tests/speed/lri.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append rolling_ta to sys path.\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# Assuming rolling_ta is in the same level as tests/\n",
    "module_path = os.path.abspath(os.path.join(os.getcwd(), \"..\", \"..\"))\n",
    "\n",
    "# Add the path to sys.path\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG - Cache [numba=True]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from rolling_ta.extras.numba import _linear_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "k10 = np.random.rand(10_000)\n",
    "k10_slope_container = slope_container = np.zeros(k10.size, dtype=np.float64)\n",
    "k10_intercept_container = intercept_container = np.zeros(k10.size, dtype=np.float64)\n",
    "\n",
    "m1 = np.random.rand(1_000_000)\n",
    "m1_slope_container = slope_container = np.zeros(m1.size, dtype=np.float64)\n",
    "m1_intercept_container = intercept_container = np.zeros(m1.size, dtype=np.float64)\n",
    "\n",
    "m500 = np.random.rand(500_000_000)\n",
    "m500_slope_container = slope_container = np.zeros(m500.size, dtype=np.float64)\n",
    "m500_intercept_container = intercept_container = np.zeros(m500.size, dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.2 μs ± 4.92 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "_linear_regression(k10, k10_slope_container, k10_intercept_container)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.44 ms ± 28.5 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "_linear_regression(m1, m1_slope_container, m1_intercept_container)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.26 s ± 51.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "_linear_regression(m500, m500_slope_container, m500_intercept_container)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": ".rolling-ta",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
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 "nbformat": 4,
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