a35f..f01183d 100644
--- a/tests/tools/test_trim.py
+++ b/tests/tools/test_trim.py
@@ -5,142 +5,184 @@
 
 TESTCASES_ALMOSTFULL = [  # start, end, freq, trimfreq, tr_start, tr_end
     # Trimming below-daily index to below-daily freq.
-    ("2020-01-01 00", "2022-01-01 15", "15T", "15T", "2020-01-01 00", "2022-01-01 15"),
-    ("2020-01-01 00", "2022-01-01 15", "15T", "H", "2020-01-01 00", "2022-01-01 15"),
-    ("2020-01-01 00", "2022-01-01 15", "H", "15T", "2020-01-01 00", "2022-01-01 15"),
-    ("2020-01-01 00", "2022-01-01 15", "H", "H", "2020-01-01 00", "2022-01-01 15"),
-    ("2020-01-01 06", "2022-01-01 15", "15T", "15T", "2020-01-01 06", "2022-01-01 15"),
-    ("2020-01-01 06", "2022-01-01 15", "15T", "H", "2020-01-01 06", "2022-01-01 15"),
-    ("2020-01-01 06", "2022-01-01 15", "H", "15T", "2020-01-01 06", "2022-01-01 15"),
-    ("2020-01-01 06", "2022-01-01 15", "H", "H", "2020-01-01 06", "2022-01-01 15"),
-    ("2020-01-01 06", "2022-01-01 04", "15T", "15T", "2020-01-01 06", "2022-01-01 04"),
-    ("2020-01-01 06", "2022-01-01 04", "15T", "H", "2020-01-01 06", "2022-01-01 04"),
-    ("2020-01-01 06", "2022-01-01 04", "H", "15T", "2020-01-01 06", "2022-01-01 04"),
-    ("2020-01-01 06", "2022-01-01 04", "H", "H", "2020-01-01 06", "2022-01-01 04"),
+    (
+        "2020-01-01 00",
+        "2022-01-01 15",
+        "15min",
+        "15min",
+        "2020-01-01 00",
+        "2022-01-01 15",
+    ),
+    ("2020-01-01 00", "2022-01-01 15", "15min", "h", "2020-01-01 00", "2022-01-01 15"),
+    ("2020-01-01 00", "2022-01-01 15", "h", "15min", "2020-01-01 00", "2022-01-01 15"),
+    ("2020-01-01 00", "2022-01-01 15", "h", "h", "2020-01-01 00", "2022-01-01 15"),
+    (
+        "2020-01-01 06",
+        "2022-01-01 15",
+        "15min",
+        "15min",
+        "2020-01-01 06",
+        "2022-01-01 15",
+    ),
+    ("2020-01-01 06", "2022-01-01 15", "15min", "h", "2020-01-01 06", "2022-01-01 15"),
+    ("2020-01-01 06", "2022-01-01 15", "h", "15min", "2020-01-01 06", "2022-01-01 15"),
+    ("2020-01-01 06", "2022-01-01 15", "h", "h", "2020-01-01 06", "2022-01-01 15"),
+    (
+        "2020-01-01 06",
+        "2022-01-01 04",
+        "15min",
+        "15min",
+        "2020-01-01 06",
+        "2022-01-01 04",
+    ),
+    ("2020-01-01 06", "2022-01-01 04", "15min", "h", "2020-01-01 06", "2022-01-01 04"),
+    ("2020-01-01 06", "2022-01-01 04", "h", "15min", "2020-01-01 06", "2022-01-01 04"),
+    ("2020-01-01 06", "2022-01-01 04", "h", "h", "2020-01-01 06", "2022-01-01 04"),
     # Trimming below-daily index to daily-or-longer freq.
-    ("2020-01-01 00", "2022-01-01 15", "15T", "D", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 15", "15T", "MS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 15", "15T", "QS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 15", "15T", "AS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 15", "H", "D", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 15", "H", "MS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 15", "H", "QS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 15", "H", "AS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 06", "2022-01-01 15", "15T", "D", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 15", "15T", "MS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 15", "15T", "QS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 15", "15T", "AS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 15", "H", "D", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 15", "H", "MS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 15", "H", "QS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 15", "H", "AS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 04", "15T", "D", "2020-01-01 06", "2021-12-31 06"),
-    ("2020-01-01 06", "2022-01-01 04", "15T", "MS", "2020-01-01 06", "2021-12-01 06"),
-    ("2020-01-01 06", "2022-01-01 04", "15T", "QS", "2020-01-01 06", "2021-10-01 06"),
-    ("2020-01-01 06", "2022-01-01 04", "15T", "AS", "2020-01-01 06", "2021-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 04", "H", "D", "2020-01-01 06", "2021-12-31 06"),
-    ("2020-01-01 06", "2022-01-01 04", "H", "MS", "2020-01-01 06", "2021-12-01 06"),
-    ("2020-01-01 06", "2022-01-01 04", "H", "QS", "2020-01-01 06", "2021-10-01 06"),
-    ("2020-01-01 06", "2022-01-01 04", "H", "AS", "2020-01-01 06", "2021-01-01 06"),
+    ("2020-01-01 00", "2022-01-01 15", "15min", "D", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 15", "15min", "MS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 15", "15min", "QS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 15", "15min", "YS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 15", "h", "D", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 15", "h", "MS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 15", "h", "QS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 15", "h", "YS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 06", "2022-01-01 15", "15min", "D", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 15", "15min", "MS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 15", "15min", "QS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 15", "15min", "YS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 15", "h", "D", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 15", "h", "MS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 15", "h", "QS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 15", "h", "YS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 04", "15min", "D", "2020-01-01 06", "2021-12-31 06"),
+    ("2020-01-01 06", "2022-01-01 04", "15min", "MS", "2020-01-01 06", "2021-12-01 06"),
+    ("2020-01-01 06", "2022-01-01 04", "15min", "QS", "2020-01-01 06", "2021-10-01 06"),
+    ("2020-01-01 06", "2022-01-01 04", "15min", "YS", "2020-01-01 06", "2021-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 04", "h", "D", "2020-01-01 06", "2021-12-31 06"),
+    ("2020-01-01 06", "2022-01-01 04", "h", "MS", "2020-01-01 06", "2021-12-01 06"),
+    ("2020-01-01 06", "2022-01-01 04", "h", "QS", "2020-01-01 06", "2021-10-01 06"),
+    ("2020-01-01 06", "2022-01-01 04", "h", "YS", "2020-01-01 06", "2021-01-01 06"),
 ]
 
 TESTCASES_MIDYEAR = [  # start, end, freq, trimfreq, tr_start, tr_end
     # Trimming below-daily index to below-daily freq.
-    ("2020-04-21 00", "2022-12-15 15", "15T", "15T", "2020-04-21 00", "2022-12-15 15"),
-    ("2020-04-21 00", "2022-12-15 15", "15T", "H", "2020-04-21 00", "2022-12-15 15"),
-    ("2020-04-21 00", "2022-12-15 15", "H", "15T", "2020-04-21 00", "2022-12-15 15"),
-    ("2020-04-21 00", "2022-12-15 15", "H", "H", "2020-04-21 00", "2022-12-15 15"),
-    ("2020-04-21 06", "2022-12-15 15", "15T", "15T", "2020-04-21 06", "2022-12-15 15"),
-    ("2020-04-21 06", "2022-12-15 15", "15T", "H", "2020-04-21 06", "2022-12-15 15"),
-    ("2020-04-21 06", "2022-12-15 15", "H", "15T", "2020-04-21 06", "2022-12-15 15"),
-    ("2020-04-21 06", "2022-12-15 15", "H", "H", "2020-04-21 06", "2022-12-15 15"),
-    ("2020-04-21 06", "2022-12-15 04", "15T", "15T", "2020-04-21 06", "2022-12-15 04"),
-    ("2020-04-21 06", "2022-12-15 04", "15T", "H", "2020-04-21 06", "2022-12-15 04"),
-    ("2020-04-21 06", "2022-12-15 04", "H", "15T", "2020-04-21 06", "2022-12-15 04"),
-    ("2020-04-21 06", "2022-12-15 04", "H", "H", "2020-04-21 06", "2022-12-15 04"),
+    (
+        "2020-04-21 00",
+        "2022-12-15 15",
+        "15min",
+        "15min",
+        "2020-04-21 00",
+        "2022-12-15 15",
+    ),
+    ("2020-04-21 00", "2022-12-15 15", "15min", "h", "2020-04-21 00", "2022-12-15 15"),
+    ("2020-04-21 00", "2022-12-15 15", "h", "15min", "2020-04-21 00", "2022-12-15 15"),
+    ("2020-04-21 00", "2022-12-15 15", "h", "h", "2020-04-21 00", "2022-12-15 15"),
+    (
+        "2020-04-21 06",
+        "2022-12-15 15",
+        "15min",
+        "15min",
+        "2020-04-21 06",
+        "2022-12-15 15",
+    ),
+    ("2020-04-21 06", "2022-12-15 15", "15min", "h", "2020-04-21 06", "2022-12-15 15"),
+    ("2020-04-21 06", "2022-12-15 15", "h", "15min", "2020-04-21 06", "2022-12-15 15"),
+    ("2020-04-21 06", "2022-12-15 15", "h", "h", "2020-04-21 06", "2022-12-15 15"),
+    (
+        "2020-04-21 06",
+        "2022-12-15 04",
+        "15min",
+        "15min",
+        "2020-04-21 06",
+        "2022-12-15 04",
+    ),
+    ("2020-04-21 06", "2022-12-15 04", "15min", "h", "2020-04-21 06", "2022-12-15 04"),
+    ("2020-04-21 06", "2022-12-15 04", "h", "15min", "2020-04-21 06", "2022-12-15 04"),
+    ("2020-04-21 06", "2022-12-15 04", "h", "h", "2020-04-21 06", "2022-12-15 04"),
     # Trimming below-daily index to daily-or-longer freq.
-    ("2020-04-21 00", "2022-12-15 15", "15T", "D", "2020-04-21 00", "2022-12-15 00"),
-    ("2020-04-21 00", "2022-12-15 15", "15T", "MS", "2020-05-01 00", "2022-12-01 00"),
-    ("2020-04-21 00", "2022-12-15 15", "15T", "QS", "2020-07-01 00", "2022-10-01 00"),
-    ("2020-04-21 00", "2022-12-15 15", "15T", "AS", "2021-01-01 00", "2022-01-01 00"),
-    ("2020-04-21 00", "2022-12-15 15", "H", "D", "2020-04-21 00", "2022-12-15 00"),
-    ("2020-04-21 00", "2022-12-15 15", "H", "MS", "2020-05-01 00", "2022-12-01 00"),
-    ("2020-04-21 00", "2022-12-15 15", "H", "QS", "2020-07-01 00", "2022-10-01 00"),
-    ("2020-04-21 00", "2022-12-15 15", "H", "AS", "2021-01-01 00", "2022-01-01 00"),
-    ("2020-04-21 06", "2022-12-15 15", "15T", "D", "2020-04-21 06", "2022-12-15 06"),
-    ("2020-04-21 06", "2022-12-15 15", "15T", "MS", "2020-05-01 06", "2022-12-01 06"),
-    ("2020-04-21 06", "2022-12-15 15", "15T", "QS", "2020-07-01 06", "2022-10-01 06"),
-    ("2020-04-21 06", "2022-12-15 15", "15T", "AS", "2021-01-01 06", "2022-01-01 06"),
-    ("2020-04-21 06", "2022-12-15 15", "H", "D", "2020-04-21 06", "2022-12-15 06"),
-    ("2020-04-21 06", "2022-12-15 15", "H", "MS", "2020-05-01 06", "2022-12-01 06"),
-    ("2020-04-21 06", "2022-12-15 15", "H", "QS", "2020-07-01 06", "2022-10-01 06"),
-    ("2020-04-21 06", "2022-12-15 15", "H", "AS", "2021-01-01 06", "2022-01-01 06"),
-    ("2020-04-21 06", "2022-12-15 04", "15T", "D", "2020-04-21 06", "2022-12-14 06"),
-    ("2020-04-21 06", "2022-12-15 04", "15T", "MS", "2020-05-01 06", "2022-12-01 06"),
-    ("2020-04-21 06", "2022-12-15 04", "15T", "QS", "2020-07-01 06", "2022-10-01 06"),
-    ("2020-04-21 06", "2022-12-15 04", "15T", "AS", "2021-01-01 06", "2022-01-01 06"),
-    ("2020-04-21 06", "2022-12-15 04", "H", "D", "2020-04-21 06", "2022-12-14 06"),
-    ("2020-04-21 06", "2022-12-15 04", "H", "MS", "2020-05-01 06", "2022-12-01 06"),
-    ("2020-04-21 06", "2022-12-15 04", "H", "QS", "2020-07-01 06", "2022-10-01 06"),
-    ("2020-04-21 06", "2022-12-15 04", "H", "AS", "2021-01-01 06", "2022-01-01 06"),
+    ("2020-04-21 00", "2022-12-15 15", "15min", "D", "2020-04-21 00", "2022-12-15 00"),
+    ("2020-04-21 00", "2022-12-15 15", "15min", "MS", "2020-05-01 00", "2022-12-01 00"),
+    ("2020-04-21 00", "2022-12-15 15", "15min", "QS", "2020-07-01 00", "2022-10-01 00"),
+    ("2020-04-21 00", "2022-12-15 15", "15min", "YS", "2021-01-01 00", "2022-01-01 00"),
+    ("2020-04-21 00", "2022-12-15 15", "h", "D", "2020-04-21 00", "2022-12-15 00"),
+    ("2020-04-21 00", "2022-12-15 15", "h", "MS", "2020-05-01 00", "2022-12-01 00"),
+    ("2020-04-21 00", "2022-12-15 15", "h", "QS", "2020-07-01 00", "2022-10-01 00"),
+    ("2020-04-21 00", "2022-12-15 15", "h", "YS", "2021-01-01 00", "2022-01-01 00"),
+    ("2020-04-21 06", "2022-12-15 15", "15min", "D", "2020-04-21 06", "2022-12-15 06"),
+    ("2020-04-21 06", "2022-12-15 15", "15min", "MS", "2020-05-01 06", "2022-12-01 06"),
+    ("2020-04-21 06", "2022-12-15 15", "15min", "QS", "2020-07-01 06", "2022-10-01 06"),
+    ("2020-04-21 06", "2022-12-15 15", "15min", "YS", "2021-01-01 06", "2022-01-01 06"),
+    ("2020-04-21 06", "2022-12-15 15", "h", "D", "2020-04-21 06", "2022-12-15 06"),
+    ("2020-04-21 06", "2022-12-15 15", "h", "MS", "2020-05-01 06", "2022-12-01 06"),
+    ("2020-04-21 06", "2022-12-15 15", "h", "QS", "2020-07-01 06", "2022-10-01 06"),
+    ("2020-04-21 06", "2022-12-15 15", "h", "YS", "2021-01-01 06", "2022-01-01 06"),
+    ("2020-04-21 06", "2022-12-15 04", "15min", "D", "2020-04-21 06", "2022-12-14 06"),
+    ("2020-04-21 06", "2022-12-15 04", "15min", "MS", "2020-05-01 06", "2022-12-01 06"),
+    ("2020-04-21 06", "2022-12-15 04", "15min", "QS", "2020-07-01 06", "2022-10-01 06"),
+    ("2020-04-21 06", "2022-12-15 04", "15min", "YS", "2021-01-01 06", "2022-01-01 06"),
+    ("2020-04-21 06", "2022-12-15 04", "h", "D", "2020-04-21 06", "2022-12-14 06"),
+    ("2020-04-21 06", "2022-12-15 04", "h", "MS", "2020-05-01 06", "2022-12-01 06"),
+    ("2020-04-21 06", "2022-12-15 04", "h", "QS", "2020-07-01 06", "2022-10-01 06"),
+    ("2020-04-21 06", "2022-12-15 04", "h", "YS", "2021-01-01 06", "2022-01-01 06"),
     # Trimming daily-or-longer index to shorter-than-daily freq.
-    ("2020-04-21 00", "2022-12-15 00", "D", "15T", "2020-04-21 00", "2022-12-15 00"),
-    ("2020-04-21 00", "2022-12-15 00", "D", "H", "2020-04-21 00", "2022-12-15 00"),
-    ("2020-04-01 00", "2022-12-01 00", "MS", "15T", "2020-04-01 00", "2022-12-01 00"),
-    ("2020-04-01 00", "2022-12-01 00", "MS", "H", "2020-04-01 00", "2022-12-01 00"),
-    ("2020-04-01 00", "2022-10-01 00", "QS", "15T", "2020-04-01 00", "2022-10-01 00"),
-    ("2020-04-01 00", "2022-10-01 00", "QS", "H", "2020-04-01 00", "2022-10-01 00"),
-    ("2020-01-01 00", "2022-01-01 00", "AS", "15T", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 00", "AS", "H", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-04-21 06", "2022-12-15 06", "D", "15T", "2020-04-21 06", "2022-12-15 06"),
-    ("2020-04-21 06", "2022-12-15 06", "D", "H", "2020-04-21 06", "2022-12-15 06"),
-    ("2020-04-01 06", "2022-12-01 06", "MS", "15T", "2020-04-01 06", "2022-12-01 06"),
-    ("2020-04-01 06", "2022-12-01 06", "MS", "H", "2020-04-01 06", "2022-12-01 06"),
-    ("2020-04-01 06", "2022-10-01 06", "QS", "15T", "2020-04-01 06", "2022-10-01 06"),
-    ("2020-04-01 06", "2022-10-01 06", "QS", "H", "2020-04-01 06", "2022-10-01 06"),
-    ("2020-01-01 06", "2022-01-01 06", "AS", "15T", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 06", "AS", "H", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-04-21 00", "2022-12-15 00", "D", "15min", "2020-04-21 00", "2022-12-15 00"),
+    ("2020-04-21 00", "2022-12-15 00", "D", "h", "2020-04-21 00", "2022-12-15 00"),
+    ("2020-04-01 00", "2022-12-01 00", "MS", "15min", "2020-04-01 00", "2022-12-01 00"),
+    ("2020-04-01 00", "2022-12-01 00", "MS", "h", "2020-04-01 00", "2022-12-01 00"),
+    ("2020-04-01 00", "2022-10-01 00", "QS", "15min", "2020-04-01 00", "2022-10-01 00"),
+    ("2020-04-01 00", "2022-10-01 00", "QS", "h", "2020-04-01 00", "2022-10-01 00"),
+    ("2020-01-01 00", "2022-01-01 00", "YS", "15min", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 00", "YS", "h", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-04-21 06", "2022-12-15 06", "D", "15min", "2020-04-21 06", "2022-12-15 06"),
+    ("2020-04-21 06", "2022-12-15 06", "D", "h", "2020-04-21 06", "2022-12-15 06"),
+    ("2020-04-01 06", "2022-12-01 06", "MS", "15min", "2020-04-01 06", "2022-12-01 06"),
+    ("2020-04-01 06", "2022-12-01 06", "MS", "h", "2020-04-01 06", "2022-12-01 06"),
+    ("2020-04-01 06", "2022-10-01 06", "QS", "15min", "2020-04-01 06", "2022-10-01 06"),
+    ("2020-04-01 06", "2022-10-01 06", "QS", "h", "2020-04-01 06", "2022-10-01 06"),
+    ("2020-01-01 06", "2022-01-01 06", "YS", "15min", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 06", "YS", "h", "2020-01-01 06", "2022-01-01 06"),
     # Trimming daily-or-longer index to daily-or-longer freq.
     ("2020-04-21 00", "2022-12-15 00", "D", "D", "2020-04-21 00", "2022-12-15 00"),
     ("2020-04-21 00", "2022-12-15 00", "D", "MS", "2020-05-01 00", "2022-12-01 00"),
     ("2020-04-21 00", "2022-12-15 00", "D", "QS", "2020-07-01 00", "2022-10-01 00"),
-    ("2020-04-21 00", "2022-12-15 00", "D", "AS", "2021-01-01 00", "2022-01-01 00"),
+    ("2020-04-21 00", "2022-12-15 00", "D", "YS", "2021-01-01 00", "2022-01-01 00"),
     ("2020-04-01 00", "2022-12-01 00", "MS", "D", "2020-04-01 00", "2022-12-01 00"),
     ("2020-04-01 00", "2022-12-01 00", "MS", "MS", "2020-04-01 00", "2022-12-01 00"),
     ("2020-04-01 00", "2022-12-01 00", "MS", "QS", "2020-04-01 00", "2022-10-01 00"),
-    ("2020-04-01 00", "2022-12-01 00", "MS", "AS", "2021-01-01 00", "2022-01-01 00"),
+    ("2020-04-01 00", "2022-12-01 00", "MS", "YS", "2021-01-01 00", "2022-01-01 00"),
     ("2020-04-01 00", "2022-10-01 00", "QS", "D", "2020-04-01 00", "2022-10-01 00"),
     ("2020-04-01 00", "2022-10-01 00", "QS", "MS", "2020-04-01 00", "2022-10-01 00"),
     ("2020-04-01 00", "2022-10-01 00", "QS", "QS", "2020-04-01 00", "2022-10-01 00"),
-    ("2020-04-01 00", "2022-10-01 00", "QS", "AS", "2021-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 00", "AS", "D", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 00", "AS", "MS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 00", "AS", "QS", "2020-01-01 00", "2022-01-01 00"),
-    ("2020-01-01 00", "2022-01-01 00", "AS", "AS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-04-01 00", "2022-10-01 00", "QS", "YS", "2021-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 00", "YS", "D", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 00", "YS", "MS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 00", "YS", "QS", "2020-01-01 00", "2022-01-01 00"),
+    ("2020-01-01 00", "2022-01-01 00", "YS", "YS", "2020-01-01 00", "2022-01-01 00"),
     ("2020-04-21 06", "2022-12-15 06", "D", "D", "2020-04-21 06", "2022-12-15 06"),
     ("2020-04-21 06", "2022-12-15 06", "D", "MS", "2020-05-01 06", "2022-12-01 06"),
     ("2020-04-21 06", "2022-12-15 06", "D", "QS", "2020-07-01 06", "2022-10-01 06"),
-    ("2020-04-21 06", "2022-12-15 06", "D", "AS", "2021-01-01 06", "2022-01-01 06"),
+    ("2020-04-21 06", "2022-12-15 06", "D", "YS", "2021-01-01 06", "2022-01-01 06"),
     ("2020-04-01 06", "2022-12-01 06", "MS", "D", "2020-04-01 06", "2022-12-01 06"),
     ("2020-04-01 06", "2022-12-01 06", "MS", "MS", "2020-04-01 06", "2022-12-01 06"),
     ("2020-04-01 06", "2022-12-01 06", "MS", "QS", "2020-04-01 06", "2022-10-01 06"),
-    ("2020-04-01 06", "2022-12-01 06", "MS", "AS", "2021-01-01 06", "2022-01-01 06"),
+    ("2020-04-01 06", "2022-12-01 06", "MS", "YS", "2021-01-01 06", "2022-01-01 06"),
     ("2020-04-01 06", "2022-10-01 06", "QS", "D", "2020-04-01 06", "2022-10-01 06"),
     ("2020-04-01 06", "2022-10-01 06", "QS", "MS", "2020-04-01 06", "2022-10-01 06"),
     ("2020-04-01 06", "2022-10-01 06", "QS", "QS", "2020-04-01 06", "2022-10-01 06"),
-    ("2020-04-01 06", "2022-10-01 06", "QS", "AS", "2021-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 06", "AS", "D", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 06", "AS", "MS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 06", "AS", "QS", "2020-01-01 06", "2022-01-01 06"),
-    ("2020-01-01 06", "2022-01-01 06", "AS", "AS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-04-01 06", "2022-10-01 06", "QS", "YS", "2021-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 06", "YS", "D", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 06", "YS", "MS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 06", "YS", "QS", "2020-01-01 06", "2022-01-01 06"),
+    ("2020-01-01 06", "2022-01-01 06", "YS", "YS", "2020-01-01 06", "2022-01-01 06"),
 ]
 
 
 @pytest.mark.parametrize("indexorframe", ["index", "s", "s_unit", "df", "df_unit"])
 @pytest.mark.parametrize("starttime", ["00:00", "06:00"])
 @pytest.mark.parametrize("tz", [None, "Europe/Berlin", "Asia/Kolkata"])
-@pytest.mark.parametrize("freq", ["15T", "H", "D", "MS", "QS", "AS"])
-@pytest.mark.parametrize("trimfreq", ["15T", "H", "D", "MS", "QS", "AS"])
+@pytest.mark.parametrize("freq", ["15min", "h", "D", "MS", "QS", "YS"])
+@pytest.mark.parametrize("trimfreq", ["15min", "h", "D", "MS", "QS", "YS"])
 def test_trim_notrimming(
     indexorframe: str, freq: str, tz: str, trimfreq: str, starttime: str
 ):
@@ -197,7 +239,7 @@ def test_trim_short2short(indexorframe: str, tz: str):
     # If input and output are below-daily, the offset parameter to the trim function should be ignored.
     start, tr_start = "2020-01-05 04:15:00", "2020-01-05 05:00"
     end, tr_end = "2022-12-21 04:15:00", "2022-12-21 04:00"
-    do_test_general(indexorframe, start, end, "15T", tz, "H", tr_start, tr_end)
+    do_test_general(indexorframe, start, end, "15min", tz, "h", tr_start, tr_end)
 
 
 @pytest.mark.parametrize("indexorframe", ["index", "s", "s_unit", "df", "df_unit"])
@@ -208,7 +250,7 @@ def test_trim_short2short(indexorframe: str, tz: str):
     [
         ("MS", "2020-02-01", "2022-12-01"),
         ("QS", "2020-04-01", "2022-10-01"),
-        ("AS", "2021-01-01", "2022-01-01"),
+        ("YS", "2021-01-01", "2022-01-01"),
     ],
 )
 def test_trim_long2long(
@@ -234,10 +276,10 @@ def test_trim_long2long(
     [
         ("MS", "2020-02-01", "2022-12-01"),
         ("QS", "2020-04-01", "2022-10-01"),
-        ("AS", "2021-01-01", "2022-01-01"),
+        ("YS", "2021-01-01", "2022-01-01"),
     ],
 )
-@pytest.mark.parametrize("trimfreq", ["H", "15T"])
+@pytest.mark.parametrize("trimfreq", ["h", "15min"])
 def test_trim_long2short(
     indexorframe: str,
     start: str,
@@ -263,7 +305,7 @@ def test_trim_long2short(
         ("D", "2020-01-05", "2022-12-21"),
         ("MS", "2020-02-01", "2022-12-01"),
         ("QS", "2020-04-01", "2022-10-01"),
-        ("AS", "2021-01-01", "2022-01-01"),
+        ("YS", "2021-01-01", "2022-01-01"),
     ],
 )
 def test_trim_short2long(
@@ -278,7 +320,7 @@ def test_trim_short2long(
     # Only in case a below-daily index is trimmed to a daily-or-longer frequency is the offset parameter used.
     start, tr_start = f"2020-01-05 {starttime}", f"{tr_startdate} {starttime}"
     end, tr_end = "2022-12-21 23:00:00", f"{tr_enddate} {starttime}"
-    do_test_general(indexorframe, start, end, "H", tz, trimfreq, tr_start, tr_end)
+    do_test_general(indexorframe, start, end, "h", tz, trimfreq, tr_start, tr_end)
 
 
 def do_test_general(
diff --git a/tests/tools/test_tzone.py b/tests/tools/test_tzone.py
index 23ca810..214f2e6 100644
--- a/tests/tools/test_tzone.py
+++ b/tests/tools/test_tzone.py
@@ -27,7 +27,7 @@ class TzType(Enum):
 def get_df_fromexcel(aggfreq, tzt_in: TzType, tzt_out: TzType) -> pd.DataFrame:
     path = Path(__file__).parent / "test_tzone_data.xlsx"
 
-    if tzt_in is TzType.A_NONFLOAT and aggfreq != "15T":
+    if tzt_in is TzType.A_NONFLOAT and aggfreq != "15min":
         return None, None  # does not exist as starting point for our tests
 
     df = pd.read_excel(path, aggfreq, header=[5, 6])
@@ -64,7 +64,7 @@ def prep_df(idx, data, tz):
 
 @pytest.mark.only_on_pr
 @pytest.mark.parametrize("seriesordf", ["series", "df"])
-@pytest.mark.parametrize("aggfreq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("aggfreq", ["15min", "h", "D", "MS"])
 @pytest.mark.parametrize(
     ("tzt_in", "tzt_out"),
     [
@@ -85,7 +85,7 @@ def conversion_fn(fr):
 
 @pytest.mark.only_on_pr
 @pytest.mark.parametrize("seriesordf", ["series", "df"])
-@pytest.mark.parametrize("aggfreq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("aggfreq", ["15min", "h", "D", "MS"])
 @pytest.mark.parametrize(
     ("tzt_in", "tzt_out"),
     [
@@ -105,7 +105,7 @@ def conversion_fn(fr):
 
 @pytest.mark.only_on_pr
 @pytest.mark.parametrize("seriesordf", ["series", "df"])
-@pytest.mark.parametrize("aggfreq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("aggfreq", ["15min", "h", "D", "MS"])
 @pytest.mark.parametrize(
     ("tzt_in", "tzt_out"),
     [
@@ -128,7 +128,7 @@ def conversion_fn(fr):
 
 
 @pytest.mark.parametrize("seriesordf", ["series", "df"])
-@pytest.mark.parametrize("aggfreq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("aggfreq", ["15min", "h", "D", "MS"])
 @pytest.mark.parametrize(
     ("tzt_in", "tzt_out"), [(TzType.A, TzType.B), (TzType.A_FLOAT, TzType.B)]
 )
@@ -143,7 +143,7 @@ def conversion_fn(fr):
 
 
 @pytest.mark.parametrize("seriesordf", ["series", "df"])
-@pytest.mark.parametrize("aggfreq", ["15T", "H", "D", "MS"])
+@pytest.mark.parametrize("aggfreq", ["15min", "h", "D", "MS"])
 @pytest.mark.parametrize(("tzt_in", "tzt_out"), [(TzType.B, TzType.A)])
 def test_conversionBtoA_fromexcel(aggfreq, tzt_in, tzt_out, seriesordf):
     """Test if frames can be correctly converted from type B to type A."""
diff --git a/tests/tools/test_tzone_data.xlsx b/tests/tools/test_tzone_data.xlsx
index d2795fa..d76bf30 100644
Binary files a/tests/tools/test_tzone_data.xlsx and b/tests/tools/test_tzone_data.xlsx differ
diff --git a/tests/tools/visualize/test_plot.py b/tests/tools/visualize/test_plot.py
index 2c66277..66096ef 100644
--- a/tests/tools/visualize/test_plot.py
+++ b/tests/tools/visualize/test_plot.py
@@ -20,7 +20,7 @@ def test_pfline_plot(
     index = pd.date_range("2020-01-01", "2021-01-01", freq=freq, tz=None)
     pfl = pf.dev.get_pfline(index, nlevels=levels, childcount=childcount, kind=kind)
     pfl.plot(children=children)
-    # plt.show()
+    plt.close()
 
 
 @pytest.mark.parametrize("childcount", [1, 2, 3])
@@ -44,6 +44,7 @@ def test_pfstate_plot(
     )
     pfs = PfState(-1 * offtakevolume, unsourcedprice, sourced)
     pfs.plot(children=children)
+    plt.close()
 
 
 @pytest.mark.parametrize("children", [True, False])
@@ -51,6 +52,7 @@ def test_flatpfline_plot(children: bool):
     """Test if plotting flatpfline with children attribute gives an error."""
     pfl = pf.dev.get_flatpfline()
     pfl.plot(children=children)
+    plt.close()
 
 
 @pytest.mark.parametrize("freq", ["MS", "D"])
@@ -78,3 +80,4 @@ def test_plot_to_ax(levels: int, childcount: int, children: bool, freq: str):
     pfl_vol.plot_to_ax(axs[0][1], children=children, kind=Kind.VOLUME)
     pfl_price.plot_to_ax(axs[1][0], children=children, kind=Kind.PRICE)
     pfl_rev.plot_to_ax(axs[1][1], children=children, kind=Kind.REVENUE)
+    plt.close()
diff --git a/tests/tools2/test_concat_error_cases.py b/tests/tools2/test_concat_error_cases.py
index e250fed..01825e4 100644
--- a/tests/tools2/test_concat_error_cases.py
+++ b/tests/tools2/test_concat_error_cases.py
@@ -22,7 +22,7 @@ def test_general():
 def test_diff_freq():
     """Test if concatenating of two flat PfLines with different freq raises error."""
     index = pd.date_range("2020", "2024", freq="QS", inclusive="left")
-    index2 = pd.date_range("2024", "2025", freq="AS", inclusive="left")
+    index2 = pd.date_range("2024", "2025", freq="YS", inclusive="left")
     pfl = dev.get_flatpfline(index)
     pfl2 = dev.get_flatpfline(index2)
     with pytest.raises(TypeError):
@@ -41,9 +41,9 @@ def test_diff_sod():
 
 def test_slice_not_sod():
     """Test if concatenating of two flat PfLines with different sod raises error."""
-    index = pd.date_range("2020-01-01 00:00", "2020-03-01", freq="H", inclusive="left")
+    index = pd.date_range("2020-01-01 00:00", "2020-03-01", freq="h", inclusive="left")
     index2 = pd.date_range(
-        "2020-02-01 06:00", "2020-04-01 06:00", freq="H", inclusive="left"
+        "2020-02-01 06:00", "2020-04-01 06:00", freq="h", inclusive="left"
     )
     pfl_a = dev.get_flatpfline(index)
     pfl_b = dev.get_flatpfline(index2)
diff --git a/tests/tools2/test_concat_pfline.py b/tests/tools2/test_concat_pfline.py
index 5e30722..3ee8df2 100644
--- a/tests/tools2/test_concat_pfline.py
+++ b/tests/tools2/test_concat_pfline.py
@@ -12,7 +12,7 @@
         "QS",
         "2022-04-01",
     ),
-    (("2020", "2022"), "AS", "2021-01-01"),
+    (("2020", "2022"), "YS", "2021-01-01"),
     (
         ("2020-05-01", "2023-04-01"),
         "MS",
@@ -27,7 +27,7 @@
         "QS",
         ("2022-04-01", "2023-01-01"),
     ),
-    (("2020", "2023"), "AS", ("2021-01-01", "2022-01-01")),
+    (("2020", "2023"), "YS", ("2021-01-01", "2022-01-01")),
     (
         ("2020-05-01", "2023-04-01"),
         "MS",
diff --git a/tests/tools2/test_concat_pfstate.py b/tests/tools2/test_concat_pfstate.py
index 6ca378b..9a3bb7c 100644
--- a/tests/tools2/test_concat_pfstate.py
+++ b/tests/tools2/test_concat_pfstate.py
@@ -12,7 +12,7 @@
         "QS",
         "2022-04-01",
     ),
-    (("2020", "2022"), "AS", "2021-01-01"),
+    (("2020", "2022"), "YS", "2021-01-01"),
     (
         ("2020-05-01", "2023-04-01"),
         "MS",
@@ -27,7 +27,7 @@
         "QS",
         ("2022-04-01", "2023-01-01"),
     ),
-    (("2020", "2023"), "AS", ("2021-01-01", "2022-01-01")),
+    (("2020", "2023"), "YS", ("2021-01-01", "2022-01-01")),
     (
         ("2020-05-01", "2023-04-01"),
         "MS",
diff --git a/tests/tools2/test_indexable.py b/tests/tools2/test_indexable.py
index d757e31..517a895 100644
--- a/tests/tools2/test_indexable.py
+++ b/tests/tools2/test_indexable.py
@@ -189,7 +189,7 @@ def test_intersect_ignore_all_3obj(
     idx2 = get_idx("2021-01-01", "06:00", None, "MS", "2024-01-01")
     s2 = pd.Series(range(len(idx2)), idx2)
 
-    idx3 = get_idx("2023-01-01", "00:00", "Asia/Kolkata", "AS", "2025-01-01")
+    idx3 = get_idx("2023-01-01", "00:00", "Asia/Kolkata", "YS", "2025-01-01")
     s3 = pd.Series(range(len(idx3)), idx3)
 
     first = create_obj(s1, first_obj) if first_obj != "series" else s1