feature-engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72222897",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "899a3eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install plotly\n",
    "#!pip install --upgrade mplfinance\n",
    "#!pip install ta-lib\n",
    "#!pip install --upgrade numpy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "import talib\n",
    "import plotly.graph_objects as go\n",
    "import mplfinance as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04759da",
   "metadata": {},
   "source": [
    "## Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ccf3e0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "url=\"https://raw.githubusercontent.com/vishalprakash97/Financial-Trading-using-RRL/main/Security%20Data/NYSE_20yr_OHLC.csv\"\n",
    "download = requests.get(url).content\n",
    "\n",
    "df = pd.read_csv(io.StringIO(download.decode('utf-8')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77d8799b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Name'],inplace=True)\n",
    "df['Date']=pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50d7b2e",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e181a8b7",
   "metadata": {},
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5265.000000</td>\n",
       "      <td>5265.000000</td>\n",
       "      <td>5265.000000</td>\n",
       "      <td>5265.000000</td>\n",
       "      <td>5265.000000</td>\n",
       "      <td>5.265000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9213.969064</td>\n",
       "      <td>9259.499930</td>\n",
       "      <td>9164.051780</td>\n",
       "      <td>9214.984676</td>\n",
       "      <td>9214.984676</td>\n",
       "      <td>3.330560e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2825.468601</td>\n",
       "      <td>2834.216365</td>\n",
       "      <td>2816.245765</td>\n",
       "      <td>2825.540731</td>\n",
       "      <td>2825.540731</td>\n",
       "      <td>1.490668e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4226.310059</td>\n",
       "      <td>4321.759766</td>\n",
       "      <td>4181.750000</td>\n",
       "      <td>4226.310059</td>\n",
       "      <td>4226.310059</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6978.859863</td>\n",
       "      <td>7020.339844</td>\n",
       "      <td>6937.189941</td>\n",
       "      <td>6980.620117</td>\n",
       "      <td>6980.620117</td>\n",
       "      <td>2.174890e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8797.160156</td>\n",
       "      <td>8845.830078</td>\n",
       "      <td>8738.690430</td>\n",
       "      <td>8797.290039</td>\n",
       "      <td>8797.290039</td>\n",
       "      <td>3.403230e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11008.759766</td>\n",
       "      <td>11045.639648</td>\n",
       "      <td>10969.940430</td>\n",
       "      <td>11013.240234</td>\n",
       "      <td>11013.240234</td>\n",
       "      <td>4.114040e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17310.509766</td>\n",
       "      <td>17364.310547</td>\n",
       "      <td>17285.550781</td>\n",
       "      <td>17310.509766</td>\n",
       "      <td>17310.509766</td>\n",
       "      <td>1.145623e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open          High           Low         Close     Adj Close  \\\n",
       "count   5265.000000   5265.000000   5265.000000   5265.000000   5265.000000   \n",
       "mean    9213.969064   9259.499930   9164.051780   9214.984676   9214.984676   \n",
       "std     2825.468601   2834.216365   2816.245765   2825.540731   2825.540731   \n",
       "min     4226.310059   4321.759766   4181.750000   4226.310059   4226.310059   \n",
       "25%     6978.859863   7020.339844   6937.189941   6980.620117   6980.620117   \n",
       "50%     8797.160156   8845.830078   8738.690430   8797.290039   8797.290039   \n",
       "75%    11008.759766  11045.639648  10969.940430  11013.240234  11013.240234   \n",
       "max    17310.509766  17364.310547  17285.550781  17310.509766  17310.509766   \n",
       "\n",
       "             Volume  \n",
       "count  5.265000e+03  \n",
       "mean   3.330560e+09  \n",
       "std    1.490668e+09  \n",
       "min    0.000000e+00  \n",
       "25%    2.174890e+09  \n",
       "50%    3.403230e+09  \n",
       "75%    4.114040e+09  \n",
       "max    1.145623e+10  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1830b9e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5265 entries, 0 to 5264\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       5265 non-null   datetime64[ns]\n",
      " 1   Open       5265 non-null   float64       \n",
      " 2   High       5265 non-null   float64       \n",
      " 3   Low        5265 non-null   float64       \n",
      " 4   Close      5265 non-null   float64       \n",
      " 5   Adj Close  5265 non-null   float64       \n",
      " 6   Volume     5265 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
      "memory usage: 288.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5407f9c8",
   "metadata": {},
   "source": [
    "#### No null values in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c9d5662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x575 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dt=df.set_index(df['Date'])\n",
    "df_dt.drop(columns=[\"Date\",\"Adj Close\",'Volume'],inplace=True)\n",
    "df_dt[:20]\n",
    "name=\"NYSE\"\n",
    "mpl.plot(df_dt[400:600],type=\"candle\",title = f\"{name} Price\",  style=\"yahoo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f30f5262",
   "metadata": {
    "scrolled": false
   },
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-01-02</td>\n",
       "      <td>6785.689941</td>\n",
       "      <td>6785.689941</td>\n",
       "      <td>6785.689941</td>\n",
       "      <td>6785.689941</td>\n",
       "      <td>6785.689941</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>6968.830078</td>\n",
       "      <td>6968.830078</td>\n",
       "      <td>6968.830078</td>\n",
       "      <td>6968.830078</td>\n",
       "      <td>6968.830078</td>\n",
       "      <td>1880700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-01-04</td>\n",
       "      <td>6894.810059</td>\n",
       "      <td>6894.810059</td>\n",
       "      <td>6894.810059</td>\n",
       "      <td>6894.810059</td>\n",
       "      <td>6894.810059</td>\n",
       "      <td>2169400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-01-05</td>\n",
       "      <td>6774.060059</td>\n",
       "      <td>6774.060059</td>\n",
       "      <td>6774.060059</td>\n",
       "      <td>6774.060059</td>\n",
       "      <td>6774.060059</td>\n",
       "      <td>1430800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-08</td>\n",
       "      <td>6759.049805</td>\n",
       "      <td>6759.049805</td>\n",
       "      <td>6759.049805</td>\n",
       "      <td>6759.049805</td>\n",
       "      <td>6759.049805</td>\n",
       "      <td>1115500000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2003-01-02</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>5146.000000</td>\n",
       "      <td>1229200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>2003-01-03</td>\n",
       "      <td>5148.450195</td>\n",
       "      <td>5148.450195</td>\n",
       "      <td>5148.450195</td>\n",
       "      <td>5148.450195</td>\n",
       "      <td>5148.450195</td>\n",
       "      <td>1130800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>2003-01-06</td>\n",
       "      <td>5255.390137</td>\n",
       "      <td>5255.390137</td>\n",
       "      <td>5255.390137</td>\n",
       "      <td>5255.390137</td>\n",
       "      <td>5255.390137</td>\n",
       "      <td>1398100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2003-01-07</td>\n",
       "      <td>5186.919922</td>\n",
       "      <td>5186.919922</td>\n",
       "      <td>5186.919922</td>\n",
       "      <td>5186.919922</td>\n",
       "      <td>5186.919922</td>\n",
       "      <td>1545200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>5124.189941</td>\n",
       "      <td>5124.189941</td>\n",
       "      <td>5124.189941</td>\n",
       "      <td>5124.189941</td>\n",
       "      <td>5124.189941</td>\n",
       "      <td>1467600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date         Open         High          Low        Close  \\\n",
       "0   2001-01-02  6785.689941  6785.689941  6785.689941  6785.689941   \n",
       "1   2001-01-03  6968.830078  6968.830078  6968.830078  6968.830078   \n",
       "2   2001-01-04  6894.810059  6894.810059  6894.810059  6894.810059   \n",
       "3   2001-01-05  6774.060059  6774.060059  6774.060059  6774.060059   \n",
       "4   2001-01-08  6759.049805  6759.049805  6759.049805  6759.049805   \n",
       "..         ...          ...          ...          ...          ...   \n",
       "500 2003-01-02  5146.000000  5146.000000  5146.000000  5146.000000   \n",
       "501 2003-01-03  5148.450195  5148.450195  5148.450195  5148.450195   \n",
       "502 2003-01-06  5255.390137  5255.390137  5255.390137  5255.390137   \n",
       "503 2003-01-07  5186.919922  5186.919922  5186.919922  5186.919922   \n",
       "504 2003-01-08  5124.189941  5124.189941  5124.189941  5124.189941   \n",
       "\n",
       "       Adj Close      Volume  \n",
       "0    6785.689941           0  \n",
       "1    6968.830078  1880700000  \n",
       "2    6894.810059  2169400000  \n",
       "3    6774.060059  1430800000  \n",
       "4    6759.049805  1115500000  \n",
       "..           ...         ...  \n",
       "500  5146.000000  1229200000  \n",
       "501  5148.450195  1130800000  \n",
       "502  5255.390137  1398100000  \n",
       "503  5186.919922  1545200000  \n",
       "504  5124.189941  1467600000  \n",
       "\n",
       "[505 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['High']==df['Low']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de980196",
   "metadata": {},
   "source": [
    "Open=High=Low=Close for the first 505 values. I'm not sure if data is buggy, or it doesn't exist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d64ced",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3befccd",
   "metadata": {},
   "source": [
    " #### Techincal Indicators being used:\n",
    " 1. Momentum Indicators: \n",
    "    * Momentum (MOM)\n",
    "    * Moving Average Convergence Divergence (MACD)\n",
    "    * Money Flow Index (MFI)\n",
    "    * Relative Strength Index (RSI)\n",
    "    \n",
    "2. Volatility indicators: \n",
    "    * Average True Range (ATR)\n",
    "    * Normalized Average True Range (NATR)\n",
    "    \n",
    "3. cycle indicators:\n",
    "    * Hilbert Transform Dominant Cycle Phase (HTDCP)\n",
    "    * Hilbert Transform Sinewave (HTS)\n",
    "    * Hilbert Transform Trend Market Mode (HTTMM)\n",
    "\n",
    "4. volume indicators: \n",
    "    * Chaikin Oscillator (CO), \n",
    "    * On Balance Volume (OBV).\n",
    "    \n",
    "#### References:\n",
    "1. [TA-Lib Documentation](https://mrjbq7.github.io/ta-lib/doc_index.html)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6bb86f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Momentum indicators\n",
    "\n",
    "#real = MOM(close, timeperiod=10)\n",
    "#macd, macdsignal, macdhist = MACD(df['Close'], fastperiod=12, slowperiod=26, signalperiod=9)\n",
    "#real = MFI(high, low, close, volume, timeperiod=14)\n",
    "#real = RSI(close, timeperiod=14)\n",
    "\n",
    "ta=pd.DataFrame()\n",
    "ta['mom']=pd.DataFrame(talib.MOM(df['Close']))\n",
    "ta['macd'], ta['macdsignal'], ta['macdhist'] = talib.MACD(df['Close'])\n",
    "ta['mfi']=talib.MFI(df['High'],df['Low'],df['Close'],df['Volume'])\n",
    "ta['rsi']=talib.RSI(df['Close'])\n",
    "\n",
    "#Volatility Indicators\n",
    "\n",
    "#real = ATR(high, low, close, timeperiod=14)\n",
    "#real = NATR(high, low, close, timeperiod=14)\n",
    "ta['atr'] = talib.ATR(df['High'], df['Low'], df['Close'])\n",
    "ta['natr']=talib.NATR(df['High'], df['Low'], df['Close'])\n",
    "\n",
    "\n",
    "#Cycle Indicators\n",
    "\n",
    "#real = HT_DCPHASE(close)\n",
    "#sine, leadsine = HT_SINE(close)\n",
    "#integer = HT_TRENDMODE(close)\n",
    "ta['htdcp']=talib.HT_DCPHASE(df['Close'])\n",
    "ta['hts'],ta['hts_lead']=talib.HT_SINE(df['Close'])\n",
    "ta['httmm']=talib.HT_TRENDMODE(df['Close'])\n",
    "\n",
    "#Volume Indicators\n",
    "\n",
    "#real = ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)\n",
    "#real = OBV(close, volume)\n",
    "ta['co']=talib.ADOSC(df['High'], df['Low'], df['Close'], df['Volume'])\n",
    "ta['obv'] = talib.OBV(df['Close'], df['Volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15e149cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5265, 14)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5197d4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mom           10\n",
       "macd          33\n",
       "macdsignal    33\n",
       "macdhist      33\n",
       "mfi           14\n",
       "rsi           14\n",
       "atr           14\n",
       "natr          14\n",
       "htdcp         63\n",
       "hts           63\n",
       "hts_lead      63\n",
       "httmm          0\n",
       "co             9\n",
       "obv            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ta.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09218462",
   "metadata": {},
   "source": [
    "### Notes \n",
    "1. talib.MACD outputs 3 variables: macd, macdsignal and macdhist\n",
    "2. talib.ht_SINE ouputs 2 variables: sine, leadsine\n",
    "3. Leading null values in generated TAs due to aggregation\n",
    "4. Drop extra columns and null values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f20e43",
   "metadata": {},
   "source": [
    "### Further Steps\n",
    "1. Explore inconsistency in dataset\n",
    "2. Normalization\n",
    "3. PCA\n",
    "4. DWT\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}