MATICUSDT_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "pd.set_option('display.max_columns', None)\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "dataframe = pd.read_csv('MATICUSDT_TA.csv', delimiter=';', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([648210, 646733], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(dataframe['signal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe['openTime'] = pd.to_datetime(dataframe['openTime'])\n",
    "dataframe['openTime'] = dataframe['openTime'].apply(lambda x: x.toordinal())\n",
    "dataframe['closeTime'] = pd.to_datetime(dataframe['closeTime'])\n",
    "dataframe['closeTime'] = dataframe['closeTime'].apply(lambda x: x.toordinal())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1123 infinite values.\n"
     ]
    }
   ],
   "source": [
    "before = len(dataframe)\n",
    "dataframe = dataframe[~dataframe.isin([np.inf, -np.inf]).any(1)]\n",
    "after = len(dataframe)\n",
    "print(str(before - after) + ' infinite values.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataframe['signal']\n",
    "dataframe.drop(['signal'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_test = int(len(dataframe) / 5)\n",
    "x_train = dataframe[:-num_test]\n",
    "y_train = y[:-num_test]\n",
    "x_test = dataframe[-num_test:]\n",
    "y_test = y[-num_test:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1035056, 93), (1035056,), (258764, 93), (258764,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 2 of 100\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:  3.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=4, random_state=17, verbose=2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time\n",
    "clf = RandomForestClassifier(max_depth=4, random_state=17, verbose=2)\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8593312825586249\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 100 out of 100 | elapsed:    1.0s finished\n"
     ]
    }
   ],
   "source": [
    "preds = clf.predict(x_test)\n",
    "print(accuracy_score(preds, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.86      0.86      0.86    127358\n",
      "         1.0       0.86      0.86      0.86    131406\n",
      "\n",
      "    accuracy                           0.86    258764\n",
      "   macro avg       0.86      0.86      0.86    258764\n",
      "weighted avg       0.86      0.86      0.86    258764\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(preds, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('RandomForest_depth_4.pickle', 'wb') as out:\n",
    "#     pickle.dump(clf, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('RandomForest_depth_4.pickle', 'rb') as f:\n",
    "#     my_clf = pickle.load(f)"
   ]
  }
 ],
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