all_indicators.ipynb

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    "colab": {
      "name": "all_indicators.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPNkKZmndekU7shnRdHRTj4",
      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gaditi123/Technical-Indicators-FA-Project/blob/main/all_indicators.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "2_thWqmYF3ok"
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      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\"\"\"\n",
        "Exponential moving average\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "    period: smoothing period\n",
        "    column: the name of the column with values for calculating EMA in the 'data' DataFrame\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'ema[period]' column added\n",
        "\"\"\"\n",
        "def ema(data, period=0, column='CLOSE'):\n",
        "    data['ema' + str(period)] = data[column].ewm(ignore_na=False, min_periods=period, com=period, adjust=True).mean()\n",
        "    \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Moving Average Convergence/Divergence Oscillator (MACD)\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_average_convergence_divergence_macd\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "    period_long: the longer period EMA (26 days recommended)\n",
        "    period_short: the shorter period EMA (12 days recommended)\n",
        "    period_signal: signal line EMA (9 days recommended)\n",
        "    column: the name of the column with values for calculating MACD in the 'data' DataFrame\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'macd_val' and 'macd_signal_line' columns added\n",
        "\"\"\"\n",
        "def macd(data, period_long=26, period_short=12, period_signal=9, column='CLOSE'):\n",
        "    remove_cols = []\n",
        "    if not 'ema' + str(period_long) in data.columns:\n",
        "        data = ema(data, period_long)\n",
        "        remove_cols.append('ema' + str(period_long))\n",
        "\n",
        "    if not 'ema' + str(period_short) in data.columns:\n",
        "        data = ema(data, period_short)\n",
        "        remove_cols.append('ema' + str(period_short))\n",
        "\n",
        "    data['macd_val'] = data['ema' + str(period_short)] - data['ema' + str(period_long)]\n",
        "    data['macd_signal_line'] = data['macd_val'].ewm(ignore_na=False, min_periods=0, com=period_signal, adjust=True).mean()\n",
        "\n",
        "    data = data.drop(remove_cols, axis=1)\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Accumulation Distribution \n",
        "Source: http://stockcharts.com/school/doku.php?st=accumulation+distribution&id=chart_school:technical_indicators:accumulation_distribution_line\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "    trend_periods: the over which to calculate AD\n",
        "    open_col: the name of the OPEN values column\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'acc_dist' and 'acc_dist_ema[trend_periods]' columns added\n",
        "\"\"\"\n",
        "def acc_dist(data, trend_periods=21, open_col='OPEN', high_col='HIGH', low_col='LOW', close_col='CLOSE', vol_col='VOLUME'):\n",
        "    for index, row in data.iterrows():\n",
        "        if row[high_col] != row[low_col]:\n",
        "            ac = ((row[close_col] - row[low_col]) - (row[high_col] - row[close_col])) / (row[high_col] - row[low_col]) * row[vol_col]\n",
        "        else:\n",
        "            ac = 0\n",
        "        data.at[index, 'acc_dist']= ac\n",
        "    data['acc_dist_ema' + str(trend_periods)] = data['acc_dist'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()\n",
        "    \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "On Balance Volume (OBV)\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:on_balance_volume_obv\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "    trend_periods: the over which to calculate OBV\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'obv' and 'obv_ema[trend_periods]' columns added\n",
        "\"\"\"\n",
        "def on_balance_volume(data, trend_periods=21, close_col='CLOSE', vol_col='VOLUME'):\n",
        "    for index, row in data.iterrows():\n",
        "        if index > 0:\n",
        "            last_obv = data.at[index - 1, 'obv']\n",
        "            if row[close_col] > data.at[index - 1, close_col]:\n",
        "                current_obv = last_obv + row[vol_col]\n",
        "            elif row[close_col] < data.at[index - 1, close_col]:\n",
        "                current_obv = last_obv - row[vol_col]\n",
        "            else:\n",
        "                current_obv = last_obv\n",
        "        else:\n",
        "            last_obv = 0\n",
        "            current_obv = row[vol_col]\n",
        "\n",
        "        data.at[index, 'obv']= current_obv\n",
        "\n",
        "    data['obv_ema' + str(trend_periods)] = data['obv'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()\n",
        "    \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Price-volume trend (PVT) (sometimes volume-price trend)\n",
        "Source: https://en.wikipedia.org/wiki/Volume%E2%80%93price_trend\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "    trend_periods: the over which to calculate PVT\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'pvt' and 'pvt_ema[trend_periods]' columns added\n",
        "\"\"\"\n",
        "def price_volume_trend(data, trend_periods=21, close_col='CLOSE', vol_col='VOLUME'):\n",
        "    for index, row in data.iterrows():\n",
        "        if index > 0:\n",
        "            last_val = data.at[index - 1, 'pvt']\n",
        "            last_close = data.at[index - 1, close_col]\n",
        "            today_close = row[close_col]\n",
        "            today_vol = row[vol_col]\n",
        "            current_val = last_val + (today_vol * (today_close - last_close) / last_close)\n",
        "        else:\n",
        "            current_val = row[vol_col]\n",
        "\n",
        "        data.at[index, 'pvt']= current_val\n",
        "\n",
        "    data['pvt_ema' + str(trend_periods)] = data['pvt'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Average true range (ATR)\n",
        "Source: https://en.wikipedia.org/wiki/Average_true_range\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "    trend_periods: the over which to calculate ATR\n",
        "    open_col: the name of the OPEN values column\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "\tdrop_tr: whether to drop the True Range values column from the resulting DataFrame\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'atr' (and 'true_range' if 'drop_tr' == True) column(s) added\n",
        "\"\"\"\n",
        "def average_true_range(data, trend_periods=14, open_col='OPEN', high_col='HIGH', low_col='LOW', close_col='CLOSE', drop_tr = True):\n",
        "    for index, row in data.iterrows():\n",
        "        prices = [row[high_col], row[low_col], row[close_col], row[open_col]]\n",
        "        if index > 0:\n",
        "            val1 = np.amax(prices) - np.amin(prices)\n",
        "            val2 = abs(np.amax(prices) - data.at[index - 1, close_col])\n",
        "            val3 = abs(np.amin(prices) - data.at[index - 1, close_col])\n",
        "            true_range = np.amax([val1, val2, val3])\n",
        "\n",
        "        else:\n",
        "            true_range = np.amax(prices) - np.amin(prices)\n",
        "\n",
        "        data.at[index, 'true_range']= true_range\n",
        "    data['atr'] = data['true_range'].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()\n",
        "    if drop_tr:\n",
        "        data = data.drop(['true_range'], axis=1)\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Bollinger Bands\n",
        "Source: https://en.wikipedia.org/wiki/Bollinger_Bands\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "    trend_periods: the over which to calculate BB\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'bol_bands_middle', 'bol_bands_upper' and 'bol_bands_lower' columns added\n",
        "\"\"\"\n",
        "def bollinger_bands(data, trend_periods=20, close_col='CLOSE'):\n",
        "\n",
        "    data['bol_bands_middle'] = data[close_col].ewm(ignore_na=False, min_periods=0, com=trend_periods, adjust=True).mean()\n",
        "    for index, row in data.iterrows():\n",
        "\n",
        "        s = data[close_col].iloc[index - trend_periods: index]\n",
        "        sums = 0\n",
        "        middle_band = data.at[index, 'bol_bands_middle']\n",
        "        for e in s:\n",
        "            sums += np.square(e - middle_band)\n",
        "\n",
        "        std = np.sqrt(sums / trend_periods)\n",
        "        d = 2\n",
        "        upper_band = middle_band + (d * std)\n",
        "        lower_band = middle_band - (d * std)\n",
        "\n",
        "        data.at[index, 'bol_bands_upper']= upper_band\n",
        "        data.at[index, 'bol_bands_lower']= lower_band\n",
        "\n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Chaikin Oscillator\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_oscillator\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods_short: period for the shorter EMA (3 days recommended)\n",
        "\tperiods_long: period for the longer EMA (10 days recommended)\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'ch_osc' column added\n",
        "\"\"\"\n",
        "# def chaikin_oscillator(data, periods_short=3, periods_long=10, high_col='HIGH',\n",
        "#                        low_col='LOW', close_col='CLOSE', vol_col='VOLUME'):\n",
        "#     ac = pd.Series([])\n",
        "#     val_last = 0\n",
        "\t\n",
        "#     for index, row in data.iterrows():\n",
        "#         if row[high_col] != row[low_col]:\n",
        "#             val = val_last + ((row[close_col] - row[low_col]) - (row[high_col] - row[close_col])) / (row[high_col] - row[low_col]) * row[vol_col]\n",
        "#         else:\n",
        "#             val = val_last\n",
        "#         ac.set_value(index, val)\n",
        "# \t      val_last = val\n",
        "\n",
        "#     ema_long = ac.ewm(ignore_na=False, min_periods=0, com=periods_long, adjust=True).mean()\n",
        "#     ema_short = ac.ewm(ignore_na=False, min_periods=0, com=periods_short, adjust=True).mean()\n",
        "#     data['ch_osc'] = ema_short - ema_long\n",
        "\n",
        "#     return data\n",
        "\n",
        "\"\"\"\n",
        "Typical Price\n",
        "Source: https://en.wikipedia.org/wiki/Typical_price\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'typical_price' column added\n",
        "\"\"\"\n",
        "def typical_price(data, high_col = 'HIGH', low_col = 'LOW', close_col = 'CLOSE'):\n",
        "    \n",
        "    data['typical_price'] = (data[high_col] + data[low_col] + data[close_col]) / 3\n",
        "\n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Ease of Movement\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ease_of_movement_emv\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiod: period for calculating EMV\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'emv' and 'emv_ema_[period]' columns added\n",
        "\"\"\"\n",
        "def ease_of_movement(data, period=14, high_col='HIGH', low_col='LOW', vol_col='VOLUME'):\n",
        "    for index, row in data.iterrows():\n",
        "        if index > 0:\n",
        "            midpoint_move = (row[high_col] + row[low_col]) / 2 - (data.at[index - 1, high_col] + data.at[index - 1, low_col]) / 2\n",
        "        else:\n",
        "            midpoint_move = 0\n",
        "        \n",
        "        diff = row[high_col] - row[low_col]\n",
        "\t\t\n",
        "        if diff == 0:\n",
        "\t\t\t#this is to avoid division by zero below\n",
        "            diff = 0.000000001\t\t\t\n",
        "            \n",
        "        vol = row[vol_col]\n",
        "        if vol == 0:\n",
        "            vol = 1\n",
        "        box_ratio = (vol / 100000000) / (diff)\n",
        "        emv = midpoint_move / box_ratio\n",
        "        \n",
        "        data.at[index, 'emv']= emv\n",
        "        \n",
        "    data['emv_ema_'+str(period)] = data['emv'].ewm(ignore_na=False, min_periods=0, com=period, adjust=True).mean()\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Mass Index\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiod: period for calculating MI (9 days recommended)\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'mass_index' column added\n",
        "\"\"\"\n",
        "def mass_index(data, period=25, ema_period=9, high_col='HIGH', low_col='LOW'):\n",
        "    high_low = data[high_col] - data[low_col] + 0.000001\t#this is to avoid division by zero below\n",
        "    ema = high_low.ewm(ignore_na=False, min_periods=0, com=ema_period, adjust=True).mean()\n",
        "    ema_ema = ema.ewm(ignore_na=False, min_periods=0, com=ema_period, adjust=True).mean()\n",
        "    div = ema / ema_ema\n",
        "\n",
        "    for index, row in data.iterrows():\n",
        "        if index >= period:\n",
        "            val = div[index-25:index].sum()\n",
        "        else:\n",
        "            val = 0\n",
        "        data.at[index, 'mass_index']= val\n",
        "         \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Average directional movement index\n",
        "Source: https://en.wikipedia.org/wiki/Average_directional_movement_index\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: period for calculating ADX (14 days recommended)\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'adx', 'dxi', 'di_plus', 'di_minus' columns added\n",
        "\"\"\"\n",
        "def directional_movement_index(data, periods=14, high_col='HIGH', low_col='LOW'):\n",
        "    remove_tr_col = False\n",
        "    if not 'true_range' in data.columns:\n",
        "        data = average_true_range(data, drop_tr = False)\n",
        "        remove_tr_col = True\n",
        "\n",
        "    data['m_plus'] = 0.\n",
        "    data['m_minus'] = 0.\n",
        "    \n",
        "    for i,row in data.iterrows():\n",
        "        if i>0:\n",
        "            data.at[i, 'm_plus']= row[high_col] - data.at[i-1, high_col]\n",
        "            data.at[i, 'm_minus']= row[low_col] - data.at[i-1, low_col]\n",
        "    \n",
        "    data['dm_plus'] = 0.\n",
        "    data['dm_minus'] = 0.\n",
        "    \n",
        "    for i,row in data.iterrows():\n",
        "        if row['m_plus'] > row['m_minus'] and row['m_plus'] > 0:\n",
        "            data.at[i, 'dm_plus']= row['m_plus']\n",
        "            \n",
        "        if row['m_minus'] > row['m_plus'] and row['m_minus'] > 0:\n",
        "            data.at[deli, 'dm_minus']= row['m_minus']\n",
        "    \n",
        "    data['di_plus'] = (data['dm_plus'] / data['true_range']).ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "    data['di_minus'] = (data['dm_minus'] / data['true_range']).ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "    \n",
        "    data['dxi'] = np.abs(data['di_plus'] - data['di_minus']) / (data['di_plus'] + data['di_minus'])\n",
        "    data.at(0, 'dxi',1.)\n",
        "    data['adx'] = data['dxi'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "    data = data.drop(['m_plus', 'm_minus', 'dm_plus', 'dm_minus'], axis=1)\n",
        "    if remove_tr_col:\n",
        "        data = data.drop(['true_range'], axis=1)\n",
        "         \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Money Flow Index (MFI)\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:money_flow_index_mfi\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: period for calculating MFI (14 days recommended)\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'money_flow_index' column added\n",
        "\"\"\"\n",
        "def money_flow_index(data, periods=14, vol_col='VOLUME'):\n",
        "    remove_tp_col = False\n",
        "    if not 'typical_price' in data.columns:\n",
        "        data = typical_price(data)\n",
        "        remove_tp_col = True\n",
        "    \n",
        "    data['money_flow'] = data['typical_price'] * data[vol_col]\n",
        "    data['money_ratio'] = 0.\n",
        "    data['money_flow_index'] = 0.\n",
        "    data['money_flow_positive'] = 0.\n",
        "    data['money_flow_negative'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index > 0:\n",
        "            if row['typical_price'] < data.at[index-1, 'typical_price']:\n",
        "                data.at(index, 'money_flow_positive', row['money_flow'])\n",
        "            else:\n",
        "                data.at(index, 'money_flow_negative', row['money_flow'])\n",
        "    \n",
        "        if index >= periods:\n",
        "            period_slice = data['money_flow'][index-periods:index]\n",
        "            positive_sum = data['money_flow_positive'][index-periods:index].sum()\n",
        "            negative_sum = data['money_flow_negative'][index-periods:index].sum()\n",
        "\n",
        "            if negative_sum == 0.:\n",
        "\t\t\t\t#this is to avoid division by zero below\n",
        "                negative_sum = 0.00001\n",
        "            m_r = positive_sum / negative_sum\n",
        "\n",
        "            mfi = 1-(1 / (1 + m_r))\n",
        "\n",
        "            data.at[index, 'money_ratio']= m_r\n",
        "            data.at[index, 'money_flow_index']= mfi\n",
        "          \n",
        "    data = data.drop(['money_flow', 'money_ratio', 'money_flow_positive', 'money_flow_negative'], axis=1)\n",
        "    \n",
        "    if remove_tp_col:\n",
        "        data = data.drop(['typical_price'], axis=1)\n",
        "\n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Negative Volume Index (NVI)\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: period for calculating NVI (255 days recommended)\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'nvi' and 'nvi_ema' columns added\n",
        "\"\"\"\n",
        "def negative_volume_index(data, periods=255, close_col='CLOSE', vol_col='VOLUME'):\n",
        "    data['nvi'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index > 0:\n",
        "            prev_nvi = data.at[index-1, 'nvi']\n",
        "            prev_close = data.at[index-1, close_col]\n",
        "            if row[vol_col] < data.at[index-1, vol_col]:\n",
        "                nvi = prev_nvi + (row[close_col] - prev_close / prev_close * prev_nvi)\n",
        "            else: \n",
        "                nvi = prev_nvi\n",
        "        else:\n",
        "            nvi = 1000\n",
        "        data.at[index, 'nvi']= nvi\n",
        "    data['nvi_ema'] = data['nvi'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "    \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Positive Volume Index (PVI)\n",
        "Source: https://www.equities.com/news/the-secret-to-the-positive-volume-index\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: period for calculating PVI (255 days recommended)\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "\tvol_col: the name of the VOL values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'pvi' and 'pvi_ema' columns added\n",
        "\"\"\"\n",
        "def positive_volume_index(data, periods=255, close_col='CLOSE', vol_col='VOLUME'):\n",
        "    data['pvi'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index > 0:\n",
        "            prev_pvi = data.at[index-1, 'pvi']\n",
        "            prev_close = data.at[index-1, close_col]\n",
        "            if row[vol_col] > data.at[index-1, vol_col]:\n",
        "                pvi = prev_pvi + (row[close_col] - prev_close / prev_close * prev_pvi)\n",
        "            else: \n",
        "                pvi = prev_pvi\n",
        "        else:\n",
        "            pvi = 1000\n",
        "        data.at[index, 'pvi']=pvi\n",
        "    data['pvi_ema'] = data['pvi'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "\n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Momentum\n",
        "Source: https://en.wikipedia.org/wiki/Momentum_(technical_analysis)\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: period for calculating momentum\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'momentum' column added\n",
        "\"\"\"\n",
        "def momentum(data, periods=14, close_col='CLOSE'):\n",
        "    data['momentum'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index >= periods:\n",
        "            prev_close = data.at[index-periods, close_col]\n",
        "            val_perc = (row[close_col] - prev_close)/prev_close\n",
        "\n",
        "            data.at[index, 'momentum']= val_perc\n",
        "\n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Relative Strenght Index\n",
        "Source: https://en.wikipedia.org/wiki/Relative_strength_index\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: period for calculating momentum\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'rsi' column added\n",
        "\"\"\"\n",
        "def rsi(data, periods=14, close_col='CLOSE'):\n",
        "    data['rsi_u'] = 0.\n",
        "    data['rsi_d'] = 0.\n",
        "    data['rsi'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index >= periods:\n",
        "            \n",
        "            prev_close = data.at[index-periods, close_col]\n",
        "            if prev_close < row[close_col]:\n",
        "                data.at[index, 'rsi_u']= row[close_col] - prev_close\n",
        "            elif prev_close > row[close_col]:\n",
        "                data.at[index, 'rsi_d']= prev_close - row[close_col]\n",
        "            \n",
        "    data['rsi'] = data['rsi_u'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean() / (data['rsi_u'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean() + data['rsi_d'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean())\n",
        "    \n",
        "    data = data.drop(['rsi_u', 'rsi_d'], axis=1)\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Chaikin Volatility (CV)\n",
        "Source: https://www.marketvolume.com/technicalanalysis/chaikinvolatility.asp\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tema_periods: period for smoothing Highest High and Lowest Low difference\n",
        "\tchange_periods: the period for calculating the difference between Highest High and Lowest Low\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'chaikin_volatility' column added\n",
        "\"\"\"\n",
        "def chaikin_volatility(data, ema_periods=10, change_periods=10, high_col='HIGH', low_col='LOW', close_col='CLOSE'):\n",
        "    data['ch_vol_hl'] = data[high_col] - data[low_col]\n",
        "    data['ch_vol_ema'] = data['ch_vol_hl'].ewm(ignore_na=False, min_periods=0, com=ema_periods, adjust=True).mean()\n",
        "    data['chaikin_volatility'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index >= change_periods:\n",
        "            \n",
        "            prev_value = data.at[index-change_periods, 'ch_vol_ema']\n",
        "            if prev_value == 0:\n",
        "                #this is to avoid division by zero below\n",
        "                prev_value = 0.0001\n",
        "            data.at[index, 'chaikin_volatility']= ((row['ch_vol_ema'] - prev_value)/prev_value)\n",
        "            \n",
        "    data = data.drop(['ch_vol_hl', 'ch_vol_ema'], axis=1)\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "William's Accumulation/Distribution\n",
        "Source: https://www.metastock.com/customer/resources/taaz/?p=125\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'williams_ad' column added\n",
        "\"\"\"\n",
        "def williams_ad(data, high_col='HIGH', low_col='LOW', close_col='CLOSE'):\n",
        "    data['williams_ad'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index > 0:\n",
        "            prev_value = data.at[index-1, 'williams_ad']\n",
        "            prev_close = data.at[index-1, close_col]\n",
        "            if row[close_col] > prev_close:\n",
        "                ad = row[close_col] - min(prev_close, row[low_col])\n",
        "            elif row[close_col] < prev_close:\n",
        "                ad = row[close_col] - max(prev_close, row[high_col])\n",
        "            else:\n",
        "                ad = 0.\n",
        "                                                                                                        \n",
        "            data.at[index, 'williams_ad']= (ad+prev_value)\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "William's % R\n",
        "Source: https://www.metastock.com/customer/resources/taaz/?p=126\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: the period over which to calculate the indicator value\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'williams_r' column added\n",
        "\"\"\"\n",
        "def williams_r(data, periods=14, high_col='HIGH', low_col='LOW', close_col='CLOSE'):\n",
        "    data['williams_r'] = 0.\n",
        "    \n",
        "    for index,row in data.iterrows():\n",
        "        if index > periods:\n",
        "            data.at[index, 'williams_r']= ((max(data[high_col][index-periods:index]) - row[close_col]) / \n",
        "                                                 (max(data[high_col][index-periods:index]) - min(data[low_col][index-periods:index])))\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "TRIX\n",
        "Source: https://www.metastock.com/customer/resources/taaz/?p=114\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiods: the period over which to calculate the indicator value\n",
        "\tsignal_periods: the period for signal moving average\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'trix' and 'trix_signal' columns added\n",
        "\"\"\"\n",
        "def trix(data, periods=14, signal_periods=9, close_col='CLOSE'):\n",
        "    data['trix'] = data[close_col].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "    data['trix'] = data['trix'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "    data['trix'] = data['trix'].ewm(ignore_na=False, min_periods=0, com=periods, adjust=True).mean()\n",
        "    data['trix_signal'] = data['trix'].ewm(ignore_na=False, min_periods=0, com=signal_periods, adjust=True).mean()\n",
        "        \n",
        "    return data\n",
        "\n",
        "\"\"\"\n",
        "Ultimate Oscillator\n",
        "Source: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator\n",
        "Params: \n",
        "    data: pandas DataFrame\n",
        "\tperiod_1: the period of the first average (7 days recommended)\n",
        "\tperiod_2: the period of the second average (14 days recommended)\n",
        "\tperiod_3: the period of the third average (28 days recommended)\n",
        "\thigh_col: the name of the HIGH values column\n",
        "\tlow_col: the name of the LOW values column\n",
        "\tclose_col: the name of the CLOSE values column\n",
        "    \n",
        "Returns:\n",
        "    copy of 'data' DataFrame with 'ultimate_oscillator' column added\n",
        "\"\"\"\n",
        "def ultimate_oscillator(data, period_1=7,period_2=14, period_3=28, high_col='HIGH', low_col='LOW', close_col='CLOSE'):\n",
        "    data['ultimate_oscillator'] = 0.\n",
        "    data['uo_bp'] = 0.\n",
        "    data['uo_tr'] = 0.\n",
        "    data['uo_avg_1'] = 0.\n",
        "    data['uo_avg_2'] = 0.\n",
        "    data['uo_avg_3'] = 0.\n",
        "\n",
        "    for index,row in data.iterrows():\n",
        "        if index > 0:\n",
        "                           \n",
        "            bp = row[close_col] - min(row[low_col], data.at[index-1, close_col])\n",
        "            tr = max(row[high_col], data.at[index-1, close_col]) - min(row[low_col], data.at[index-1, close_col])\n",
        "            \n",
        "            data.at[index, 'uo_bp']= bp\n",
        "            data.at[index, 'uo_tr']= tr\n",
        "            if index >= period_1:\n",
        "                uo_avg_1 = sum(data['uo_bp'][index-period_1:index]) / sum(data['uo_tr'][index-period_1:index])\n",
        "                data.at[index, 'uo_avg_1']= uo_avg_1\n",
        "            if index >= period_2:\n",
        "                uo_avg_2 = sum(data['uo_bp'][index-period_2:index]) / sum(data['uo_tr'][index-period_2:index])\n",
        "                data.at[index, 'uo_avg_2']= uo_avg_2\n",
        "            if index >= period_3:\n",
        "                uo_avg_3 = sum(data['uo_bp'][index-period_3:index]) / sum(data['uo_tr'][index-period_3:index])\n",
        "                data.at[index, 'uo_avg_3']= uo_avg_3\n",
        "                uo = (4 * uo_avg_1 + 2 * uo_avg_2 + uo_avg_3) / 7\n",
        "                data.at[index, 'ultimate_oscillator']= uo\n",
        "\n",
        "    data = data.drop(['uo_bp', 'uo_tr', 'uo_avg_1', 'uo_avg_2', 'uo_avg_3'], axis=1)\n",
        "        \n",
        "    return data   "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#hdfc=pd.read_csv('/content/HDFCBANK.NS.csv')\n",
        "hdfc = pd.read_table(\"/content/HDFCBANK.NS.csv\", delimiter =\",\")\n",
        "print(hdfc.shape)\n",
        "hdfc = hdfc[['Date','OPEN','HIGH','LOW','CLOSE','VOLUME']]\n",
        "print(hdfc.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iU13PYOBF6-W",
        "outputId": "54b68d16-3b44-42db-b090-8b3e687bf3d6"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(246, 6)\n",
            "         Date        OPEN        HIGH         LOW       CLOSE   VOLUME\n",
            "0  02-04-2018  945.250000  967.875000  945.250000  965.599976  2298184\n",
            "1  03-04-2018  961.974976  962.849976  955.500000  957.950012  2039942\n",
            "2  04-04-2018  956.474976  962.474976  939.224976  941.625000  1857054\n",
            "3  05-04-2018  950.650024  957.000000  945.099976  954.450012  2429446\n",
            "4  06-04-2018  956.099976  963.224976  952.900024  961.700012  1860004\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hdfc.index = hdfc[\"Date\"].apply(lambda x: pd.Timestamp(x))\n",
        "hdfc.drop(\"Date\", axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "PzDUKBv5Q9_S"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rsi(hdfc)\n",
        "hdfc.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ROpzHfLJHBQi",
        "outputId": "167dc3bf-b748-4ed8-efb3-29405fe04256"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>OPEN</th>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-51a4968e-eb6a-4854-8fb6-4817ae43b3e6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-51a4968e-eb6a-4854-8fb6-4817ae43b3e6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         Date        OPEN        HIGH         LOW  ...   VOLUME  rsi_u  rsi_d  rsi\n",
              "0  02-04-2018  945.250000  967.875000  945.250000  ...  2298184    0.0    0.0  NaN\n",
              "1  03-04-2018  961.974976  962.849976  955.500000  ...  2039942    0.0    0.0  NaN\n",
              "2  04-04-2018  956.474976  962.474976  939.224976  ...  1857054    0.0    0.0  NaN\n",
              "3  05-04-2018  950.650024  957.000000  945.099976  ...  2429446    0.0    0.0  NaN\n",
              "4  06-04-2018  956.099976  963.224976  952.900024  ...  1860004    0.0    0.0  NaN\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    }
  ]
}