TA_XGBOOST_STK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hemangjoshi37a_gmail_com/.local/lib/python3.8/site-packages/pandas/compat/__init__.py:120: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "# pandas numpy sklearn joblib livelossplot seaborn tqdm matplotlib keras_tqdm kiteconnect ipywidgets ta\n",
    "import threading\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "import joblib\n",
    "import livelossplot\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "from matplotlib.pyplot import figure\n",
    "from numpy import loadtxt\n",
    "from stkdef60 import One_trian_stk_class\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost\n",
    "import math\n",
    "import csv as csv\n",
    "from xgboost import plot_importance\n",
    "from matplotlib import pyplot\n",
    "from sklearn.model_selection import cross_val_score, KFold, train_test_split, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import skew\n",
    "from collections import OrderedDict\n",
    "from stkdef60 import One_trian_stk_class\n",
    "import logging\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from kiteconnect import KiteConnect\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interact_manual\n",
    "from ta import add_all_ta_features\n",
    "import ta\n",
    "logging.basicConfig(level=logging.ERROR)\n",
    "# from pactools.grid_search import GridSearchCVProgressBar\n",
    "homediraddr = '/home/hemangjoshi37a_gmail_com'\n",
    "kite = KiteConnect(api_key=\"w19o0chuo929jxkp\")\n",
    "kite.login_url()\n",
    "# plt.style.use(\"seaborn\")\n",
    "kite = joblib.load('kitefile.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_for_testing = {\n",
    "    'estimator__colsample_bytree': [0.4, 0.8],\n",
    "    'estimator__gamma': [0.03, 0.3],\n",
    "    'estimator__min_child_weight': [1.5, 10],\n",
    "    'estimator__learning_rate': [0.1, 0.07],\n",
    "    'estimator__max_depth': [3, 15],\n",
    "    'estimator__n_estimators': [10000],\n",
    "    'estimator__reg_alpha': [1e-5, 0.75],\n",
    "    'estimator__reg_lambda': [1e-5, 0.45],\n",
    "    'estimator__subsample': [0.6, 0.95],\n",
    "}\n",
    "\n",
    "xgbmodel = xgboost.XGBRegressor(colsample_bytree=0.4,\n",
    "                                gamma=0.5,\n",
    "                                learning_rate=0.05,\n",
    "                                max_depth=20,\n",
    "                                min_child_weight=1.1,\n",
    "                                n_estimators=10000,\n",
    "                                reg_alpha=0.50,\n",
    "                                gpu_id=0,\n",
    "                                tree_method='gpu_hist',\n",
    "                                reg_lambda=0.40,\n",
    "                                subsample=0.6,\n",
    "                                seed=50,\n",
    "                                n_jobs=-1,\n",
    "                                objective='reg:squarederror')\n",
    "\n",
    "xx = {\n",
    "    'name': 'TATASTEEL',\n",
    "    'insttkn': 895745,\n",
    "    'nbatch': 1,\n",
    "    'nepochs': 5,\n",
    "    'trainlen': 'all',\n",
    "    'kite': kite\n",
    "}\n",
    "trainit = One_trian_stk_class(xx['name'], xx['insttkn'], xx['nbatch'],\n",
    "                              xx['nepochs'], xx['trainlen'], xx['kite'])\n",
    "trainit.load()\n",
    "trainit.ta_xtrain_def()\n",
    "\n",
    "reshapedxt = trainit.ta_x_train.reshape([\n",
    "    trainit.ta_x_train.shape[0],\n",
    "    trainit.ta_x_train.shape[1] * trainit.ta_x_train.shape[2]\n",
    "])\n",
    "xtreshaped = trainit.ta_x_test.reshape([\n",
    "    trainit.ta_x_test.shape[0],\n",
    "    trainit.ta_x_train.shape[1] * trainit.ta_x_train.shape[2]\n",
    "])\n",
    "\n",
    "multioutputregressor = MultiOutputRegressor(xgbmodel)\n",
    "\n",
    "eval_set = [(reshapedxt, trainit.ta_y_train), (xtreshaped, trainit.ta_y_test)]\n",
    "\n",
    "gsearch1 = GridSearchCV(estimator=multioutputregressor,\n",
    "                        param_grid=parameters_for_testing,\n",
    "                        n_jobs=8,\n",
    "                        iid=False,\n",
    "                        verbose=1,\n",
    "                        scoring='neg_mean_squared_error')\n",
    "\n",
    "gsearch1.fit(reshapedxt,\n",
    "             trainit.ta_y_train,\n",
    "             eval_metric=[\"error\", \"logloss\"],\n",
    "             eval_set=eval_set,\n",
    "             verbose=False)\n",
    "print(gsearch1.grid_scores_)\n",
    "print('best params')\n",
    "print(gsearch1.best_params_)\n",
    "print('best score')\n",
    "print(gsearch1.best_score_)\n",
    "joblib.dump(gsearch1, \"gsearch1.p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(xtrain, \"xtrain97.p\")\n",
    "joblib.dump(trainit.ta_y_train, \"ytrain97.p\")\n",
    "joblib.dump(xtest, \"xtest97.p\")\n",
    "joblib.dump(trainit.ta_y_test, \"ytest97.p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainit.dxtrain.save_binary(\"dxtrain.p\")\n",
    "trainit.dytrain.save_binary(\"dytrain.p\")\n",
    "trainit.dxtest.save_binary(\"dxtest.p\")\n",
    "trainit.dytest.save_binary(\"dytest.p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain=joblib.load('xtrain.p')\n",
    "ytrain=joblib.load('ytrain.p')\n",
    "xtest=joblib.load('xtest.p')\n",
    "ytest=joblib.load('ytest.p')\n",
    "eval_set = [(xtrain, ytrain), (xtest,ytest)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrain_o=ytrain\n",
    "ytest_o=ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx={'name':'TATASTEEL',\n",
    "    'insttkn':895745,\n",
    "    'nbatch':1,\n",
    "    'nepochs':5,\n",
    "    'trainlen':'all',\n",
    "    'kite':kite}\n",
    "trainit= One_trian_stk_class(xx['name'], xx['insttkn'],xx['nbatch'],xx['nepochs'],\n",
    "                             xx['trainlen'],xx['kite'])\n",
    "trainit.load()\n",
    "trainit.ta_xtrain_def()\n",
    "\n",
    "xtrain=trainit.ta_x_train.reshape([trainit.ta_x_train.shape[0],\n",
    "                                   trainit.ta_x_train.shape[1]*trainit.ta_x_train.shape[2]])\n",
    "xtest=trainit.ta_x_test.reshape([trainit.ta_x_test.shape[0],\n",
    "                                  trainit.ta_x_train.shape[1]*trainit.ta_x_train.shape[2]])\n",
    "eval_set = [(xtrain, trainit.ta_y_train), (xtest, trainit.ta_y_test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain = (291216, 4620)\n",
      "ytrain = (291216, 60)\n",
      "xtest = (4842, 4620)\n",
      "ytest = (4842, 60)\n"
     ]
    }
   ],
   "source": [
    "percent=0.60\n",
    "\n",
    "xtrain=xtrain[-math.ceil((xtrain.shape[0])*percent):]\n",
    "ytrain=ytrain[-math.ceil((ytrain.shape[0])*percent):]\n",
    "eval_set = [(xtrain, ytrain), (xtest,ytest)]\n",
    "print('xtrain = '+str(xtrain.shape))            \n",
    "print('ytrain = '+str(ytrain.shape))\n",
    "\n",
    "print('xtest = '+str(xtest.shape))      \n",
    "print('ytest = '+str(ytest.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(291216, 1)\n",
      "(4842, 1)\n"
     ]
    }
   ],
   "source": [
    "ytrain = ytrain[:, :1]\n",
    "ytest = ytest[:, :1]\n",
    "print(ytrain.shape)\n",
    "print(ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "['xgbregressor16.p']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nr=xgboost.XGBRegressor(\n",
    "                 colsample_bytree=0.6,\n",
    "                 gamma=0.5,\n",
    "                 learning_rate=0.07,\n",
    "                 max_depth=25,\n",
    "                 min_child_weight=1.2,\n",
    "                 n_estimators=1000,\n",
    "                 gpu_id=0,\n",
    "                 tree_method='gpu_hist',\n",
    "                 objective='reg:squarederror',\n",
    "                 reg_alpha=0.75,\n",
    "                 reg_lambda=0.45,\n",
    "                 subsample=0.6,\n",
    "                 seed=70,\n",
    "                 verbosity=1,\n",
    "                 predictor = 'gpu_predictor',\n",
    "                 #n_jobs=-1,\n",
    ")\n",
    "\n",
    "eval_set=[(xtrain,ytrain),(xtest,ytest)]\n",
    "\n",
    "nr.fit(xtrain,ytrain,\n",
    "        eval_metric=[\"error\", \"logloss\"],\n",
    "            eval_set=eval_set,\n",
    "            verbose=True\n",
    "      )\n",
    "joblib.dump(nr, \"xgbregressor16.p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multioutputregressor = MultiOutputRegressor(xgboost.XGBRegressor(\n",
    "                colsample_bytree=0.4,\n",
    "                 gamma=0.5,\n",
    "                 learning_rate=0.1,\n",
    "                 max_depth=10,\n",
    "                 min_child_weight=1.3,\n",
    "                 n_estimators=100,\n",
    "                 gpu_id=0,\n",
    "                 tree_method='gpu_hist',\n",
    "                 objective='reg:squarederror',\n",
    "                 reg_alpha=0.75,\n",
    "                 reg_lambda=0.45,\n",
    "                 subsample=0.6,\n",
    "                 seed=15,\n",
    "                 verbosity=1,\n",
    "                 n_jobs=-1))\n",
    "\n",
    "# multioutputregressor = MultiOutputRegressor(xgboost.XGBRegressor(objective='reg:squaredlogerror',\n",
    "#                                                                  verbosity=1,\n",
    "#                                                                  gpu_id=0,\n",
    "#                                                                  n_jobs=-1\n",
    "#                                                                  ),\n",
    "#                                                                  n_jobs=-1\n",
    "#                                                                  )\n",
    "\n",
    "print(multioutputregressor.get_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "res1=multioutputregressor.fit(xtrain,ytrain,\n",
    "                          eval_metric=[\"error\",\"logloss\"], \n",
    "                          eval_set=eval_set,\n",
    "                          verbose=True)\n",
    "\n",
    "joblib.dump(multioutputregressor, \"multioutputregressor11.p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multioutputregressor.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrMatrix = trainit.talibdf.corr()\n",
    "figure(num=None, figsize=(8, 8), dpi=150)\n",
    "sns.heatmap(corrMatrix)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36994628906251137\n"
     ]
    }
   ],
   "source": [
    "pred0=nr.predict(xtest[0].reshape(1,-1))\n",
    "act0=ytest[0][0]\n",
    "diff=np.array(pred0[0])-np.array(act0)\n",
    "print(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.17260132  0.22260132  0.57260132  0.57260132  0.87260132  0.42260132\n",
      " -0.07739868 -0.02739868 -0.07739868  0.27260132  0.27260132  0.17260132\n",
      " -0.22739868 -0.12739868 -0.12739868 -0.27739868 -0.37739868 -0.32739868\n",
      " -0.32739868 -0.07739868  0.17260132  0.12260132  0.07260132  0.02260132\n",
      "  0.02260132  0.02260132 -0.12739868  3.02260132  4.62260132  3.87260132\n",
      "  3.97260132  3.02260132  3.77260132  4.32260132  5.07260132  4.27260132\n",
      "  4.92260132  4.82260132  4.22260132  3.82260132  3.97260132  4.22260132\n",
      "  3.92260132  5.22260132  5.07260132  5.42260132  4.92260132  5.02260132\n",
      "  6.12260132  6.62260132  7.62260132  8.37260132  8.32260132  9.27260132\n",
      "  9.97260132  9.12260132  8.87260132  8.27260132  8.42260132  9.37260132]\n"
     ]
    }
   ],
   "source": [
    "pred1=nr.predict(xtest[1].reshape(1,-1))\n",
    "act1=ytest[1]\n",
    "diff1=np.array(pred1[0])-np.array(act1)\n",
    "print(diff1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.43933105e-01  5.93933105e-01  5.93933105e-01  8.93933105e-01\n",
      "  4.43933105e-01 -5.60668945e-02 -6.06689453e-03 -5.60668945e-02\n",
      "  2.93933105e-01  2.93933105e-01  1.93933105e-01 -2.06066895e-01\n",
      " -1.06066895e-01 -1.06066895e-01 -2.56066895e-01 -3.56066895e-01\n",
      " -3.06066895e-01 -3.06066895e-01 -5.60668945e-02  1.93933105e-01\n",
      "  1.43933105e-01  9.39331055e-02  4.39331055e-02  4.39331055e-02\n",
      "  4.39331055e-02 -1.06066895e-01  3.04393311e+00  4.64393311e+00\n",
      "  3.89393311e+00  3.99393311e+00  3.04393311e+00  3.79393311e+00\n",
      "  4.34393311e+00  5.09393311e+00  4.29393311e+00  4.94393311e+00\n",
      "  4.84393311e+00  4.24393311e+00  3.84393311e+00  3.99393311e+00\n",
      "  4.24393311e+00  3.94393311e+00  5.24393311e+00  5.09393311e+00\n",
      "  5.44393311e+00  4.94393311e+00  5.04393311e+00  6.14393311e+00\n",
      "  6.64393311e+00  7.64393311e+00  8.39393311e+00  8.34393311e+00\n",
      "  9.29393311e+00  9.99393311e+00  9.14393311e+00  8.89393311e+00\n",
      "  8.29393311e+00  8.44393311e+00  9.39393311e+00  9.44393311e+00]\n"
     ]
    }
   ],
   "source": [
    "pred2=nr.predict(xtest[2].reshape(1,-1))\n",
    "act2=ytest[2]\n",
    "diff2=np.array(pred2[0])-np.array(act2)\n",
    "print(diff2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred0=multioutputregressor.predict(trainit.ta_x_test[0].reshape(1,60*trainit.ta_x_test.shape[2]))\n",
    "act0=trainit.ta_y_test[0]\n",
    "diff=np.array(pred0[0])-np.array(act0)\n",
    "difflist=diff.tolist()\n",
    "table0=pd.DataFrame({'true':act0,'pred':pred0[0]})\n",
    "table0.plot(figsize=(13, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1=multioutputregressor.predict(trainit.ta_x_test[21].reshape(1,60*trainit.ta_x_test.shape[2]))\n",
    "act1=trainit.ta_y_test[21]\n",
    "diff=np.array(pred1[0])-np.array(act1)\n",
    "difflist1=diff.tolist()\n",
    "table1=pd.DataFrame({'true':act1,'pred':pred1[0]})\n",
    "table1.plot(figsize=(13, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred2=multioutputregressor.predict(trainit.ta_x_test[25].reshape(1,60*trainit.ta_x_test.shape[2]))\n",
    "act2=trainit.ta_y_test[25]\n",
    "diff=np.array(pred2[0])-np.array(act2)\n",
    "difflist2=diff.tolist()\n",
    "table2=pd.DataFrame({'true':act2,'pred':pred2[0]})\n",
    "table2.plot(figsize=(13, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# newdict={'table1_pred':table0['diff'].values.tolist(),'table2_pred':table1['diff'].values.tolist()} \n",
    "# hjdf=pd.DataFrame(newdict)\n",
    "# hjdf.plot(figsize=(13, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_no=0\n",
    "table2=pd.DataFrame({'true':trainit.ta_y_test[sample_no],'pred':multioutputregressor.predict(trainit.ta_x_test[sample_no].reshape(1,60*trainit.ta_x_test.shape[2]))[0]})\n",
    "table2.plot(figsize=(13, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@interact\n",
    "def shwo_gra(x=189):\n",
    "    sample_no=x\n",
    "#     sample_no=int(y)\n",
    "    table2=pd.DataFrame({'true':trainit.ta_y_test[sample_no],'pred':multioutputregressor.predict(trainit.ta_x_test[sample_no].reshape(1,60*trainit.ta_x_test.shape[2]))[0]})\n",
    "    print(table2.head())\n",
    "    actar=np.array(trainit.ta_y_test[sample_no])\n",
    "    predar=np.array(multioutputregressor.predict(trainit.ta_x_test[sample_no].reshape(1,60*trainit.ta_x_test.shape[2]))[0])\n",
    "    diffdf=pd.DataFrame(actar-predar)\n",
    "    diffdf.plot()\n",
    "    diffa=(np.mean(np.abs(actar-predar)))\n",
    "    print('acc : '+ str(diffa))\n",
    "    return table2.plot(figsize=(10, 6))\n",
    "#     return table2\n",
    "#     return trainit.talibdf.loc[trainit.talibdf[column] = x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from ta import add_all_ta_features\n",
    "from ta.utils import dropna\n",
    "\n",
    "df =trainit.df\n",
    "\n",
    "df = add_all_ta_features(\n",
    "    df, open=\"open\", high=\"high\", low=\"low\", close=\"close\", volume=\"volume\")"
   ]
  }
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