stocks_svm_window.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Price  William's %R     Roc  Momentum     RSI     CCI     ADX  TRIX  \\\n",
      "0    274500        0.0000 -50.000  0.000000 -1.0000  0.0000 -1.0000   0.0   \n",
      "1    278500        0.0000 -50.000  0.000000 -1.0000  0.0000 -1.0000   0.0   \n",
      "2    276500       -0.2500 -50.000  0.000000 -1.0000  0.0000 -1.0000   0.1   \n",
      "3    275500       -0.3750 -50.000  0.000000 -1.0000  0.0000 -1.0000   0.1   \n",
      "4    275500       -0.3750 -50.000  0.000000 -1.0000  0.0000 -1.0000   0.2   \n",
      "..      ...           ...     ...       ...     ...     ...     ...   ...   \n",
      "595  317500       -0.2105   0.720  0.012598  0.2666  0.5372 -0.0354   0.9   \n",
      "596  318500       -0.1053   0.795  0.014129  0.2904  0.6458 -0.0198   0.9   \n",
      "597  319000       -0.0526   1.040  0.018809  0.4000  0.8410  0.0092   1.0   \n",
      "598  319500       -0.0500   0.795  0.018779  0.4000  1.1561  0.0586   1.0   \n",
      "599  320000        0.0000   0.475  0.023438  0.5172  1.5659  0.1014   1.0   \n",
      "\n",
      "         MACD        OBV      _TSF_       ATR     MFI  \n",
      "0         NaN        NaN        NaN       NaN -1.0000  \n",
      "1    5.765473  11.442878        NaN       NaN  1.0000  \n",
      "2    6.006131  10.265802        NaN       NaN  0.1824  \n",
      "3    5.964941  -9.332470        NaN       NaN -0.0564  \n",
      "4    5.919593  -9.332470        NaN       NaN -0.1734  \n",
      "..        ...        ...        ...       ...     ...  \n",
      "595  7.437595  13.641964  12.672843  7.653087  0.2312  \n",
      "596  7.462892  13.682268  12.673824  7.635994  0.2702  \n",
      "597  7.493490  13.749542  12.674237  7.618600  0.4250  \n",
      "598  7.527326  13.830855  12.674753  7.618600  0.5918  \n",
      "599  7.562827  13.869666  12.674547  7.600902  0.7172  \n",
      "\n",
      "[600 rows x 13 columns]\n",
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      "\n",
      "         0.0       0.43      0.43      0.43       233\n",
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      "\n",
      "    accuracy                           0.39       563\n",
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      "\n",
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      "\n",
      "    accuracy                           0.41       528\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "98.326360 completed\n",
      "90 0.3891213389121339\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.41      0.46      0.43       199\n",
      "         1.0       0.42      0.42      0.42       202\n",
      "         2.0       0.18      0.13      0.15        77\n",
      "\n",
      "    accuracy                           0.39       478\n",
      "   macro avg       0.34      0.34      0.34       478\n",
      "weighted avg       0.38      0.39      0.38       478\n",
      "\n",
      "Window Size: 95\n",
      "0.000000 completed\n",
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      "97.251586 completed\n",
      "99.365751 completed\n",
      "95 0.4080338266384778\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.42      0.52      0.47       197\n",
      "         1.0       0.47      0.40      0.43       200\n",
      "         2.0       0.19      0.14      0.16        76\n",
      "\n",
      "    accuracy                           0.41       473\n",
      "   macro avg       0.36      0.35      0.35       473\n",
      "weighted avg       0.40      0.41      0.40       473\n",
      "\n",
      "0.4324817518248175 20\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Sliding window attempt: Not Successful\n",
    "\n",
    "'''\n",
    "warnings.filterwarnings('ignore') \n",
    "\n",
    "def csv_extract(filename = \"/NAVER_DATA.csv\"):\n",
    "    '''\n",
    "    Input: Number of samples, Number of Stocks, filename\n",
    "    Output: numpy array of (number of samples, number of stocks)\n",
    "    '''\n",
    "  \n",
    "    df = pd.read_csv(filename)\n",
    "    df['Roc'] = (df['Roc'] - 100)/2\n",
    "    df['Momentum'] = df['Momentum']/df['Price']\n",
    "    df['RSI'] = 2*df['RSI'] - 1 \n",
    "    df['CCI'] = df['CCI']/100\n",
    "    df['ADX'] = 2*df['ADX'] - 1\n",
    "    df['TRIX'] = 10*df['TRIX']\n",
    "    df['MACD'] = np.log(np.abs(df['MACD'])) * np.sign(df['MACD'])\n",
    "    df['OBV'] = np.log(np.abs(df['OBV'])) * np.sign(df['OBV'])\n",
    "    df['_TSF_'] = np.log(np.abs(df['_TSF_'])) * np.sign(df['_TSF_'])\n",
    "    df['ATR'] = np.log(np.abs(df['ATR'])) * np.sign(df['ATR'])\n",
    "    df['MFI'] = 2*df['MFI']/100 - 1\n",
    "    day_num = 1\n",
    "    X = np.empty((600 - day_num, 0))\n",
    "    y = np.empty(0)\n",
    "    for n in range(df['Price'].shape[0] - day_num):\n",
    "        temp = 1 if df['Price'][n + day_num] >= df['Price'][n] else 0\n",
    "        if df['Price'][n + day_num] > df['Price'][n]:\n",
    "            temp = 1\n",
    "        elif  df['Price'][n + day_num] < df['Price'][n]:\n",
    "            temp = 0\n",
    "        else:\n",
    "            temp = 2\n",
    "        y = np.append(y, temp)\n",
    "    remove = ['Price'] \n",
    "    #remove = ['Price', '_TSF_', 'MFI', 'MACD', 'OBV', 'Momentum', 'Roc', 'RSI', 'TRIX']\n",
    "    #remove = ['Momentum', 'RSI', 'MACD', 'OBV', 'ADX', 'MFI'] \n",
    "    remove = []\n",
    "    for row in df:\n",
    "        if row in remove:\n",
    "            continue\n",
    "        X = np.column_stack((X, df[row][0:600 - day_num]))\n",
    "    print(df)\n",
    "    #X = preprocessing.scale(X)\n",
    "    return X, y\n",
    "\n",
    "filename = \"./NAVER_DATA.csv\"\n",
    "X, y = csv_extract(filename = filename)\n",
    "X = X[31:]\n",
    "y = y[31:]\n",
    "verbose = False\n",
    "d = {}\n",
    "\n",
    "for window in np.arange(5, 100, 5):\n",
    "    print('Window Size: %d' %(window))\n",
    "    tot_right = 0; tot = 0\n",
    "    y_pred_ = []\n",
    "    y_test_ = []\n",
    "    for n in range(y.shape[0] - window):\n",
    "        if n%10 == 0:\n",
    "            print('%f completed' %(100*n/(y.shape[0] - window)))\n",
    "        X_train, y_train = X[n:n+window-1], y[n:n+window-1]\n",
    "        X_test, y_test = np.resize(X[n+window], (1, X.shape[1])), y[n+window]\n",
    "        #tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-2, 1e-1, 1e+0, 1e+1],\n",
    "        #              'C': [1e+0, 1e+1, 1e+2, 1e+3, 1e+4]}]\n",
    "        tuned_parameters = [{'kernel': ['linear'], 'C': [1e+0, 1e+1]}]\n",
    "        score = 'recall'\n",
    "        '''clf = GridSearchCV(\n",
    "            SVC(), tuned_parameters, scoring='%s_macro' % score\n",
    "        )'''\n",
    "        clf = SVC(kernel = 'linear', C = 10) \n",
    "        try:\n",
    "            clf.fit(X_train, y_train)\n",
    "            y_pred = clf.predict(X_test)\n",
    "        except Exception:\n",
    "            #print(y_train)\n",
    "            y_pred = y_train[0]\n",
    "        tot = tot + 1\n",
    "        tot_right = tot_right + 1 if y_pred == y_test else tot_right\n",
    "        try:\n",
    "            y_pred_.append(y_pred[0])\n",
    "        except Exception:\n",
    "            y_pred_.append(y_pred)\n",
    "        y_test_.append(y_test)\n",
    "        if verbose:\n",
    "            print(\"Best parameters set found on development set:\")\n",
    "            print()\n",
    "            print(clf.best_params_)\n",
    "            print()\n",
    "            print(\"Grid scores on development set:\")\n",
    "            print()\n",
    "            means = clf.cv_results_['mean_test_score']\n",
    "            stds = clf.cv_results_['std_test_score']\n",
    "            for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "                print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "                % (mean, std * 2, params))\n",
    "    d[window] = tot_right/tot\n",
    "    print(window, tot_right/tot)\n",
    "    print(classification_report(y_test_, y_pred_))\n",
    "\n",
    "v=list(d.values())\n",
    "k=list(d.keys())\n",
    "print(max(v), k[v.index(max(v))])"
   ]
  }
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