Technical Indicator's of Indian Stock market.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INDICATORS (ATR,RSI,SMA,EMA,Bollinger band ) on Historical_data 'SBI'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* What Is a Technical Indicator?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Technical indicators are heuristic or mathematical calculations based on the price, volume, or open interest of a security or contract used by traders who follow technical analysis.\n",
    "By analyzing historical data, technical analysts use indicators to predict future price movements. Examples of common technical indicators include the Relative Strength Index, Money Flow Index, Stochastics, MACD and Bollinger Bands®."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* How Technical Indicators Work ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analyzing statistical trends gathered from trading activity, such as price movement and volume. Unlike fundamental analysts, who attempt to evaluate a security's intrinsic value based on financial or economic data, technical analysts focus on patterns of price movements, trading signals and various other analytical charting tools to evaluate a security's strength or weakness.\n",
    "Technical analysis can be used on any security with historical trading data. This includes stocks, futures, commodities, fixed-income, currencies, and other securities. In this tutorial, we’ll usually analyze stocks in our examples, but keep in mind that these concepts can be applied to any type of security. In fact, technical analysis is far more prevalent in commodities and forex markets where traders focus on short-term price movements.\n",
    "Technical indicators, also known as \"technicals\", are focused on historical trading data, such as price, volume, and open interest, rather than the fundamentals of a business, like earnings, revenue, or profit margins. Technical indicators are commonly used by active traders, since they're designed to analyze short-term price movements, but long-term investors may also use technical indicators to identify entry and exit points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two basic types of technical indicators:\n",
    "\n",
    "* Overlays - Technical indicators that use the same scale as prices are plotted over the top of the prices on a stock chart. Examples include moving averages and Bollinger Bands®.\n",
    "* Oscillators - Technical indicators that oscillate between a local minimum and maximum are plotted above or below a price chart. Examples include the stochastic oscillator, MACD or RSI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Login to Zerodha and fetching Histrical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import secrets \n",
    "import threading\n",
    "import webbrowser\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "from math import modf\n",
    "from pprint import pprint\n",
    "from pytz import timezone\n",
    "from datetime import datetime \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from time import gmtime, strftime\n",
    "from kiteconnect import KiteTicker \n",
    "from kiteconnect import KiteConnect\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# API KEY and API secter \n",
    "api_key='******************* @@enter your api key here@@@**************' \n",
    "api_secret='**********************@@enter your api key here****************'  \n",
    "\n",
    "webbrowser.open_new_tab(KiteConnect(api_key,api_secret).login_url())\n",
    "kite = KiteConnect(api_key=api_key)\n",
    "data = kite.generate_session(((str(input(\"ENTER full link generated in URL :- \")).split(\"request_token=\")[1]).split(\"&action\"))[0],api_secret)\n",
    "kite.set_access_token(data[\"access_token\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Getting Historical Data_Download from zerodha for indicator's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2019-08-01 00:00:00+05:30</td>\n",
       "      <td>330.80</td>\n",
       "      <td>331.50</td>\n",
       "      <td>311.35</td>\n",
       "      <td>317.15</td>\n",
       "      <td>40853304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2019-08-02 00:00:00+05:30</td>\n",
       "      <td>315.55</td>\n",
       "      <td>322.25</td>\n",
       "      <td>307.05</td>\n",
       "      <td>308.45</td>\n",
       "      <td>64472348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>2019-08-05 00:00:00+05:30</td>\n",
       "      <td>298.45</td>\n",
       "      <td>302.85</td>\n",
       "      <td>291.70</td>\n",
       "      <td>300.25</td>\n",
       "      <td>48815899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2019-08-06 00:00:00+05:30</td>\n",
       "      <td>298.80</td>\n",
       "      <td>304.25</td>\n",
       "      <td>297.25</td>\n",
       "      <td>301.40</td>\n",
       "      <td>30442970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2019-08-07 00:00:00+05:30</td>\n",
       "      <td>302.40</td>\n",
       "      <td>302.45</td>\n",
       "      <td>288.80</td>\n",
       "      <td>289.90</td>\n",
       "      <td>30820114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2019-08-08 00:00:00+05:30</td>\n",
       "      <td>290.00</td>\n",
       "      <td>295.50</td>\n",
       "      <td>285.60</td>\n",
       "      <td>294.35</td>\n",
       "      <td>32929233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2019-08-09 00:00:00+05:30</td>\n",
       "      <td>296.30</td>\n",
       "      <td>298.00</td>\n",
       "      <td>290.05</td>\n",
       "      <td>291.35</td>\n",
       "      <td>23377581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2019-08-13 00:00:00+05:30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>291.55</td>\n",
       "      <td>282.50</td>\n",
       "      <td>283.35</td>\n",
       "      <td>24231272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2019-08-14 00:00:00+05:30</td>\n",
       "      <td>285.05</td>\n",
       "      <td>291.25</td>\n",
       "      <td>284.50</td>\n",
       "      <td>289.75</td>\n",
       "      <td>18523649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2019-08-16 00:00:00+05:30</td>\n",
       "      <td>287.95</td>\n",
       "      <td>292.80</td>\n",
       "      <td>284.30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>20047399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        date    open    high     low   close    volume\n",
       "73 2019-08-01 00:00:00+05:30  330.80  331.50  311.35  317.15  40853304\n",
       "74 2019-08-02 00:00:00+05:30  315.55  322.25  307.05  308.45  64472348\n",
       "75 2019-08-05 00:00:00+05:30  298.45  302.85  291.70  300.25  48815899\n",
       "76 2019-08-06 00:00:00+05:30  298.80  304.25  297.25  301.40  30442970\n",
       "77 2019-08-07 00:00:00+05:30  302.40  302.45  288.80  289.90  30820114\n",
       "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233\n",
       "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581\n",
       "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272\n",
       "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649\n",
       "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "    # Date must be present as a Pandas DataFrame with ['date', 'open', 'high', 'low', 'close', 'volume'] as columns\n",
    "    df = pd.DataFrame(data[\"data\"][\"candles\"], columns=['date', 'open', 'high', 'low', 'close', 'volume']) \n",
    "'''\n",
    "df=pd.DataFrame(kite.historical_data(779521,\"2019-04-14 15:00:00\",\"2019-08-16 09:16:00\",\"day\",0))[[\"date\",\"open\",\"high\",\"low\",\"close\",\"volume\"]]\n",
    "display(df.tail(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ploting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  \t'---- ABOVE GRAPH OF OHLC Historical data ----- ' \n",
      " \n",
      " \n"
     ]
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "df['high'].plot()\n",
    "df['low'].plot()\n",
    "df['close'].plot()\n",
    "df['open'].plot()\n",
    "plt.show()\n",
    "print (\"  \\t'---- ABOVE GRAPH OF OHLC Historical data ----- ' \\n \\n \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indicator's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. ATR (Average True Range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>ATR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2019-08-08 00:00:00+05:30</td>\n",
       "      <td>290.00</td>\n",
       "      <td>295.50</td>\n",
       "      <td>285.60</td>\n",
       "      <td>294.35</td>\n",
       "      <td>32929233</td>\n",
       "      <td>11.851230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2019-08-09 00:00:00+05:30</td>\n",
       "      <td>296.30</td>\n",
       "      <td>298.00</td>\n",
       "      <td>290.05</td>\n",
       "      <td>291.35</td>\n",
       "      <td>23377581</td>\n",
       "      <td>11.331060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2019-08-13 00:00:00+05:30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>291.55</td>\n",
       "      <td>282.50</td>\n",
       "      <td>283.35</td>\n",
       "      <td>24231272</td>\n",
       "      <td>11.026916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2019-08-14 00:00:00+05:30</td>\n",
       "      <td>285.05</td>\n",
       "      <td>291.25</td>\n",
       "      <td>284.50</td>\n",
       "      <td>289.75</td>\n",
       "      <td>18523649</td>\n",
       "      <td>10.609991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2019-08-16 00:00:00+05:30</td>\n",
       "      <td>287.95</td>\n",
       "      <td>292.80</td>\n",
       "      <td>284.30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>20047399</td>\n",
       "      <td>10.328657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        date    open    high     low   close    volume  \\\n",
       "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
       "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
       "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
       "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
       "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
       "\n",
       "          ATR  \n",
       "78  11.851230  \n",
       "79  11.331060  \n",
       "80  11.026916  \n",
       "81  10.609991  \n",
       "82  10.328657  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Function to compute Average True Range (ATR)\n",
    "\n",
    "Args :\n",
    "    df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "    period : Integer indicates the period of computation in terms of number of candles\n",
    "    ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
    "\n",
    "Returns :\n",
    "    df : Pandas DataFrame with new columns added for \n",
    "        True Range (TR)\n",
    "        ATR (ATR_$period)\n",
    "\"\"\"\n",
    "\n",
    "def atr(df,period):\n",
    "    df['hl']=abs(df['high']-df['low'])\n",
    "    df['hpc']=abs(df['high']-df['close'].shift())\n",
    "    df['lpc']=abs(df['low']-df['close'].shift())\n",
    "    df['tr']=df[['hl','hpc','lpc']].max(axis=1)\n",
    "    df['ATR']=pd.DataFrame.ewm(df[\"tr\"], span=period,min_periods=period).mean()\n",
    "    df.drop([\"hl\",\"hpc\",\"lpc\",\"tr\"],axis = 1 , inplace =True)\n",
    "atr(df,14)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. RSI (Relative Strength Index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RSI is classified as a momentum oscillator, measuring the velocity and magnitude of directional price movements. Momentum is the rate of the rise or fall in price. The RSI computes momentum as the ratio of higher closes to lower closes. Stocks which have had more or stronger positive changes have a higher RSI than stocks which have had more or stronger negative changes.\n",
    "The indicator has an upper line, typically at 70, a lower line at 30, and a dashed mid-line at 50. When price moves up very rapidly, at some point it is considered overbought (When the RSI crosses 70). Likewise, when price falls very rapidly, at some point it is considered oversold (When the RSI crosses 30) the level of the RSI is a measure of the stock's recent trading strength. The slope of the RSI is directly proportional to the velocity of a change in the trend. The distance travelled by the RSI is proportional to the magnitude of the move.\n",
    "*** https://www.businesstoday.in/markets/stocks/five-technical-indicators-you-should-know-as-an-investor-rohit-gadia-capital-via/story/221116.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>volume</th>\n",
       "      <th>ATR</th>\n",
       "      <th>rsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2019-08-08 00:00:00+05:30</td>\n",
       "      <td>290.00</td>\n",
       "      <td>295.50</td>\n",
       "      <td>285.60</td>\n",
       "      <td>294.35</td>\n",
       "      <td>32929233</td>\n",
       "      <td>11.851230</td>\n",
       "      <td>26.529639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2019-08-09 00:00:00+05:30</td>\n",
       "      <td>296.30</td>\n",
       "      <td>298.00</td>\n",
       "      <td>290.05</td>\n",
       "      <td>291.35</td>\n",
       "      <td>23377581</td>\n",
       "      <td>11.331060</td>\n",
       "      <td>25.567214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2019-08-13 00:00:00+05:30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>291.55</td>\n",
       "      <td>282.50</td>\n",
       "      <td>283.35</td>\n",
       "      <td>24231272</td>\n",
       "      <td>11.026916</td>\n",
       "      <td>23.154909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2019-08-14 00:00:00+05:30</td>\n",
       "      <td>285.05</td>\n",
       "      <td>291.25</td>\n",
       "      <td>284.50</td>\n",
       "      <td>289.75</td>\n",
       "      <td>18523649</td>\n",
       "      <td>10.609991</td>\n",
       "      <td>28.931857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2019-08-16 00:00:00+05:30</td>\n",
       "      <td>287.95</td>\n",
       "      <td>292.80</td>\n",
       "      <td>284.30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>20047399</td>\n",
       "      <td>10.328657</td>\n",
       "      <td>29.950888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        date    open    high     low   close    volume  \\\n",
       "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
       "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
       "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
       "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
       "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
       "\n",
       "          ATR        rsi  \n",
       "78  11.851230  26.529639  \n",
       "79  11.331060  25.567214  \n",
       "80  11.026916  23.154909  \n",
       "81  10.609991  28.931857  \n",
       "82  10.328657  29.950888  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "    Function to compute Relative Strength Index (RSI)\n",
    "    \n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        period : Integer indicates the period of computation in terms of number of candles\n",
    "        \n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new columns added for \n",
    "            Relative Strength Index (RSI_$period)\n",
    "    \"\"\"\n",
    "\n",
    "rsi_period=14\n",
    "chg=df[\"close\"].diff(1)\n",
    "gain=chg.mask(chg<0,0) \n",
    "loss=chg.mask(chg>0,0)\n",
    "avg_gain=gain.ewm(com=rsi_period-1,min_periods=rsi_period).mean()\n",
    "avg_loss=loss.ewm(com=rsi_period-1,min_periods=rsi_period).mean()\n",
    "rs =abs(avg_gain / avg_loss)\n",
    "rsi =100 -(100/(1+rs))\n",
    "df['rsi']=rsi\n",
    "display(df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. SMA (simple moving average)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving averages is one of the oldest and most useful technical indicators in technical analysis. Moving shows a trend in a \"smoothed\" manner and can give reliable signals when used in tandem with other oscillators like MACD and RSI.\n",
    "Simple moving average (SMA), exponential moving average (EMA) and weighted moving average (WMA) are the three types of moving averages.\n",
    "For stocks, common time periods for moving averages are 10 days, 21 days, 50 days, 100 days and 200 days. The most commonly used moving average is the simple moving average (SMA). Single SMAs can be used to identify a trend but we find that dual or triple moving average is more powerful when combined together.                  \n",
    "https://www.businesstoday.in/markets/stocks/five-technical-indicators-you-should-know-as-an-investor-rohit-gadia-capital-via/story/221116.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
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       "    <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>volume</th>\n",
       "      <th>ATR</th>\n",
       "      <th>rsi</th>\n",
       "      <th>SMA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2019-08-08 00:00:00+05:30</td>\n",
       "      <td>290.00</td>\n",
       "      <td>295.50</td>\n",
       "      <td>285.60</td>\n",
       "      <td>294.35</td>\n",
       "      <td>32929233</td>\n",
       "      <td>11.851230</td>\n",
       "      <td>26.529639</td>\n",
       "      <td>323.685714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2019-08-09 00:00:00+05:30</td>\n",
       "      <td>296.30</td>\n",
       "      <td>298.00</td>\n",
       "      <td>290.05</td>\n",
       "      <td>291.35</td>\n",
       "      <td>23377581</td>\n",
       "      <td>11.331060</td>\n",
       "      <td>25.567214</td>\n",
       "      <td>319.435714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2019-08-13 00:00:00+05:30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>291.55</td>\n",
       "      <td>282.50</td>\n",
       "      <td>283.35</td>\n",
       "      <td>24231272</td>\n",
       "      <td>11.026916</td>\n",
       "      <td>23.154909</td>\n",
       "      <td>315.232143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2019-08-14 00:00:00+05:30</td>\n",
       "      <td>285.05</td>\n",
       "      <td>291.25</td>\n",
       "      <td>284.50</td>\n",
       "      <td>289.75</td>\n",
       "      <td>18523649</td>\n",
       "      <td>10.609991</td>\n",
       "      <td>28.931857</td>\n",
       "      <td>311.671429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2019-08-16 00:00:00+05:30</td>\n",
       "      <td>287.95</td>\n",
       "      <td>292.80</td>\n",
       "      <td>284.30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>20047399</td>\n",
       "      <td>10.328657</td>\n",
       "      <td>29.950888</td>\n",
       "      <td>308.071429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        date    open    high     low   close    volume  \\\n",
       "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
       "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
       "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
       "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
       "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
       "\n",
       "          ATR        rsi         SMA  \n",
       "78  11.851230  26.529639  323.685714  \n",
       "79  11.331060  25.567214  319.435714  \n",
       "80  11.026916  23.154909  315.232143  \n",
       "81  10.609991  28.931857  311.671429  \n",
       "82  10.328657  29.950888  308.071429  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "    Function to compute Simple Moving Average (SMA)\n",
    "    \n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        base : String indicating the column name from which the SMA needs to be computed from\n",
    "        target : String indicates the column name to which the computed data needs to be stored\n",
    "        period : Integer indicates the period of computation in terms of number of candles\n",
    "        \n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new column added with name 'target'\n",
    "    \"\"\"\n",
    "\n",
    "def SMA(df, column=\"close\", period=14):\n",
    "    smavg = df.close.rolling(14).mean()\n",
    "    return df.join(smavg.to_frame('SMA'))\n",
    "a=SMA(df)\n",
    "a.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. EMA ( Exponential Moving Average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78    321.111774\n",
       "79    317.329208\n",
       "80    313.293848\n",
       "81    309.758441\n",
       "82    307.172770\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "    Function to compute Exponential Moving Average (EMA)\n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        base : String indicating the column name from which the EMA needs to be computed from\n",
    "        target : String indicates the column name to which the computed data needs to be stored\n",
    "        period : Integer indicates the period of computation in terms of number of candles\n",
    "        alpha : Boolean if True indicates to use the formula for computing EMA using alpha (default is False)\n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new column added with name 'target'\n",
    "    \"\"\"\n",
    "def EMA(df, column=\"close\", period=14):\n",
    "    ema = df[column].ewm(span=period, min_periods=period - 1).mean()\n",
    "    return df.join(ema.to_frame('EMA'))\n",
    "pp= ( EMA(df)[\"EMA\"].shift(1)*13 + df['close'] ) /  14\n",
    "pp.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Bollinger band"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bollinger Bands are a technical trading tool created by John Bollinger in the early 1980s. The purpose of Bollinger Bands is to provide a relative definition of high and low. By definition prices are high at the upper band and low at the lower band. This definition can aid in rigorous pattern recognition and is useful in comparing price action to the action of indicators. Bollinger Bands consist of a set of three curves drawn in relation to securities prices. The middle band is a measure of the intermediate-term trend, usually a simple moving average that serves as the base for the upper band and lower band. The interval between the upper and lower bands and the middle band is determined by volatility, typically the standard deviation of the same data that were used for the average. The default parameters, 20 periods and two standard deviations, may be adjusted to suit your purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>volume</th>\n",
       "      <th>ATR</th>\n",
       "      <th>rsi</th>\n",
       "      <th>BBANDUP</th>\n",
       "      <th>BBANDLO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2019-08-08 00:00:00+05:30</td>\n",
       "      <td>290.00</td>\n",
       "      <td>295.50</td>\n",
       "      <td>285.60</td>\n",
       "      <td>294.35</td>\n",
       "      <td>32929233</td>\n",
       "      <td>11.851230</td>\n",
       "      <td>26.529639</td>\n",
       "      <td>387.024069</td>\n",
       "      <td>284.140931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2019-08-09 00:00:00+05:30</td>\n",
       "      <td>296.30</td>\n",
       "      <td>298.00</td>\n",
       "      <td>290.05</td>\n",
       "      <td>291.35</td>\n",
       "      <td>23377581</td>\n",
       "      <td>11.331060</td>\n",
       "      <td>25.567214</td>\n",
       "      <td>385.242185</td>\n",
       "      <td>278.697815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2019-08-13 00:00:00+05:30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>291.55</td>\n",
       "      <td>282.50</td>\n",
       "      <td>283.35</td>\n",
       "      <td>24231272</td>\n",
       "      <td>11.026916</td>\n",
       "      <td>23.154909</td>\n",
       "      <td>383.881457</td>\n",
       "      <td>272.388543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2019-08-14 00:00:00+05:30</td>\n",
       "      <td>285.05</td>\n",
       "      <td>291.25</td>\n",
       "      <td>284.50</td>\n",
       "      <td>289.75</td>\n",
       "      <td>18523649</td>\n",
       "      <td>10.609991</td>\n",
       "      <td>28.931857</td>\n",
       "      <td>379.931275</td>\n",
       "      <td>268.878725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>2019-08-16 00:00:00+05:30</td>\n",
       "      <td>287.95</td>\n",
       "      <td>292.80</td>\n",
       "      <td>284.30</td>\n",
       "      <td>290.90</td>\n",
       "      <td>20047399</td>\n",
       "      <td>10.328657</td>\n",
       "      <td>29.950888</td>\n",
       "      <td>372.909758</td>\n",
       "      <td>267.750242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        date    open    high     low   close    volume  \\\n",
       "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
       "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
       "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
       "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
       "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
       "\n",
       "          ATR        rsi     BBANDUP     BBANDLO  \n",
       "78  11.851230  26.529639  387.024069  284.140931  \n",
       "79  11.331060  25.567214  385.242185  278.697815  \n",
       "80  11.026916  23.154909  383.881457  272.388543  \n",
       "81  10.609991  28.931857  379.931275  268.878725  \n",
       "82  10.328657  29.950888  372.909758  267.750242  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def BollingerBand(df, column=\"close\", period=20):\n",
    "    sma = df[column].rolling(window=period, min_periods=period - 1).mean()\n",
    "    std = df[column].rolling(window=period, min_periods=period - 1).std()\n",
    "    up = (sma + (std * 2)).to_frame('BBANDUP')\n",
    "    lower = (sma - (std * 2)).to_frame('BBANDLO')\n",
    "    return df.join(up).join(lower)\n",
    "c=BollingerBand(df)\n",
    "c.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* * * * * "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other indicator's hint  (HA,MACD, STDDEV, SuperTrend, Ichimoku)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. HA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def HA(df, ohlc=['Open', 'High', 'Low', 'Close']):\n",
    "    \"\"\"\n",
    "    Function to compute Heiken Ashi Candles (HA)\n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new columns added for \n",
    "            Heiken Ashi Close (HA_$ohlc[3])\n",
    "            Heiken Ashi Open (HA_$ohlc[0])\n",
    "            Heiken Ashi High (HA_$ohlc[1])\n",
    "            Heiken Ashi Low (HA_$ohlc[2])\n",
    "    \"\"\"\n",
    "    ha_open = 'HA_' + ohlc[0]\n",
    "    ha_high = 'HA_' + ohlc[1]\n",
    "    ha_low = 'HA_' + ohlc[2]\n",
    "    ha_close = 'HA_' + ohlc[3]\n",
    "    df[ha_close] = (df[ohlc[0]] + df[ohlc[1]] + df[ohlc[2]] + df[ohlc[3]]) / 4\n",
    "    df[ha_open] = 0.00\n",
    "    for i in range(0, len(df)):\n",
    "        if i == 0:\n",
    "            df[ha_open].iat[i] = (df[ohlc[0]].iat[i] + df[ohlc[3]].iat[i]) / 2\n",
    "        else:\n",
    "            df[ha_open].iat[i] = (df[ha_open].iat[i - 1] + df[ha_close].iat[i - 1]) / 2\n",
    "    df[ha_high]=df[[ha_open, ha_close, ohlc[1]]].max(axis=1)\n",
    "    df[ha_low]=df[[ha_open, ha_close, ohlc[2]]].min(axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. STDDEV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def STDDEV(df, base, target, period):\n",
    "    \"\"\"\n",
    "    Function to compute Standard Deviation (STDDEV)\n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        base : String indicating the column name from which the SMA needs to be computed from\n",
    "        target : String indicates the column name to which the computed data needs to be stored\n",
    "        period : Integer indicates the period of computation in terms of number of candles\n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new column added with name 'target'\n",
    "    \"\"\"\n",
    "    df[target] = df[base].rolling(window=period).std()\n",
    "    df[target].fillna(0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. SuperTrend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SuperTrend(df, period, multiplier, ohlc=['Open', 'High', 'Low', 'Close']):\n",
    "    \"\"\"\n",
    "    Function to compute SuperTrend\n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        period : Integer indicates the period of computation in terms of number of candles\n",
    "        multiplier : Integer indicates value to multiply the ATR\n",
    "        ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new columns added for \n",
    "            True Range (TR), ATR (ATR_$period)\n",
    "            SuperTrend (ST_$period_$multiplier)\n",
    "            SuperTrend Direction (STX_$period_$multiplier)\n",
    "    \"\"\"\n",
    "    ATR(df, period, ohlc=ohlc)\n",
    "    atr = 'ATR_' + str(period)\n",
    "    st = 'ST_' + str(period) + '_' + str(multiplier)\n",
    "    stx = 'STX_' + str(period) + '_' + str(multiplier)\n",
    "    \"\"\"\n",
    "    SuperTrend Algorithm :\n",
    "        BASIC UPPERBAND = (HIGH + LOW) / 2 + Multiplier * ATR\n",
    "        BASIC LOWERBAND = (HIGH + LOW) / 2 - Multiplier * ATR\n",
    "        FINAL UPPERBAND = IF( (Current BASICUPPERBAND < Previous FINAL UPPERBAND) or (Previous Close > Previous FINAL UPPERBAND))\n",
    "                            THEN (Current BASIC UPPERBAND) ELSE Previous FINALUPPERBAND)\n",
    "        FINAL LOWERBAND = IF( (Current BASIC LOWERBAND > Previous FINAL LOWERBAND) or (Previous Close < Previous FINAL LOWERBAND)) \n",
    "                            THEN (Current BASIC LOWERBAND) ELSE Previous FINAL LOWERBAND)\n",
    "        SUPERTREND = IF((Previous SUPERTREND = Previous FINAL UPPERBAND) and (Current Close <= Current FINAL UPPERBAND)) THEN\n",
    "                        Current FINAL UPPERBAND\n",
    "                    ELSE\n",
    "                        IF((Previous SUPERTREND = Previous FINAL UPPERBAND) and (Current Close > Current FINAL UPPERBAND)) THEN\n",
    "                            Current FINAL LOWERBAND\n",
    "                        ELSE\n",
    "                            IF((Previous SUPERTREND = Previous FINAL LOWERBAND) and (Current Close >= Current FINAL LOWERBAND)) THEN\n",
    "                                Current FINAL LOWERBAND\n",
    "                            ELSE\n",
    "                                IF((Previous SUPERTREND = Previous FINAL LOWERBAND) and (Current Close < Current FINAL LOWERBAND)) THEN\n",
    "                                    Current FINAL UPPERBAND\n",
    "    \"\"\"\n",
    "    # Compute basic upper and lower bands\n",
    "    df['basic_ub'] = (df[ohlc[1]] + df[ohlc[2]]) / 2 + multiplier * df[atr]\n",
    "    df['basic_lb'] = (df[ohlc[1]] + df[ohlc[2]]) / 2 - multiplier * df[atr]\n",
    "    # Compute final upper and lower bands\n",
    "    df['final_ub'] = 0.00\n",
    "    df['final_lb'] = 0.00\n",
    "    for i in range(period, len(df)):\n",
    "        df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df[ohlc[3]].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]\n",
    "        df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df[ohlc[3]].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]\n",
    "    # Set the Supertrend value\n",
    "    df[st] = 0.00\n",
    "    for i in range(period, len(df)):\n",
    "        df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df[ohlc[3]].iat[i] <= df['final_ub'].iat[i] else \\\n",
    "                        df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df[ohlc[3]].iat[i] >  df['final_ub'].iat[i] else \\\n",
    "                        df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df[ohlc[3]].iat[i] >= df['final_lb'].iat[i] else \\\n",
    "                        df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df[ohlc[3]].iat[i] <  df['final_lb'].iat[i] else 0.00     \n",
    "    # Mark the trend direction up/down\n",
    "    df[stx] = np.where((df[st] > 0.00), np.where((df[ohlc[3]] < df[st]), 'down',  'up'), np.NaN)\n",
    "    # Remove basic and final bands from the columns\n",
    "    df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)\n",
    "    df.fillna(0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. MACD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Trend and momentum indicator\n",
    "* The difference between 12 and 26-day moving averages.\n",
    "* Rising Moving Average Convergence Divergence (MACD) indicates an upward price trend and falling MACD indicates a downward price trend.\n",
    "* Usually, nine-day moving average of MACD is used for buy and sell calls. Buy when MACD goes above the nine-day moving average of MACD, sell when MACD go below the nineday moving average.                 \n",
    "https://economictimes.indiatimes.com/wealth/invest/7-stock-market-technical-indicators-that-can-help-you-invest/articleshow/63342749.cms?from=mdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MACD(df, fastEMA=12, slowEMA=26, signal=9, base='Close'):\n",
    "    \"\"\"\n",
    "    Function to compute Moving Average Convergence Divergence (MACD)\n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        fastEMA : Integer indicates faster EMA\n",
    "        slowEMA : Integer indicates slower EMA\n",
    "        signal : Integer indicates the signal generator for MACD\n",
    "        base : String indicating the column name from which the MACD needs to be computed from (Default Close)\n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new columns added for \n",
    "            Fast EMA (ema_$fastEMA)\n",
    "            Slow EMA (ema_$slowEMA)\n",
    "            MACD (macd_$fastEMA_$slowEMA_$signal)\n",
    "            MACD Signal (signal_$fastEMA_$slowEMA_$signal)\n",
    "            MACD Histogram (MACD (hist_$fastEMA_$slowEMA_$signal)) \n",
    "    \"\"\"\n",
    "    fE = \"ema_\" + str(fastEMA)\n",
    "    sE = \"ema_\" + str(slowEMA)\n",
    "    macd = \"macd_\" + str(fastEMA) + \"_\" + str(slowEMA) + \"_\" + str(signal)\n",
    "    sig = \"signal_\" + str(fastEMA) + \"_\" + str(slowEMA) + \"_\" + str(signal)\n",
    "    hist = \"hist_\" + str(fastEMA) + \"_\" + str(slowEMA) + \"_\" + str(signal)\n",
    "    # Compute fast and slow EMA    \n",
    "    EMA(df, base, fE, fastEMA)\n",
    "    EMA(df, base, sE, slowEMA)\n",
    "    # Compute MACD\n",
    "    df[macd] = np.where(np.logical_and(np.logical_not(df[fE] == 0), np.logical_not(df[sE] == 0)), df[fE] - df[sE], 0)\n",
    "    # Compute MACD Signal\n",
    "    EMA(df, macd, sig, signal)\n",
    "    # Compute MACD Histogram\n",
    "    df[hist] = np.where(np.logical_and(np.logical_not(df[macd] == 0), np.logical_not(df[sig] == 0)), df[macd] - df[sig], 0)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. BBand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BBand(df, base='Close', period=20, multiplier=2):\n",
    "    \"\"\"\n",
    "    Function to compute Bollinger Band (BBand)\n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        base : String indicating the column name from which the MACD needs to be computed from (Default Close)\n",
    "        period : Integer indicates the period of computation in terms of number of candles\n",
    "        multiplier : Integer indicates value to multiply the SD\n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new columns added for \n",
    "            Upper Band (UpperBB_$period_$multiplier)\n",
    "            Lower Band (LowerBB_$period_$multiplier)\n",
    "    \"\"\"\n",
    "    upper = 'UpperBB_' + str(period) + '_' + str(multiplier)\n",
    "    lower = 'LowerBB_' + str(period) + '_' + str(multiplier)\n",
    "    sma = df[base].rolling(window=period, min_periods=period - 1).mean()\n",
    "    sd = df[base].rolling(window=period).std()\n",
    "    df[upper] = sma + (multiplier * sd)\n",
    "    df[lower] = sma - (multiplier * sd)\n",
    "    df[upper].fillna(0, inplace=True)\n",
    "    df[lower].fillna(0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Ichimoku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Ichimoku(df, ohlc=['Open', 'High', 'Low', 'Close'], param=[9, 26, 52, 26]):\n",
    "    \"\"\"\n",
    "    Function to compute Ichimoku Cloud parameter (Ichimoku)    \n",
    "    Args :\n",
    "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
    "        ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
    "        param: Periods to be used in computation (default [tenkan_sen_period, kijun_sen_period, senkou_span_period, chikou_span_period] = [9, 26, 52, 26])\n",
    "    Returns :\n",
    "        df : Pandas DataFrame with new columns added for ['Tenkan Sen', 'Kijun Sen', 'Senkou Span A', 'Senkou Span B', 'Chikou Span']\n",
    "    \"\"\"\n",
    "    high = df[ohlc[1]]\n",
    "    low = df[ohlc[2]]\n",
    "    close = df[ohlc[3]]\n",
    "    tenkan_sen_period = param[0]\n",
    "    kijun_sen_period = param[1]\n",
    "    senkou_span_period = param[2]\n",
    "    chikou_span_period = param[3]\n",
    "    tenkan_sen_column = 'Tenkan Sen'\n",
    "    kijun_sen_column = 'Kijun Sen'\n",
    "    senkou_span_a_column = 'Senkou Span A'\n",
    "    senkou_span_b_column = 'Senkou Span B'\n",
    "    chikou_span_column = 'Chikou Span'\n",
    "    # Tenkan-sen (Conversion Line)\n",
    "    tenkan_sen_high = high.rolling(window=tenkan_sen_period).max()\n",
    "    tenkan_sen_low = low.rolling(window=tenkan_sen_period).min()\n",
    "    df[tenkan_sen_column] = (tenkan_sen_high + tenkan_sen_low) / 2\n",
    "    # Kijun-sen (Base Line)\n",
    "    kijun_sen_high = high.rolling(window=kijun_sen_period).max()\n",
    "    kijun_sen_low = low.rolling(window=kijun_sen_period).min()\n",
    "    df[kijun_sen_column] = (kijun_sen_high + kijun_sen_low) / 2\n",
    "    # Senkou Span A (Leading Span A)\n",
    "    df[senkou_span_a_column] = ((df[tenkan_sen_column] + df[kijun_sen_column]) / 2).shift(kijun_sen_period)\n",
    "    # Senkou Span B (Leading Span B)\n",
    "    senkou_span_high = high.rolling(window=senkou_span_period).max()\n",
    "    senkou_span_low = low.rolling(window=senkou_span_period).min()\n",
    "    df[senkou_span_b_column] = ((senkou_span_high + senkou_span_low) / 2).shift(kijun_sen_period)\n",
    "    # The most current closing price plotted chikou_span_period time periods behind\n",
    "    df[chikou_span_column] = close.shift(-1 * chikou_span_period)\n",
    "    return df"
   ]
  }
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