supervise learning and pca feature.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b206c2af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:04:16.351358Z",
     "start_time": "2023-11-15T17:04:15.725307Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier \n",
    "from sklearn.ensemble import AdaBoostClassifier \n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn import svm\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout,Input,Normalization\n",
    "from eval_class import *\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f936cac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:04:35.492453Z",
     "start_time": "2023-11-15T17:04:35.478451Z"
    }
   },
   "outputs": [],
   "source": [
    "Predict_price_train = pd.read_csv(r'Predict_price_train.csv')\n",
    "Predict_price_valid = pd.read_csv(r'Predict_price_valid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b46ad7d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:05:53.095578Z",
     "start_time": "2023-11-15T17:05:53.062561Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\Sharif\\Downloads\\feature-rasad-target1 (2).csv')\n",
    "df=df.drop(['Unnamed: 0'],axis=1)\n",
    "pca = pd.read_csv(r'C:\\Users\\Sharif\\Downloads\\data_main2(1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03493397",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:05:56.705139Z",
     "start_time": "2023-11-15T17:05:56.631106Z"
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   "outputs": [
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      "text/plain": [
       "          Date    Open    High     Low   Close  ...  EMA_10_EMA_30  MACD_Signal_MACD    return  target  RSI_Signal\n",
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       "...        ...     ...     ...     ...     ...  ...            ...               ...       ...     ...         ...\n",
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       "\n",
       "[2641 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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    "df"
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  {
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     "start_time": "2023-11-15T17:06:00.113415Z"
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       "\n",
       "[1512 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "pca"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ba3ef6d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:06:53.433691Z",
     "start_time": "2023-11-15T17:06:53.419688Z"
    }
   },
   "outputs": [],
   "source": [
    "pca= pca.merge(df, left_on='date', right_on='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf3b953d",
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       "      <td>11390</td>\n",
       "      <td>23167.0</td>\n",
       "      <td>156000.0</td>\n",
       "      <td>58574.0</td>\n",
       "      <td>120593.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.302319</td>\n",
       "      <td>0.272291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964686</td>\n",
       "      <td>0.089127</td>\n",
       "      <td>3.513850</td>\n",
       "      <td>0.008395</td>\n",
       "      <td>0.009361</td>\n",
       "      <td>0.001217</td>\n",
       "      <td>34.678255</td>\n",
       "      <td>54.038724</td>\n",
       "      <td>-0.000713</td>\n",
       "      <td>-0.001234</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.105033</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.816963e+05</td>\n",
       "      <td>-423108.571216</td>\n",
       "      <td>-62169.873081</td>\n",
       "      <td>59737.415945</td>\n",
       "      <td>3470.120139</td>\n",
       "      <td>-439.797580</td>\n",
       "      <td>1807.934082</td>\n",
       "      <td>-24.327617</td>\n",
       "      <td>-12.872542</td>\n",
       "      <td>4.088564</td>\n",
       "      <td>-1.850089</td>\n",
       "      <td>0.217305</td>\n",
       "      <td>0.624123</td>\n",
       "      <td>-4.384927</td>\n",
       "      <td>-0.647308</td>\n",
       "      <td>20120722</td>\n",
       "      <td>10150</td>\n",
       "      <td>20120722</td>\n",
       "      <td>10396</td>\n",
       "      <td>10400</td>\n",
       "      <td>10051</td>\n",
       "      <td>10150</td>\n",
       "      <td>47322.0</td>\n",
       "      <td>104752.0</td>\n",
       "      <td>140705.0</td>\n",
       "      <td>11369.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.334809</td>\n",
       "      <td>0.493551</td>\n",
       "      <td>0.999681</td>\n",
       "      <td>1.008728</td>\n",
       "      <td>-0.000319</td>\n",
       "      <td>-4.696763</td>\n",
       "      <td>0.007668</td>\n",
       "      <td>0.008843</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>30.559490</td>\n",
       "      <td>41.873304</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>-0.000955</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.067006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.472541e+05</td>\n",
       "      <td>-235511.061248</td>\n",
       "      <td>-57714.396507</td>\n",
       "      <td>47894.941007</td>\n",
       "      <td>4584.540215</td>\n",
       "      <td>-406.263772</td>\n",
       "      <td>758.790007</td>\n",
       "      <td>-45.894674</td>\n",
       "      <td>-8.455026</td>\n",
       "      <td>3.333220</td>\n",
       "      <td>-5.452699</td>\n",
       "      <td>0.465177</td>\n",
       "      <td>0.150495</td>\n",
       "      <td>-2.216129</td>\n",
       "      <td>1.087821</td>\n",
       "      <td>20120723</td>\n",
       "      <td>10799</td>\n",
       "      <td>20120723</td>\n",
       "      <td>10388</td>\n",
       "      <td>10890</td>\n",
       "      <td>10388</td>\n",
       "      <td>10799</td>\n",
       "      <td>151216.0</td>\n",
       "      <td>282355.0</td>\n",
       "      <td>343571.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.219389</td>\n",
       "      <td>0.413639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.009488</td>\n",
       "      <td>0.007746</td>\n",
       "      <td>-6.355936</td>\n",
       "      <td>0.007810</td>\n",
       "      <td>0.008817</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>28.990915</td>\n",
       "      <td>49.397290</td>\n",
       "      <td>-0.000701</td>\n",
       "      <td>-0.000904</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.263794</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.810090e+05</td>\n",
       "      <td>329289.835162</td>\n",
       "      <td>-35980.382017</td>\n",
       "      <td>49383.170571</td>\n",
       "      <td>1447.008824</td>\n",
       "      <td>-252.349664</td>\n",
       "      <td>1031.497917</td>\n",
       "      <td>-34.854055</td>\n",
       "      <td>-5.463971</td>\n",
       "      <td>1.263067</td>\n",
       "      <td>0.858669</td>\n",
       "      <td>-1.813554</td>\n",
       "      <td>0.653703</td>\n",
       "      <td>-5.178204</td>\n",
       "      <td>-0.399496</td>\n",
       "      <td>20120724</td>\n",
       "      <td>10701</td>\n",
       "      <td>20120724</td>\n",
       "      <td>10801</td>\n",
       "      <td>10956</td>\n",
       "      <td>10503</td>\n",
       "      <td>10701</td>\n",
       "      <td>16976.0</td>\n",
       "      <td>115097.0</td>\n",
       "      <td>118573.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.221280</td>\n",
       "      <td>0.439413</td>\n",
       "      <td>0.961042</td>\n",
       "      <td>1.040233</td>\n",
       "      <td>-0.038958</td>\n",
       "      <td>0.170306</td>\n",
       "      <td>0.007872</td>\n",
       "      <td>0.008774</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>27.419529</td>\n",
       "      <td>48.378945</td>\n",
       "      <td>-0.000625</td>\n",
       "      <td>-0.000848</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.035640</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>-9.033843e+05</td>\n",
       "      <td>-978941.060823</td>\n",
       "      <td>59572.962222</td>\n",
       "      <td>-9250.593101</td>\n",
       "      <td>-2648.261668</td>\n",
       "      <td>-124.593469</td>\n",
       "      <td>998.546516</td>\n",
       "      <td>446.792964</td>\n",
       "      <td>-25.633822</td>\n",
       "      <td>-2.869416</td>\n",
       "      <td>-1.643769</td>\n",
       "      <td>-0.520154</td>\n",
       "      <td>0.678705</td>\n",
       "      <td>1.723787</td>\n",
       "      <td>-0.764789</td>\n",
       "      <td>20230610</td>\n",
       "      <td>154950</td>\n",
       "      <td>20230610</td>\n",
       "      <td>163450</td>\n",
       "      <td>163450</td>\n",
       "      <td>154950</td>\n",
       "      <td>154950</td>\n",
       "      <td>254722.0</td>\n",
       "      <td>478000.0</td>\n",
       "      <td>400685.0</td>\n",
       "      <td>332037.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.445613</td>\n",
       "      <td>0.489516</td>\n",
       "      <td>0.975648</td>\n",
       "      <td>0.879090</td>\n",
       "      <td>0.249219</td>\n",
       "      <td>-8.661301</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.516023</td>\n",
       "      <td>0.013731</td>\n",
       "      <td>21.583799</td>\n",
       "      <td>25.963645</td>\n",
       "      <td>-0.023126</td>\n",
       "      <td>-0.015602</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.049454</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>-1.611956e+06</td>\n",
       "      <td>-640930.081962</td>\n",
       "      <td>2988.013612</td>\n",
       "      <td>-2753.561370</td>\n",
       "      <td>-3179.352797</td>\n",
       "      <td>31.562840</td>\n",
       "      <td>38.435055</td>\n",
       "      <td>-21.030952</td>\n",
       "      <td>-27.019773</td>\n",
       "      <td>-0.981566</td>\n",
       "      <td>-1.320975</td>\n",
       "      <td>0.094992</td>\n",
       "      <td>-1.205429</td>\n",
       "      <td>1.026250</td>\n",
       "      <td>-0.099406</td>\n",
       "      <td>20230611</td>\n",
       "      <td>152950</td>\n",
       "      <td>20230611</td>\n",
       "      <td>150000</td>\n",
       "      <td>157950</td>\n",
       "      <td>149000</td>\n",
       "      <td>152950</td>\n",
       "      <td>309129.0</td>\n",
       "      <td>273545.0</td>\n",
       "      <td>469459.0</td>\n",
       "      <td>113215.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.562187</td>\n",
       "      <td>0.572593</td>\n",
       "      <td>0.478911</td>\n",
       "      <td>0.894507</td>\n",
       "      <td>-0.521089</td>\n",
       "      <td>-8.587677</td>\n",
       "      <td>0.476240</td>\n",
       "      <td>0.510958</td>\n",
       "      <td>0.012827</td>\n",
       "      <td>23.437571</td>\n",
       "      <td>24.939356</td>\n",
       "      <td>-0.025623</td>\n",
       "      <td>-0.017606</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.013704</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>-5.222906e+05</td>\n",
       "      <td>-500017.588834</td>\n",
       "      <td>-35597.670587</td>\n",
       "      <td>-13607.657076</td>\n",
       "      <td>-143.291285</td>\n",
       "      <td>-422.980286</td>\n",
       "      <td>2904.351400</td>\n",
       "      <td>-39.583968</td>\n",
       "      <td>-30.522106</td>\n",
       "      <td>-0.466694</td>\n",
       "      <td>-0.552104</td>\n",
       "      <td>-1.133779</td>\n",
       "      <td>0.493897</td>\n",
       "      <td>1.942851</td>\n",
       "      <td>-0.062873</td>\n",
       "      <td>20230612</td>\n",
       "      <td>146050</td>\n",
       "      <td>20230612</td>\n",
       "      <td>152050</td>\n",
       "      <td>155000</td>\n",
       "      <td>146050</td>\n",
       "      <td>146050</td>\n",
       "      <td>466472.0</td>\n",
       "      <td>185341.0</td>\n",
       "      <td>421166.0</td>\n",
       "      <td>230647.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.679242</td>\n",
       "      <td>0.698577</td>\n",
       "      <td>0.227455</td>\n",
       "      <td>0.811151</td>\n",
       "      <td>-0.717445</td>\n",
       "      <td>-8.698489</td>\n",
       "      <td>0.465411</td>\n",
       "      <td>0.504875</td>\n",
       "      <td>0.012067</td>\n",
       "      <td>25.373302</td>\n",
       "      <td>21.751171</td>\n",
       "      <td>-0.028949</td>\n",
       "      <td>-0.019875</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.048773</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>-2.604537e+05</td>\n",
       "      <td>66112.796014</td>\n",
       "      <td>-76081.998276</td>\n",
       "      <td>3839.756298</td>\n",
       "      <td>-3065.714595</td>\n",
       "      <td>-24.441763</td>\n",
       "      <td>436.768348</td>\n",
       "      <td>-24.112073</td>\n",
       "      <td>-31.389450</td>\n",
       "      <td>-0.086056</td>\n",
       "      <td>-0.684275</td>\n",
       "      <td>-0.225807</td>\n",
       "      <td>0.704404</td>\n",
       "      <td>0.998876</td>\n",
       "      <td>-0.219016</td>\n",
       "      <td>20230613</td>\n",
       "      <td>146300</td>\n",
       "      <td>20230613</td>\n",
       "      <td>140100</td>\n",
       "      <td>146400</td>\n",
       "      <td>140100</td>\n",
       "      <td>146300</td>\n",
       "      <td>506160.0</td>\n",
       "      <td>39540.0</td>\n",
       "      <td>454549.0</td>\n",
       "      <td>91151.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.813108</td>\n",
       "      <td>0.805722</td>\n",
       "      <td>0.620109</td>\n",
       "      <td>0.737153</td>\n",
       "      <td>-0.355539</td>\n",
       "      <td>-8.766849</td>\n",
       "      <td>0.456688</td>\n",
       "      <td>0.499234</td>\n",
       "      <td>0.011658</td>\n",
       "      <td>27.561245</td>\n",
       "      <td>22.139546</td>\n",
       "      <td>-0.031165</td>\n",
       "      <td>-0.022133</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>1.384566e+06</td>\n",
       "      <td>-200857.123155</td>\n",
       "      <td>-44911.520162</td>\n",
       "      <td>64785.543929</td>\n",
       "      <td>-3113.379174</td>\n",
       "      <td>-25.604928</td>\n",
       "      <td>71.420067</td>\n",
       "      <td>-26.305029</td>\n",
       "      <td>-23.850882</td>\n",
       "      <td>-1.033926</td>\n",
       "      <td>-0.805566</td>\n",
       "      <td>-0.299326</td>\n",
       "      <td>0.484786</td>\n",
       "      <td>2.144407</td>\n",
       "      <td>-0.062952</td>\n",
       "      <td>20230614</td>\n",
       "      <td>151250</td>\n",
       "      <td>20230614</td>\n",
       "      <td>147250</td>\n",
       "      <td>151250</td>\n",
       "      <td>145500</td>\n",
       "      <td>151250</td>\n",
       "      <td>769806.0</td>\n",
       "      <td>205732.0</td>\n",
       "      <td>549328.0</td>\n",
       "      <td>426210.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.811585</td>\n",
       "      <td>0.860965</td>\n",
       "      <td>0.201522</td>\n",
       "      <td>0.610591</td>\n",
       "      <td>-0.277390</td>\n",
       "      <td>-6.628724</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.494920</td>\n",
       "      <td>0.011980</td>\n",
       "      <td>28.659784</td>\n",
       "      <td>29.591154</td>\n",
       "      <td>-0.031354</td>\n",
       "      <td>-0.023977</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.035166</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1512 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0              1              2             3  ...  MACD_Signal_MACD    return  target  RSI_Signal\n",
       "0     8.034553e+05 -116928.643640  153367.769215  61180.329866  ...                -1  0.105361       0           0\n",
       "1    -7.496663e+05   10355.990718    5710.280402  15889.769461  ...                -1  0.105033       0           0\n",
       "2    -2.816963e+05 -423108.571216  -62169.873081  59737.415945  ...                -1 -0.067006       0           0\n",
       "3     7.472541e+05 -235511.061248  -57714.396507  47894.941007  ...                -1  0.263794       0           0\n",
       "4    -5.810090e+05  329289.835162  -35980.382017  49383.170571  ...                -1 -0.035640       0           0\n",
       "...            ...            ...            ...           ...  ...               ...       ...     ...         ...\n",
       "1507 -9.033843e+05 -978941.060823   59572.962222  -9250.593101  ...                 1 -0.049454       0          -1\n",
       "1508 -1.611956e+06 -640930.081962    2988.013612  -2753.561370  ...                 1 -0.013704       0          -1\n",
       "1509 -5.222906e+05 -500017.588834  -35597.670587 -13607.657076  ...                 1 -0.048773       0          -1\n",
       "1510 -2.604537e+05   66112.796014  -76081.998276   3839.756298  ...                 1  0.001809       0          -1\n",
       "1511  1.384566e+06 -200857.123155  -44911.520162  64785.543929  ...                 1  0.035166       0          -1\n",
       "\n",
       "[1512 rows x 49 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef1468ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:08:24.948318Z",
     "start_time": "2023-11-15T17:08:24.937325Z"
    }
   },
   "outputs": [],
   "source": [
    "train=pca[:1058]\n",
    "validation=pca[10588:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9a6478da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:09:49.457686Z",
     "start_time": "2023-11-15T17:09:49.439421Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sharif\\AppData\\Local\\Temp\\ipykernel_5896\\1460141690.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['return1'] = np.log(train['Close_x'].shift(-10)/train['Close_x'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1048.000000\n",
       "mean        0.027598\n",
       "std         0.151287\n",
       "min        -0.368651\n",
       "25%        -0.043846\n",
       "50%         0.023607\n",
       "75%         0.084445\n",
       "max         1.055440\n",
       "Name: Close_x, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['return1'] = np.log(train['Close_x'].shift(-10)/train['Close_x'])\n",
    "a=np.log(train['Close_x'].shift(-10)/train['Close_x'])\n",
    "a.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cfa287af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:10:49.233051Z",
     "start_time": "2023-11-15T17:10:49.221465Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sharif\\AppData\\Local\\Temp\\ipykernel_5896\\3157014742.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['target1'] = np.where((train['return1'] <-0.06) , 1, np.where(train['return1'] >   0.06, -1, 0))\n"
     ]
    }
   ],
   "source": [
    "train['target1'] = np.where((train['return1'] <-0.06) , 1, np.where(train['return1'] >   0.06, -1, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58a6ae82",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:11:07.877622Z",
     "start_time": "2023-11-15T17:11:07.836626Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "profit: 649.554 \n",
      "win_rate: 0.913\n"
     ]
    }
   ],
   "source": [
    "datas={\n",
    "    \"signal\":train.target1.tolist(),\n",
    "    \"close\":train.Close_x.tolist() \n",
    "}\n",
    "y1=pd.DataFrame(datas)\n",
    "profit, win_rate = eval_strategy(y1)\n",
    "print(\"profit:\", profit,\"\\nwin_rate:\", win_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d48963b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:11:31.874901Z",
     "start_time": "2023-11-15T17:11:31.830901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "profit: 15.5342 \n",
      "win_rate: 1.0\n"
     ]
    }
   ],
   "source": [
    "L=[-1]\n",
    "for i in range(len(train)-2):\n",
    "    L.append(0)\n",
    "L.append(1)\n",
    "\n",
    "datas={\n",
    "    \"signal\":L,\n",
    "    \"close\":train.Close_x.tolist() \n",
    "}\n",
    "y1=pd.DataFrame(datas)\n",
    "profit, win_rate = eval_strategy(y1)\n",
    "print(\"profit:\", profit,\"\\nwin_rate:\", win_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3fbf3545",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:25:11.993984Z",
     "start_time": "2023-11-15T17:25:11.981986Z"
    }
   },
   "outputs": [],
   "source": [
    "a = train[['Date','Close_x','0', '1','2', '3', '4', '5', '6','7', '8', '9', '10', '11','12','13','14','target1','ATR', 'ADX','RSI', 'RSI_Signal', 'Close_EMA_10', 'EMA_10_EMA_30', 'MACD_Signal_MACD']]\n",
    "a = a.dropna()\n",
    "x_train = a[['Date','Close_x','0', '1','2', '3', '4', '5', '6','7', '8', '9', '10', '11','12','13','14','ATR', 'ADX','RSI', 'RSI_Signal', 'Close_EMA_10', 'EMA_10_EMA_30', 'MACD_Signal_MACD']]\n",
    "y_train = a['target1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ccefff2c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:19:16.526414Z",
     "start_time": "2023-11-15T17:19:16.514390Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sharif\\AppData\\Local\\Temp\\ipykernel_5896\\598013292.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation['return1'] = np.log(validation['Close_x'].shift(-10)/validation['Close_x'])\n",
      "C:\\Users\\Sharif\\AppData\\Local\\Temp\\ipykernel_5896\\598013292.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  validation['target1'] = np.where((validation['return1'] < -0.06) , 1, np.where(validation['return1'] >  0.06, -1, 0))\n"
     ]
    }
   ],
   "source": [
    "validation['return1'] = np.log(validation['Close_x'].shift(-10)/validation['Close_x'])\n",
    "validation['target1'] = np.where((validation['return1'] < -0.06) , 1, np.where(validation['return1'] >  0.06, -1, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "710461e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:19:04.244828Z",
     "start_time": "2023-11-15T17:19:04.174784Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>date</th>\n",
       "      <th>Close_x</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close_y</th>\n",
       "      <th>v_i_buy</th>\n",
       "      <th>v_n_buy</th>\n",
       "      <th>v_i_sell</th>\n",
       "      <th>v_n_sell</th>\n",
       "      <th>n_i_buy</th>\n",
       "      <th>n_n_buy</th>\n",
       "      <th>n_i_sell</th>\n",
       "      <th>n_n_sell</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
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       "      <th>f4</th>\n",
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       "      <th>f6</th>\n",
       "      <th>EMA_10</th>\n",
       "      <th>EMA_30</th>\n",
       "      <th>ATR</th>\n",
       "      <th>ADX</th>\n",
       "      <th>RSI</th>\n",
       "      <th>MACD</th>\n",
       "      <th>MACD_signal</th>\n",
       "      <th>Close_EMA_10</th>\n",
       "      <th>EMA_10_EMA_30</th>\n",
       "      <th>MACD_Signal_MACD</th>\n",
       "      <th>return</th>\n",
       "      <th>target</th>\n",
       "      <th>RSI_Signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.024934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>-2.034207e+06</td>\n",
       "      <td>8.966341e+05</td>\n",
       "      <td>-1707.588639</td>\n",
       "      <td>18275.238541</td>\n",
       "      <td>-3007.186229</td>\n",
       "      <td>123.682447</td>\n",
       "      <td>-301.990240</td>\n",
       "      <td>-13.729953</td>\n",
       "      <td>6.897668</td>\n",
       "      <td>0.499335</td>\n",
       "      <td>3.959664</td>\n",
       "      <td>-0.347334</td>\n",
       "      <td>2.801914</td>\n",
       "      <td>1.259782</td>\n",
       "      <td>-0.053327</td>\n",
       "      <td>20210310</td>\n",
       "      <td>200030</td>\n",
       "      <td>20210310</td>\n",
       "      <td>201959</td>\n",
       "      <td>208500</td>\n",
       "      <td>199972</td>\n",
       "      <td>200030</td>\n",
       "      <td>381097.0</td>\n",
       "      <td>225880.0</td>\n",
       "      <td>527672.0</td>\n",
       "      <td>79305.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.695238</td>\n",
       "      <td>0.855845</td>\n",
       "      <td>0.878107</td>\n",
       "      <td>0.994663</td>\n",
       "      <td>-0.121893</td>\n",
       "      <td>5.765487</td>\n",
       "      <td>0.559673</td>\n",
       "      <td>0.552091</td>\n",
       "      <td>0.016177</td>\n",
       "      <td>16.422892</td>\n",
       "      <td>57.857229</td>\n",
       "      <td>0.004999</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-0.000364</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>-9.033843e+05</td>\n",
       "      <td>-9.789411e+05</td>\n",
       "      <td>59572.962222</td>\n",
       "      <td>-9250.593101</td>\n",
       "      <td>-2648.261668</td>\n",
       "      <td>-124.593469</td>\n",
       "      <td>998.546516</td>\n",
       "      <td>446.792964</td>\n",
       "      <td>-25.633822</td>\n",
       "      <td>-2.869416</td>\n",
       "      <td>-1.643769</td>\n",
       "      <td>-0.520154</td>\n",
       "      <td>0.678705</td>\n",
       "      <td>1.723787</td>\n",
       "      <td>-0.764789</td>\n",
       "      <td>20230610</td>\n",
       "      <td>154950</td>\n",
       "      <td>20230610</td>\n",
       "      <td>163450</td>\n",
       "      <td>163450</td>\n",
       "      <td>154950</td>\n",
       "      <td>154950</td>\n",
       "      <td>254722.0</td>\n",
       "      <td>478000.0</td>\n",
       "      <td>400685.0</td>\n",
       "      <td>332037.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.445613</td>\n",
       "      <td>0.489516</td>\n",
       "      <td>0.975648</td>\n",
       "      <td>0.879090</td>\n",
       "      <td>0.249219</td>\n",
       "      <td>-8.661301</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.516023</td>\n",
       "      <td>0.013731</td>\n",
       "      <td>21.583799</td>\n",
       "      <td>25.963645</td>\n",
       "      <td>-0.023126</td>\n",
       "      <td>-0.015602</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.049454</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>-1.611956e+06</td>\n",
       "      <td>-6.409301e+05</td>\n",
       "      <td>2988.013612</td>\n",
       "      <td>-2753.561370</td>\n",
       "      <td>-3179.352797</td>\n",
       "      <td>31.562840</td>\n",
       "      <td>38.435055</td>\n",
       "      <td>-21.030952</td>\n",
       "      <td>-27.019773</td>\n",
       "      <td>-0.981566</td>\n",
       "      <td>-1.320975</td>\n",
       "      <td>0.094992</td>\n",
       "      <td>-1.205429</td>\n",
       "      <td>1.026250</td>\n",
       "      <td>-0.099406</td>\n",
       "      <td>20230611</td>\n",
       "      <td>152950</td>\n",
       "      <td>20230611</td>\n",
       "      <td>150000</td>\n",
       "      <td>157950</td>\n",
       "      <td>149000</td>\n",
       "      <td>152950</td>\n",
       "      <td>309129.0</td>\n",
       "      <td>273545.0</td>\n",
       "      <td>469459.0</td>\n",
       "      <td>113215.0</td>\n",
       "      <td>458.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.562187</td>\n",
       "      <td>0.572593</td>\n",
       "      <td>0.478911</td>\n",
       "      <td>0.894507</td>\n",
       "      <td>-0.521089</td>\n",
       "      <td>-8.587677</td>\n",
       "      <td>0.476240</td>\n",
       "      <td>0.510958</td>\n",
       "      <td>0.012827</td>\n",
       "      <td>23.437571</td>\n",
       "      <td>24.939356</td>\n",
       "      <td>-0.025623</td>\n",
       "      <td>-0.017606</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.013704</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>-5.222906e+05</td>\n",
       "      <td>-5.000176e+05</td>\n",
       "      <td>-35597.670587</td>\n",
       "      <td>-13607.657076</td>\n",
       "      <td>-143.291285</td>\n",
       "      <td>-422.980286</td>\n",
       "      <td>2904.351400</td>\n",
       "      <td>-39.583968</td>\n",
       "      <td>-30.522106</td>\n",
       "      <td>-0.466694</td>\n",
       "      <td>-0.552104</td>\n",
       "      <td>-1.133779</td>\n",
       "      <td>0.493897</td>\n",
       "      <td>1.942851</td>\n",
       "      <td>-0.062873</td>\n",
       "      <td>20230612</td>\n",
       "      <td>146050</td>\n",
       "      <td>20230612</td>\n",
       "      <td>152050</td>\n",
       "      <td>155000</td>\n",
       "      <td>146050</td>\n",
       "      <td>146050</td>\n",
       "      <td>466472.0</td>\n",
       "      <td>185341.0</td>\n",
       "      <td>421166.0</td>\n",
       "      <td>230647.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.679242</td>\n",
       "      <td>0.698577</td>\n",
       "      <td>0.227455</td>\n",
       "      <td>0.811151</td>\n",
       "      <td>-0.717445</td>\n",
       "      <td>-8.698489</td>\n",
       "      <td>0.465411</td>\n",
       "      <td>0.504875</td>\n",
       "      <td>0.012067</td>\n",
       "      <td>25.373302</td>\n",
       "      <td>21.751171</td>\n",
       "      <td>-0.028949</td>\n",
       "      <td>-0.019875</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.048773</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>-2.604537e+05</td>\n",
       "      <td>6.611280e+04</td>\n",
       "      <td>-76081.998276</td>\n",
       "      <td>3839.756298</td>\n",
       "      <td>-3065.714595</td>\n",
       "      <td>-24.441763</td>\n",
       "      <td>436.768348</td>\n",
       "      <td>-24.112073</td>\n",
       "      <td>-31.389450</td>\n",
       "      <td>-0.086056</td>\n",
       "      <td>-0.684275</td>\n",
       "      <td>-0.225807</td>\n",
       "      <td>0.704404</td>\n",
       "      <td>0.998876</td>\n",
       "      <td>-0.219016</td>\n",
       "      <td>20230613</td>\n",
       "      <td>146300</td>\n",
       "      <td>20230613</td>\n",
       "      <td>140100</td>\n",
       "      <td>146400</td>\n",
       "      <td>140100</td>\n",
       "      <td>146300</td>\n",
       "      <td>506160.0</td>\n",
       "      <td>39540.0</td>\n",
       "      <td>454549.0</td>\n",
       "      <td>91151.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.813108</td>\n",
       "      <td>0.805722</td>\n",
       "      <td>0.620109</td>\n",
       "      <td>0.737153</td>\n",
       "      <td>-0.355539</td>\n",
       "      <td>-8.766849</td>\n",
       "      <td>0.456688</td>\n",
       "      <td>0.499234</td>\n",
       "      <td>0.011658</td>\n",
       "      <td>27.561245</td>\n",
       "      <td>22.139546</td>\n",
       "      <td>-0.031165</td>\n",
       "      <td>-0.022133</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001809</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>1.384566e+06</td>\n",
       "      <td>-2.008571e+05</td>\n",
       "      <td>-44911.520162</td>\n",
       "      <td>64785.543929</td>\n",
       "      <td>-3113.379174</td>\n",
       "      <td>-25.604928</td>\n",
       "      <td>71.420067</td>\n",
       "      <td>-26.305029</td>\n",
       "      <td>-23.850882</td>\n",
       "      <td>-1.033926</td>\n",
       "      <td>-0.805566</td>\n",
       "      <td>-0.299326</td>\n",
       "      <td>0.484786</td>\n",
       "      <td>2.144407</td>\n",
       "      <td>-0.062952</td>\n",
       "      <td>20230614</td>\n",
       "      <td>151250</td>\n",
       "      <td>20230614</td>\n",
       "      <td>147250</td>\n",
       "      <td>151250</td>\n",
       "      <td>145500</td>\n",
       "      <td>151250</td>\n",
       "      <td>769806.0</td>\n",
       "      <td>205732.0</td>\n",
       "      <td>549328.0</td>\n",
       "      <td>426210.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.811585</td>\n",
       "      <td>0.860965</td>\n",
       "      <td>0.201522</td>\n",
       "      <td>0.610591</td>\n",
       "      <td>-0.277390</td>\n",
       "      <td>-6.628724</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.494920</td>\n",
       "      <td>0.011980</td>\n",
       "      <td>28.659784</td>\n",
       "      <td>29.591154</td>\n",
       "      <td>-0.031354</td>\n",
       "      <td>-0.023977</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.035166</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>454 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0             1              2              3  ...  MACD_Signal_MACD    return  target  RSI_Signal\n",
       "1058 -7.886185e+06 -5.057550e+06  230242.744149 -307961.407809  ...                -1  0.005189       0           0\n",
       "1059 -3.024126e+06 -3.311615e+06  -26811.180217  -38509.835516  ...                -1  0.048310       0           0\n",
       "1060 -3.060310e+06 -9.309814e+05   44845.042548   18743.470127  ...                -1  0.044526       0           0\n",
       "1061  5.315437e+05 -1.583153e+06   35837.663010   61278.358249  ...                -1 -0.024934       0           0\n",
       "1062 -2.034207e+06  8.966341e+05   -1707.588639   18275.238541  ...                -1 -0.000364       0           0\n",
       "...            ...           ...            ...            ...  ...               ...       ...     ...         ...\n",
       "1507 -9.033843e+05 -9.789411e+05   59572.962222   -9250.593101  ...                 1 -0.049454       0          -1\n",
       "1508 -1.611956e+06 -6.409301e+05    2988.013612   -2753.561370  ...                 1 -0.013704       0          -1\n",
       "1509 -5.222906e+05 -5.000176e+05  -35597.670587  -13607.657076  ...                 1 -0.048773       0          -1\n",
       "1510 -2.604537e+05  6.611280e+04  -76081.998276    3839.756298  ...                 1  0.001809       0          -1\n",
       "1511  1.384566e+06 -2.008571e+05  -44911.520162   64785.543929  ...                 1  0.035166       0          -1\n",
       "\n",
       "[454 rows x 49 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation=pca[1058:]\n",
    "validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1b1f654a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:18:15.187454Z",
     "start_time": "2023-11-15T17:18:15.174423Z"
    }
   },
   "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>Date</th>\n",
       "      <th>Close_x</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>target1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Date, Close_x, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, target1]\n",
       "Index: []"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = validation[['Date','Close_x','0', '1','2', '3', '4', '5', '6','7', '8', '9', '10', '11','12','13','14','target1']]\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0167868b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:25:36.189656Z",
     "start_time": "2023-11-15T17:25:36.174621Z"
    }
   },
   "outputs": [],
   "source": [
    "b = validation[['Date','Close_x','0', '1','2', '3', '4', '5', '6','7', '8', '9', '10', '11','12','13','14','target1','ATR', 'ADX','RSI', 'RSI_Signal', 'Close_EMA_10', 'EMA_10_EMA_30', 'MACD_Signal_MACD']]\n",
    "b = b.dropna()\n",
    "x_valid = b[['Date','Close_x','0', '1','2', '3', '4', '5', '6','7', '8', '9', '10', '11','12','13','14','ATR', 'ADX','RSI', 'RSI_Signal', 'Close_EMA_10', 'EMA_10_EMA_30', 'MACD_Signal_MACD']]\n",
    "y_valid = b['target1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ca137e58",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:25:37.251293Z",
     "start_time": "2023-11-15T17:25:37.224175Z"
    }
   },
   "outputs": [],
   "source": [
    "def model_selection(X1,Y):\n",
    "    seed = 7\n",
    "    models = []\n",
    "    models.append(('LogisticRegression', LogisticRegression(random_state=seed)))\n",
    "    models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "    models.append(('KNeighborsClassifier', KNeighborsClassifier()))\n",
    "    models.append(('DecisionTreeClassifier', DecisionTreeClassifier()))\n",
    "    models.append(('GaussianNB', GaussianNB()))\n",
    "    models.append(('RandomForestClassifier', RandomForestClassifier()))\n",
    "    models.append(('ExtraTreesClassifier',ExtraTreesClassifier(random_state=seed)))\n",
    "    models.append(('AdaBoostClassifier',AdaBoostClassifier(DecisionTreeClassifier(random_state=seed),random_state=seed,learning_rate=0.01)))\n",
    "    models.append(('SVM',svm.SVC(random_state=seed)))\n",
    "    models.append(('GradientBoostingClassifier',GradientBoostingClassifier(random_state=seed)))\n",
    "    models.append(('MLPClassifier',MLPClassifier(random_state=seed)))\n",
    "    # evaluate each model in turn\n",
    "    results = []\n",
    "    names = []\n",
    "    output = pd.DataFrame() \n",
    "    output = X1.reset_index(drop=True)\n",
    "    output['Target'] = Y.reset_index(drop=True)\n",
    "    output1 = pd.DataFrame() \n",
    "    output1 = x_valid.reset_index(drop=True)\n",
    "    output1['Target'] = y_valid.reset_index(drop=True)\n",
    "    #output2 = pd.DataFrame() \n",
    "   # output2 = x_test.reset_index(drop=True)\n",
    "    #output2['Target'] = y_test.reset_index(drop=True)\n",
    "    scoring = 'accuracy'\n",
    "    scores = []\n",
    "    for name, model in models:\n",
    "        kfold = KFold(n_splits=10, shuffle=True, random_state=seed) \n",
    "        cv_results = cross_val_score(model, X1, Y, cv=kfold, scoring=scoring)\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "        model = model.fit(X1,Y)\n",
    "        output[name] = model.predict(X1)\n",
    "        msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "        print(msg)\n",
    "        score = model.score(x_valid, y_valid)\n",
    "        scores.append(score)\n",
    "        output1[name] = model.predict(x_valid) \n",
    "       # output2[name] = model.predict(x_test)\n",
    "        msg1 = \"%s: %f\" % (name, score)\n",
    "        print(msg1)\n",
    "    return results, names, output, scores, output1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "15ca31ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:26:47.251243Z",
     "start_time": "2023-11-15T17:25:38.344277Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Sharif\\anaconda3\\envs\\notebook\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression: 0.448023 (0.028769)\n",
      "LogisticRegression: 0.409692\n",
      "LDA: 0.500000 (0.038353)\n",
      "LDA: 0.337004\n",
      "KNeighborsClassifier: 0.419587 (0.039117)\n",
      "KNeighborsClassifier: 0.337004\n",
      "DecisionTreeClassifier: 0.642686 (0.043320)\n",
      "DecisionTreeClassifier: 0.253304\n",
      "GaussianNB: 0.344007 (0.052822)\n",
      "GaussianNB: 0.350220\n",
      "RandomForestClassifier: 0.704142 (0.039027)\n",
      "RandomForestClassifier: 0.314978\n",
      "ExtraTreesClassifier: 0.747637 (0.018016)\n",
      "ExtraTreesClassifier: 0.317181\n",
      "AdaBoostClassifier: 0.650261 (0.046758)\n",
      "AdaBoostClassifier: 0.253304\n",
      "SVM: 0.447062 (0.035435)\n",
      "SVM: 0.414097\n",
      "GradientBoostingClassifier: 0.726783 (0.047439)\n",
      "GradientBoostingClassifier: 0.365639\n",
      "MLPClassifier: 0.341285 (0.049302)\n",
      "MLPClassifier: 0.273128\n"
     ]
    }
   ],
   "source": [
    "results, names, output, scores ,output1  = model_selection(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cd5b7b2d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:26:48.200691Z",
     "start_time": "2023-11-15T17:26:48.044658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_models(results, names):\n",
    "    fig = plt.figure()\n",
    "    fig.suptitle('Algorithm Comparison')\n",
    "    ax = fig.add_subplot(111)\n",
    "    plt.boxplot(results)\n",
    "    ax.set_xticklabels(names)\n",
    "    plt.show()\n",
    "plot_models(results, names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "91716403",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:26:49.152678Z",
     "start_time": "2023-11-15T17:26:48.993689Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "means = np.array(results).mean(axis=1)\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "# creating the bar plot\n",
    "plt.barh(names,means)\n",
    "plt.xlabel(\"Mean Accuracy\")\n",
    "plt.ylabel(\"Algorithm\")\n",
    "plt.title(\"CV Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ad5459bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:26:50.117834Z",
     "start_time": "2023-11-15T17:26:49.963691Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "# creating the bar plot\n",
    "plt.barh(names,scores,color='r')\n",
    "plt.xlabel(\"Scores\")\n",
    "plt.ylabel(\"Algorithm\")\n",
    "plt.title(\"Test Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "59f82353",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:26:51.585803Z",
     "start_time": "2023-11-15T17:26:50.896775Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA profit: 27.9121 \n",
      "win_rate: 0.5185\n",
      "KNN profit: 37.0614 \n",
      "win_rate: 0.5849\n",
      "Dtree profit: 649.554 \n",
      "win_rate: 0.913\n",
      "GausianNB profit: 3.9984 \n",
      "win_rate: 0.5775\n",
      "RF profit: 649.554 \n",
      "win_rate: 0.913\n",
      "Etree profit: 649.554 \n",
      "win_rate: 0.913\n",
      "AdaBoot profit: 649.554 \n",
      "win_rate: 0.913\n",
      "GBooting profit: 427.1314 \n",
      "win_rate: 0.9524\n"
     ]
    }
   ],
   "source": [
    "#LogisticRegression \tLMz6XaxP7FaH5uUuJbRyeEuQ2XGadC84Cu \tKNeighborsClassifier \tDecisionTreeClassifier \tGaussianNB \tRandomForestClassifier \tExtraTreesClassifier \tAdaBoostClassifier \tSVM \tGradientBoostingClassifier \tMLPClassifier\n",
    "out_puts=output.copy()\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.LDA.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist() \n",
    "}\n",
    "sample_eval_LDA=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_LDA)):\n",
    "    if a == 1 and sample_eval_LDA.signal[i] == 1 or a == -1 and sample_eval_LDA.signal[i] == -1:\n",
    "        sample_eval_LDA.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_LDA.signal[i] == 1 or a == 1 and sample_eval_LDA.signal[i] == -1:\n",
    "        a = sample_eval_LDA.signal[i]\n",
    "    elif a == 0 and sample_eval_LDA.signal[i] == -1:\n",
    "        a = sample_eval_LDA.signal[i]\n",
    "    elif a == 0 and sample_eval_LDA.signal[i] == 1:       \n",
    "        sample_eval_LDA.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_LDA)\n",
    "print(\"LDA\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.KNeighborsClassifier.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist() \n",
    "}\n",
    "sample_eval_KNN=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_KNN)):\n",
    "    if a == 1 and sample_eval_KNN.signal[i] == 1 or a == -1 and sample_eval_KNN.signal[i] == -1:\n",
    "        sample_eval_KNN.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_KNN.signal[i] == 1 or a == 1 and sample_eval_KNN.signal[i] == -1:\n",
    "        a = sample_eval_KNN.signal[i]\n",
    "    elif a == 0 and sample_eval_KNN.signal[i] == -1:\n",
    "        a = sample_eval_KNN.signal[i]\n",
    "    elif a == 0 and sample_eval_KNN.signal[i] == 1:       \n",
    "        sample_eval_KNN.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_KNN)\n",
    "print(\"KNN\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.DecisionTreeClassifier.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist() \n",
    "}\n",
    "sample_eval_Dtree=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_Dtree)):\n",
    "    if a == 1 and sample_eval_Dtree.signal[i] == 1 or a == -1 and sample_eval_Dtree.signal[i] == -1:\n",
    "        sample_eval_Dtree.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_Dtree.signal[i] == 1 or a == 1 and sample_eval_Dtree.signal[i] == -1:\n",
    "        a = sample_eval_Dtree.signal[i]\n",
    "    elif a == 0 and sample_eval_Dtree.signal[i] == -1:\n",
    "        a = sample_eval_Dtree.signal[i]\n",
    "    elif a == 0 and sample_eval_Dtree.signal[i] == 1:       \n",
    "        sample_eval_Dtree.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_Dtree)\n",
    "print(\"Dtree\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.GaussianNB.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist() \n",
    "}\n",
    "sample_eval_GausianNB=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_GausianNB)):\n",
    "    if a == 1 and sample_eval_GausianNB.signal[i] == 1 or a == -1 and sample_eval_GausianNB.signal[i] == -1:\n",
    "        sample_eval_GausianNB.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_GausianNB.signal[i] == 1 or a == 1 and sample_eval_GausianNB.signal[i] == -1:\n",
    "        a = sample_eval_GausianNB.signal[i]\n",
    "    elif a == 0 and sample_eval_GausianNB.signal[i] == -1:\n",
    "        a = sample_eval_GausianNB.signal[i]\n",
    "    elif a == 0 and sample_eval_GausianNB.signal[i] == 1:       \n",
    "        sample_eval_GausianNB.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_GausianNB)\n",
    "print(\"GausianNB\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.RandomForestClassifier.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist() \n",
    "}\n",
    "sample_eval_RF=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_RF)):\n",
    "    if a == 1 and sample_eval_RF.signal[i] == 1 or a == -1 and sample_eval_RF.signal[i] == -1:\n",
    "        sample_eval_RF.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_RF.signal[i] == 1 or a == 1 and sample_eval_RF.signal[i] == -1:\n",
    "        a = sample_eval_RF.signal[i]\n",
    "    elif a == 0 and sample_eval_RF.signal[i] == -1:\n",
    "        a = sample_eval_RF.signal[i]\n",
    "    elif a == 0 and sample_eval_RF.signal[i] == 1:       \n",
    "        sample_eval_RF.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_RF)\n",
    "print(\"RF\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.ExtraTreesClassifier.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist() \n",
    "}\n",
    "sample_eval_Etree=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_Etree)):\n",
    "    if a == 1 and sample_eval_Etree.signal[i] == 1 or a == -1 and sample_eval_Etree.signal[i] == -1:\n",
    "        sample_eval_Etree.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_Etree.signal[i] == 1 or a == 1 and sample_eval_Etree.signal[i] == -1:\n",
    "        a = sample_eval_Etree.signal[i]\n",
    "    elif a == 0 and sample_eval_Etree.signal[i] == -1:\n",
    "        a = sample_eval_Etree.signal[i]\n",
    "    elif a == 0 and sample_eval_Etree.signal[i] == 1:       \n",
    "        sample_eval_Etree.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_Etree)\n",
    "print(\"Etree\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.AdaBoostClassifier.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist() \n",
    "}\n",
    "sample_eval_AdaBoot=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_AdaBoot)):\n",
    "    if a == 1 and sample_eval_AdaBoot.signal[i] == 1 or a == -1 and sample_eval_AdaBoot.signal[i] == -1:\n",
    "        sample_eval_AdaBoot.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_AdaBoot.signal[i] == 1 or a == 1 and sample_eval_AdaBoot.signal[i] == -1:\n",
    "        a = sample_eval_AdaBoot.signal[i]\n",
    "    elif a == 0 and sample_eval_AdaBoot.signal[i] == -1:\n",
    "        a = sample_eval_AdaBoot.signal[i]\n",
    "    elif a == 0 and sample_eval_AdaBoot.signal[i] == 1:       \n",
    "        sample_eval_AdaBoot.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_AdaBoot)\n",
    "print(\"AdaBoot\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts.GradientBoostingClassifier.tolist(),\n",
    "    \"close\":out_puts.Close_x.tolist(),\n",
    "    \"Date\":out_puts.Date.tolist()\n",
    "}\n",
    "sample_eval_GBooting=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_GBooting)):\n",
    "    if a == 1 and sample_eval_GBooting.signal[i] == 1 or a == -1 and sample_eval_GBooting.signal[i] == -1:\n",
    "        sample_eval_GBooting.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_GBooting.signal[i] == 1 or a == 1 and sample_eval_GBooting.signal[i] == -1:\n",
    "        a = sample_eval_GBooting.signal[i]\n",
    "    elif a == 0 and sample_eval_GBooting.signal[i] == -1:\n",
    "        a = sample_eval_GBooting.signal[i]\n",
    "    elif a == 0 and sample_eval_GBooting.signal[i] == 1:       \n",
    "        sample_eval_GBooting.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_GBooting)\n",
    "print(\"GBooting\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcd3d613",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "777b84e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:27:06.539188Z",
     "start_time": "2023-11-15T17:27:06.203157Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LDA profit: 1.2331 \n",
      "win_rate: 0.75\n",
      "KNN profit: 0.6964 \n",
      "win_rate: 0.4828\n",
      "Dtree profit: 0.7059 \n",
      "win_rate: 0.375\n",
      "GausianNB profit: 1.3211 \n",
      "win_rate: 0.7273\n",
      "RF profit: 0.7431 \n",
      "win_rate: 0.3333\n",
      "Etree profit: 0.8626 \n",
      "win_rate: 0.8\n",
      "AdaBoot profit: 0.5544 \n",
      "win_rate: 0.5385\n",
      "GBooting profit: 1.6106 \n",
      "win_rate: 0.8\n"
     ]
    }
   ],
   "source": [
    "out_puts1=output1.copy()\n",
    "\n",
    "#LogisticRegression \tLMz6XaxP7FaH5uUuJbRyeEuQ2XGadC84Cu \tKNeighborsClassifier \tDecisionTreeClassifier \tGaussianNB \tRandomForestClassifier \tExtraTreesClassifier \tAdaBoostClassifier \tSVM \tGradientBoostingClassifier \tMLPClassifier\n",
    "\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.LDA.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist() \n",
    "}\n",
    "sample_eval_LDA=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_LDA)):\n",
    "    if a == 1 and sample_eval_LDA.signal[i] == 1 or a == -1 and sample_eval_LDA.signal[i] == -1:\n",
    "        sample_eval_LDA.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_LDA.signal[i] == 1 or a == 1 and sample_eval_LDA.signal[i] == -1:\n",
    "        a = sample_eval_LDA.signal[i]\n",
    "    elif a == 0 and sample_eval_LDA.signal[i] == -1:\n",
    "        a = sample_eval_LDA.signal[i]\n",
    "    elif a == 0 and sample_eval_LDA.signal[i] == 1:       \n",
    "        sample_eval_LDA.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_LDA)\n",
    "print(\"LDA\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.KNeighborsClassifier.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist() \n",
    "}\n",
    "sample_eval_KNN=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_KNN)):\n",
    "    if a == 1 and sample_eval_KNN.signal[i] == 1 or a == -1 and sample_eval_KNN.signal[i] == -1:\n",
    "        sample_eval_KNN.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_KNN.signal[i] == 1 or a == 1 and sample_eval_KNN.signal[i] == -1:\n",
    "        a = sample_eval_KNN.signal[i]\n",
    "    elif a == 0 and sample_eval_KNN.signal[i] == -1:\n",
    "        a = sample_eval_KNN.signal[i]\n",
    "    elif a == 0 and sample_eval_KNN.signal[i] == 1:       \n",
    "        sample_eval_KNN.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_KNN)\n",
    "print(\"KNN\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.DecisionTreeClassifier.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist() \n",
    "}\n",
    "sample_eval_Dtree=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_Dtree)):\n",
    "    if a == 1 and sample_eval_Dtree.signal[i] == 1 or a == -1 and sample_eval_Dtree.signal[i] == -1:\n",
    "        sample_eval_Dtree.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_Dtree.signal[i] == 1 or a == 1 and sample_eval_Dtree.signal[i] == -1:\n",
    "        a = sample_eval_Dtree.signal[i]\n",
    "    elif a == 0 and sample_eval_Dtree.signal[i] == -1:\n",
    "        a = sample_eval_Dtree.signal[i]\n",
    "    elif a == 0 and sample_eval_Dtree.signal[i] == 1:       \n",
    "        sample_eval_Dtree.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_Dtree)\n",
    "print(\"Dtree\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.GaussianNB.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist() \n",
    "}\n",
    "sample_eval_GausianNB=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_GausianNB)):\n",
    "    if a == 1 and sample_eval_GausianNB.signal[i] == 1 or a == -1 and sample_eval_GausianNB.signal[i] == -1:\n",
    "        sample_eval_GausianNB.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_GausianNB.signal[i] == 1 or a == 1 and sample_eval_GausianNB.signal[i] == -1:\n",
    "        a = sample_eval_GausianNB.signal[i]\n",
    "    elif a == 0 and sample_eval_GausianNB.signal[i] == -1:\n",
    "        a = sample_eval_GausianNB.signal[i]\n",
    "    elif a == 0 and sample_eval_GausianNB.signal[i] == 1:       \n",
    "        sample_eval_GausianNB.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_GausianNB)\n",
    "print(\"GausianNB\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.RandomForestClassifier.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist() \n",
    "}\n",
    "sample_eval_RF=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_RF)):\n",
    "    if a == 1 and sample_eval_RF.signal[i] == 1 or a == -1 and sample_eval_RF.signal[i] == -1:\n",
    "        sample_eval_RF.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_RF.signal[i] == 1 or a == 1 and sample_eval_RF.signal[i] == -1:\n",
    "        a = sample_eval_RF.signal[i]\n",
    "    elif a == 0 and sample_eval_RF.signal[i] == -1:\n",
    "        a = sample_eval_RF.signal[i]\n",
    "    elif a == 0 and sample_eval_RF.signal[i] == 1:       \n",
    "        sample_eval_RF.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_RF)\n",
    "print(\"RF\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.ExtraTreesClassifier.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist() \n",
    "}\n",
    "sample_eval_Etree=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_Etree)):\n",
    "    if a == 1 and sample_eval_Etree.signal[i] == 1 or a == -1 and sample_eval_Etree.signal[i] == -1:\n",
    "        sample_eval_Etree.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_Etree.signal[i] == 1 or a == 1 and sample_eval_Etree.signal[i] == -1:\n",
    "        a = sample_eval_Etree.signal[i]\n",
    "    elif a == 0 and sample_eval_Etree.signal[i] == -1:\n",
    "        a = sample_eval_Etree.signal[i]\n",
    "    elif a == 0 and sample_eval_Etree.signal[i] == 1:       \n",
    "        sample_eval_Etree.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_Etree)\n",
    "print(\"Etree\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.AdaBoostClassifier.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist() \n",
    "}\n",
    "sample_eval_AdaBoot=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_AdaBoot)):\n",
    "    if a == 1 and sample_eval_AdaBoot.signal[i] == 1 or a == -1 and sample_eval_AdaBoot.signal[i] == -1:\n",
    "        sample_eval_AdaBoot.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_AdaBoot.signal[i] == 1 or a == 1 and sample_eval_AdaBoot.signal[i] == -1:\n",
    "        a = sample_eval_AdaBoot.signal[i]\n",
    "    elif a == 0 and sample_eval_AdaBoot.signal[i] == -1:\n",
    "        a = sample_eval_AdaBoot.signal[i]\n",
    "    elif a == 0 and sample_eval_AdaBoot.signal[i] == 1:       \n",
    "        sample_eval_AdaBoot.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_AdaBoot)\n",
    "print(\"AdaBoot\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n",
    "\n",
    "datas={\n",
    "    \"signal\":out_puts1.GradientBoostingClassifier.tolist(),\n",
    "    \"close\":out_puts1.Close_x.tolist(),\n",
    "    \"Date\":out_puts1.Date.tolist()\n",
    "    \n",
    "}\n",
    "sample_eval_GBooting=pd.DataFrame(datas)\n",
    "a=0\n",
    "for i in range(len(sample_eval_GBooting)):\n",
    "    if a == 1 and sample_eval_GBooting.signal[i] == 1 or a == -1 and sample_eval_GBooting.signal[i] == -1:\n",
    "        sample_eval_GBooting.signal[i] = 0\n",
    "    elif a == -1 and sample_eval_GBooting.signal[i] == 1 or a == 1 and sample_eval_GBooting.signal[i] == -1:\n",
    "        a = sample_eval_GBooting.signal[i]\n",
    "    elif a == 0 and sample_eval_GBooting.signal[i] == -1:\n",
    "        a = sample_eval_GBooting.signal[i]\n",
    "    elif a == 0 and sample_eval_GBooting.signal[i] == 1:       \n",
    "        sample_eval_GBooting.signal[i] = 0\n",
    "profit, win_rate = eval_strategy(sample_eval_GBooting)\n",
    "print(\"GBooting\",\"profit:\", profit,\"\\nwin_rate:\", win_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d2cf6d34",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:21:56.522665Z",
     "start_time": "2023-11-15T17:21:56.503639Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "profit: 0.7942 \n",
      "win_rate: 0.0\n"
     ]
    }
   ],
   "source": [
    "L=[-1]\n",
    "for i in range(len(validation)-2):\n",
    "    L.append(0)\n",
    "L.append(1)\n",
    "\n",
    "datas={\n",
    "    \"signal\":L,\n",
    "    \"close\":validation.Close_x.tolist() \n",
    "}\n",
    "y1=pd.DataFrame(datas)\n",
    "profit, win_rate = eval_strategy(y1)\n",
    "print(\"profit:\", profit,\"\\nwin_rate:\", win_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1bf33d07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:27:54.357000Z",
     "start_time": "2023-11-15T17:27:19.294324Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: this type of plot wants 5 columns/args, but you've only supplied 4\n",
      " - Assuming time column is missing and using index instead.\n"
     ]
    }
   ],
   "source": [
    "import finplot as fplt\n",
    "# CREATE A TICKER INSTANCE FOR TESLA\n",
    "#Rasad = yf.Ticker('TSLA')\n",
    "\n",
    "# RETRIEVE 1 YEAR WORTH OF DAILY DATA OF TESLA\n",
    "#df = Rasad.history(interval='1d',period='1y')\n",
    "\n",
    "# PLOT THE OHLC CANDLE CHART\n",
    "aa=validation[['Open','Close_x','High','Low','Date']]\n",
    "ab=sample_eval_GBooting\n",
    "aa=ab.merge(aa, left_on='Date', right_on='Date')\n",
    "\n",
    "fplt.candlestick_ochl(aa[['Open','Close_x','High','Low']])\n",
    "#train1['AdaBoostClassifier']=sample_eval_AdaBoot['signal']\n",
    "lo_wicks=sample_eval_GBooting['signal']\n",
    "aa.loc[(lo_wicks==1),'marker'] = aa['Close_x']\n",
    "fplt.plot(aa.index, aa['marker'],color='navy', style='^', legend='dumb mark')\n",
    "aa.loc[(lo_wicks==-1),'marker1'] = aa['Close_x']\n",
    "fplt.plot(aa.index, aa['marker1'],color='lawngreen', style='v', legend='dumb mark')\n",
    "\n",
    "#fplt.plot(a , color='black', legend='Log price')\n",
    "fplt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "38451310",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-15T17:22:40.265856Z",
     "start_time": "2023-11-15T17:22:40.194862Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>date</th>\n",
       "      <th>Close_x</th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close_y</th>\n",
       "      <th>v_i_buy</th>\n",
       "      <th>v_n_buy</th>\n",
       "      <th>v_i_sell</th>\n",
       "      <th>v_n_sell</th>\n",
       "      <th>n_i_buy</th>\n",
       "      <th>n_n_buy</th>\n",
       "      <th>n_i_sell</th>\n",
       "      <th>n_n_sell</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>f3</th>\n",
       "      <th>f4</th>\n",
       "      <th>f5</th>\n",
       "      <th>f6</th>\n",
       "      <th>EMA_10</th>\n",
       "      <th>EMA_30</th>\n",
       "      <th>ATR</th>\n",
       "      <th>ADX</th>\n",
       "      <th>RSI</th>\n",
       "      <th>MACD</th>\n",
       "      <th>MACD_signal</th>\n",
       "      <th>Close_EMA_10</th>\n",
       "      <th>EMA_10_EMA_30</th>\n",
       "      <th>MACD_Signal_MACD</th>\n",
       "      <th>return</th>\n",
       "      <th>target</th>\n",
       "      <th>RSI_Signal</th>\n",
       "      <th>return1</th>\n",
       "      <th>target1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>-7.886185e+06</td>\n",
       "      <td>-5.057550e+06</td>\n",
       "      <td>230242.744149</td>\n",
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       "      <td>191328.0</td>\n",
       "      <td>292884.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.240959</td>\n",
       "      <td>0.328751</td>\n",
       "      <td>0.995710</td>\n",
       "      <td>0.931925</td>\n",
       "      <td>-0.004290</td>\n",
       "      <td>4.313209</td>\n",
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       "      <td>0.543328</td>\n",
       "      <td>0.015271</td>\n",
       "      <td>9.038941</td>\n",
       "      <td>49.886361</td>\n",
       "      <td>-0.007338</td>\n",
       "      <td>-0.008124</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.173940</td>\n",
       "      <td>-1</td>\n",
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       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>-3.024126e+06</td>\n",
       "      <td>-3.311615e+06</td>\n",
       "      <td>-26811.180217</td>\n",
       "      <td>-38509.835516</td>\n",
       "      <td>-1866.421124</td>\n",
       "      <td>29.241210</td>\n",
       "      <td>-809.187489</td>\n",
       "      <td>-23.726417</td>\n",
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       "      <td>5.673683</td>\n",
       "      <td>1.947640</td>\n",
       "      <td>2.459634</td>\n",
       "      <td>2.611361</td>\n",
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       "      <td>20210307</td>\n",
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       "      <td>548346.0</td>\n",
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       "      <td>6.526396</td>\n",
       "      <td>0.539854</td>\n",
       "      <td>0.544957</td>\n",
       "      <td>0.015433</td>\n",
       "      <td>10.305807</td>\n",
       "      <td>56.927783</td>\n",
       "      <td>-0.004065</td>\n",
       "      <td>-0.007312</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.048310</td>\n",
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       "      <td>0.108818</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>-3.060310e+06</td>\n",
       "      <td>-9.309814e+05</td>\n",
       "      <td>44845.042548</td>\n",
       "      <td>18743.470127</td>\n",
       "      <td>-2805.552635</td>\n",
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       "      <td>-362.965359</td>\n",
       "      <td>-13.265457</td>\n",
       "      <td>14.463339</td>\n",
       "      <td>-0.450772</td>\n",
       "      <td>4.881519</td>\n",
       "      <td>0.209764</td>\n",
       "      <td>2.931325</td>\n",
       "      <td>0.810980</td>\n",
       "      <td>-0.235295</td>\n",
       "      <td>20210308</td>\n",
       "      <td>204950</td>\n",
       "      <td>20210308</td>\n",
       "      <td>198836</td>\n",
       "      <td>204980</td>\n",
       "      <td>197001</td>\n",
       "      <td>204950</td>\n",
       "      <td>573369.0</td>\n",
       "      <td>148119.0</td>\n",
       "      <td>662262.0</td>\n",
       "      <td>59226.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.398180</td>\n",
       "      <td>0.544223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985395</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.707662</td>\n",
       "      <td>0.549782</td>\n",
       "      <td>0.548151</td>\n",
       "      <td>0.016284</td>\n",
       "      <td>12.270741</td>\n",
       "      <td>62.418070</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>-0.005727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>5.315437e+05</td>\n",
       "      <td>-1.583153e+06</td>\n",
       "      <td>35837.663010</td>\n",
       "      <td>61278.358249</td>\n",
       "      <td>-3108.050668</td>\n",
       "      <td>-15.576101</td>\n",
       "      <td>-241.441466</td>\n",
       "      <td>-27.992649</td>\n",
       "      <td>9.468191</td>\n",
       "      <td>-2.567775</td>\n",
       "      <td>6.467719</td>\n",
       "      <td>-0.068272</td>\n",
       "      <td>2.137580</td>\n",
       "      <td>2.270962</td>\n",
       "      <td>-0.288522</td>\n",
       "      <td>20210309</td>\n",
       "      <td>200100</td>\n",
       "      <td>20210309</td>\n",
       "      <td>206530</td>\n",
       "      <td>209212</td>\n",
       "      <td>198051</td>\n",
       "      <td>200100</td>\n",
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       "      <td>1120566.0</td>\n",
       "      <td>404319.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.734363</td>\n",
       "      <td>0.930543</td>\n",
       "      <td>0.989369</td>\n",
       "      <td>-0.065167</td>\n",
       "      <td>8.331355</td>\n",
       "      <td>0.555243</td>\n",
       "      <td>0.550194</td>\n",
       "      <td>0.015462</td>\n",
       "      <td>14.423708</td>\n",
       "      <td>57.922006</td>\n",
       "      <td>0.003101</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1062</th>\n",
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       "      <td>-1707.588639</td>\n",
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       "      <td>-301.990240</td>\n",
       "      <td>-13.729953</td>\n",
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       "      <td>-0.347334</td>\n",
       "      <td>2.801914</td>\n",
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       "      <td>-0.053327</td>\n",
       "      <td>20210310</td>\n",
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       "      <td>369.0</td>\n",
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       "      <td>0.552091</td>\n",
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       "      <td>16.422892</td>\n",
       "      <td>57.857229</td>\n",
       "      <td>0.004999</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-0.000364</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>-9.033843e+05</td>\n",
       "      <td>-9.789411e+05</td>\n",
       "      <td>59572.962222</td>\n",
       "      <td>-9250.593101</td>\n",
       "      <td>-2648.261668</td>\n",
       "      <td>-124.593469</td>\n",
       "      <td>998.546516</td>\n",
       "      <td>446.792964</td>\n",
       "      <td>-25.633822</td>\n",
       "      <td>-2.869416</td>\n",
       "      <td>-1.643769</td>\n",
       "      <td>-0.520154</td>\n",
       "      <td>0.678705</td>\n",
       "      <td>1.723787</td>\n",
       "      <td>-0.764789</td>\n",
       "      <td>20230610</td>\n",
       "      <td>154950</td>\n",
       "      <td>20230610</td>\n",
       "      <td>163450</td>\n",
       "      <td>163450</td>\n",
       "      <td>154950</td>\n",
       "      <td>154950</td>\n",
       "      <td>254722.0</td>\n",
       "      <td>478000.0</td>\n",
       "      <td>400685.0</td>\n",
       "      <td>332037.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.445613</td>\n",
       "      <td>0.489516</td>\n",
       "      <td>0.975648</td>\n",
       "      <td>0.879090</td>\n",
       "      <td>0.249219</td>\n",
       "      <td>-8.661301</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.516023</td>\n",
       "      <td>0.013731</td>\n",
       "      <td>21.583799</td>\n",
       "      <td>25.963645</td>\n",
       "      <td>-0.023126</td>\n",
       "      <td>-0.015602</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.049454</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>-1.611956e+06</td>\n",
       "      <td>-6.409301e+05</td>\n",
       "      <td>2988.013612</td>\n",
       "      <td>-2753.561370</td>\n",
       "      <td>-3179.352797</td>\n",
       "      <td>31.562840</td>\n",
       "      <td>38.435055</td>\n",
       "      <td>-21.030952</td>\n",
       "      <td>-27.019773</td>\n",
       "      <td>-0.981566</td>\n",
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       "    <tr>\n",
       "      <th>1509</th>\n",
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       "      <td>0.465411</td>\n",
       "      <td>0.504875</td>\n",
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       "      <td>25.373302</td>\n",
       "      <td>21.751171</td>\n",
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       "    <tr>\n",
       "      <th>1510</th>\n",
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       "      <td>-76081.998276</td>\n",
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       "      <td>-3065.714595</td>\n",
       "      <td>-24.441763</td>\n",
       "      <td>436.768348</td>\n",
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       "      <td>-31.389450</td>\n",
       "      <td>-0.086056</td>\n",
       "      <td>-0.684275</td>\n",
       "      <td>-0.225807</td>\n",
       "      <td>0.704404</td>\n",
       "      <td>0.998876</td>\n",
       "      <td>-0.219016</td>\n",
       "      <td>20230613</td>\n",
       "      <td>146300</td>\n",
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       "      <td>140100</td>\n",
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       "      <td>91151.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>368.0</td>\n",
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       "      <td>0.805722</td>\n",
       "      <td>0.620109</td>\n",
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       "      <td>-0.355539</td>\n",
       "      <td>-8.766849</td>\n",
       "      <td>0.456688</td>\n",
       "      <td>0.499234</td>\n",
       "      <td>0.011658</td>\n",
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       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>1.384566e+06</td>\n",
       "      <td>-2.008571e+05</td>\n",
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       "      <td>0.452267</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>454 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0             1              2              3  ...  target  RSI_Signal   return1  target1\n",
       "1058 -7.886185e+06 -5.057550e+06  230242.744149 -307961.407809  ...       0           0  0.173940       -1\n",
       "1059 -3.024126e+06 -3.311615e+06  -26811.180217  -38509.835516  ...       0           0  0.108818       -1\n",
       "1060 -3.060310e+06 -9.309814e+05   44845.042548   18743.470127  ...       0           0  0.054640        0\n",
       "1061  5.315437e+05 -1.583153e+06   35837.663010   61278.358249  ...       0           0  0.081080       -1\n",
       "1062 -2.034207e+06  8.966341e+05   -1707.588639   18275.238541  ...       0           0  0.054485        0\n",
       "...            ...           ...            ...            ...  ...     ...         ...       ...      ...\n",
       "1507 -9.033843e+05 -9.789411e+05   59572.962222   -9250.593101  ...       0          -1       NaN        0\n",
       "1508 -1.611956e+06 -6.409301e+05    2988.013612   -2753.561370  ...       0          -1       NaN        0\n",
       "1509 -5.222906e+05 -5.000176e+05  -35597.670587  -13607.657076  ...       0          -1       NaN        0\n",
       "1510 -2.604537e+05  6.611280e+04  -76081.998276    3839.756298  ...       0          -1       NaN        0\n",
       "1511  1.384566e+06 -2.008571e+05  -44911.520162   64785.543929  ...       0          -1       NaN        0\n",
       "\n",
       "[454 rows x 51 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
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   "source": []
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