├── 1.1 - Getting Instrument Token of a Scrip Using Python and Zerodha API.ipynb
├── 1.2 - Getting Instrument Token of a Scrip Using Python and Zerodha Kite LTP Method.ipynb
├── 1.3 - Getting Historical Data of Reliance​ in JSON Using Python and Zerodha API.ipynb
├── 1.4 - Getting Historical Data of Reliance​ In Pandas Using Python and Zerodha API.ipynb
├── 1.5 - Organizing Expiry Dates of Financial Instruments with Python.ipynb
├── 1.7 - Buy on RSI Strategy Coding in Python using Zerodha.ipynb
├── 1.8 - Guppy Strategy Screener Using Python and Zerodha.ipynb
├── 1.9 - Guppy Strategy Trading Bot Using Python and Zerodha.ipynb
├── 2.1 - Guppy Indicator Trading Bot Using Python and Zerodha.ipynb
├── 2.2 - Plotting OHLC to Candlestick Chart.ipynb
├── 2.2 - Plotting Zerodha OHLC to Candlestick Chart.ipynb
├── 2.3 - Converting Candles to Heikin Ashi Using Zerodha KiteConnect.ipynb
├── 2.3 - Converting Candlestick to Heikin Ashi Using Zerodha KiteConnect.ipynb
├── 2.4 - Creating TimeFrames from Minute-Based Data Using Python.ipynb
├── 2.5 - Multi-Stock Bot Using Guppy Strategy with Screener and Backtesting.ipynb
├── 2.6 - RSI Based Trading Bot with Python using Zerodha API.ipynb
├── 2.7 - Multi-Timeframe Bot Using Guppy Strategy and Screener.ipynb
├── 2.8 - Backtesting Guppy Multiple Moving Average (GMMA) with Python using Zerodha API.ipynb
├── 2.9 - Exploring Technical Indicators in the Indian Stock Market with Zerodha API and Python.ipynb
└── README.md


/1.1 - Getting Instrument Token of a Scrip Using Python and Zerodha API.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "id": "2eb7c931",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "# https://unofficed.com/courses/mastering-algotrading-a-beginners-guide-using-kiteconnect-api/lessons/getting-instrument-token-of-a-scrip-using-python-and-zerodha-api/"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "id": "a59c49d2",
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "name": "stderr",
 21 |      "output_type": "stream",
 22 |      "text": [
 23 |       "/tmp/ipykernel_4126091/2308159906.py:10: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
 24 |       "  from kite.kitecustom import *\n"
 25 |      ]
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "import logging\n",
 30 |     "from kiteconnect import KiteConnect\n",
 31 |     "\n",
 32 |     "logging.basicConfig(level=logging.DEBUG)\n",
 33 |     "\n",
 34 |     "kite = KiteConnect(api_key=\"your_api_key\")\n",
 35 |     "\n",
 36 |     "# Redirect the user to the login url obtained\n",
 37 |     "# from kite.login_url(), and receive the request_token\n",
 38 |     "# from the registered redirect url after the login flow.\n",
 39 |     "# Once you have the request_token, obtain the access_token\n",
 40 |     "# as follows.\n",
 41 |     "\n",
 42 |     "data = kite.generate_session(\"request_token_here\", api_secret=\"your_secret\")\n",
 43 |     "kite.set_access_token(data[\"access_token\"])"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": 2,
 49 |    "id": "56ed6214",
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "name": "stdout",
 54 |      "output_type": "stream",
 55 |      "text": [
 56 |       "       instrument_token  exchange_token       tradingsymbol  \\\n",
 57 |       "0             537733638         2100522  EURINR23DEC83.25CE   \n",
 58 |       "1             537730310         2100509  EURINR23DEC83.25PE   \n",
 59 |       "2             537617158         2100067   EURINR23DEC83.5CE   \n",
 60 |       "3             537614342         2100056   EURINR23DEC83.5PE   \n",
 61 |       "4             537747718         2100577  EURINR23DEC83.75CE   \n",
 62 |       "...                 ...             ...                 ...   \n",
 63 |       "98011           2916865           11394                ZOTA   \n",
 64 |       "98012           7436801           29050               ZUARI   \n",
 65 |       "98013            979713            3827            ZUARIIND   \n",
 66 |       "98014           2029825            7929           ZYDUSLIFE   \n",
 67 |       "98015           4514561           17635           ZYDUSWELL   \n",
 68 |       "\n",
 69 |       "                       name  last_price      expiry  strike  tick_size  \\\n",
 70 |       "0                    EURINR           0  2023-12-27   83.25     0.0025   \n",
 71 |       "1                    EURINR           0  2023-12-27   83.25     0.0025   \n",
 72 |       "2                    EURINR           0  2023-12-27   83.50     0.0025   \n",
 73 |       "3                    EURINR           0  2023-12-27   83.50     0.0025   \n",
 74 |       "4                    EURINR           0  2023-12-27   83.75     0.0025   \n",
 75 |       "...                     ...         ...         ...     ...        ...   \n",
 76 |       "98011      ZOTA HEALTH CARE           0         NaN    0.00     0.0500   \n",
 77 |       "98012  ZUARI AGRO CHEMICALS           0         NaN    0.00     0.0500   \n",
 78 |       "98013      ZUARI INDUSTRIES           0         NaN    0.00     0.0500   \n",
 79 |       "98014    ZYDUS LIFESCIENCES           0         NaN    0.00     0.0500   \n",
 80 |       "98015        ZYDUS WELLNESS           0         NaN    0.00     0.0500   \n",
 81 |       "\n",
 82 |       "       lot_size instrument_type  segment exchange  \n",
 83 |       "0             1              CE  BCD-OPT      BCD  \n",
 84 |       "1             1              PE  BCD-OPT      BCD  \n",
 85 |       "2             1              CE  BCD-OPT      BCD  \n",
 86 |       "3             1              PE  BCD-OPT      BCD  \n",
 87 |       "4             1              CE  BCD-OPT      BCD  \n",
 88 |       "...         ...             ...      ...      ...  \n",
 89 |       "98011         1              EQ      NSE      NSE  \n",
 90 |       "98012         1              EQ      NSE      NSE  \n",
 91 |       "98013         1              EQ      NSE      NSE  \n",
 92 |       "98014         1              EQ      NSE      NSE  \n",
 93 |       "98015         1              EQ      NSE      NSE  \n",
 94 |       "\n",
 95 |       "[98016 rows x 12 columns]\n"
 96 |      ]
 97 |     }
 98 |    ],
 99 |    "source": [
100 |     "import pandas as pd\n",
101 |     "\n",
102 |     "instrumentList = pd.read_csv(\"https://api.kite.trade/instruments\")\n",
103 |     "print(instrumentList)"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": 3,
109 |    "id": "219b9be3",
110 |    "metadata": {},
111 |    "outputs": [
112 |     {
113 |      "name": "stdout",
114 |      "output_type": "stream",
115 |      "text": [
116 |       "       instrument_token  exchange_token tradingsymbol                 name  \\\n",
117 |       "97272            738561            2885      RELIANCE  RELIANCE INDUSTRIES   \n",
118 |       "\n",
119 |       "       last_price expiry  strike  tick_size  lot_size instrument_type segment  \\\n",
120 |       "97272           0    NaN     0.0       0.05         1              EQ     NSE   \n",
121 |       "\n",
122 |       "      exchange  \n",
123 |       "97272      NSE  \n"
124 |      ]
125 |     }
126 |    ],
127 |    "source": [
128 |     "tradesymbol = \"RELIANCE\"\n",
129 |     "exchange = \"NSE\"\n",
130 |     "dataToken = instrumentList[(instrumentList['tradingsymbol'] == tradesymbol)&(instrumentList['exchange']==exchange)]\n",
131 |     "print(dataToken)"
132 |    ]
133 |   },
134 |   {
135 |    "cell_type": "code",
136 |    "execution_count": 5,
137 |    "id": "8c813dd5",
138 |    "metadata": {},
139 |    "outputs": [],
140 |    "source": [
141 |     "def get_insToken(tradesymbol,exchange=\"NFO\"):\n",
142 |     "    if(exchange==\"NSE\"):\n",
143 |     "        if(tradesymbol==\"NIFTY\"):tradesymbol=\"NIFTY 50\"\n",
144 |     "        if(tradesymbol==\"BANKNIFTY\"):tradesymbol=\"NIFTY BANK\"\n",
145 |     "        if(tradesymbol==\"FINNIFTY\"):tradesymbol=\"NIFTY FIN SERVICE\"\n",
146 |     "    dataToken = instrumentList[(instrumentList['tradingsymbol'] == tradesymbol)&(instrumentList['exchange']==exchange)]\n",
147 |     "    return dataToken.instrument_token.iloc[0]"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": 6,
153 |    "id": "df9ec918",
154 |    "metadata": {},
155 |    "outputs": [
156 |     {
157 |      "name": "stdout",
158 |      "output_type": "stream",
159 |      "text": [
160 |       "738561\n"
161 |      ]
162 |     }
163 |    ],
164 |    "source": [
165 |     "symbol = \"RELIANCE\"\n",
166 |     "token = get_insToken(symbol,\"NSE\")\n",
167 |     "print(token)"
168 |    ]
169 |   },
170 |   {
171 |    "cell_type": "code",
172 |    "execution_count": null,
173 |    "id": "036e1aa7",
174 |    "metadata": {},
175 |    "outputs": [],
176 |    "source": []
177 |   }
178 |  ],
179 |  "metadata": {
180 |   "kernelspec": {
181 |    "display_name": "Python 3 (ipykernel)",
182 |    "language": "python",
183 |    "name": "python3"
184 |   },
185 |   "language_info": {
186 |    "codemirror_mode": {
187 |     "name": "ipython",
188 |     "version": 3
189 |    },
190 |    "file_extension": ".py",
191 |    "mimetype": "text/x-python",
192 |    "name": "python",
193 |    "nbconvert_exporter": "python",
194 |    "pygments_lexer": "ipython3",
195 |    "version": "3.10.12"
196 |   }
197 |  },
198 |  "nbformat": 4,
199 |  "nbformat_minor": 5
200 | }
201 | 


--------------------------------------------------------------------------------
/1.2 - Getting Instrument Token of a Scrip Using Python and Zerodha Kite LTP Method.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "id": "1a6b80c8",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "# https://unofficed.com/courses/mastering-algotrading-a-beginners-guide-using-kiteconnect-api/lessons/getting-instrument-token-zerodha-kite-ltp-method/"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "id": "2d47c7e8",
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "name": "stderr",
 21 |      "output_type": "stream",
 22 |      "text": [
 23 |       "/tmp/ipykernel_4126731/1105643221.py:8: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
 24 |       "  from kite.kitecustom import *\n"
 25 |      ]
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "import logging\n",
 30 |     "from kiteconnect import KiteConnect\n",
 31 |     "\n",
 32 |     "logging.basicConfig(level=logging.DEBUG)\n",
 33 |     "\n",
 34 |     "kite = KiteConnect(api_key=\"your_api_key\")\n",
 35 |     "\n",
 36 |     "# Redirect the user to the login url obtained\n",
 37 |     "# from kite.login_url(), and receive the request_token\n",
 38 |     "# from the registered redirect url after the login flow.\n",
 39 |     "# Once you have the request_token, obtain the access_token\n",
 40 |     "# as follows.\n",
 41 |     "\n",
 42 |     "data = kite.generate_session(\"request_token_here\", api_secret=\"your_secret\")\n",
 43 |     "kite.set_access_token(data[\"access_token\"])"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": 3,
 49 |    "id": "cb04de4d",
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "data": {
 54 |       "text/plain": [
 55 |        "{'NSE:RELIANCE': {'instrument_token': 738561, 'last_price': 2397.65}}"
 56 |       ]
 57 |      },
 58 |      "execution_count": 3,
 59 |      "metadata": {},
 60 |      "output_type": "execute_result"
 61 |     }
 62 |    ],
 63 |    "source": [
 64 |     "# Use Kite Connect's search instruments API to find RELIANCE's instrument token\n",
 65 |     "instruments = kite.ltp(['NSE:RELIANCE'])\n",
 66 |     "instruments"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 4,
 72 |    "id": "110282f3",
 73 |    "metadata": {},
 74 |    "outputs": [
 75 |     {
 76 |      "name": "stdout",
 77 |      "output_type": "stream",
 78 |      "text": [
 79 |       "738561\n"
 80 |      ]
 81 |     }
 82 |    ],
 83 |    "source": [
 84 |     "# Check if the instrument token for RELIANCE is in the instruments dictionary\n",
 85 |     "if 'NSE:RELIANCE' in instruments:\n",
 86 |     "    instrument_token = instruments['NSE:RELIANCE']['instrument_token']\n",
 87 |     "    print(instrument_token)"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": null,
 93 |    "id": "fd00ee05",
 94 |    "metadata": {},
 95 |    "outputs": [],
 96 |    "source": []
 97 |   }
 98 |  ],
 99 |  "metadata": {
100 |   "kernelspec": {
101 |    "display_name": "Python 3 (ipykernel)",
102 |    "language": "python",
103 |    "name": "python3"
104 |   },
105 |   "language_info": {
106 |    "codemirror_mode": {
107 |     "name": "ipython",
108 |     "version": 3
109 |    },
110 |    "file_extension": ".py",
111 |    "mimetype": "text/x-python",
112 |    "name": "python",
113 |    "nbconvert_exporter": "python",
114 |    "pygments_lexer": "ipython3",
115 |    "version": "3.10.12"
116 |   }
117 |  },
118 |  "nbformat": 4,
119 |  "nbformat_minor": 5
120 | }
121 | 


--------------------------------------------------------------------------------
/1.3 - Getting Historical Data of Reliance​ in JSON Using Python and Zerodha API.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": null,
   6 |    "id": "278cf859",
   7 |    "metadata": {},
   8 |    "outputs": [],
   9 |    "source": [
  10 |     "# https://unofficed.com/courses/mastering-algotrading-a-beginners-guide-using-kiteconnect-api/lessons/getting-historical-data-of-reliance-in-json-using-python-and-zerodha-api/"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 1,
  16 |    "id": "a0737d28",
  17 |    "metadata": {},
  18 |    "outputs": [
  19 |     {
  20 |      "name": "stderr",
  21 |      "output_type": "stream",
  22 |      "text": [
  23 |       "/tmp/ipykernel_4126809/1105643221.py:8: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
  24 |       "  from kite.kitecustom import *\n"
  25 |      ]
  26 |     }
  27 |    ],
  28 |    "source": [
  29 |     "import logging\n",
  30 |     "from kiteconnect import KiteConnect\n",
  31 |     "\n",
  32 |     "logging.basicConfig(level=logging.DEBUG)\n",
  33 |     "\n",
  34 |     "kite = KiteConnect(api_key=\"your_api_key\")\n",
  35 |     "\n",
  36 |     "# Redirect the user to the login url obtained\n",
  37 |     "# from kite.login_url(), and receive the request_token\n",
  38 |     "# from the registered redirect url after the login flow.\n",
  39 |     "# Once you have the request_token, obtain the access_token\n",
  40 |     "# as follows.\n",
  41 |     "\n",
  42 |     "data = kite.generate_session(\"request_token_here\", api_secret=\"your_secret\")\n",
  43 |     "kite.set_access_token(data[\"access_token\"])"
  44 |    ]
  45 |   },
  46 |   {
  47 |    "cell_type": "code",
  48 |    "execution_count": 3,
  49 |    "id": "5d81a004",
  50 |    "metadata": {},
  51 |    "outputs": [],
  52 |    "source": [
  53 |     "instrument_token = 738561"
  54 |    ]
  55 |   },
  56 |   {
  57 |    "cell_type": "code",
  58 |    "execution_count": 4,
  59 |    "id": "35116fa4",
  60 |    "metadata": {},
  61 |    "outputs": [
  62 |     {
  63 |      "ename": "InputException",
  64 |      "evalue": "interval exceeds max limit: 60 days",
  65 |      "output_type": "error",
  66 |      "traceback": [
  67 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  68 |       "\u001b[0;31mInputException\u001b[0m                            Traceback (most recent call last)",
  69 |       "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Fetch historical data for RELIANCE\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m historical_data \u001b[38;5;241m=\u001b[39m \u001b[43mkite\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistorical_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43minstrument_token\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2023-01-01\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m2023-09-01\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mminute\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m historical_data\n",
  70 |       "File \u001b[0;32m~/apps/zerodha/../kiteconnect/connect.py:503\u001b[0m, in \u001b[0;36mKiteConnect.historical_data\u001b[0;34m(self, instrument_token, from_date, to_date, interval, continuous, oi)\u001b[0m\n\u001b[1;32m    500\u001b[0m from_date_string \u001b[38;5;241m=\u001b[39m from_date\u001b[38;5;241m.\u001b[39mstrftime(date_string_format) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(from_date) \u001b[38;5;241m==\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime \u001b[38;5;28;01melse\u001b[39;00m from_date\n\u001b[1;32m    501\u001b[0m to_date_string \u001b[38;5;241m=\u001b[39m to_date\u001b[38;5;241m.\u001b[39mstrftime(date_string_format) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(to_date) \u001b[38;5;241m==\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime \u001b[38;5;28;01melse\u001b[39;00m to_date\n\u001b[0;32m--> 503\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmarket.historical\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minstrument_token\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minstrument_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    505\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrom\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_date_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    506\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_date_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minterval\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontinuous\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcontinuous\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moi\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\n\u001b[1;32m    510\u001b[0m \u001b[43m\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_historical(data, oi)\n",
  71 |       "File \u001b[0;32m~/apps/zerodha/../kiteconnect/connect.py:601\u001b[0m, in \u001b[0;36mKiteConnect._get\u001b[0;34m(self, route, params)\u001b[0m\n\u001b[1;32m    599\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get\u001b[39m(\u001b[38;5;28mself\u001b[39m, route, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    600\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Alias for sending a GET request.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 601\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroute\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n",
  72 |       "File \u001b[0;32m~/apps/zerodha/../kiteconnect/connect.py:671\u001b[0m, in \u001b[0;36mKiteConnect._request\u001b[0;34m(self, route, method, parameters)\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[38;5;66;03m# native Kite errors\u001b[39;00m\n\u001b[1;32m    670\u001b[0m         exp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ex, data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merror_type\u001b[39m\u001b[38;5;124m\"\u001b[39m], ex\u001b[38;5;241m.\u001b[39mGeneralException)\n\u001b[0;32m--> 671\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exp(data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m\"\u001b[39m], code\u001b[38;5;241m=\u001b[39mr\u001b[38;5;241m.\u001b[39mstatus_code)\n\u001b[1;32m    673\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    674\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsv\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m r\u001b[38;5;241m.\u001b[39mheaders[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
  73 |       "\u001b[0;31mInputException\u001b[0m: interval exceeds max limit: 60 days"
  74 |      ]
  75 |     }
  76 |    ],
  77 |    "source": [
  78 |     "# Fetch historical data for RELIANCE\n",
  79 |     "historical_data = kite.historical_data(instrument_token, '2023-01-01', '2023-09-01', interval='minute')\n",
  80 |     "historical_data"
  81 |    ]
  82 |   },
  83 |   {
  84 |    "cell_type": "code",
  85 |    "execution_count": 5,
  86 |    "id": "0368b7c7",
  87 |    "metadata": {},
  88 |    "outputs": [
  89 |     {
  90 |      "data": {
  91 |       "text/plain": [
  92 |        "[{'date': datetime.datetime(2023, 1, 2, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
  93 |        "  'open': 2430.65,\n",
  94 |        "  'high': 2458.3,\n",
  95 |        "  'low': 2428.95,\n",
  96 |        "  'close': 2455.35,\n",
  97 |        "  'volume': 2573872},\n",
  98 |        " {'date': datetime.datetime(2023, 1, 3, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
  99 |        "  'open': 2445,\n",
 100 |        "  'high': 2452.6,\n",
 101 |        "  'low': 2428.55,\n",
 102 |        "  'close': 2437.4,\n",
 103 |        "  'volume': 3708138},\n",
 104 |        " {'date': datetime.datetime(2023, 1, 4, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 105 |        "  'open': 2437.35,\n",
 106 |        "  'high': 2441.2,\n",
 107 |        "  'low': 2396.35,\n",
 108 |        "  'close': 2400.7,\n",
 109 |        "  'volume': 4485677},\n",
 110 |        " {'date': datetime.datetime(2023, 1, 5, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 111 |        "  'open': 2405.4,\n",
 112 |        "  'high': 2417.7,\n",
 113 |        "  'low': 2386.8,\n",
 114 |        "  'close': 2396.4,\n",
 115 |        "  'volume': 6602518},\n",
 116 |        " {'date': datetime.datetime(2023, 1, 6, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 117 |        "  'open': 2408.4,\n",
 118 |        "  'high': 2428.7,\n",
 119 |        "  'low': 2400.45,\n",
 120 |        "  'close': 2418.15,\n",
 121 |        "  'volume': 3074212},\n",
 122 |        " {'date': datetime.datetime(2023, 1, 9, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 123 |        "  'open': 2426.2,\n",
 124 |        "  'high': 2480.25,\n",
 125 |        "  'low': 2424.3,\n",
 126 |        "  'close': 2475.25,\n",
 127 |        "  'volume': 5191513},\n",
 128 |        " {'date': datetime.datetime(2023, 1, 10, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 129 |        "  'open': 2484.05,\n",
 130 |        "  'high': 2484.05,\n",
 131 |        "  'low': 2426.85,\n",
 132 |        "  'close': 2438.65,\n",
 133 |        "  'volume': 3896093},\n",
 134 |        " {'date': datetime.datetime(2023, 1, 11, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 135 |        "  'open': 2433.05,\n",
 136 |        "  'high': 2438.3,\n",
 137 |        "  'low': 2403.1,\n",
 138 |        "  'close': 2407.95,\n",
 139 |        "  'volume': 5572449},\n",
 140 |        " {'date': datetime.datetime(2023, 1, 12, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 141 |        "  'open': 2406.7,\n",
 142 |        "  'high': 2414,\n",
 143 |        "  'low': 2349.65,\n",
 144 |        "  'close': 2355.95,\n",
 145 |        "  'volume': 8564171},\n",
 146 |        " {'date': datetime.datetime(2023, 1, 13, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 147 |        "  'open': 2343.35,\n",
 148 |        "  'high': 2357.15,\n",
 149 |        "  'low': 2320.65,\n",
 150 |        "  'close': 2352.1,\n",
 151 |        "  'volume': 9982664},\n",
 152 |        " {'date': datetime.datetime(2023, 1, 16, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 153 |        "  'open': 2357,\n",
 154 |        "  'high': 2363.6,\n",
 155 |        "  'low': 2313.4,\n",
 156 |        "  'close': 2329.7,\n",
 157 |        "  'volume': 6596106},\n",
 158 |        " {'date': datetime.datetime(2023, 1, 17, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 159 |        "  'open': 2342.95,\n",
 160 |        "  'high': 2366.8,\n",
 161 |        "  'low': 2335.9,\n",
 162 |        "  'close': 2362.8,\n",
 163 |        "  'volume': 5205189},\n",
 164 |        " {'date': datetime.datetime(2023, 1, 18, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 165 |        "  'open': 2357.75,\n",
 166 |        "  'high': 2374.5,\n",
 167 |        "  'low': 2345.2,\n",
 168 |        "  'close': 2358.9,\n",
 169 |        "  'volume': 6511103},\n",
 170 |        " {'date': datetime.datetime(2023, 1, 19, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 171 |        "  'open': 2357.15,\n",
 172 |        "  'high': 2365.05,\n",
 173 |        "  'low': 2341.7,\n",
 174 |        "  'close': 2356.35,\n",
 175 |        "  'volume': 5780879},\n",
 176 |        " {'date': datetime.datetime(2023, 1, 20, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 177 |        "  'open': 2359.15,\n",
 178 |        "  'high': 2359.15,\n",
 179 |        "  'low': 2323.2,\n",
 180 |        "  'close': 2328.35,\n",
 181 |        "  'volume': 7228626},\n",
 182 |        " {'date': datetime.datetime(2023, 1, 23, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 183 |        "  'open': 2334.4,\n",
 184 |        "  'high': 2350.8,\n",
 185 |        "  'low': 2311.5,\n",
 186 |        "  'close': 2316.55,\n",
 187 |        "  'volume': 5303530},\n",
 188 |        " {'date': datetime.datetime(2023, 1, 24, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 189 |        "  'open': 2325.8,\n",
 190 |        "  'high': 2329.3,\n",
 191 |        "  'low': 2275.6,\n",
 192 |        "  'close': 2302.9,\n",
 193 |        "  'volume': 7983172},\n",
 194 |        " {'date': datetime.datetime(2023, 1, 25, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 195 |        "  'open': 2299.55,\n",
 196 |        "  'high': 2301.7,\n",
 197 |        "  'low': 2268.6,\n",
 198 |        "  'close': 2271.05,\n",
 199 |        "  'volume': 5996313},\n",
 200 |        " {'date': datetime.datetime(2023, 1, 27, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 201 |        "  'open': 2272.8,\n",
 202 |        "  'high': 2275.6,\n",
 203 |        "  'low': 2203.45,\n",
 204 |        "  'close': 2227.95,\n",
 205 |        "  'volume': 12506288},\n",
 206 |        " {'date': datetime.datetime(2023, 1, 30, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 207 |        "  'open': 2222.6,\n",
 208 |        "  'high': 2260.9,\n",
 209 |        "  'low': 2193.3,\n",
 210 |        "  'close': 2249.3,\n",
 211 |        "  'volume': 10541150},\n",
 212 |        " {'date': datetime.datetime(2023, 1, 31, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 213 |        "  'open': 2270.5,\n",
 214 |        "  'high': 2275.9,\n",
 215 |        "  'low': 2231.45,\n",
 216 |        "  'close': 2243.7,\n",
 217 |        "  'volume': 10986329},\n",
 218 |        " {'date': datetime.datetime(2023, 2, 1, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 219 |        "  'open': 2268.55,\n",
 220 |        "  'high': 2268.55,\n",
 221 |        "  'low': 2197.15,\n",
 222 |        "  'close': 2230.4,\n",
 223 |        "  'volume': 9367403},\n",
 224 |        " {'date': datetime.datetime(2023, 2, 2, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 225 |        "  'open': 2209.5,\n",
 226 |        "  'high': 2238.1,\n",
 227 |        "  'low': 2202.85,\n",
 228 |        "  'close': 2218.05,\n",
 229 |        "  'volume': 6614895},\n",
 230 |        " {'date': datetime.datetime(2023, 2, 3, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 231 |        "  'open': 2239.05,\n",
 232 |        "  'high': 2239.05,\n",
 233 |        "  'low': 2185.7,\n",
 234 |        "  'close': 2220,\n",
 235 |        "  'volume': 11958511},\n",
 236 |        " {'date': datetime.datetime(2023, 2, 6, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 237 |        "  'open': 2206.65,\n",
 238 |        "  'high': 2212.4,\n",
 239 |        "  'low': 2197.9,\n",
 240 |        "  'close': 2203.25,\n",
 241 |        "  'volume': 7183886},\n",
 242 |        " {'date': datetime.datetime(2023, 2, 7, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 243 |        "  'open': 2203.8,\n",
 244 |        "  'high': 2218.5,\n",
 245 |        "  'low': 2185.7,\n",
 246 |        "  'close': 2198,\n",
 247 |        "  'volume': 7327951},\n",
 248 |        " {'date': datetime.datetime(2023, 2, 8, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 249 |        "  'open': 2204.75,\n",
 250 |        "  'high': 2249.45,\n",
 251 |        "  'low': 2199.05,\n",
 252 |        "  'close': 2241.9,\n",
 253 |        "  'volume': 10244956},\n",
 254 |        " {'date': datetime.datetime(2023, 2, 9, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 255 |        "  'open': 2243.3,\n",
 256 |        "  'high': 2259.9,\n",
 257 |        "  'low': 2224.75,\n",
 258 |        "  'close': 2245.8,\n",
 259 |        "  'volume': 6377706},\n",
 260 |        " {'date': datetime.datetime(2023, 2, 10, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 261 |        "  'open': 2244.7,\n",
 262 |        "  'high': 2244.7,\n",
 263 |        "  'low': 2212.65,\n",
 264 |        "  'close': 2227.3,\n",
 265 |        "  'volume': 5224287},\n",
 266 |        " {'date': datetime.datetime(2023, 2, 13, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 267 |        "  'open': 2230.7,\n",
 268 |        "  'high': 2240,\n",
 269 |        "  'low': 2205.3,\n",
 270 |        "  'close': 2214.6,\n",
 271 |        "  'volume': 5012248},\n",
 272 |        " {'date': datetime.datetime(2023, 2, 14, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 273 |        "  'open': 2220.9,\n",
 274 |        "  'high': 2270.45,\n",
 275 |        "  'low': 2214.9,\n",
 276 |        "  'close': 2266.8,\n",
 277 |        "  'volume': 6934080},\n",
 278 |        " {'date': datetime.datetime(2023, 2, 15, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
 279 |        "  'open': 2264.8,\n",
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1065 |        "  'open': 2452.05,\n",
1066 |        "  'high': 2453.45,\n",
1067 |        "  'low': 2408.15,\n",
1068 |        "  'close': 2420.35,\n",
1069 |        "  'volume': 8438322},\n",
1070 |        " {'date': datetime.datetime(2023, 8, 30, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
1071 |        "  'open': 2432,\n",
1072 |        "  'high': 2443,\n",
1073 |        "  'low': 2415,\n",
1074 |        "  'close': 2418.05,\n",
1075 |        "  'volume': 6084473},\n",
1076 |        " {'date': datetime.datetime(2023, 8, 31, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
1077 |        "  'open': 2423,\n",
1078 |        "  'high': 2425,\n",
1079 |        "  'low': 2399.9,\n",
1080 |        "  'close': 2407,\n",
1081 |        "  'volume': 10819300},\n",
1082 |        " {'date': datetime.datetime(2023, 9, 1, 0, 0, tzinfo=tzoffset(None, 19800)),\n",
1083 |        "  'open': 2406.55,\n",
1084 |        "  'high': 2425.65,\n",
1085 |        "  'low': 2401.65,\n",
1086 |        "  'close': 2412.65,\n",
1087 |        "  'volume': 9056421}]"
1088 |       ]
1089 |      },
1090 |      "execution_count": 5,
1091 |      "metadata": {},
1092 |      "output_type": "execute_result"
1093 |     }
1094 |    ],
1095 |    "source": [
1096 |     "# Fetch historical data for RELIANCE\n",
1097 |     "historical_data = kite.historical_data(instrument_token, '2023-01-01', '2023-09-01', interval='day')\n",
1098 |     "historical_data"
1099 |    ]
1100 |   },
1101 |   {
1102 |    "cell_type": "code",
1103 |    "execution_count": null,
1104 |    "id": "eaf97a37",
1105 |    "metadata": {},
1106 |    "outputs": [],
1107 |    "source": []
1108 |   }
1109 |  ],
1110 |  "metadata": {
1111 |   "kernelspec": {
1112 |    "display_name": "Python 3 (ipykernel)",
1113 |    "language": "python",
1114 |    "name": "python3"
1115 |   },
1116 |   "language_info": {
1117 |    "codemirror_mode": {
1118 |     "name": "ipython",
1119 |     "version": 3
1120 |    },
1121 |    "file_extension": ".py",
1122 |    "mimetype": "text/x-python",
1123 |    "name": "python",
1124 |    "nbconvert_exporter": "python",
1125 |    "pygments_lexer": "ipython3",
1126 |    "version": "3.10.12"
1127 |   }
1128 |  },
1129 |  "nbformat": 4,
1130 |  "nbformat_minor": 5
1131 | }
1132 | 


--------------------------------------------------------------------------------
/1.4 - Getting Historical Data of Reliance​ In Pandas Using Python and Zerodha API.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "id": "80cf8822",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "# https://unofficed.com/courses/mastering-algotrading-a-beginners-guide-using-kiteconnect-api/lessons/getting-historical-data-of-reliance-stocks-using-python-and-zerodha-api/"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "id": "1d446c7b",
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "name": "stderr",
 21 |      "output_type": "stream",
 22 |      "text": [
 23 |       "/tmp/ipykernel_4154679/3058104947.py:7: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
 24 |       "  from kite.kitecustom import *\n"
 25 |      ]
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "import logging\n",
 30 |     "from kiteconnect import KiteConnect\n",
 31 |     "\n",
 32 |     "logging.basicConfig(level=logging.DEBUG)\n",
 33 |     "\n",
 34 |     "kite = KiteConnect(api_key=\"your_api_key\")\n",
 35 |     "\n",
 36 |     "# Redirect the user to the login url obtained\n",
 37 |     "# from kite.login_url(), and receive the request_token\n",
 38 |     "# from the registered redirect url after the login flow.\n",
 39 |     "# Once you have the request_token, obtain the access_token\n",
 40 |     "# as follows.\n",
 41 |     "\n",
 42 |     "data = kite.generate_session(\"request_token_here\", api_secret=\"your_secret\")\n",
 43 |     "kite.set_access_token(data[\"access_token\"])"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": 2,
 49 |    "id": "58dbcb1d",
 50 |    "metadata": {},
 51 |    "outputs": [],
 52 |    "source": [
 53 |     "token = 738561"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": 3,
 59 |    "id": "3a7cc37a",
 60 |    "metadata": {},
 61 |    "outputs": [],
 62 |    "source": [
 63 |     "import datetime\n",
 64 |     "starting_date=datetime.datetime(2019, 1, 1)\n",
 65 |     "ending_date=datetime.datetime.today()"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "code",
 70 |    "execution_count": 4,
 71 |    "id": "eb26ad4c",
 72 |    "metadata": {},
 73 |    "outputs": [
 74 |     {
 75 |      "name": "stdout",
 76 |      "output_type": "stream",
 77 |      "text": [
 78 |       "                       date     open     high      low    close   volume\n",
 79 |       "0 2019-01-01 00:00:00+05:30  1072.60  1074.55  1058.15  1068.55  4674623\n",
 80 |       "1 2019-01-02 00:00:00+05:30  1062.35  1074.25  1049.45  1054.60  7495774\n",
 81 |       "2 2019-01-03 00:00:00+05:30  1055.65  1062.45  1039.05  1041.60  7812063\n",
 82 |       "3 2019-01-04 00:00:00+05:30  1046.05  1052.75  1030.50  1047.25  8880762\n",
 83 |       "4 2019-01-07 00:00:00+05:30  1055.20  1066.10  1049.45  1053.05  5784264\n",
 84 |       "5 2019-01-08 00:00:00+05:30  1053.35  1058.00  1044.70  1052.95  5901337\n",
 85 |       "6 2019-01-09 00:00:00+05:30  1059.95  1064.70  1047.30  1058.75  6049944\n",
 86 |       "7 2019-01-10 00:00:00+05:30  1055.90  1059.00  1051.40  1055.65  4280617\n",
 87 |       "8 2019-01-11 00:00:00+05:30  1055.75  1061.65  1037.65  1046.65  6781268\n",
 88 |       "9 2019-01-14 00:00:00+05:30  1043.75  1049.00  1035.55  1045.45  4313662\n"
 89 |      ]
 90 |     }
 91 |    ],
 92 |    "source": [
 93 |     "zap=kite.historical_data(token,starting_date,ending_date,\"day\")\n",
 94 |     "zap= pd.DataFrame(zap)\n",
 95 |     "print(zap.head(10))"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "code",
100 |    "execution_count": 6,
101 |    "id": "a94ee70f",
102 |    "metadata": {},
103 |    "outputs": [
104 |     {
105 |      "ename": "InputException",
106 |      "evalue": "interval exceeds max limit: 60 days",
107 |      "output_type": "error",
108 |      "traceback": [
109 |       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
110 |       "\u001b[0;31mInputException\u001b[0m                            Traceback (most recent call last)",
111 |       "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m zap\u001b[38;5;241m=\u001b[39m\u001b[43mkite\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhistorical_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\u001b[43mstarting_date\u001b[49m\u001b[43m,\u001b[49m\u001b[43mending_date\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mminute\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m zap\u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(zap)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(zap\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m10\u001b[39m))\n",
112 |       "File \u001b[0;32m~/apps/zerodha/../kiteconnect/connect.py:503\u001b[0m, in \u001b[0;36mKiteConnect.historical_data\u001b[0;34m(self, instrument_token, from_date, to_date, interval, continuous, oi)\u001b[0m\n\u001b[1;32m    500\u001b[0m from_date_string \u001b[38;5;241m=\u001b[39m from_date\u001b[38;5;241m.\u001b[39mstrftime(date_string_format) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(from_date) \u001b[38;5;241m==\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime \u001b[38;5;28;01melse\u001b[39;00m from_date\n\u001b[1;32m    501\u001b[0m to_date_string \u001b[38;5;241m=\u001b[39m to_date\u001b[38;5;241m.\u001b[39mstrftime(date_string_format) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(to_date) \u001b[38;5;241m==\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime \u001b[38;5;28;01melse\u001b[39;00m to_date\n\u001b[0;32m--> 503\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmarket.historical\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    504\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minstrument_token\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minstrument_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    505\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrom\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_date_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    506\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mto_date_string\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    507\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minterval\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontinuous\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcontinuous\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moi\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\n\u001b[1;32m    510\u001b[0m \u001b[43m\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_historical(data, oi)\n",
113 |       "File \u001b[0;32m~/apps/zerodha/../kiteconnect/connect.py:601\u001b[0m, in \u001b[0;36mKiteConnect._get\u001b[0;34m(self, route, params)\u001b[0m\n\u001b[1;32m    599\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get\u001b[39m(\u001b[38;5;28mself\u001b[39m, route, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    600\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Alias for sending a GET request.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 601\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroute\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n",
114 |       "File \u001b[0;32m~/apps/zerodha/../kiteconnect/connect.py:671\u001b[0m, in \u001b[0;36mKiteConnect._request\u001b[0;34m(self, route, method, parameters)\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[38;5;66;03m# native Kite errors\u001b[39;00m\n\u001b[1;32m    670\u001b[0m         exp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(ex, data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124merror_type\u001b[39m\u001b[38;5;124m\"\u001b[39m], ex\u001b[38;5;241m.\u001b[39mGeneralException)\n\u001b[0;32m--> 671\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exp(data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m\"\u001b[39m], code\u001b[38;5;241m=\u001b[39mr\u001b[38;5;241m.\u001b[39mstatus_code)\n\u001b[1;32m    673\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m    674\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsv\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m r\u001b[38;5;241m.\u001b[39mheaders[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
115 |       "\u001b[0;31mInputException\u001b[0m: interval exceeds max limit: 60 days"
116 |      ]
117 |     }
118 |    ],
119 |    "source": [
120 |     "zap=kite.historical_data(token,starting_date,ending_date,\"minute\")\n",
121 |     "zap= pd.DataFrame(zap)\n",
122 |     "print(zap.head(10))"
123 |    ]
124 |   },
125 |   {
126 |    "cell_type": "code",
127 |    "execution_count": 7,
128 |    "id": "127aa37e",
129 |    "metadata": {},
130 |    "outputs": [
131 |     {
132 |      "data": {
133 |       "text/html": [
134 |        "<div>\n",
135 |        "<style scoped>\n",
136 |        "    .dataframe tbody tr th:only-of-type {\n",
137 |        "        vertical-align: middle;\n",
138 |        "    }\n",
139 |        "\n",
140 |        "    .dataframe tbody tr th {\n",
141 |        "        vertical-align: top;\n",
142 |        "    }\n",
143 |        "\n",
144 |        "    .dataframe thead th {\n",
145 |        "        text-align: right;\n",
146 |        "    }\n",
147 |        "</style>\n",
148 |        "<table border=\"1\" class=\"dataframe\">\n",
149 |        "  <thead>\n",
150 |        "    <tr style=\"text-align: right;\">\n",
151 |        "      <th></th>\n",
152 |        "      <th>date</th>\n",
153 |        "      <th>open</th>\n",
154 |        "      <th>high</th>\n",
155 |        "      <th>low</th>\n",
156 |        "      <th>close</th>\n",
157 |        "      <th>volume</th>\n",
158 |        "    </tr>\n",
159 |        "  </thead>\n",
160 |        "  <tbody>\n",
161 |        "    <tr>\n",
162 |        "      <th>0</th>\n",
163 |        "      <td>2019-01-01 09:15:00+05:30</td>\n",
164 |        "      <td>1062.55</td>\n",
165 |        "      <td>1063.90</td>\n",
166 |        "      <td>1060.25</td>\n",
167 |        "      <td>1061.65</td>\n",
168 |        "      <td>53427</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>1</th>\n",
172 |        "      <td>2019-01-01 09:16:00+05:30</td>\n",
173 |        "      <td>1062.25</td>\n",
174 |        "      <td>1063.85</td>\n",
175 |        "      <td>1061.65</td>\n",
176 |        "      <td>1063.40</td>\n",
177 |        "      <td>18209</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>2</th>\n",
181 |        "      <td>2019-01-01 09:17:00+05:30</td>\n",
182 |        "      <td>1063.40</td>\n",
183 |        "      <td>1063.90</td>\n",
184 |        "      <td>1063.25</td>\n",
185 |        "      <td>1063.40</td>\n",
186 |        "      <td>42785</td>\n",
187 |        "    </tr>\n",
188 |        "    <tr>\n",
189 |        "      <th>3</th>\n",
190 |        "      <td>2019-01-01 09:18:00+05:30</td>\n",
191 |        "      <td>1063.40</td>\n",
192 |        "      <td>1063.50</td>\n",
193 |        "      <td>1062.30</td>\n",
194 |        "      <td>1062.60</td>\n",
195 |        "      <td>40647</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>4</th>\n",
199 |        "      <td>2019-01-01 09:19:00+05:30</td>\n",
200 |        "      <td>1062.30</td>\n",
201 |        "      <td>1062.30</td>\n",
202 |        "      <td>1059.50</td>\n",
203 |        "      <td>1059.50</td>\n",
204 |        "      <td>44316</td>\n",
205 |        "    </tr>\n",
206 |        "    <tr>\n",
207 |        "      <th>...</th>\n",
208 |        "      <td>...</td>\n",
209 |        "      <td>...</td>\n",
210 |        "      <td>...</td>\n",
211 |        "      <td>...</td>\n",
212 |        "      <td>...</td>\n",
213 |        "      <td>...</td>\n",
214 |        "    </tr>\n",
215 |        "    <tr>\n",
216 |        "      <th>454063</th>\n",
217 |        "      <td>2023-11-24 15:25:00+05:30</td>\n",
218 |        "      <td>2395.95</td>\n",
219 |        "      <td>2396.75</td>\n",
220 |        "      <td>2395.00</td>\n",
221 |        "      <td>2395.65</td>\n",
222 |        "      <td>18228</td>\n",
223 |        "    </tr>\n",
224 |        "    <tr>\n",
225 |        "      <th>454064</th>\n",
226 |        "      <td>2023-11-24 15:26:00+05:30</td>\n",
227 |        "      <td>2395.55</td>\n",
228 |        "      <td>2396.00</td>\n",
229 |        "      <td>2394.85</td>\n",
230 |        "      <td>2394.85</td>\n",
231 |        "      <td>12166</td>\n",
232 |        "    </tr>\n",
233 |        "    <tr>\n",
234 |        "      <th>454065</th>\n",
235 |        "      <td>2023-11-24 15:27:00+05:30</td>\n",
236 |        "      <td>2395.00</td>\n",
237 |        "      <td>2395.30</td>\n",
238 |        "      <td>2394.50</td>\n",
239 |        "      <td>2394.80</td>\n",
240 |        "      <td>17623</td>\n",
241 |        "    </tr>\n",
242 |        "    <tr>\n",
243 |        "      <th>454066</th>\n",
244 |        "      <td>2023-11-24 15:28:00+05:30</td>\n",
245 |        "      <td>2394.55</td>\n",
246 |        "      <td>2394.80</td>\n",
247 |        "      <td>2394.00</td>\n",
248 |        "      <td>2394.00</td>\n",
249 |        "      <td>19879</td>\n",
250 |        "    </tr>\n",
251 |        "    <tr>\n",
252 |        "      <th>454067</th>\n",
253 |        "      <td>2023-11-24 15:29:00+05:30</td>\n",
254 |        "      <td>2394.40</td>\n",
255 |        "      <td>2394.90</td>\n",
256 |        "      <td>2394.00</td>\n",
257 |        "      <td>2394.25</td>\n",
258 |        "      <td>9111</td>\n",
259 |        "    </tr>\n",
260 |        "  </tbody>\n",
261 |        "</table>\n",
262 |        "<p>454068 rows × 6 columns</p>\n",
263 |        "</div>"
264 |       ],
265 |       "text/plain": [
266 |        "                            date     open     high      low    close  volume\n",
267 |        "0      2019-01-01 09:15:00+05:30  1062.55  1063.90  1060.25  1061.65   53427\n",
268 |        "1      2019-01-01 09:16:00+05:30  1062.25  1063.85  1061.65  1063.40   18209\n",
269 |        "2      2019-01-01 09:17:00+05:30  1063.40  1063.90  1063.25  1063.40   42785\n",
270 |        "3      2019-01-01 09:18:00+05:30  1063.40  1063.50  1062.30  1062.60   40647\n",
271 |        "4      2019-01-01 09:19:00+05:30  1062.30  1062.30  1059.50  1059.50   44316\n",
272 |        "...                          ...      ...      ...      ...      ...     ...\n",
273 |        "454063 2023-11-24 15:25:00+05:30  2395.95  2396.75  2395.00  2395.65   18228\n",
274 |        "454064 2023-11-24 15:26:00+05:30  2395.55  2396.00  2394.85  2394.85   12166\n",
275 |        "454065 2023-11-24 15:27:00+05:30  2395.00  2395.30  2394.50  2394.80   17623\n",
276 |        "454066 2023-11-24 15:28:00+05:30  2394.55  2394.80  2394.00  2394.00   19879\n",
277 |        "454067 2023-11-24 15:29:00+05:30  2394.40  2394.90  2394.00  2394.25    9111\n",
278 |        "\n",
279 |        "[454068 rows x 6 columns]"
280 |       ]
281 |      },
282 |      "execution_count": 7,
283 |      "metadata": {},
284 |      "output_type": "execute_result"
285 |     }
286 |    ],
287 |    "source": [
288 |     "def fetch_historical_data(kite, token, start_date, end_date, interval):\n",
289 |     "    # Initialize an empty DataFrame to store the historical data\n",
290 |     "    full_data = pd.DataFrame()\n",
291 |     "\n",
292 |     "    # Split the date range into 60-day intervals\n",
293 |     "    while start_date < end_date:\n",
294 |     "        # Calculate the end date of the 60-day interval\n",
295 |     "        interval_end = start_date + datetime.timedelta(days=60)\n",
296 |     "\n",
297 |     "        # Ensure the interval end does not exceed the overall end date\n",
298 |     "        if interval_end > end_date:\n",
299 |     "            interval_end = end_date\n",
300 |     "\n",
301 |     "        # Fetch historical data for the 60-day interval\n",
302 |     "        data = kite.historical_data(token, start_date, interval_end, interval)\n",
303 |     "        data_df = pd.DataFrame(data)\n",
304 |     "\n",
305 |     "        # Append the fetched data to the full dataset\n",
306 |     "        full_data = full_data.append(data_df, ignore_index=True)\n",
307 |     "\n",
308 |     "        # Update the start date for the next interval\n",
309 |     "        start_date = interval_end\n",
310 |     "\n",
311 |     "    return full_data\n",
312 |     "\n",
313 |     "historical_data = fetch_historical_data(kite, token, starting_date, ending_date, \"minute\")\n",
314 |     "historical_data"
315 |    ]
316 |   },
317 |   {
318 |    "cell_type": "code",
319 |    "execution_count": null,
320 |    "id": "e51b086c",
321 |    "metadata": {},
322 |    "outputs": [],
323 |    "source": []
324 |   }
325 |  ],
326 |  "metadata": {
327 |   "kernelspec": {
328 |    "display_name": "Python 3 (ipykernel)",
329 |    "language": "python",
330 |    "name": "python3"
331 |   },
332 |   "language_info": {
333 |    "codemirror_mode": {
334 |     "name": "ipython",
335 |     "version": 3
336 |    },
337 |    "file_extension": ".py",
338 |    "mimetype": "text/x-python",
339 |    "name": "python",
340 |    "nbconvert_exporter": "python",
341 |    "pygments_lexer": "ipython3",
342 |    "version": "3.10.12"
343 |   }
344 |  },
345 |  "nbformat": 4,
346 |  "nbformat_minor": 5
347 | }
348 | 


--------------------------------------------------------------------------------
/2.4 - Creating TimeFrames from Minute-Based Data Using Python.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "id": "93e36e7e",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "# https://unofficed.com/courses/mastering-algotrading-a-beginners-guide-using-kiteconnect-api/lessons/creating-timeframes-from-minute-based-data-using-python/"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "id": "ba490eb9",
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "name": "stderr",
 21 |      "output_type": "stream",
 22 |      "text": [
 23 |       "/tmp/ipykernel_4148432/1105643221.py:8: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
 24 |       "  from kite.kitecustom import *\n"
 25 |      ]
 26 |     }
 27 |    ],
 28 |    "source": [
 29 |     "import logging\n",
 30 |     "from kiteconnect import KiteConnect\n",
 31 |     "\n",
 32 |     "logging.basicConfig(level=logging.DEBUG)\n",
 33 |     "\n",
 34 |     "kite = KiteConnect(api_key=\"your_api_key\")\n",
 35 |     "\n",
 36 |     "# Redirect the user to the login url obtained\n",
 37 |     "# from kite.login_url(), and receive the request_token\n",
 38 |     "# from the registered redirect url after the login flow.\n",
 39 |     "# Once you have the request_token, obtain the access_token\n",
 40 |     "# as follows.\n",
 41 |     "\n",
 42 |     "data = kite.generate_session(\"request_token_here\", api_secret=\"your_secret\")\n",
 43 |     "kite.set_access_token(data[\"access_token\"])"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": 6,
 49 |    "id": "51e31406",
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "data": {
 54 |       "text/html": [
 55 |        "<div>\n",
 56 |        "<style scoped>\n",
 57 |        "    .dataframe tbody tr th:only-of-type {\n",
 58 |        "        vertical-align: middle;\n",
 59 |        "    }\n",
 60 |        "\n",
 61 |        "    .dataframe tbody tr th {\n",
 62 |        "        vertical-align: top;\n",
 63 |        "    }\n",
 64 |        "\n",
 65 |        "    .dataframe thead th {\n",
 66 |        "        text-align: right;\n",
 67 |        "    }\n",
 68 |        "</style>\n",
 69 |        "<table border=\"1\" class=\"dataframe\">\n",
 70 |        "  <thead>\n",
 71 |        "    <tr style=\"text-align: right;\">\n",
 72 |        "      <th></th>\n",
 73 |        "      <th>date</th>\n",
 74 |        "      <th>open</th>\n",
 75 |        "      <th>high</th>\n",
 76 |        "      <th>low</th>\n",
 77 |        "      <th>close</th>\n",
 78 |        "      <th>volume</th>\n",
 79 |        "    </tr>\n",
 80 |        "  </thead>\n",
 81 |        "  <tbody>\n",
 82 |        "    <tr>\n",
 83 |        "      <th>0</th>\n",
 84 |        "      <td>2023-11-10 09:15:00+05:30</td>\n",
 85 |        "      <td>2305.55</td>\n",
 86 |        "      <td>2308.90</td>\n",
 87 |        "      <td>2303.55</td>\n",
 88 |        "      <td>2305.00</td>\n",
 89 |        "      <td>41248</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <th>1</th>\n",
 93 |        "      <td>2023-11-10 09:16:00+05:30</td>\n",
 94 |        "      <td>2304.95</td>\n",
 95 |        "      <td>2306.25</td>\n",
 96 |        "      <td>2303.00</td>\n",
 97 |        "      <td>2305.70</td>\n",
 98 |        "      <td>28564</td>\n",
 99 |        "    </tr>\n",
100 |        "    <tr>\n",
101 |        "      <th>2</th>\n",
102 |        "      <td>2023-11-10 09:17:00+05:30</td>\n",
103 |        "      <td>2305.05</td>\n",
104 |        "      <td>2308.80</td>\n",
105 |        "      <td>2305.05</td>\n",
106 |        "      <td>2308.60</td>\n",
107 |        "      <td>33568</td>\n",
108 |        "    </tr>\n",
109 |        "    <tr>\n",
110 |        "      <th>3</th>\n",
111 |        "      <td>2023-11-10 09:18:00+05:30</td>\n",
112 |        "      <td>2308.60</td>\n",
113 |        "      <td>2310.45</td>\n",
114 |        "      <td>2308.20</td>\n",
115 |        "      <td>2310.45</td>\n",
116 |        "      <td>39478</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>4</th>\n",
120 |        "      <td>2023-11-10 09:19:00+05:30</td>\n",
121 |        "      <td>2310.45</td>\n",
122 |        "      <td>2311.95</td>\n",
123 |        "      <td>2310.00</td>\n",
124 |        "      <td>2310.10</td>\n",
125 |        "      <td>23513</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>...</th>\n",
129 |        "      <td>...</td>\n",
130 |        "      <td>...</td>\n",
131 |        "      <td>...</td>\n",
132 |        "      <td>...</td>\n",
133 |        "      <td>...</td>\n",
134 |        "      <td>...</td>\n",
135 |        "    </tr>\n",
136 |        "    <tr>\n",
137 |        "      <th>3805</th>\n",
138 |        "      <td>2023-11-24 15:25:00+05:30</td>\n",
139 |        "      <td>2395.95</td>\n",
140 |        "      <td>2396.75</td>\n",
141 |        "      <td>2395.00</td>\n",
142 |        "      <td>2395.65</td>\n",
143 |        "      <td>18228</td>\n",
144 |        "    </tr>\n",
145 |        "    <tr>\n",
146 |        "      <th>3806</th>\n",
147 |        "      <td>2023-11-24 15:26:00+05:30</td>\n",
148 |        "      <td>2395.55</td>\n",
149 |        "      <td>2396.00</td>\n",
150 |        "      <td>2394.85</td>\n",
151 |        "      <td>2394.85</td>\n",
152 |        "      <td>12166</td>\n",
153 |        "    </tr>\n",
154 |        "    <tr>\n",
155 |        "      <th>3807</th>\n",
156 |        "      <td>2023-11-24 15:27:00+05:30</td>\n",
157 |        "      <td>2395.00</td>\n",
158 |        "      <td>2395.30</td>\n",
159 |        "      <td>2394.50</td>\n",
160 |        "      <td>2394.80</td>\n",
161 |        "      <td>17623</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>3808</th>\n",
165 |        "      <td>2023-11-24 15:28:00+05:30</td>\n",
166 |        "      <td>2394.55</td>\n",
167 |        "      <td>2394.80</td>\n",
168 |        "      <td>2394.00</td>\n",
169 |        "      <td>2394.00</td>\n",
170 |        "      <td>19879</td>\n",
171 |        "    </tr>\n",
172 |        "    <tr>\n",
173 |        "      <th>3809</th>\n",
174 |        "      <td>2023-11-24 15:29:00+05:30</td>\n",
175 |        "      <td>2394.40</td>\n",
176 |        "      <td>2394.90</td>\n",
177 |        "      <td>2394.00</td>\n",
178 |        "      <td>2394.25</td>\n",
179 |        "      <td>9111</td>\n",
180 |        "    </tr>\n",
181 |        "  </tbody>\n",
182 |        "</table>\n",
183 |        "<p>3810 rows × 6 columns</p>\n",
184 |        "</div>"
185 |       ],
186 |       "text/plain": [
187 |        "                          date     open     high      low    close  volume\n",
188 |        "0    2023-11-10 09:15:00+05:30  2305.55  2308.90  2303.55  2305.00   41248\n",
189 |        "1    2023-11-10 09:16:00+05:30  2304.95  2306.25  2303.00  2305.70   28564\n",
190 |        "2    2023-11-10 09:17:00+05:30  2305.05  2308.80  2305.05  2308.60   33568\n",
191 |        "3    2023-11-10 09:18:00+05:30  2308.60  2310.45  2308.20  2310.45   39478\n",
192 |        "4    2023-11-10 09:19:00+05:30  2310.45  2311.95  2310.00  2310.10   23513\n",
193 |        "...                        ...      ...      ...      ...      ...     ...\n",
194 |        "3805 2023-11-24 15:25:00+05:30  2395.95  2396.75  2395.00  2395.65   18228\n",
195 |        "3806 2023-11-24 15:26:00+05:30  2395.55  2396.00  2394.85  2394.85   12166\n",
196 |        "3807 2023-11-24 15:27:00+05:30  2395.00  2395.30  2394.50  2394.80   17623\n",
197 |        "3808 2023-11-24 15:28:00+05:30  2394.55  2394.80  2394.00  2394.00   19879\n",
198 |        "3809 2023-11-24 15:29:00+05:30  2394.40  2394.90  2394.00  2394.25    9111\n",
199 |        "\n",
200 |        "[3810 rows x 6 columns]"
201 |       ]
202 |      },
203 |      "execution_count": 6,
204 |      "metadata": {},
205 |      "output_type": "execute_result"
206 |     }
207 |    ],
208 |    "source": [
209 |     "from datetime import date,timedelta\n",
210 |     "starting_date= (date.today() - timedelta(days=15)).strftime(\"%Y-%m-%d\")\n",
211 |     "ending_date=date.today().strftime(\"%Y-%m-%d\")\n",
212 |     "\n",
213 |     "token = \"738561\" #Reliance Token \n",
214 |     "\n",
215 |     "zap=kite.historical_data(token,starting_date,ending_date,\"minute\")\n",
216 |     "zap= pd.DataFrame(zap)\n",
217 |     "zap"
218 |    ]
219 |   },
220 |   {
221 |    "cell_type": "code",
222 |    "execution_count": 8,
223 |    "id": "cade917c",
224 |    "metadata": {},
225 |    "outputs": [
226 |     {
227 |      "data": {
228 |       "text/html": [
229 |        "<div>\n",
230 |        "<style scoped>\n",
231 |        "    .dataframe tbody tr th:only-of-type {\n",
232 |        "        vertical-align: middle;\n",
233 |        "    }\n",
234 |        "\n",
235 |        "    .dataframe tbody tr th {\n",
236 |        "        vertical-align: top;\n",
237 |        "    }\n",
238 |        "\n",
239 |        "    .dataframe thead th {\n",
240 |        "        text-align: right;\n",
241 |        "    }\n",
242 |        "</style>\n",
243 |        "<table border=\"1\" class=\"dataframe\">\n",
244 |        "  <thead>\n",
245 |        "    <tr style=\"text-align: right;\">\n",
246 |        "      <th></th>\n",
247 |        "      <th>date</th>\n",
248 |        "      <th>open</th>\n",
249 |        "      <th>high</th>\n",
250 |        "      <th>low</th>\n",
251 |        "      <th>close</th>\n",
252 |        "      <th>volume</th>\n",
253 |        "    </tr>\n",
254 |        "  </thead>\n",
255 |        "  <tbody>\n",
256 |        "    <tr>\n",
257 |        "      <th>0</th>\n",
258 |        "      <td>2023-11-10 08:00:00+05:30</td>\n",
259 |        "      <td>2305.55</td>\n",
260 |        "      <td>2316.35</td>\n",
261 |        "      <td>2298.90</td>\n",
262 |        "      <td>2301.00</td>\n",
263 |        "      <td>1417820</td>\n",
264 |        "    </tr>\n",
265 |        "    <tr>\n",
266 |        "      <th>1</th>\n",
267 |        "      <td>2023-11-10 10:00:00+05:30</td>\n",
268 |        "      <td>2301.00</td>\n",
269 |        "      <td>2307.35</td>\n",
270 |        "      <td>2298.05</td>\n",
271 |        "      <td>2305.60</td>\n",
272 |        "      <td>645230</td>\n",
273 |        "    </tr>\n",
274 |        "    <tr>\n",
275 |        "      <th>2</th>\n",
276 |        "      <td>2023-11-10 12:00:00+05:30</td>\n",
277 |        "      <td>2305.65</td>\n",
278 |        "      <td>2315.10</td>\n",
279 |        "      <td>2305.10</td>\n",
280 |        "      <td>2309.10</td>\n",
281 |        "      <td>813375</td>\n",
282 |        "    </tr>\n",
283 |        "    <tr>\n",
284 |        "      <th>3</th>\n",
285 |        "      <td>2023-11-10 14:00:00+05:30</td>\n",
286 |        "      <td>2309.15</td>\n",
287 |        "      <td>2316.00</td>\n",
288 |        "      <td>2307.00</td>\n",
289 |        "      <td>2315.90</td>\n",
290 |        "      <td>942613</td>\n",
291 |        "    </tr>\n",
292 |        "    <tr>\n",
293 |        "      <th>4</th>\n",
294 |        "      <td>2023-11-12 18:00:00+05:30</td>\n",
295 |        "      <td>2326.05</td>\n",
296 |        "      <td>2332.00</td>\n",
297 |        "      <td>2322.70</td>\n",
298 |        "      <td>2330.90</td>\n",
299 |        "      <td>685748</td>\n",
300 |        "    </tr>\n",
301 |        "    <tr>\n",
302 |        "      <th>5</th>\n",
303 |        "      <td>2023-11-13 08:00:00+05:30</td>\n",
304 |        "      <td>2322.90</td>\n",
305 |        "      <td>2324.60</td>\n",
306 |        "      <td>2316.20</td>\n",
307 |        "      <td>2317.00</td>\n",
308 |        "      <td>295834</td>\n",
309 |        "    </tr>\n",
310 |        "    <tr>\n",
311 |        "      <th>6</th>\n",
312 |        "      <td>2023-11-13 10:00:00+05:30</td>\n",
313 |        "      <td>2317.05</td>\n",
314 |        "      <td>2318.85</td>\n",
315 |        "      <td>2311.70</td>\n",
316 |        "      <td>2315.50</td>\n",
317 |        "      <td>418459</td>\n",
318 |        "    </tr>\n",
319 |        "    <tr>\n",
320 |        "      <th>7</th>\n",
321 |        "      <td>2023-11-13 12:00:00+05:30</td>\n",
322 |        "      <td>2315.30</td>\n",
323 |        "      <td>2318.00</td>\n",
324 |        "      <td>2312.00</td>\n",
325 |        "      <td>2314.75</td>\n",
326 |        "      <td>367694</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>8</th>\n",
330 |        "      <td>2023-11-13 14:00:00+05:30</td>\n",
331 |        "      <td>2314.95</td>\n",
332 |        "      <td>2316.75</td>\n",
333 |        "      <td>2312.00</td>\n",
334 |        "      <td>2315.10</td>\n",
335 |        "      <td>798828</td>\n",
336 |        "    </tr>\n",
337 |        "    <tr>\n",
338 |        "      <th>9</th>\n",
339 |        "      <td>2023-11-15 08:00:00+05:30</td>\n",
340 |        "      <td>2340.00</td>\n",
341 |        "      <td>2342.60</td>\n",
342 |        "      <td>2327.00</td>\n",
343 |        "      <td>2335.55</td>\n",
344 |        "      <td>1015224</td>\n",
345 |        "    </tr>\n",
346 |        "    <tr>\n",
347 |        "      <th>10</th>\n",
348 |        "      <td>2023-11-15 10:00:00+05:30</td>\n",
349 |        "      <td>2335.55</td>\n",
350 |        "      <td>2348.00</td>\n",
351 |        "      <td>2335.00</td>\n",
352 |        "      <td>2344.10</td>\n",
353 |        "      <td>1867153</td>\n",
354 |        "    </tr>\n",
355 |        "    <tr>\n",
356 |        "      <th>11</th>\n",
357 |        "      <td>2023-11-15 12:00:00+05:30</td>\n",
358 |        "      <td>2344.00</td>\n",
359 |        "      <td>2352.90</td>\n",
360 |        "      <td>2343.85</td>\n",
361 |        "      <td>2346.85</td>\n",
362 |        "      <td>992835</td>\n",
363 |        "    </tr>\n",
364 |        "    <tr>\n",
365 |        "      <th>12</th>\n",
366 |        "      <td>2023-11-15 14:00:00+05:30</td>\n",
367 |        "      <td>2346.95</td>\n",
368 |        "      <td>2361.95</td>\n",
369 |        "      <td>2346.80</td>\n",
370 |        "      <td>2356.10</td>\n",
371 |        "      <td>2003118</td>\n",
372 |        "    </tr>\n",
373 |        "    <tr>\n",
374 |        "      <th>13</th>\n",
375 |        "      <td>2023-11-16 08:00:00+05:30</td>\n",
376 |        "      <td>2351.10</td>\n",
377 |        "      <td>2364.00</td>\n",
378 |        "      <td>2346.95</td>\n",
379 |        "      <td>2355.25</td>\n",
380 |        "      <td>923205</td>\n",
381 |        "    </tr>\n",
382 |        "    <tr>\n",
383 |        "      <th>14</th>\n",
384 |        "      <td>2023-11-16 10:00:00+05:30</td>\n",
385 |        "      <td>2355.00</td>\n",
386 |        "      <td>2369.60</td>\n",
387 |        "      <td>2354.70</td>\n",
388 |        "      <td>2366.45</td>\n",
389 |        "      <td>1049582</td>\n",
390 |        "    </tr>\n",
391 |        "    <tr>\n",
392 |        "      <th>15</th>\n",
393 |        "      <td>2023-11-16 12:00:00+05:30</td>\n",
394 |        "      <td>2366.50</td>\n",
395 |        "      <td>2369.35</td>\n",
396 |        "      <td>2363.80</td>\n",
397 |        "      <td>2367.00</td>\n",
398 |        "      <td>727641</td>\n",
399 |        "    </tr>\n",
400 |        "    <tr>\n",
401 |        "      <th>16</th>\n",
402 |        "      <td>2023-11-16 14:00:00+05:30</td>\n",
403 |        "      <td>2366.75</td>\n",
404 |        "      <td>2374.15</td>\n",
405 |        "      <td>2350.55</td>\n",
406 |        "      <td>2359.00</td>\n",
407 |        "      <td>3758974</td>\n",
408 |        "    </tr>\n",
409 |        "    <tr>\n",
410 |        "      <th>17</th>\n",
411 |        "      <td>2023-11-17 08:00:00+05:30</td>\n",
412 |        "      <td>2352.90</td>\n",
413 |        "      <td>2370.30</td>\n",
414 |        "      <td>2352.05</td>\n",
415 |        "      <td>2369.95</td>\n",
416 |        "      <td>513251</td>\n",
417 |        "    </tr>\n",
418 |        "    <tr>\n",
419 |        "      <th>18</th>\n",
420 |        "      <td>2023-11-17 10:00:00+05:30</td>\n",
421 |        "      <td>2370.05</td>\n",
422 |        "      <td>2373.25</td>\n",
423 |        "      <td>2363.80</td>\n",
424 |        "      <td>2367.00</td>\n",
425 |        "      <td>1017076</td>\n",
426 |        "    </tr>\n",
427 |        "    <tr>\n",
428 |        "      <th>19</th>\n",
429 |        "      <td>2023-11-17 12:00:00+05:30</td>\n",
430 |        "      <td>2366.80</td>\n",
431 |        "      <td>2371.60</td>\n",
432 |        "      <td>2364.25</td>\n",
433 |        "      <td>2366.80</td>\n",
434 |        "      <td>849327</td>\n",
435 |        "    </tr>\n",
436 |        "    <tr>\n",
437 |        "      <th>20</th>\n",
438 |        "      <td>2023-11-17 14:00:00+05:30</td>\n",
439 |        "      <td>2366.80</td>\n",
440 |        "      <td>2368.00</td>\n",
441 |        "      <td>2353.05</td>\n",
442 |        "      <td>2354.85</td>\n",
443 |        "      <td>1454868</td>\n",
444 |        "    </tr>\n",
445 |        "    <tr>\n",
446 |        "      <th>21</th>\n",
447 |        "      <td>2023-11-20 08:00:00+05:30</td>\n",
448 |        "      <td>2348.55</td>\n",
449 |        "      <td>2358.40</td>\n",
450 |        "      <td>2342.00</td>\n",
451 |        "      <td>2345.45</td>\n",
452 |        "      <td>507002</td>\n",
453 |        "    </tr>\n",
454 |        "    <tr>\n",
455 |        "      <th>22</th>\n",
456 |        "      <td>2023-11-20 10:00:00+05:30</td>\n",
457 |        "      <td>2346.35</td>\n",
458 |        "      <td>2347.25</td>\n",
459 |        "      <td>2336.40</td>\n",
460 |        "      <td>2341.30</td>\n",
461 |        "      <td>478746</td>\n",
462 |        "    </tr>\n",
463 |        "    <tr>\n",
464 |        "      <th>23</th>\n",
465 |        "      <td>2023-11-20 12:00:00+05:30</td>\n",
466 |        "      <td>2341.25</td>\n",
467 |        "      <td>2347.95</td>\n",
468 |        "      <td>2340.35</td>\n",
469 |        "      <td>2342.25</td>\n",
470 |        "      <td>354916</td>\n",
471 |        "    </tr>\n",
472 |        "    <tr>\n",
473 |        "      <th>24</th>\n",
474 |        "      <td>2023-11-20 14:00:00+05:30</td>\n",
475 |        "      <td>2342.25</td>\n",
476 |        "      <td>2352.70</td>\n",
477 |        "      <td>2341.15</td>\n",
478 |        "      <td>2349.00</td>\n",
479 |        "      <td>826822</td>\n",
480 |        "    </tr>\n",
481 |        "    <tr>\n",
482 |        "      <th>25</th>\n",
483 |        "      <td>2023-11-21 08:00:00+05:30</td>\n",
484 |        "      <td>2366.00</td>\n",
485 |        "      <td>2380.00</td>\n",
486 |        "      <td>2360.20</td>\n",
487 |        "      <td>2372.70</td>\n",
488 |        "      <td>876474</td>\n",
489 |        "    </tr>\n",
490 |        "    <tr>\n",
491 |        "      <th>26</th>\n",
492 |        "      <td>2023-11-21 10:00:00+05:30</td>\n",
493 |        "      <td>2372.95</td>\n",
494 |        "      <td>2379.65</td>\n",
495 |        "      <td>2371.00</td>\n",
496 |        "      <td>2379.00</td>\n",
497 |        "      <td>857184</td>\n",
498 |        "    </tr>\n",
499 |        "    <tr>\n",
500 |        "      <th>27</th>\n",
501 |        "      <td>2023-11-21 12:00:00+05:30</td>\n",
502 |        "      <td>2378.80</td>\n",
503 |        "      <td>2388.00</td>\n",
504 |        "      <td>2378.30</td>\n",
505 |        "      <td>2381.20</td>\n",
506 |        "      <td>1150310</td>\n",
507 |        "    </tr>\n",
508 |        "    <tr>\n",
509 |        "      <th>28</th>\n",
510 |        "      <td>2023-11-21 14:00:00+05:30</td>\n",
511 |        "      <td>2381.20</td>\n",
512 |        "      <td>2382.85</td>\n",
513 |        "      <td>2375.00</td>\n",
514 |        "      <td>2377.00</td>\n",
515 |        "      <td>1065302</td>\n",
516 |        "    </tr>\n",
517 |        "    <tr>\n",
518 |        "      <th>29</th>\n",
519 |        "      <td>2023-11-22 08:00:00+05:30</td>\n",
520 |        "      <td>2375.00</td>\n",
521 |        "      <td>2394.45</td>\n",
522 |        "      <td>2375.00</td>\n",
523 |        "      <td>2387.90</td>\n",
524 |        "      <td>1362028</td>\n",
525 |        "    </tr>\n",
526 |        "    <tr>\n",
527 |        "      <th>30</th>\n",
528 |        "      <td>2023-11-22 10:00:00+05:30</td>\n",
529 |        "      <td>2387.70</td>\n",
530 |        "      <td>2392.80</td>\n",
531 |        "      <td>2378.05</td>\n",
532 |        "      <td>2379.95</td>\n",
533 |        "      <td>1190981</td>\n",
534 |        "    </tr>\n",
535 |        "    <tr>\n",
536 |        "      <th>31</th>\n",
537 |        "      <td>2023-11-22 12:00:00+05:30</td>\n",
538 |        "      <td>2379.95</td>\n",
539 |        "      <td>2385.00</td>\n",
540 |        "      <td>2372.20</td>\n",
541 |        "      <td>2380.00</td>\n",
542 |        "      <td>921422</td>\n",
543 |        "    </tr>\n",
544 |        "    <tr>\n",
545 |        "      <th>32</th>\n",
546 |        "      <td>2023-11-22 14:00:00+05:30</td>\n",
547 |        "      <td>2379.75</td>\n",
548 |        "      <td>2390.00</td>\n",
549 |        "      <td>2378.05</td>\n",
550 |        "      <td>2387.00</td>\n",
551 |        "      <td>702310</td>\n",
552 |        "    </tr>\n",
553 |        "    <tr>\n",
554 |        "      <th>33</th>\n",
555 |        "      <td>2023-11-23 08:00:00+05:30</td>\n",
556 |        "      <td>2388.20</td>\n",
557 |        "      <td>2399.75</td>\n",
558 |        "      <td>2388.20</td>\n",
559 |        "      <td>2396.25</td>\n",
560 |        "      <td>1124039</td>\n",
561 |        "    </tr>\n",
562 |        "    <tr>\n",
563 |        "      <th>34</th>\n",
564 |        "      <td>2023-11-23 10:00:00+05:30</td>\n",
565 |        "      <td>2396.20</td>\n",
566 |        "      <td>2400.00</td>\n",
567 |        "      <td>2390.25</td>\n",
568 |        "      <td>2392.75</td>\n",
569 |        "      <td>1194248</td>\n",
570 |        "    </tr>\n",
571 |        "    <tr>\n",
572 |        "      <th>35</th>\n",
573 |        "      <td>2023-11-23 12:00:00+05:30</td>\n",
574 |        "      <td>2393.00</td>\n",
575 |        "      <td>2399.00</td>\n",
576 |        "      <td>2392.15</td>\n",
577 |        "      <td>2398.45</td>\n",
578 |        "      <td>819897</td>\n",
579 |        "    </tr>\n",
580 |        "    <tr>\n",
581 |        "      <th>36</th>\n",
582 |        "      <td>2023-11-23 14:00:00+05:30</td>\n",
583 |        "      <td>2398.45</td>\n",
584 |        "      <td>2399.10</td>\n",
585 |        "      <td>2390.50</td>\n",
586 |        "      <td>2393.90</td>\n",
587 |        "      <td>1067299</td>\n",
588 |        "    </tr>\n",
589 |        "    <tr>\n",
590 |        "      <th>37</th>\n",
591 |        "      <td>2023-11-24 08:00:00+05:30</td>\n",
592 |        "      <td>2391.60</td>\n",
593 |        "      <td>2402.60</td>\n",
594 |        "      <td>2391.05</td>\n",
595 |        "      <td>2397.50</td>\n",
596 |        "      <td>493994</td>\n",
597 |        "    </tr>\n",
598 |        "    <tr>\n",
599 |        "      <th>38</th>\n",
600 |        "      <td>2023-11-24 10:00:00+05:30</td>\n",
601 |        "      <td>2397.40</td>\n",
602 |        "      <td>2401.00</td>\n",
603 |        "      <td>2393.55</td>\n",
604 |        "      <td>2400.05</td>\n",
605 |        "      <td>971111</td>\n",
606 |        "    </tr>\n",
607 |        "    <tr>\n",
608 |        "      <th>39</th>\n",
609 |        "      <td>2023-11-24 12:00:00+05:30</td>\n",
610 |        "      <td>2400.10</td>\n",
611 |        "      <td>2401.00</td>\n",
612 |        "      <td>2395.10</td>\n",
613 |        "      <td>2397.85</td>\n",
614 |        "      <td>941540</td>\n",
615 |        "    </tr>\n",
616 |        "    <tr>\n",
617 |        "      <th>40</th>\n",
618 |        "      <td>2023-11-24 14:00:00+05:30</td>\n",
619 |        "      <td>2397.90</td>\n",
620 |        "      <td>2399.50</td>\n",
621 |        "      <td>2392.05</td>\n",
622 |        "      <td>2394.25</td>\n",
623 |        "      <td>934921</td>\n",
624 |        "    </tr>\n",
625 |        "  </tbody>\n",
626 |        "</table>\n",
627 |        "</div>"
628 |       ],
629 |       "text/plain": [
630 |        "                        date     open     high      low    close   volume\n",
631 |        "0  2023-11-10 08:00:00+05:30  2305.55  2316.35  2298.90  2301.00  1417820\n",
632 |        "1  2023-11-10 10:00:00+05:30  2301.00  2307.35  2298.05  2305.60   645230\n",
633 |        "2  2023-11-10 12:00:00+05:30  2305.65  2315.10  2305.10  2309.10   813375\n",
634 |        "3  2023-11-10 14:00:00+05:30  2309.15  2316.00  2307.00  2315.90   942613\n",
635 |        "4  2023-11-12 18:00:00+05:30  2326.05  2332.00  2322.70  2330.90   685748\n",
636 |        "5  2023-11-13 08:00:00+05:30  2322.90  2324.60  2316.20  2317.00   295834\n",
637 |        "6  2023-11-13 10:00:00+05:30  2317.05  2318.85  2311.70  2315.50   418459\n",
638 |        "7  2023-11-13 12:00:00+05:30  2315.30  2318.00  2312.00  2314.75   367694\n",
639 |        "8  2023-11-13 14:00:00+05:30  2314.95  2316.75  2312.00  2315.10   798828\n",
640 |        "9  2023-11-15 08:00:00+05:30  2340.00  2342.60  2327.00  2335.55  1015224\n",
641 |        "10 2023-11-15 10:00:00+05:30  2335.55  2348.00  2335.00  2344.10  1867153\n",
642 |        "11 2023-11-15 12:00:00+05:30  2344.00  2352.90  2343.85  2346.85   992835\n",
643 |        "12 2023-11-15 14:00:00+05:30  2346.95  2361.95  2346.80  2356.10  2003118\n",
644 |        "13 2023-11-16 08:00:00+05:30  2351.10  2364.00  2346.95  2355.25   923205\n",
645 |        "14 2023-11-16 10:00:00+05:30  2355.00  2369.60  2354.70  2366.45  1049582\n",
646 |        "15 2023-11-16 12:00:00+05:30  2366.50  2369.35  2363.80  2367.00   727641\n",
647 |        "16 2023-11-16 14:00:00+05:30  2366.75  2374.15  2350.55  2359.00  3758974\n",
648 |        "17 2023-11-17 08:00:00+05:30  2352.90  2370.30  2352.05  2369.95   513251\n",
649 |        "18 2023-11-17 10:00:00+05:30  2370.05  2373.25  2363.80  2367.00  1017076\n",
650 |        "19 2023-11-17 12:00:00+05:30  2366.80  2371.60  2364.25  2366.80   849327\n",
651 |        "20 2023-11-17 14:00:00+05:30  2366.80  2368.00  2353.05  2354.85  1454868\n",
652 |        "21 2023-11-20 08:00:00+05:30  2348.55  2358.40  2342.00  2345.45   507002\n",
653 |        "22 2023-11-20 10:00:00+05:30  2346.35  2347.25  2336.40  2341.30   478746\n",
654 |        "23 2023-11-20 12:00:00+05:30  2341.25  2347.95  2340.35  2342.25   354916\n",
655 |        "24 2023-11-20 14:00:00+05:30  2342.25  2352.70  2341.15  2349.00   826822\n",
656 |        "25 2023-11-21 08:00:00+05:30  2366.00  2380.00  2360.20  2372.70   876474\n",
657 |        "26 2023-11-21 10:00:00+05:30  2372.95  2379.65  2371.00  2379.00   857184\n",
658 |        "27 2023-11-21 12:00:00+05:30  2378.80  2388.00  2378.30  2381.20  1150310\n",
659 |        "28 2023-11-21 14:00:00+05:30  2381.20  2382.85  2375.00  2377.00  1065302\n",
660 |        "29 2023-11-22 08:00:00+05:30  2375.00  2394.45  2375.00  2387.90  1362028\n",
661 |        "30 2023-11-22 10:00:00+05:30  2387.70  2392.80  2378.05  2379.95  1190981\n",
662 |        "31 2023-11-22 12:00:00+05:30  2379.95  2385.00  2372.20  2380.00   921422\n",
663 |        "32 2023-11-22 14:00:00+05:30  2379.75  2390.00  2378.05  2387.00   702310\n",
664 |        "33 2023-11-23 08:00:00+05:30  2388.20  2399.75  2388.20  2396.25  1124039\n",
665 |        "34 2023-11-23 10:00:00+05:30  2396.20  2400.00  2390.25  2392.75  1194248\n",
666 |        "35 2023-11-23 12:00:00+05:30  2393.00  2399.00  2392.15  2398.45   819897\n",
667 |        "36 2023-11-23 14:00:00+05:30  2398.45  2399.10  2390.50  2393.90  1067299\n",
668 |        "37 2023-11-24 08:00:00+05:30  2391.60  2402.60  2391.05  2397.50   493994\n",
669 |        "38 2023-11-24 10:00:00+05:30  2397.40  2401.00  2393.55  2400.05   971111\n",
670 |        "39 2023-11-24 12:00:00+05:30  2400.10  2401.00  2395.10  2397.85   941540\n",
671 |        "40 2023-11-24 14:00:00+05:30  2397.90  2399.50  2392.05  2394.25   934921"
672 |       ]
673 |      },
674 |      "execution_count": 8,
675 |      "metadata": {},
676 |      "output_type": "execute_result"
677 |     }
678 |    ],
679 |    "source": [
680 |     "import pandas as pd\n",
681 |     "\n",
682 |     "def minute_conversion(data, timeframe):\n",
683 |     "    # Ensure 'date' column is in datetime format\n",
684 |     "    data['date'] = pd.to_datetime(data['date'])\n",
685 |     "\n",
686 |     "    # Initialize an empty list to store the aggregated data\n",
687 |     "    aggregated_data = []\n",
688 |     "\n",
689 |     "    # Group data by the desired timeframe\n",
690 |     "    grouped = data.groupby(pd.Grouper(key='date', freq=f'{timeframe}T'))\n",
691 |     "\n",
692 |     "    # Aggregate data within each group\n",
693 |     "    for name, group in grouped:\n",
694 |     "        if not group.empty:\n",
695 |     "            open_price = group.iloc[0]['open']\n",
696 |     "            high_price = group['high'].max()\n",
697 |     "            low_price = group['low'].min()\n",
698 |     "            close_price = group.iloc[-1]['close']\n",
699 |     "            volume = group['volume'].sum()\n",
700 |     "\n",
701 |     "            # Append the aggregated values to the list\n",
702 |     "            aggregated_data.append([name, open_price, high_price, low_price, close_price, volume])\n",
703 |     "\n",
704 |     "    # Convert the list to a DataFrame\n",
705 |     "    new_df = pd.DataFrame(aggregated_data, columns=['date', 'open', 'high', 'low', 'close', 'volume'])\n",
706 |     "\n",
707 |     "    return new_df\n",
708 |     "\n",
709 |     "# Example usage:\n",
710 |     "# zap is your original DataFrame\n",
711 |     "timeframe = 120  # 120 minutes\n",
712 |     "converted_df = minute_conversion(zap, timeframe)\n",
713 |     "converted_df"
714 |    ]
715 |   },
716 |   {
717 |    "cell_type": "code",
718 |    "execution_count": null,
719 |    "id": "8531cc0f",
720 |    "metadata": {},
721 |    "outputs": [],
722 |    "source": []
723 |   }
724 |  ],
725 |  "metadata": {
726 |   "kernelspec": {
727 |    "display_name": "Python 3 (ipykernel)",
728 |    "language": "python",
729 |    "name": "python3"
730 |   },
731 |   "language_info": {
732 |    "codemirror_mode": {
733 |     "name": "ipython",
734 |     "version": 3
735 |    },
736 |    "file_extension": ".py",
737 |    "mimetype": "text/x-python",
738 |    "name": "python",
739 |    "nbconvert_exporter": "python",
740 |    "pygments_lexer": "ipython3",
741 |    "version": "3.10.12"
742 |   }
743 |  },
744 |  "nbformat": 4,
745 |  "nbformat_minor": 5
746 | }
747 | 


--------------------------------------------------------------------------------
/2.6 - RSI Based Trading Bot with Python using Zerodha API.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": null,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "#https://unofficed.com/courses/mastering-algotrading-a-beginners-guide-using-kiteconnect-api/lessons/rsi-based-trading-bot-with-python-using-zerodha-api/"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "code",
  14 |    "execution_count": 5,
  15 |    "metadata": {
  16 |     "colab": {},
  17 |     "colab_type": "code",
  18 |     "id": "kOrIloxAuSGw"
  19 |    },
  20 |    "outputs": [],
  21 |    "source": [
  22 |     "import logging\n",
  23 |     "from kiteconnect import KiteConnect\n",
  24 |     "\n",
  25 |     "logging.basicConfig(level=logging.DEBUG)\n",
  26 |     "\n",
  27 |     "kite = KiteConnect(api_key=\"your_api_key\")\n",
  28 |     "\n",
  29 |     "# Redirect the user to the login url obtained\n",
  30 |     "# from kite.login_url(), and receive the request_token\n",
  31 |     "# from the registered redirect url after the login flow.\n",
  32 |     "# Once you have the request_token, obtain the access_token\n",
  33 |     "# as follows.\n",
  34 |     "\n",
  35 |     "data = kite.generate_session(\"request_token_here\", api_secret=\"your_secret\")\n",
  36 |     "kite.set_access_token(data[\"access_token\"])"
  37 |    ]
  38 |   },
  39 |   {
  40 |    "cell_type": "code",
  41 |    "execution_count": null,
  42 |    "metadata": {
  43 |     "colab": {
  44 |      "base_uri": "https://localhost:8080/",
  45 |      "height": 34
  46 |     },
  47 |     "colab_type": "code",
  48 |     "executionInfo": {
  49 |      "elapsed": 898,
  50 |      "status": "ok",
  51 |      "timestamp": 1562581444138,
  52 |      "user": {
  53 |       "displayName": "ashish kumar",
  54 |       "photoUrl": "https://lh4.googleusercontent.com/-gyDeK-Cm9Bw/AAAAAAAAAAI/AAAAAAAAAAc/zxhe91XBOvQ/s64/photo.jpg",
  55 |       "userId": "08220617051679634216"
  56 |      },
  57 |      "user_tz": 0
  58 |     },
  59 |     "id": "0hFcbLHsuSG2",
  60 |     "outputId": "4bd14f63-ee7f-45bc-e2d9-ed6d890c5ddb"
  61 |    },
  62 |    "outputs": [],
  63 |    "source": [
  64 |     "import numpy as np\n",
  65 |     "import pandas as pd\n",
  66 |     "from pytz import timezone\n",
  67 |     "from pprint import pprint\n",
  68 |     "from datetime import datetime\n",
  69 |     "from time import gmtime, strftime"
  70 |    ]
  71 |   },
  72 |   {
  73 |    "cell_type": "code",
  74 |    "execution_count": null,
  75 |    "metadata": {},
  76 |    "outputs": [],
  77 |    "source": [
  78 |     "print(\"input token name\")\n",
  79 |     "token_n=str(input())\n",
  80 |     "print(\"enter exchange\")\n",
  81 |     "exc=str(input())\n",
  82 |     "pd.DataFrame(kite.instruments(exc))[[\"tradingsymbol\",\"instrument_token\",\"name\",\"exchange\"]][pd.DataFrame(kite.instruments(exc))[[\"tradingsymbol\",\"instrument_token\",\"name\",\"exchange\"]]['tradingsymbol'].str.contains(token_n)] "
  83 |    ]
  84 |   },
  85 |   {
  86 |    "cell_type": "code",
  87 |    "execution_count": 7,
  88 |    "metadata": {
  89 |     "colab": {},
  90 |     "colab_type": "code",
  91 |     "id": "NxSDVBuSuSHD"
  92 |    },
  93 |    "outputs": [],
  94 |    "source": [
  95 |     "ttradingsymbol =\"SBIN\"\n",
  96 |     "eexchange      =\"NSE\"\n",
  97 |     "productt       =\"MIS\"\n",
  98 |     "qu             =1\n",
  99 |     "RSI_VAL        =30\n",
 100 |     "RSI_VAL2       =30\n",
 101 |     "profit_booking =70\n",
 102 |     "time_frame     =\"minute\"\n",
 103 |     "sdate          =\"2019-06-05\"\n",
 104 |     "todate         =\"2019-10-02\"\n",
 105 |     "#Do not change here#\n",
 106 |     "buy_add=.50\n",
 107 |     "sell_add=.50\n",
 108 |     "ttoken=int(pd.DataFrame(kite.ltp(eexchange+\":\"+ttradingsymbol)).iloc[-2,0])"
 109 |    ]
 110 |   },
 111 |   {
 112 |    "cell_type": "code",
 113 |    "execution_count": null,
 114 |    "metadata": {
 115 |     "colab": {
 116 |      "base_uri": "https://localhost:8080/",
 117 |      "height": 818
 118 |     },
 119 |     "colab_type": "code",
 120 |     "executionInfo": {
 121 |      "elapsed": 2553,
 122 |      "status": "error",
 123 |      "timestamp": 1562344675553,
 124 |      "user": {
 125 |       "displayName": "ashish kumar",
 126 |       "photoUrl": "https://lh4.googleusercontent.com/-gyDeK-Cm9Bw/AAAAAAAAAAI/AAAAAAAAAAc/zxhe91XBOvQ/s64/photo.jpg",
 127 |       "userId": "08220617051679634216"
 128 |      },
 129 |      "user_tz": 0
 130 |     },
 131 |     "id": "5HoNC5kYuSHd",
 132 |     "outputId": "88d56bd2-8a90-435d-ccc2-6be9e9a3b384",
 133 |     "scrolled": true
 134 |    },
 135 |    "outputs": [
 136 |     {
 137 |      "name": "stdout",
 138 |      "output_type": "stream",
 139 |      "text": [
 140 |       " \n",
 141 |       " \t \t \t \t  ZERODHA RSI 'Relative Strength Index BOT' [5]\n",
 142 |       " \n",
 143 |       " \t \t \t \t  ZERODHA RSI 'Relative Strength Index BOT'\n",
 144 |       "\n",
 145 |       " \n",
 146 |       "Zerodha RSI BOT start Time              2019-08-31 _ 21:26:07 \n",
 147 |       "\n",
 148 |       " \n",
 149 |       "BOT working succeesfully on time:       2019-08-31 _ 21:27:05 \n",
 150 |       "\n",
 151 |       " \n",
 152 |       "Trading symbol is SBIN Token is 779521 Exchange is NSE RSI condition is 30 30 and profit booking RSI level is 70  \t \n",
 153 |       " and product type is MIS Quantity is 1 time frame for Historical Data is minute Starting and Ending Date of Historical Data is 2019-06-05 2019-10-02\n",
 154 |       "\n",
 155 |       " \n",
 156 |       "RSI condition Occured but high Not break 0\n",
 157 |       "\n",
 158 |       " \n",
 159 |       "\n",
 160 |       " \n",
 161 |       "\n",
 162 |       " \n",
 163 |       "Current  ATR status is                  0.41822857770302235\n",
 164 |       "Current  RSI status is                  50.23005471188229\n",
 165 |       "Previous RSI status is                  50.32567294164271\n",
 166 |       "Current  HIGH is                        273.6\n",
 167 |       "Previous HIGH satus is                  273.6\n",
 168 |       "Lowest-Low when enter in RSI Condition  []\n",
 169 |       "RSI at ENTRY in Condition Rough detail  0\n",
 170 |       "\n",
 171 |       " \n",
 172 |       "\t  \t \t   BUY & SELL details and Number of Order Placed \n",
 173 |       " \n",
 174 |       "Orignal Price while placing order is    []\n",
 175 |       "BUY bid trigger price is                []\n",
 176 |       "BUY bid price is                        []\n",
 177 |       "SELL SL trigger price is                []\n",
 178 |       "SELL stoploss is price is               []\n",
 179 |       "Number of BUY/SELL order till now is                    0\n",
 180 |       "Number of time BOT complete RSI profit booking strategy 0\n",
 181 |       "Number of time BOT complet with execution of sell bid   0\n",
 182 |       "\n",
 183 |       " \n"
 184 |      ]
 185 |     }
 186 |    ],
 187 |    "source": [
 188 |     "InteractiveShell.ast_node_interactivity = \"all\"\n",
 189 |     "changesver=[5]\n",
 190 |     "a=[0]\n",
 191 |     "b=[0]\n",
 192 |     "hh=[]\n",
 193 |     "ii=[]\n",
 194 |     "ggg=[]\n",
 195 |     "firs=[]\n",
 196 |     "inpu=[]\n",
 197 |     "glkk=[]\n",
 198 |     "akki=[]\n",
 199 |     "ame=[21]\n",
 200 |     "check=[]\n",
 201 |     "bii_by=[]\n",
 202 |     "orprice=[]\n",
 203 |     "datalow=[]\n",
 204 |     "progrun=[]\n",
 205 |     "rsichec=[]\n",
 206 |     "datarsi=[0]\n",
 207 |     "buyprice=[]\n",
 208 |     "beak_high=[]\n",
 209 |     "sellorder=[]\n",
 210 |     "org_price=[]\n",
 211 |     "limitprice=[]\n",
 212 |     "sellorder1=[]\n",
 213 |     "orderplaced=[]\n",
 214 |     "prog_starts=[]\n",
 215 |     "high_to_high=[]\n",
 216 |     "rsi_strategy=[]\n",
 217 |     "sell_strategy=[]\n",
 218 |     "rsi_condition=[]\n",
 219 |     "orgtimersicondition=[]\n",
 220 |     "sellbit_execute_limit=[]\n",
 221 |     "buy_order_sell_status=[]\n",
 222 |     "buy_order_info_buy_orid=[]\n",
 223 |     "buy_order_info_buy_symbol=[]\n",
 224 |     "buy_order_info_buy_status=[]\n",
 225 |     "buy_order_info_buy_status=[]\n",
 226 |     "buy_order_info_buy_orderid=[]\n",
 227 |     "buy_order_info_sell_orderid=[]\n",
 228 |     "class ThreadingData(object):\n",
 229 |     "    global app_z_status\n",
 230 |     "    global app_z_order\n",
 231 |     "    global app_z_order_type\n",
 232 |     "    global app_z_transaction_type\n",
 233 |     "    app_z_status=[]\n",
 234 |     "    app_z_order=[]\n",
 235 |     "    app_z_order_type=[]\n",
 236 |     "    app_z_transaction_type=[]\n",
 237 |     "    def __init__(self):\n",
 238 |     "        thread = threading.Thread(target=self.run, args=())\n",
 239 |     "        thread.daemon = True\n",
 240 |     "        thread.start()\n",
 241 |     "    def run(self):\n",
 242 |     "        access_token=token\n",
 243 |     "        api_key=api_key\n",
 244 |     "        logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n",
 245 |     "        clear_output(wait=True)\n",
 246 |     "        kws = KiteTicker(api_key, access_token, debug=True)\n",
 247 |     "        global app_z_status\n",
 248 |     "        global app_z_order\n",
 249 |     "        global app_z_order_type\n",
 250 |     "        global app_z_transaction_type\n",
 251 |     "        tokens = [ttoken]\n",
 252 |     "        print(\"Tokens length\", len(tokens))\n",
 253 |     "        def on_connect(ws, response):\n",
 254 |     "            logging.debug(\"on connect: {}\".format(response))\n",
 255 |     "            ws.subscribe(tokens)\n",
 256 |     "            ws.set_mode(ws.MODE_FULL, tokens)\n",
 257 |     "        def on_close(ws, code, reason):\n",
 258 |     "            logging.error(\"closed connection on close: {} {}\".format(code, reason))\n",
 259 |     "        def on_error(ws, code, reason):\n",
 260 |     "            logging.error(\"closed connection on error: {} {}\".format(code, reason))\n",
 261 |     "        def on_noreconnect(ws):\n",
 262 |     "            logging.error(\"Reconnecting the websocket failed\")\n",
 263 |     "        def on_reconnect(ws, attempt_count):\n",
 264 |     "            logging.debug(\"Reconnecting the websocket: {}\".format(attempt_count))\n",
 265 |     "        def on_order_update(ws, data):\n",
 266 |     "            z_status=data['status']\n",
 267 |     "            z_order=data[\"order_id\"]\n",
 268 |     "            z_order_type=data[\"order_type\"]\n",
 269 |     "            z_transaction_type=[\"transaction_type\"]\n",
 270 |     "            app_z_status.append(z_status)\n",
 271 |     "            app_z_order.append(z_order)\n",
 272 |     "            app_z_order_type.append(z_order_type)\n",
 273 |     "            app_z_transaction_type.append(z_transaction_type)\n",
 274 |     "            print(app_z_status)\n",
 275 |     "            print(app_z_order)\n",
 276 |     "            print(app_z_order_type)\n",
 277 |     "            print(app_z_transaction_type)\n",
 278 |     "            print(data)\n",
 279 |     "        kws.on_connect      = on_connect\n",
 280 |     "        kws.on_close        = on_close\n",
 281 |     "        kws.on_error        = on_error\n",
 282 |     "        kws.on_noreconnect  = on_noreconnect\n",
 283 |     "        kws.on_reconnect    = on_reconnect\n",
 284 |     "        kws.on_order_update = on_order_update\n",
 285 |     "        kws.connect()\n",
 286 |     "def ashi():\n",
 287 |     "    while(True):\n",
 288 |     "        InteractiveShell.ast_node_interactivity = \"all\"\n",
 289 |     "        km=datetime.now().minute\n",
 290 |     "        ks=datetime.now().second\n",
 291 |     "        if km%1==0 and ks==1:\n",
 292 |     "            clear_output(wait=True)\n",
 293 |     "            z=kite.historical_data(ttoken, sdate, todate,time_frame,0)\n",
 294 |     "            za=pd.DataFrame(z)\n",
 295 |     "            rsi_period=14\n",
 296 |     "            chg=za[\"close\"].diff(1)\n",
 297 |     "            gain=chg.mask(chg<0,0) \n",
 298 |     "            loss=chg.mask(chg>0,0)\n",
 299 |     "            avg_gain=gain.ewm(com=rsi_period-1,min_periods=rsi_period).mean()\n",
 300 |     "            avg_loss=loss.ewm(com=rsi_period-1,min_periods=rsi_period).mean()\n",
 301 |     "            rs =abs(avg_gain / avg_loss)\n",
 302 |     "            rsi =100 -(100/(1+rs))\n",
 303 |     "            za['rsi']=rsi\n",
 304 |     "            def bidatrema(df,period):\n",
 305 |     "                df['hl']=abs(df['high']-df['low'])\n",
 306 |     "                df['hpc']=abs(df['high']-df['close'].shift())\n",
 307 |     "                df['lpc']=abs(df['low']-df['close'].shift())\n",
 308 |     "                df['tr']=df[['hl','hpc','lpc']].max(axis=1)\n",
 309 |     "                df['ATR']=pd.DataFrame.ewm(df[\"tr\"], span=period,min_periods=period).mean()\n",
 310 |     "                df.drop([\"hl\",\"hpc\",\"lpc\",\"tr\"],axis = 1 , inplace =True)\n",
 311 |     "            bidatrema(za,14)\n",
 312 |     "            p=za.iloc[-1,6]\n",
 313 |     "            o=za.iloc[-2,2]\n",
 314 |     "            a.append(p)\n",
 315 |     "            b.append(o)\n",
 316 |     "            def avg_ha(x):\n",
 317 |     "                a=list(modf(round(x,3)))\n",
 318 |     "                d=str(a[0])\n",
 319 |     "                aa=str(d[0:2])\n",
 320 |     "                try:\n",
 321 |     "                    ab=str(d[2])\n",
 322 |     "                except:\n",
 323 |     "                    ab='0'\n",
 324 |     "                try:\n",
 325 |     "                    ac=str(d[3])\n",
 326 |     "                except:\n",
 327 |     "                    ac='0'\n",
 328 |     "                try:\n",
 329 |     "                    ad=str(d[4])\n",
 330 |     "                except:\n",
 331 |     "                    ad='0'\n",
 332 |     "                c=aa+ab+ac+ad\n",
 333 |     "                b=0\n",
 334 |     "                if a[0]!=0:\n",
 335 |     "                    astr=c\n",
 336 |     "                    a0=astr[0]\n",
 337 |     "                    a1=astr[1]\n",
 338 |     "                    a3=int(astr[2])\n",
 339 |     "                    a2=int(astr[3:5])\n",
 340 |     "                    if a2>=0 and a2<25:\n",
 341 |     "                        a2=0\n",
 342 |     "                    elif a2>=25 and a2<75:\n",
 343 |     "                        a2=5\n",
 344 |     "                    elif a2>=75 and a2<=99:\n",
 345 |     "                        a3+=1\n",
 346 |     "                        a2=0\n",
 347 |     "                    aint=a0+a1+str(a3)+str(a2)\n",
 348 |     "                    a[0]=float(aint)\n",
 349 |     "                for k in a:\n",
 350 |     "                    b+=k\n",
 351 |     "                return b\n",
 352 |     "            InteractiveShell.ast_node_interactivity = \"all\"\n",
 353 |     "            print(\" \\n \\t \\t \\t \\t  ZERODHA RSI 'Relative Strength Index BOT'\",changesver)\n",
 354 |     "            print(\" \\n \\t \\t \\t \\t  ZERODHA RSI 'Relative Strength Index BOT'\")\n",
 355 |     "            now_utc = datetime.now(timezone('UTC'))\n",
 356 |     "            now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))\n",
 357 |     "            now_asia = now_asia.strftime(\"%Y-%m-%d _ %H:%M:%S \")\n",
 358 |     "            klp1=now_asia\n",
 359 |     "            prog_starts.append(klp1)\n",
 360 |     "            print(\"\\n \")\n",
 361 |     "            print(\"Zerodha RSI BOT start Time             \"  , prog_starts[0])\n",
 362 |     "            print(\"\\n \")\n",
 363 |     "            print(\"BOT working succeesfully on time:      \"  , now_asia )\n",
 364 |     "            print(\"\\n \")\n",
 365 |     "            print(\"Trading symbol is\",ttradingsymbol,\"Token is\",ttoken,\"Exchange is\",eexchange,\"RSI condition is\",RSI_VAL,RSI_VAL2,\"and profit booking RSI level is\",profit_booking,\" \\t \\n and product type is\",productt,\"Quantity is\",qu,\"time frame for Historical Data is\",time_frame,\"Starting and Ending Date of Historical Data is\",sdate,todate)\n",
 366 |     "            print(\"\\n \")\n",
 367 |     "            #display(data)\n",
 368 |     "            print(\"RSI condition Occured but high Not break\" , len(progrun))\n",
 369 |     "            if 2 in rsichec:\n",
 370 |     "                print(\"RSI condition's occured at time    \"  ,*rsi_condition,sep='\\n')\n",
 371 |     "            print(\"\\n \")\n",
 372 |     "            print(\"\\n \")\n",
 373 |     "            if 2 in high_to_high:\n",
 374 |     "                print(\"High-High breaks condition time    \"  ,*beak_high,sep='\\n')\n",
 375 |     "            print(\"\\n \")\n",
 376 |     "            print(\"Current  ATR status is                 \"  , za.iloc[-1,7])\n",
 377 |     "            print(\"Current  RSI status is                 \"  , a[-1])\n",
 378 |     "            print(\"Previous RSI status is                 \"  , a[-2])\n",
 379 |     "            print(\"Current  HIGH is                       \"  , b[-1])\n",
 380 |     "            print(\"Previous HIGH satus is                 \"  , b[-2])\n",
 381 |     "            \n",
 382 |     "            print(\"Lowest-Low when enter in RSI Condition \"  , datalow)\n",
 383 |     "            print(\"RSI at ENTRY in Condition Rough detail \"  , datarsi[-1])\n",
 384 |     "            print(\"\\n \")\n",
 385 |     "            print(\"\\t  \\t \\t   BUY & SELL details and Number of Order Placed \\n \")\n",
 386 |     "            print(\"Orignal Price while placing order is   \"  , orprice)\n",
 387 |     "            print(\"BUY bid trigger price is               \"  , buyprice)\n",
 388 |     "            print(\"BUY bid price is                       \"  , limitprice)\n",
 389 |     "            print(\"SELL SL trigger price is               \"  , sellorder)\n",
 390 |     "            print(\"SELL stoploss is price is              \"  , sellorder1)\n",
 391 |     "            print(\"Number of BUY/SELL order till now is                   \" , len(orderplaced))\n",
 392 |     "            print(\"Number of time BOT complete RSI profit booking strategy\" , len(rsi_strategy))\n",
 393 |     "            print(\"Number of time BOT complet with execution of sell bid  \" , len(sell_strategy))\n",
 394 |     "            if 0 in check:\n",
 395 |     "                print(\" BUY bid Order Symbol              \"  , buy_order_info_buy_symbol)\n",
 396 |     "                print(\" BUY bid Order Status              \"  , buy_order_info_buy_status)\n",
 397 |     "                print(\" BUY bid Order id                  \"  , buy_order_info_buy_orid)\n",
 398 |     "                print(\" BUY bid placed of                 \"  , buy_order_info_buy_symbol, \"and order id  is\", buy_order_info_buy_orid)\n",
 399 |     "            if 0 in sellbit_execute_limit:\n",
 400 |     "                print(\" SELL Order Status                 \"  , buy_order_sell_status)\n",
 401 |     "                print(\" SELL Order id                     \"  , buy_order_info_sell_orderid)\n",
 402 |     "                print(\" BUY Order executed on zerodha and SELL bid placed Order of\",ttradingsymbol,\"status is\",buy_order_sell_status,\"order id is\",buy_order_info_sell_orderid)\n",
 403 |     "            print(\"\\n \")\n",
 404 |     "            if 0 in check:\n",
 405 |     "                if 0 in sellbit_execute_limit:\n",
 406 |     "                    lll=str(buy_order_info_sell_orderid[0])\n",
 407 |     "                    kneww8=pd.DataFrame(kite.orders())[[\"tradingsymbol\",\"status\",\"order_id\",\"quantity\"]]\n",
 408 |     "                    zc898,=kneww8[kneww8.order_id== lll].index\n",
 409 |     "                    in898=int(zc898)\n",
 410 |     "                    if \"COMPLETE\" in kneww8.loc[in898,\"status\"]:\n",
 411 |     "                        print(\"YES Order Already Executed of SL \")\n",
 412 |     "                        a.clear()\n",
 413 |     "                        b.clear()\n",
 414 |     "                        hh.clear()\n",
 415 |     "                        ii.clear()\n",
 416 |     "                        ggg.clear()\n",
 417 |     "                        ame.clear()\n",
 418 |     "                        a.append(0)\n",
 419 |     "                        b.append(0)\n",
 420 |     "                        firs.clear()\n",
 421 |     "                        inpu.clear()\n",
 422 |     "                        glkk.clear()\n",
 423 |     "                        akki.clear()\n",
 424 |     "                        check.clear()\n",
 425 |     "                        bii_by.clear()\n",
 426 |     "                        orprice.clear()\n",
 427 |     "                        datarsi.clear()\n",
 428 |     "                        datalow.clear()\n",
 429 |     "                        buyprice.clear()\n",
 430 |     "                        datarsi.append(0)\n",
 431 |     "                        sellorder.clear()\n",
 432 |     "                        org_price.clear()\n",
 433 |     "                        limitprice.clear()\n",
 434 |     "                        sellorder1.clear()\n",
 435 |     "                        sellbit_execute_limit.clear()\n",
 436 |     "                        buy_order_sell_status.clear()\n",
 437 |     "                        buy_order_info_buy_orid.clear()\n",
 438 |     "                        buy_order_info_buy_symbol.clear()\n",
 439 |     "                        buy_order_info_buy_status.clear()\n",
 440 |     "                        buy_order_info_buy_status.clear()\n",
 441 |     "                        buy_order_info_buy_orderid.clear()\n",
 442 |     "                        buy_order_info_sell_orderid.clear()\n",
 443 |     "                        print(\" Cycle complete with execution of SELL bid   \")\n",
 444 |     "                        orderplaced.append(1)\n",
 445 |     "                        sell_strategy.append(1)\n",
 446 |     "                        ashi()\n",
 447 |     "                    if za.iloc[-1,6]>=profit_booking:\n",
 448 |     "                        lll=buy_order_info_sell_orderid[0]\n",
 449 |     "                        print(\"SL order Cancel id is : \",buy_order_info_sell_orderid,kite.cancel_order(\"regular\",order_id=lll, parent_order_id=None))\n",
 450 |     "                        print(\"Market order placed and BOT starts again\",kite.place_order(variety=\"regular\",tradingsymbol=ttradingsymbol,quantity=qu,exchange=eexchange,order_type='MARKET',transaction_type='SELL',product=productt,tag=\"RSImkt\"))               \n",
 451 |     "                        a.clear()\n",
 452 |     "                        b.clear()\n",
 453 |     "                        hh.clear()\n",
 454 |     "                        ii.clear()\n",
 455 |     "                        ggg.clear()\n",
 456 |     "                        ame.clear()\n",
 457 |     "                        a.append(0)\n",
 458 |     "                        b.append(0)\n",
 459 |     "                        firs.clear()\n",
 460 |     "                        inpu.clear()\n",
 461 |     "                        glkk.clear()\n",
 462 |     "                        akki.clear()\n",
 463 |     "                        check.clear()\n",
 464 |     "                        bii_by.clear()\n",
 465 |     "                        datarsi.clear()\n",
 466 |     "                        orprice.clear()\n",
 467 |     "                        datalow.clear()\n",
 468 |     "                        buyprice.clear()\n",
 469 |     "                        sellorder.clear()\n",
 470 |     "                        org_price.clear()\n",
 471 |     "                        datarsi.append(0)\n",
 472 |     "                        limitprice.clear()\n",
 473 |     "                        sellorder1.clear()\n",
 474 |     "                        sellbit_execute_limit.clear()\n",
 475 |     "                        buy_order_sell_status.clear()\n",
 476 |     "                        buy_order_info_buy_orid.clear()\n",
 477 |     "                        buy_order_info_buy_symbol.clear()\n",
 478 |     "                        buy_order_info_buy_status.clear()\n",
 479 |     "                        buy_order_info_buy_status.clear()\n",
 480 |     "                        buy_order_info_buy_orderid.clear()\n",
 481 |     "                        buy_order_info_sell_orderid.clear()\n",
 482 |     "                        print(\" This Cycle completed with RSI strategy \")\n",
 483 |     "                        rsi_strategy.append(1)\n",
 484 |     "                        orderplaced.append(1)\n",
 485 |     "                        ashi()\n",
 486 |     "                    else:\n",
 487 |     "                        pass\n",
 488 |     "                if 0 not in glkk:\n",
 489 |     "                    kkkstr=str(buy_order_info_buy_orid[0])\n",
 490 |     "                    kneww=pd.DataFrame(kite.orders())[[\"tradingsymbol\",\"status\",\"order_id\",\"quantity\"]]\n",
 491 |     "                    zc89,=kneww[kneww.order_id== kkkstr].index\n",
 492 |     "                    in899=int(zc89)\n",
 493 |     "                    if \"COMPLETE\" in kneww.loc[in899,\"status\"]:\n",
 494 |     "                        print(\" YES! BUY bid Executed \")\n",
 495 |     "                        xel=min(datalow)-(za.iloc[-1,7]*.25)\n",
 496 |     "                        triggerpriceis=avg_ha(xel)\n",
 497 |     "                        sellorder.append(triggerpriceis)\n",
 498 |     "                        xelm=triggerpriceis-sell_add\n",
 499 |     "                        stoplossis=avg_ha(xelm)\n",
 500 |     "                        sellorder1.append(stoplossis)\n",
 501 |     "                        print(\"Buy Limit order executed on zerodha and now SELL order placed\")        \n",
 502 |     "                        print(\"Sell Stoploss order placed and SL trigger price is \", triggerpriceis,\"and stoploss is\",stoplossis)\n",
 503 |     "                        listt1=int(kite.place_order( variety=\"regular\",tradingsymbol=ttradingsymbol,quantity=qu,exchange=eexchange,order_type='SL',stoploss= triggerpriceis,trigger_price=stoplossis,transaction_type='SELL',product=productt,tag=\"RSIsel\"))\n",
 504 |     "                        listtt2=\"STATUS\"\n",
 505 |     "                        buy_order_sell_status.append(listtt2)\n",
 506 |     "                        zkak=int(listt1)\n",
 507 |     "                        buy_order_info_sell_orderid.append(zkak)\n",
 508 |     "                        print(buy_order_sell_status)\n",
 509 |     "                        print(buy_order_info_sell_orderid)\n",
 510 |     "                        sellbit_execute=secrets.randbelow(1)\n",
 511 |     "                        sellbit_execute_limit.append(sellbit_execute)\n",
 512 |     "                        glk=secrets.randbelow(1)\n",
 513 |     "                        glkk.append(glk)\n",
 514 |     "                    else:\n",
 515 |     "                        pass    \n",
 516 |     "                else:\n",
 517 |     "                    pass\n",
 518 |     "            if 0 in bii_by:\n",
 519 |     "                if 0 not in akki:\n",
 520 |     "                    if 21 in ame:\n",
 521 |     "                        orgprice=za.iloc[-1,2]\n",
 522 |     "                        orprice.append(orgprice)\n",
 523 |     "                        exx=(za.iloc[-1,2]+(za.iloc[-1,7]*.25))\n",
 524 |     "                        cur_high=avg_ha(exx)\n",
 525 |     "                        exxx=cur_high+buy_add\n",
 526 |     "                        lim_price=avg_ha(exxx)\n",
 527 |     "                        buyprice.append(cur_high) \n",
 528 |     "                        limitprice.append(lim_price)\n",
 529 |     "                        org_price.append(orgprice)\n",
 530 |     "                        print(\"Congrats ! One Limit BUY Order placed Price is\", lim_price , \"and trigger_price is \", cur_high)\n",
 531 |     "                        list_buy_symbol=ttradingsymbol\n",
 532 |     "                        list_buy_status=\"STATUS\"\n",
 533 |     "                        buy_order_info_buy_symbol.append(list_buy_symbol)\n",
 534 |     "                        buy_order_info_buy_status.append(list_buy_status)\n",
 535 |     "                        buy_order_info_buy_orid.append(kite.place_order(variety=\"regular\",tradingsymbol=ttradingsymbol,quantity=qu,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag=\"RSIbuy\"))         \n",
 536 |     "                        print(\" BUY ORDER SYMBOL \",buy_order_info_buy_symbol)\n",
 537 |     "                        print(\" BUY ORDER STATUS \",buy_order_info_buy_status)\n",
 538 |     "                        print(\" BUY ORDER ID IS  \",buy_order_info_buy_orid)\n",
 539 |     "                        biiiii=secrets.randbelow(1)\n",
 540 |     "                        akki.append(biiiii)\n",
 541 |     "                        p=secrets.randbelow(1)\n",
 542 |     "                        check.append(p)\n",
 543 |     "                    else:\n",
 544 |     "                        pass\n",
 545 |     "                else:\n",
 546 |     "                    pass\n",
 547 |     "            if 0 not in bii_by:\n",
 548 |     "                if 2 not in firs:\n",
 549 |     "                    if za.iloc[-2,6]>= RSI_VAL  and za.iloc[-1,6]<=RSI_VAL:\n",
 550 |     "                        rsichec.append(2)\n",
 551 |     "                        rsi_condition.append(now_asia)\n",
 552 |     "                        fiss=2\n",
 553 |     "                        apa=1\n",
 554 |     "                        firs.append(fiss)\n",
 555 |     "                        inpu.append(apa)\n",
 556 |     "                    else:\n",
 557 |     "                        pass\n",
 558 |     "                if 1 in inpu:\n",
 559 |     "                    datalow.append(za.iloc[-1,3])\n",
 560 |     "                    datarsi.append(za.iloc[-1,6])\n",
 561 |     "                    if za.iloc[-1,6]>=RSI_VAL2:\n",
 562 |     "                        print(\" Programe satisfied RSI condition \")\n",
 563 |     "                        if za.iloc[-2,2] < za.iloc[-1,2]:\n",
 564 |     "                            print(\"High breaks High condition satisfy\")\n",
 565 |     "                            high_to_high.append(2)\n",
 566 |     "                            beak_high.append(now_asia)\n",
 567 |     "                            bii = secrets.randbelow(1)\n",
 568 |     "                            bii_by.append(bii)    \n",
 569 |     "                        else:\n",
 570 |     "                            progrun.append(1)\n",
 571 |     "                            bii_by.clear()\n",
 572 |     "                            firs.clear()\n",
 573 |     "                            inpu.clear()\n",
 574 |     "                            datalow.clear()\n",
 575 |     "                            datarsi.clear()\n",
 576 |     "                            datarsi.append(0)\n",
 577 |     "                    else:\n",
 578 |     "                        pass\n",
 579 |     "                else:\n",
 580 |     "                    pass\n",
 581 |     "            else:\n",
 582 |     "                pass\n",
 583 |     "ashi()"
 584 |    ]
 585 |   },
 586 |   {
 587 |    "cell_type": "code",
 588 |    "execution_count": 14,
 589 |    "metadata": {},
 590 |    "outputs": [
 591 |     {
 592 |      "name": "stdout",
 593 |      "output_type": "stream",
 594 |      "text": [
 595 |       " \n",
 596 |       " \t \t \t \t \t ---- ALL THE HOLDINGS YOU HAVE ON THIS ACCOUNT----     \n"
 597 |      ]
 598 |     },
 599 |     {
 600 |      "data": {
 601 |       "text/html": [
 602 |        "<div>\n",
 603 |        "<style scoped>\n",
 604 |        "    .dataframe tbody tr th:only-of-type {\n",
 605 |        "        vertical-align: middle;\n",
 606 |        "    }\n",
 607 |        "\n",
 608 |        "    .dataframe tbody tr th {\n",
 609 |        "        vertical-align: top;\n",
 610 |        "    }\n",
 611 |        "\n",
 612 |        "    .dataframe thead th {\n",
 613 |        "        text-align: right;\n",
 614 |        "    }\n",
 615 |        "</style>\n",
 616 |        "<table border=\"1\" class=\"dataframe\">\n",
 617 |        "  <thead>\n",
 618 |        "    <tr style=\"text-align: right;\">\n",
 619 |        "      <th></th>\n",
 620 |        "      <th>tradingsymbol</th>\n",
 621 |        "      <th>instrument_token</th>\n",
 622 |        "      <th>pnl</th>\n",
 623 |        "      <th>average_price</th>\n",
 624 |        "      <th>close_price</th>\n",
 625 |        "      <th>collateral_quantity</th>\n",
 626 |        "      <th>collateral_type</th>\n",
 627 |        "      <th>day_change</th>\n",
 628 |        "      <th>day_change_percentage</th>\n",
 629 |        "      <th>exchange</th>\n",
 630 |        "      <th>last_price</th>\n",
 631 |        "      <th>isin</th>\n",
 632 |        "      <th>price</th>\n",
 633 |        "      <th>product</th>\n",
 634 |        "      <th>quantity</th>\n",
 635 |        "      <th>realised_quantity</th>\n",
 636 |        "      <th>t1_quantity</th>\n",
 637 |        "    </tr>\n",
 638 |        "  </thead>\n",
 639 |        "  <tbody>\n",
 640 |        "    <tr>\n",
 641 |        "      <th>0</th>\n",
 642 |        "      <td>CRESSAN</td>\n",
 643 |        "      <td>131169028</td>\n",
 644 |        "      <td>0.000000</td>\n",
 645 |        "      <td>0.200000</td>\n",
 646 |        "      <td>0.2</td>\n",
 647 |        "      <td>0</td>\n",
 648 |        "      <td></td>\n",
 649 |        "      <td>0.00</td>\n",
 650 |        "      <td>0.000000</td>\n",
 651 |        "      <td>BSE</td>\n",
 652 |        "      <td>0.20</td>\n",
 653 |        "      <td>INE716D01033</td>\n",
 654 |        "      <td>0</td>\n",
 655 |        "      <td>CNC</td>\n",
 656 |        "      <td>0</td>\n",
 657 |        "      <td>5</td>\n",
 658 |        "      <td>0</td>\n",
 659 |        "    </tr>\n",
 660 |        "    <tr>\n",
 661 |        "      <th>1</th>\n",
 662 |        "      <td>IDEA</td>\n",
 663 |        "      <td>3677697</td>\n",
 664 |        "      <td>-2.510001</td>\n",
 665 |        "      <td>11.786667</td>\n",
 666 |        "      <td>11.3</td>\n",
 667 |        "      <td>0</td>\n",
 668 |        "      <td></td>\n",
 669 |        "      <td>-0.35</td>\n",
 670 |        "      <td>-3.097345</td>\n",
 671 |        "      <td>NSE</td>\n",
 672 |        "      <td>10.95</td>\n",
 673 |        "      <td>INE669E01016</td>\n",
 674 |        "      <td>0</td>\n",
 675 |        "      <td>CNC</td>\n",
 676 |        "      <td>2</td>\n",
 677 |        "      <td>2</td>\n",
 678 |        "      <td>1</td>\n",
 679 |        "    </tr>\n",
 680 |        "  </tbody>\n",
 681 |        "</table>\n",
 682 |        "</div>"
 683 |       ],
 684 |       "text/plain": [
 685 |        "  tradingsymbol  instrument_token       pnl  average_price  close_price  \\\n",
 686 |        "0       CRESSAN         131169028  0.000000       0.200000          0.2   \n",
 687 |        "1          IDEA           3677697 -2.510001      11.786667         11.3   \n",
 688 |        "\n",
 689 |        "   collateral_quantity collateral_type  day_change  day_change_percentage  \\\n",
 690 |        "0                    0                        0.00               0.000000   \n",
 691 |        "1                    0                       -0.35              -3.097345   \n",
 692 |        "\n",
 693 |        "  exchange  last_price          isin  price product  quantity  \\\n",
 694 |        "0      BSE        0.20  INE716D01033      0     CNC         0   \n",
 695 |        "1      NSE       10.95  INE669E01016      0     CNC         2   \n",
 696 |        "\n",
 697 |        "   realised_quantity  t1_quantity  \n",
 698 |        "0                  5            0  \n",
 699 |        "1                  2            1  "
 700 |       ]
 701 |      },
 702 |      "metadata": {},
 703 |      "output_type": "display_data"
 704 |     },
 705 |     {
 706 |      "name": "stdout",
 707 |      "output_type": "stream",
 708 |      "text": [
 709 |       " \n",
 710 |       " \t \t \t \t \t ---- ALL THE POSITONS YOU HAVE ON THIS ACCOUNT----     \n"
 711 |      ]
 712 |     },
 713 |     {
 714 |      "data": {
 715 |       "text/html": [
 716 |        "<div>\n",
 717 |        "<style scoped>\n",
 718 |        "    .dataframe tbody tr th:only-of-type {\n",
 719 |        "        vertical-align: middle;\n",
 720 |        "    }\n",
 721 |        "\n",
 722 |        "    .dataframe tbody tr th {\n",
 723 |        "        vertical-align: top;\n",
 724 |        "    }\n",
 725 |        "\n",
 726 |        "    .dataframe thead th {\n",
 727 |        "        text-align: right;\n",
 728 |        "    }\n",
 729 |        "</style>\n",
 730 |        "<table border=\"1\" class=\"dataframe\">\n",
 731 |        "  <thead>\n",
 732 |        "    <tr style=\"text-align: right;\">\n",
 733 |        "      <th></th>\n",
 734 |        "      <th>tradingsymbol</th>\n",
 735 |        "      <th>average_price</th>\n",
 736 |        "      <th>buy_m2m</th>\n",
 737 |        "      <th>buy_price</th>\n",
 738 |        "      <th>buy_quantity</th>\n",
 739 |        "      <th>buy_value</th>\n",
 740 |        "      <th>close_price</th>\n",
 741 |        "      <th>sell_m2m</th>\n",
 742 |        "      <th>sell_price</th>\n",
 743 |        "      <th>sell_quantity</th>\n",
 744 |        "      <th>sell_value</th>\n",
 745 |        "      <th>unrealised</th>\n",
 746 |        "      <th>value</th>\n",
 747 |        "      <th>day_buy_price</th>\n",
 748 |        "      <th>day_buy_quantity</th>\n",
 749 |        "      <th>day_buy_value</th>\n",
 750 |        "      <th>day_sell_price</th>\n",
 751 |        "      <th>product</th>\n",
 752 |        "      <th>quantity</th>\n",
 753 |        "      <th>realised</th>\n",
 754 |        "    </tr>\n",
 755 |        "  </thead>\n",
 756 |        "  <tbody>\n",
 757 |        "    <tr>\n",
 758 |        "      <th>0</th>\n",
 759 |        "      <td>IDEA</td>\n",
 760 |        "      <td>11.2100</td>\n",
 761 |        "      <td>11.21</td>\n",
 762 |        "      <td>11.2100</td>\n",
 763 |        "      <td>1</td>\n",
 764 |        "      <td>11.21</td>\n",
 765 |        "      <td>0.0000</td>\n",
 766 |        "      <td>0.0</td>\n",
 767 |        "      <td>0.0</td>\n",
 768 |        "      <td>0</td>\n",
 769 |        "      <td>0.0</td>\n",
 770 |        "      <td>-0.31</td>\n",
 771 |        "      <td>-11.21</td>\n",
 772 |        "      <td>11.21</td>\n",
 773 |        "      <td>1</td>\n",
 774 |        "      <td>11.21</td>\n",
 775 |        "      <td>0.0</td>\n",
 776 |        "      <td>CNC</td>\n",
 777 |        "      <td>1</td>\n",
 778 |        "      <td>0</td>\n",
 779 |        "    </tr>\n",
 780 |        "    <tr>\n",
 781 |        "      <th>1</th>\n",
 782 |        "      <td>EURINR19JULFUT</td>\n",
 783 |        "      <td>77.5100</td>\n",
 784 |        "      <td>464955.00</td>\n",
 785 |        "      <td>77.5100</td>\n",
 786 |        "      <td>6</td>\n",
 787 |        "      <td>465060.00</td>\n",
 788 |        "      <td>77.4925</td>\n",
 789 |        "      <td>0.0</td>\n",
 790 |        "      <td>0.0</td>\n",
 791 |        "      <td>0</td>\n",
 792 |        "      <td>0.0</td>\n",
 793 |        "      <td>780.00</td>\n",
 794 |        "      <td>-465060.00</td>\n",
 795 |        "      <td>0.00</td>\n",
 796 |        "      <td>0</td>\n",
 797 |        "      <td>0.00</td>\n",
 798 |        "      <td>0.0</td>\n",
 799 |        "      <td>NRML</td>\n",
 800 |        "      <td>6</td>\n",
 801 |        "      <td>0</td>\n",
 802 |        "    </tr>\n",
 803 |        "    <tr>\n",
 804 |        "      <th>2</th>\n",
 805 |        "      <td>GBPINR19JULFUT</td>\n",
 806 |        "      <td>85.7900</td>\n",
 807 |        "      <td>517215.00</td>\n",
 808 |        "      <td>85.7900</td>\n",
 809 |        "      <td>6</td>\n",
 810 |        "      <td>514740.00</td>\n",
 811 |        "      <td>86.2025</td>\n",
 812 |        "      <td>0.0</td>\n",
 813 |        "      <td>0.0</td>\n",
 814 |        "      <td>0</td>\n",
 815 |        "      <td>0.0</td>\n",
 816 |        "      <td>3990.00</td>\n",
 817 |        "      <td>-514740.00</td>\n",
 818 |        "      <td>0.00</td>\n",
 819 |        "      <td>0</td>\n",
 820 |        "      <td>0.00</td>\n",
 821 |        "      <td>0.0</td>\n",
 822 |        "      <td>NRML</td>\n",
 823 |        "      <td>6</td>\n",
 824 |        "      <td>0</td>\n",
 825 |        "    </tr>\n",
 826 |        "    <tr>\n",
 827 |        "      <th>3</th>\n",
 828 |        "      <td>JPYINR19JULFUT</td>\n",
 829 |        "      <td>64.0100</td>\n",
 830 |        "      <td>384060.00</td>\n",
 831 |        "      <td>64.0100</td>\n",
 832 |        "      <td>6</td>\n",
 833 |        "      <td>384060.00</td>\n",
 834 |        "      <td>64.0100</td>\n",
 835 |        "      <td>0.0</td>\n",
 836 |        "      <td>0.0</td>\n",
 837 |        "      <td>0</td>\n",
 838 |        "      <td>0.0</td>\n",
 839 |        "      <td>765.00</td>\n",
 840 |        "      <td>-384060.00</td>\n",
 841 |        "      <td>0.00</td>\n",
 842 |        "      <td>0</td>\n",
 843 |        "      <td>0.00</td>\n",
 844 |        "      <td>0.0</td>\n",
 845 |        "      <td>NRML</td>\n",
 846 |        "      <td>6</td>\n",
 847 |        "      <td>0</td>\n",
 848 |        "    </tr>\n",
 849 |        "    <tr>\n",
 850 |        "      <th>4</th>\n",
 851 |        "      <td>USDINR19JULFUT</td>\n",
 852 |        "      <td>68.6475</td>\n",
 853 |        "      <td>207015.00</td>\n",
 854 |        "      <td>68.6475</td>\n",
 855 |        "      <td>3</td>\n",
 856 |        "      <td>205942.50</td>\n",
 857 |        "      <td>69.0050</td>\n",
 858 |        "      <td>0.0</td>\n",
 859 |        "      <td>0.0</td>\n",
 860 |        "      <td>0</td>\n",
 861 |        "      <td>0.0</td>\n",
 862 |        "      <td>1035.00</td>\n",
 863 |        "      <td>-205942.50</td>\n",
 864 |        "      <td>0.00</td>\n",
 865 |        "      <td>0</td>\n",
 866 |        "      <td>0.00</td>\n",
 867 |        "      <td>0.0</td>\n",
 868 |        "      <td>NRML</td>\n",
 869 |        "      <td>3</td>\n",
 870 |        "      <td>0</td>\n",
 871 |        "    </tr>\n",
 872 |        "  </tbody>\n",
 873 |        "</table>\n",
 874 |        "</div>"
 875 |       ],
 876 |       "text/plain": [
 877 |        "    tradingsymbol  average_price    buy_m2m  buy_price  buy_quantity  \\\n",
 878 |        "0            IDEA        11.2100      11.21    11.2100             1   \n",
 879 |        "1  EURINR19JULFUT        77.5100  464955.00    77.5100             6   \n",
 880 |        "2  GBPINR19JULFUT        85.7900  517215.00    85.7900             6   \n",
 881 |        "3  JPYINR19JULFUT        64.0100  384060.00    64.0100             6   \n",
 882 |        "4  USDINR19JULFUT        68.6475  207015.00    68.6475             3   \n",
 883 |        "\n",
 884 |        "   buy_value  close_price  sell_m2m  sell_price  sell_quantity  sell_value  \\\n",
 885 |        "0      11.21       0.0000       0.0         0.0              0         0.0   \n",
 886 |        "1  465060.00      77.4925       0.0         0.0              0         0.0   \n",
 887 |        "2  514740.00      86.2025       0.0         0.0              0         0.0   \n",
 888 |        "3  384060.00      64.0100       0.0         0.0              0         0.0   \n",
 889 |        "4  205942.50      69.0050       0.0         0.0              0         0.0   \n",
 890 |        "\n",
 891 |        "   unrealised      value  day_buy_price  day_buy_quantity  day_buy_value  \\\n",
 892 |        "0       -0.31     -11.21          11.21                 1          11.21   \n",
 893 |        "1      780.00 -465060.00           0.00                 0           0.00   \n",
 894 |        "2     3990.00 -514740.00           0.00                 0           0.00   \n",
 895 |        "3      765.00 -384060.00           0.00                 0           0.00   \n",
 896 |        "4     1035.00 -205942.50           0.00                 0           0.00   \n",
 897 |        "\n",
 898 |        "   day_sell_price product  quantity  realised  \n",
 899 |        "0             0.0     CNC         1         0  \n",
 900 |        "1             0.0    NRML         6         0  \n",
 901 |        "2             0.0    NRML         6         0  \n",
 902 |        "3             0.0    NRML         6         0  \n",
 903 |        "4             0.0    NRML         3         0  "
 904 |       ]
 905 |      },
 906 |      "metadata": {},
 907 |      "output_type": "display_data"
 908 |     },
 909 |     {
 910 |      "name": "stdout",
 911 |      "output_type": "stream",
 912 |      "text": [
 913 |       " \n",
 914 |       " \t \t \t \t \t ---- ALL THE ORDERS YOU HAVE ON THIS ACCOUNT----     \n"
 915 |      ]
 916 |     },
 917 |     {
 918 |      "data": {
 919 |       "text/html": [
 920 |        "<div>\n",
 921 |        "<style scoped>\n",
 922 |        "    .dataframe tbody tr th:only-of-type {\n",
 923 |        "        vertical-align: middle;\n",
 924 |        "    }\n",
 925 |        "\n",
 926 |        "    .dataframe tbody tr th {\n",
 927 |        "        vertical-align: top;\n",
 928 |        "    }\n",
 929 |        "\n",
 930 |        "    .dataframe thead th {\n",
 931 |        "        text-align: right;\n",
 932 |        "    }\n",
 933 |        "</style>\n",
 934 |        "<table border=\"1\" class=\"dataframe\">\n",
 935 |        "  <thead>\n",
 936 |        "    <tr style=\"text-align: right;\">\n",
 937 |        "      <th></th>\n",
 938 |        "      <th>tradingsymbol</th>\n",
 939 |        "      <th>transaction_type</th>\n",
 940 |        "      <th>order_id</th>\n",
 941 |        "      <th>quantity</th>\n",
 942 |        "      <th>order_type</th>\n",
 943 |        "      <th>exchange_update_timestamp</th>\n",
 944 |        "      <th>tag</th>\n",
 945 |        "      <th>quantity</th>\n",
 946 |        "      <th>status</th>\n",
 947 |        "    </tr>\n",
 948 |        "  </thead>\n",
 949 |        "  <tbody>\n",
 950 |        "    <tr>\n",
 951 |        "      <th>0</th>\n",
 952 |        "      <td>SBIN</td>\n",
 953 |        "      <td>BUY</td>\n",
 954 |        "      <td>190719001131173</td>\n",
 955 |        "      <td>1</td>\n",
 956 |        "      <td>SL</td>\n",
 957 |        "      <td>None</td>\n",
 958 |        "      <td>RSIbuy</td>\n",
 959 |        "      <td>1</td>\n",
 960 |        "      <td>REJECTED</td>\n",
 961 |        "    </tr>\n",
 962 |        "    <tr>\n",
 963 |        "      <th>1</th>\n",
 964 |        "      <td>SBIN</td>\n",
 965 |        "      <td>BUY</td>\n",
 966 |        "      <td>190719001136838</td>\n",
 967 |        "      <td>1</td>\n",
 968 |        "      <td>SL</td>\n",
 969 |        "      <td>None</td>\n",
 970 |        "      <td>RSIbuy</td>\n",
 971 |        "      <td>1</td>\n",
 972 |        "      <td>REJECTED</td>\n",
 973 |        "    </tr>\n",
 974 |        "    <tr>\n",
 975 |        "      <th>2</th>\n",
 976 |        "      <td>SBIN</td>\n",
 977 |        "      <td>BUY</td>\n",
 978 |        "      <td>190719001143372</td>\n",
 979 |        "      <td>1</td>\n",
 980 |        "      <td>SL</td>\n",
 981 |        "      <td>None</td>\n",
 982 |        "      <td>RSIbuy</td>\n",
 983 |        "      <td>1</td>\n",
 984 |        "      <td>REJECTED</td>\n",
 985 |        "    </tr>\n",
 986 |        "    <tr>\n",
 987 |        "      <th>3</th>\n",
 988 |        "      <td>SBIN</td>\n",
 989 |        "      <td>BUY</td>\n",
 990 |        "      <td>190719001191209</td>\n",
 991 |        "      <td>1</td>\n",
 992 |        "      <td>SL</td>\n",
 993 |        "      <td>None</td>\n",
 994 |        "      <td>RSIbuy</td>\n",
 995 |        "      <td>1</td>\n",
 996 |        "      <td>REJECTED</td>\n",
 997 |        "    </tr>\n",
 998 |        "    <tr>\n",
 999 |        "      <th>4</th>\n",
1000 |        "      <td>IDEA</td>\n",
1001 |        "      <td>BUY</td>\n",
1002 |        "      <td>190719001278642</td>\n",
1003 |        "      <td>1</td>\n",
1004 |        "      <td>SL</td>\n",
1005 |        "      <td>2019-07-19 11:24:04</td>\n",
1006 |        "      <td>RSIbuy</td>\n",
1007 |        "      <td>1</td>\n",
1008 |        "      <td>REJECTED</td>\n",
1009 |        "    </tr>\n",
1010 |        "    <tr>\n",
1011 |        "      <th>5</th>\n",
1012 |        "      <td>ADANIPOWER</td>\n",
1013 |        "      <td>BUY</td>\n",
1014 |        "      <td>190719001279199</td>\n",
1015 |        "      <td>1</td>\n",
1016 |        "      <td>SL</td>\n",
1017 |        "      <td>2019-07-19 11:24:10</td>\n",
1018 |        "      <td>RSIbuy</td>\n",
1019 |        "      <td>1</td>\n",
1020 |        "      <td>REJECTED</td>\n",
1021 |        "    </tr>\n",
1022 |        "    <tr>\n",
1023 |        "      <th>6</th>\n",
1024 |        "      <td>IDEA</td>\n",
1025 |        "      <td>BUY</td>\n",
1026 |        "      <td>190719001322367</td>\n",
1027 |        "      <td>1</td>\n",
1028 |        "      <td>LIMIT</td>\n",
1029 |        "      <td>2019-07-19 11:32:24</td>\n",
1030 |        "      <td>RSIbuy</td>\n",
1031 |        "      <td>1</td>\n",
1032 |        "      <td>COMPLETE</td>\n",
1033 |        "    </tr>\n",
1034 |        "    <tr>\n",
1035 |        "      <th>7</th>\n",
1036 |        "      <td>SBIN</td>\n",
1037 |        "      <td>BUY</td>\n",
1038 |        "      <td>190719000730248</td>\n",
1039 |        "      <td>1</td>\n",
1040 |        "      <td>SL</td>\n",
1041 |        "      <td>2019-07-19 11:44:36</td>\n",
1042 |        "      <td>RSIbuy</td>\n",
1043 |        "      <td>1</td>\n",
1044 |        "      <td>CANCELLED</td>\n",
1045 |        "    </tr>\n",
1046 |        "    <tr>\n",
1047 |        "      <th>8</th>\n",
1048 |        "      <td>IDEA</td>\n",
1049 |        "      <td>BUY</td>\n",
1050 |        "      <td>190719001368836</td>\n",
1051 |        "      <td>1</td>\n",
1052 |        "      <td>SL</td>\n",
1053 |        "      <td>2019-07-19 11:44:36</td>\n",
1054 |        "      <td>None</td>\n",
1055 |        "      <td>1</td>\n",
1056 |        "      <td>CANCELLED</td>\n",
1057 |        "    </tr>\n",
1058 |        "    <tr>\n",
1059 |        "      <th>9</th>\n",
1060 |        "      <td>ADANIPOWER</td>\n",
1061 |        "      <td>BUY</td>\n",
1062 |        "      <td>190719001388194</td>\n",
1063 |        "      <td>1</td>\n",
1064 |        "      <td>SL</td>\n",
1065 |        "      <td>2019-07-19 11:44:36</td>\n",
1066 |        "      <td>None</td>\n",
1067 |        "      <td>1</td>\n",
1068 |        "      <td>CANCELLED</td>\n",
1069 |        "    </tr>\n",
1070 |        "    <tr>\n",
1071 |        "      <th>10</th>\n",
1072 |        "      <td>ADANIPOWER</td>\n",
1073 |        "      <td>BUY</td>\n",
1074 |        "      <td>190719001393518</td>\n",
1075 |        "      <td>1</td>\n",
1076 |        "      <td>SL</td>\n",
1077 |        "      <td>2019-07-19 11:44:36</td>\n",
1078 |        "      <td>None</td>\n",
1079 |        "      <td>1</td>\n",
1080 |        "      <td>CANCELLED</td>\n",
1081 |        "    </tr>\n",
1082 |        "    <tr>\n",
1083 |        "      <th>11</th>\n",
1084 |        "      <td>IDEA</td>\n",
1085 |        "      <td>BUY</td>\n",
1086 |        "      <td>190719001691092</td>\n",
1087 |        "      <td>1</td>\n",
1088 |        "      <td>SL</td>\n",
1089 |        "      <td>None</td>\n",
1090 |        "      <td>None</td>\n",
1091 |        "      <td>1</td>\n",
1092 |        "      <td>REJECTED</td>\n",
1093 |        "    </tr>\n",
1094 |        "    <tr>\n",
1095 |        "      <th>12</th>\n",
1096 |        "      <td>IDEA</td>\n",
1097 |        "      <td>BUY</td>\n",
1098 |        "      <td>190719001700301</td>\n",
1099 |        "      <td>1</td>\n",
1100 |        "      <td>SL</td>\n",
1101 |        "      <td>None</td>\n",
1102 |        "      <td>None</td>\n",
1103 |        "      <td>1</td>\n",
1104 |        "      <td>REJECTED</td>\n",
1105 |        "    </tr>\n",
1106 |        "    <tr>\n",
1107 |        "      <th>13</th>\n",
1108 |        "      <td>IDEA</td>\n",
1109 |        "      <td>BUY</td>\n",
1110 |        "      <td>190719001697624</td>\n",
1111 |        "      <td>1</td>\n",
1112 |        "      <td>SL</td>\n",
1113 |        "      <td>2019-07-19 12:54:45</td>\n",
1114 |        "      <td>None</td>\n",
1115 |        "      <td>1</td>\n",
1116 |        "      <td>CANCELLED</td>\n",
1117 |        "    </tr>\n",
1118 |        "    <tr>\n",
1119 |        "      <th>14</th>\n",
1120 |        "      <td>ADANIPOWER</td>\n",
1121 |        "      <td>BUY</td>\n",
1122 |        "      <td>190719001706115</td>\n",
1123 |        "      <td>1</td>\n",
1124 |        "      <td>SL</td>\n",
1125 |        "      <td>2019-07-19 12:54:45</td>\n",
1126 |        "      <td>None</td>\n",
1127 |        "      <td>1</td>\n",
1128 |        "      <td>CANCELLED</td>\n",
1129 |        "    </tr>\n",
1130 |        "    <tr>\n",
1131 |        "      <th>15</th>\n",
1132 |        "      <td>ADANIPOWER</td>\n",
1133 |        "      <td>BUY</td>\n",
1134 |        "      <td>190719001709108</td>\n",
1135 |        "      <td>1</td>\n",
1136 |        "      <td>SL</td>\n",
1137 |        "      <td>2019-07-19 12:54:45</td>\n",
1138 |        "      <td>None</td>\n",
1139 |        "      <td>1</td>\n",
1140 |        "      <td>CANCELLED</td>\n",
1141 |        "    </tr>\n",
1142 |        "    <tr>\n",
1143 |        "      <th>16</th>\n",
1144 |        "      <td>ADANIPOWER</td>\n",
1145 |        "      <td>BUY</td>\n",
1146 |        "      <td>190719001711914</td>\n",
1147 |        "      <td>1</td>\n",
1148 |        "      <td>SL</td>\n",
1149 |        "      <td>2019-07-19 12:54:45</td>\n",
1150 |        "      <td>None</td>\n",
1151 |        "      <td>1</td>\n",
1152 |        "      <td>CANCELLED</td>\n",
1153 |        "    </tr>\n",
1154 |        "    <tr>\n",
1155 |        "      <th>17</th>\n",
1156 |        "      <td>ADANIPOWER</td>\n",
1157 |        "      <td>BUY</td>\n",
1158 |        "      <td>190719001725812</td>\n",
1159 |        "      <td>1</td>\n",
1160 |        "      <td>SL</td>\n",
1161 |        "      <td>2019-07-19 12:54:45</td>\n",
1162 |        "      <td>None</td>\n",
1163 |        "      <td>1</td>\n",
1164 |        "      <td>CANCELLED</td>\n",
1165 |        "    </tr>\n",
1166 |        "    <tr>\n",
1167 |        "      <th>18</th>\n",
1168 |        "      <td>IDEA</td>\n",
1169 |        "      <td>BUY</td>\n",
1170 |        "      <td>190719001759893</td>\n",
1171 |        "      <td>1</td>\n",
1172 |        "      <td>SL</td>\n",
1173 |        "      <td>None</td>\n",
1174 |        "      <td>None</td>\n",
1175 |        "      <td>1</td>\n",
1176 |        "      <td>REJECTED</td>\n",
1177 |        "    </tr>\n",
1178 |        "    <tr>\n",
1179 |        "      <th>19</th>\n",
1180 |        "      <td>IDEA</td>\n",
1181 |        "      <td>BUY</td>\n",
1182 |        "      <td>190719001782460</td>\n",
1183 |        "      <td>1</td>\n",
1184 |        "      <td>SL</td>\n",
1185 |        "      <td>None</td>\n",
1186 |        "      <td>None</td>\n",
1187 |        "      <td>1</td>\n",
1188 |        "      <td>REJECTED</td>\n",
1189 |        "    </tr>\n",
1190 |        "    <tr>\n",
1191 |        "      <th>20</th>\n",
1192 |        "      <td>CRESSAN</td>\n",
1193 |        "      <td>SELL</td>\n",
1194 |        "      <td>190719001731860</td>\n",
1195 |        "      <td>5</td>\n",
1196 |        "      <td>LIMIT</td>\n",
1197 |        "      <td>2019-07-19 12:55:01</td>\n",
1198 |        "      <td>None</td>\n",
1199 |        "      <td>5</td>\n",
1200 |        "      <td>OPEN</td>\n",
1201 |        "    </tr>\n",
1202 |        "    <tr>\n",
1203 |        "      <th>21</th>\n",
1204 |        "      <td>ADANIPOWER</td>\n",
1205 |        "      <td>BUY</td>\n",
1206 |        "      <td>190719002505971</td>\n",
1207 |        "      <td>1</td>\n",
1208 |        "      <td>MARKET</td>\n",
1209 |        "      <td>2019-07-19 15:12:10</td>\n",
1210 |        "      <td>None</td>\n",
1211 |        "      <td>1</td>\n",
1212 |        "      <td>COMPLETE</td>\n",
1213 |        "    </tr>\n",
1214 |        "    <tr>\n",
1215 |        "      <th>22</th>\n",
1216 |        "      <td>ADANIPOWER</td>\n",
1217 |        "      <td>BUY</td>\n",
1218 |        "      <td>190719002150751</td>\n",
1219 |        "      <td>1</td>\n",
1220 |        "      <td>SL</td>\n",
1221 |        "      <td>2019-07-19 15:12:35</td>\n",
1222 |        "      <td>None</td>\n",
1223 |        "      <td>1</td>\n",
1224 |        "      <td>CANCELLED</td>\n",
1225 |        "    </tr>\n",
1226 |        "    <tr>\n",
1227 |        "      <th>23</th>\n",
1228 |        "      <td>ADANIPOWER</td>\n",
1229 |        "      <td>SELL</td>\n",
1230 |        "      <td>190719002521645</td>\n",
1231 |        "      <td>1</td>\n",
1232 |        "      <td>MARKET</td>\n",
1233 |        "      <td>2019-07-19 15:14:33</td>\n",
1234 |        "      <td>None</td>\n",
1235 |        "      <td>1</td>\n",
1236 |        "      <td>COMPLETE</td>\n",
1237 |        "    </tr>\n",
1238 |        "  </tbody>\n",
1239 |        "</table>\n",
1240 |        "</div>"
1241 |       ],
1242 |       "text/plain": [
1243 |        "   tradingsymbol transaction_type         order_id  quantity order_type  \\\n",
1244 |        "0           SBIN              BUY  190719001131173         1         SL   \n",
1245 |        "1           SBIN              BUY  190719001136838         1         SL   \n",
1246 |        "2           SBIN              BUY  190719001143372         1         SL   \n",
1247 |        "3           SBIN              BUY  190719001191209         1         SL   \n",
1248 |        "4           IDEA              BUY  190719001278642         1         SL   \n",
1249 |        "5     ADANIPOWER              BUY  190719001279199         1         SL   \n",
1250 |        "6           IDEA              BUY  190719001322367         1      LIMIT   \n",
1251 |        "7           SBIN              BUY  190719000730248         1         SL   \n",
1252 |        "8           IDEA              BUY  190719001368836         1         SL   \n",
1253 |        "9     ADANIPOWER              BUY  190719001388194         1         SL   \n",
1254 |        "10    ADANIPOWER              BUY  190719001393518         1         SL   \n",
1255 |        "11          IDEA              BUY  190719001691092         1         SL   \n",
1256 |        "12          IDEA              BUY  190719001700301         1         SL   \n",
1257 |        "13          IDEA              BUY  190719001697624         1         SL   \n",
1258 |        "14    ADANIPOWER              BUY  190719001706115         1         SL   \n",
1259 |        "15    ADANIPOWER              BUY  190719001709108         1         SL   \n",
1260 |        "16    ADANIPOWER              BUY  190719001711914         1         SL   \n",
1261 |        "17    ADANIPOWER              BUY  190719001725812         1         SL   \n",
1262 |        "18          IDEA              BUY  190719001759893         1         SL   \n",
1263 |        "19          IDEA              BUY  190719001782460         1         SL   \n",
1264 |        "20       CRESSAN             SELL  190719001731860         5      LIMIT   \n",
1265 |        "21    ADANIPOWER              BUY  190719002505971         1     MARKET   \n",
1266 |        "22    ADANIPOWER              BUY  190719002150751         1         SL   \n",
1267 |        "23    ADANIPOWER             SELL  190719002521645         1     MARKET   \n",
1268 |        "\n",
1269 |        "   exchange_update_timestamp     tag  quantity     status  \n",
1270 |        "0                       None  RSIbuy         1   REJECTED  \n",
1271 |        "1                       None  RSIbuy         1   REJECTED  \n",
1272 |        "2                       None  RSIbuy         1   REJECTED  \n",
1273 |        "3                       None  RSIbuy         1   REJECTED  \n",
1274 |        "4        2019-07-19 11:24:04  RSIbuy         1   REJECTED  \n",
1275 |        "5        2019-07-19 11:24:10  RSIbuy         1   REJECTED  \n",
1276 |        "6        2019-07-19 11:32:24  RSIbuy         1   COMPLETE  \n",
1277 |        "7        2019-07-19 11:44:36  RSIbuy         1  CANCELLED  \n",
1278 |        "8        2019-07-19 11:44:36    None         1  CANCELLED  \n",
1279 |        "9        2019-07-19 11:44:36    None         1  CANCELLED  \n",
1280 |        "10       2019-07-19 11:44:36    None         1  CANCELLED  \n",
1281 |        "11                      None    None         1   REJECTED  \n",
1282 |        "12                      None    None         1   REJECTED  \n",
1283 |        "13       2019-07-19 12:54:45    None         1  CANCELLED  \n",
1284 |        "14       2019-07-19 12:54:45    None         1  CANCELLED  \n",
1285 |        "15       2019-07-19 12:54:45    None         1  CANCELLED  \n",
1286 |        "16       2019-07-19 12:54:45    None         1  CANCELLED  \n",
1287 |        "17       2019-07-19 12:54:45    None         1  CANCELLED  \n",
1288 |        "18                      None    None         1   REJECTED  \n",
1289 |        "19                      None    None         1   REJECTED  \n",
1290 |        "20       2019-07-19 12:55:01    None         5       OPEN  \n",
1291 |        "21       2019-07-19 15:12:10    None         1   COMPLETE  \n",
1292 |        "22       2019-07-19 15:12:35    None         1  CANCELLED  \n",
1293 |        "23       2019-07-19 15:14:33    None         1   COMPLETE  "
1294 |       ]
1295 |      },
1296 |      "metadata": {},
1297 |      "output_type": "display_data"
1298 |     }
1299 |    ],
1300 |    "source": [
1301 |     "print(\" \\n \\t \\t \\t \\t \\t ---- ALL THE HOLDINGS YOU HAVE ON THIS ACCOUNT----     \")\n",
1302 |     "display(pd.DataFrame(kite.holdings())[[\"tradingsymbol\",\"instrument_token\",\"pnl\",\"average_price\",\"close_price\",\"collateral_quantity\",\"collateral_type\",\"day_change\",\"day_change_percentage\",\"exchange\",\"last_price\",\"isin\",\"price\",\"product\",\"quantity\",\"realised_quantity\",\"t1_quantity\" ]])      \n",
1303 |     "print(\" \\n \\t \\t \\t \\t \\t ---- ALL THE POSITONS YOU HAVE ON THIS ACCOUNT----     \")\n",
1304 |     "display(pd.DataFrame(kite.positions()[\"net\"])[[\"tradingsymbol\",\"average_price\",\"buy_m2m\",\"buy_price\",\"buy_quantity\",\"buy_value\",\"close_price\",\"sell_m2m\",\"sell_price\",\"sell_quantity\",\"sell_value\",\"unrealised\",\"value\",\"day_buy_price\",\"day_buy_quantity\",\"day_buy_value\",\"day_sell_price\",\"product\",\"quantity\",\"realised\"]].head())\n",
1305 |     "print(\" \\n \\t \\t \\t \\t \\t ---- ALL THE ORDERS YOU HAVE ON THIS ACCOUNT----     \")\n",
1306 |     "display(pd.DataFrame(kite.orders())[[\"tradingsymbol\",\"transaction_type\",\"order_id\",\"quantity\",\"order_type\",\"exchange_update_timestamp\",'tag',\"quantity\",\"status\"]])"
1307 |    ]
1308 |   },
1309 |   {
1310 |    "cell_type": "code",
1311 |    "execution_count": null,
1312 |    "metadata": {},
1313 |    "outputs": [],
1314 |    "source": []
1315 |   }
1316 |  ],
1317 |  "metadata": {
1318 |   "colab": {
1319 |    "collapsed_sections": [],
1320 |    "name": "RSI BOT 2.0.ipynb",
1321 |    "provenance": [],
1322 |    "version": "0.3.2"
1323 |   },
1324 |   "kernelspec": {
1325 |    "display_name": "Python 3 (ipykernel)",
1326 |    "language": "python",
1327 |    "name": "python3"
1328 |   },
1329 |   "language_info": {
1330 |    "codemirror_mode": {
1331 |     "name": "ipython",
1332 |     "version": 3
1333 |    },
1334 |    "file_extension": ".py",
1335 |    "mimetype": "text/x-python",
1336 |    "name": "python",
1337 |    "nbconvert_exporter": "python",
1338 |    "pygments_lexer": "ipython3",
1339 |    "version": "3.10.12"
1340 |   }
1341 |  },
1342 |  "nbformat": 4,
1343 |  "nbformat_minor": 1
1344 | }
1345 | 


--------------------------------------------------------------------------------
/2.8 - Backtesting Guppy Multiple Moving Average (GMMA) with Python using Zerodha API.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "#https://unofficed.com/lessons/backtesting-guppy-multiple-moving-average-gmma-with-python-using-zerodha-api/"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": null,
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import logging\n",
 19 |     "from kiteconnect import KiteConnect\n",
 20 |     "\n",
 21 |     "logging.basicConfig(level=logging.DEBUG)\n",
 22 |     "\n",
 23 |     "kite = KiteConnect(api_key=\"your_api_key\")\n",
 24 |     "\n",
 25 |     "# Redirect the user to the login url obtained\n",
 26 |     "# from kite.login_url(), and receive the request_token\n",
 27 |     "# from the registered redirect url after the login flow.\n",
 28 |     "# Once you have the request_token, obtain the access_token\n",
 29 |     "# as follows.\n",
 30 |     "\n",
 31 |     "data = kite.generate_session(\"request_token_here\", api_secret=\"your_secret\")\n",
 32 |     "kite.set_access_token(data[\"access_token\"])"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 6,
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "from datetime import date, timedelta\n",
 42 |     "import pandas as pd\n",
 43 |     "from pytz import timezone"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "markdown",
 48 |    "metadata": {},
 49 |    "source": [
 50 |     "# Backtesting for BUY "
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": 4,
 56 |    "metadata": {},
 57 |    "outputs": [
 58 |     {
 59 |      "name": "stdout",
 60 |      "output_type": "stream",
 61 |      "text": [
 62 |       "SCANNING START\n",
 63 |       "complete\n"
 64 |      ]
 65 |     }
 66 |    ],
 67 |    "source": [
 68 |     "tokenall=[5633, 6401, 3861249, 4451329, 2760193, 20993, 325121, 2524673, 41729, 49409, 54273, 60417, 70401, 1510401, 1195009, 1214721, 94977, 108033, 2714625, 2911489, 2763265, 3812865, 160001, 160769, 163073, 177665, 5215745, 3876097, 197633, 3771393, 225537, 173057, 261889, 1207553, 3463169, 2796801, 315393, 3378433, 2513665, 1850625, 340481, 341249, 3789569, 345089, 2747905, 348929, 359937, 356865, 364545, 3699201, 1270529, 377857, 3677697, 3060993, 381697, 2883073, 387073, 387841, 1346049, 408065, 2393089, 415745, 3920129, 424961, 1723649, 2661633, 2933761, 3011329, 4574465, 3001089, 4632577, 492033, 2061825, 511233, 2939649, 2672641, 519937, 2815745, 2674433, 582913, 593665, 3924993, 2977281, 2748929, 633601, 2819073, 636673, 2730497, 3834113, 2906881, 3364353, 731905, 3375873, 3930881, 737793, 738561, 141569, 3078657, 779521, 780289, 1492737, 1522689, 1102337, 857857, 3431425, 3076609, 1837825, 871681, 952577, 878593, 884737, 4343041, 877057, 895745, 2953217, 3465729, 897537, 2873089, 2952193, 2752769, 920065, 951809, 3026177, 969473, 3050241, 112129, 134657, 3721473, 2800641, 3385857, 4454401, 1152769, 806401, 617473, 2905857, 3660545, 3906305, 758529, 975873]\n",
 69 |     "ss=['ACC', 'ADANIENT', 'ADANIPORTS', 'ADANIPOWER', 'ALBK', 'ALOKTEXT', 'AMBUJACEM', 'ANDHRABANK', 'APOLLOTYRE', 'ARVIND', 'ASHOKLEY', 'ASIANPAINT', 'AUROPHARMA', 'AXISBANK', 'BANKBARODA', 'BANKINDIA', 'BATAINDIA', 'BHARATFORG', 'BHARTIARTL', 'BIOCON', 'CANBK', 'CENTRALBK', 'CENTURYTEX', 'CESC', 'CHAMBLFERT', 'CIPLA', 'COALINDIA', 'COLPAL', 'DABUR', 'DLF', 'DRREDDY', 'EXIDEIND', 'FEDERALBNK', 'GAIL', 'GMRINFRA', 'GODREJIND', 'GRASIM', 'GSPL', 'HAVELLS', 'HCLTECH', 'HDFC', 'HDFCBANK', 'HDIL', 'HEROMOTOCO', 'HEXAWARE', 'HINDALCO', 'HINDPETRO', 'HINDUNILVR', 'HINDZINC', 'IBREALEST', 'ICICIBANK', 'IDBI', 'IDEA', 'IDFC', 'IFCI', 'IGL', 'INDHOTEL', 'INDIACEM', 'INDUSINDBK', 'INFY', 'IOB', 'IOC', 'IRB', 'ITC', 'JINDALSTEL', 'JISLJALEQS', 'JPASSOCIAT', 'JPPOWER', 'JSWENERGY', 'JSWSTEEL', 'JUBLFOOD', 'KOTAKBANK', 'KTKBANK', 'LICHSGFIN', 'LT', 'LUPIN', 'M&M', 'MARUTI', 'MCDOWELL-N', 'MRF', 'NCC', 'NMDC', 'NTPC', 'OFSS', 'ONGC', 'OPTOCIRCUI', 'ORIENTBANK', 'PNB', 'POWERGRID', 'PTC', 'PUNJLLOYD', 'RAYMOND', 'RCOM', 'RECLTD', 'RELCAPITAL', 'RELIANCE', 'RELINFRA', 'RENUKA', 'SBIN', 'SCI', 'SINTEX', 'SOUTHBANK', 'SRTRANSFIN', 'SUNPHARMA', 'SUNTV', 'SUZLON', 'SYNDIBANK', 'TATACHEM', 'TATACOMM', 'TATAGLOBAL', 'TATAMOTORS', 'TATAMTRDVR', 'TATAPOWER', 'TATASTEEL', 'TCS', 'TECHM', 'TITAN', 'UCOBANK', 'ULTRACEMCO', 'UNIONBANK', 'UNITECH', 'VOLTAS', 'WELCORP', 'WIPRO', 'YESBANK', 'BHEL', 'BPCL', 'DISHTV', 'DIVISLAB', 'GVKPIL', 'NHPC', 'MPHASIS', 'SIEMENS', 'PEL', 'PETRONET', 'PFC', 'RPOWER', 'SAIL', 'ZEEL']\n",
 70 |     "\n",
 71 |     "# inputs for backtesting \n",
 72 |     "\n",
 73 |     "sdate           =\"2018-08-20 00:00:00\"\n",
 74 |     "todate          =\"2019-09-16 09:16:00\"\n",
 75 |     "#sdate          =\"2019-08-19\"\n",
 76 |     "#todate         =\"2019-10-01\"\n",
 77 |     "time_frame      =\"minute\"\n",
 78 |     "quu=50000\n",
 79 |     "eexchange=\"NSE\"\n",
 80 |     "productt=\"MIS\"\n",
 81 |     "qu=int(quu)\n",
 82 |     "\n",
 83 |     "# baktesting starting and ending date \n",
 84 |     "\n",
 85 |     "sdate_backtest          =\"2018-08-14\"\n",
 86 |     "todate_backtest         =\"2019-10-02\"\n",
 87 |     "##############\n",
 88 |     "print(\"SCANNING START\")\n",
 89 |     "ttoken=5633\n",
 90 |     "ttradingsymbol=\"ACC\"\n",
 91 |     "\n",
 92 |     "strike_rate = 0\n",
 93 |     "strike_rate_list = []\n",
 94 |     "timeframe = []\n",
 95 |     "timeframe_list = []\n",
 96 |     "pnl = 0\n",
 97 |     "pnl_list = []\n",
 98 |     "\n",
 99 |     "def time_con(time,df):\n",
100 |     "    z = 0\n",
101 |     "    chg = []\n",
102 |     "    chg_index = 0\n",
103 |     "    timeframe = time\n",
104 |     "    date = np.array([])\n",
105 |     "    for i in range(len(df)):\n",
106 |     "        date = np.hstack((date,str(df.loc[i,\"date\"])))\n",
107 |     "    open_np = df.open.values\n",
108 |     "    high_np = df.high.values\n",
109 |     "    low_np = df.low.values\n",
110 |     "    close_np = df.close.values\n",
111 |     "#     volume_np = df.volume.values\n",
112 |     "#     print(date)\n",
113 |     "    for j in range(len(date)):\n",
114 |     "        if date[j][11:16] == \"09:15\":\n",
115 |     "            chg.append(j)\n",
116 |     "    np_date=np.array([])\n",
117 |     "    np_open = np.array([])\n",
118 |     "    np_high = np.array([])\n",
119 |     "    np_low = np.array([])\n",
120 |     "    np_close = np.array([])\n",
121 |     "#     np_volume = np.array([])\n",
122 |     "    for i in range(len(date)-timeframe+1):\n",
123 |     "        if i>=z:\n",
124 |     "            chg_index+=1\n",
125 |     "            if (chg_index < len(chg)):\n",
126 |     "                for k in range(i,chg[chg_index],timeframe):\n",
127 |     "#                     print(date[k])\n",
128 |     "#                     print(k,k+timeframe)\n",
129 |     "                    np_date = np.hstack((np_date,date[k]))\n",
130 |     "                    np_open = np.hstack((np_open,open_np[k]))\n",
131 |     "                    np_high = np.hstack((np_high,np.amax(high_np[k:k+timeframe])))\n",
132 |     "#                     print(high_np[k:k+timeframe])\n",
133 |     "                    np_low = np.hstack((np_low,np.amin(low_np[k:k+timeframe])))\n",
134 |     "#                     print(low_np[k:k+timeframe])\n",
135 |     "                    np_close = np.hstack((np_close,close_np[k+timeframe-1]))\n",
136 |     "#                     np_volume = np.hstack((np_volume,np.sum(volume_np[k:k+timeframe])))\n",
137 |     "                    z = i+timeframe\n",
138 |     "                    z=chg[chg_index]\n",
139 |     "    else:\n",
140 |     "        con_df = pd.DataFrame({\"date\":np_date,\"open\":np_open,\"high\":np_high,\"low\":np_low,\"close\":np_close})\n",
141 |     "        return con_df\n",
142 |     "\n",
143 |     "\n",
144 |     "for t in range(1,2):\n",
145 |     "    dfw=kite.historical_data(ttoken,sdate_backtest,todate_backtest,time_frame,0)\n",
146 |     "    dfw=pd.DataFrame(dfw)\n",
147 |     "    df=pd.DataFrame(dfw[['date','open','high','low','close']])\n",
148 |     "    df = time_con(t,df)\n",
149 |     "    slow_ema = [3,5,7,9,11,13,15,17,19,21,23]\n",
150 |     "    fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]\n",
151 |     "    def EMA(df, base, target, period, alpha=False):\n",
152 |     "        con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])\n",
153 |     "        if (alpha == True):\n",
154 |     "            # (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period\n",
155 |     "            df[target] = con.ewm(alpha=1 / period, adjust=False).mean()\n",
156 |     "        else:\n",
157 |     "            # ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)\n",
158 |     "            df[target] = con.ewm(span=period, adjust=False).mean()\n",
159 |     "        df.fillna(0,inplace = True)\n",
160 |     "    #     return df\n",
161 |     "    for j in slow_ema:\n",
162 |     "        val = \"ema\"+\"_\"+str(j)\n",
163 |     "        EMA(df,\"close\",val,j)\n",
164 |     "    for k in fast_ema:\n",
165 |     "        val = \"ema\"+\"_\"+str(k)\n",
166 |     "        EMA(df,\"close\",val,k)\n",
167 |     "    def super_guppy(interval,df,anchor=0):\n",
168 |     "        anchor = 0\n",
169 |     "        ShowBreak = True\n",
170 |     "        ShowSwing = True\n",
171 |     "        ShowCon = False\n",
172 |     "        uOCCswing = False\n",
173 |     "        Lookback = 6\n",
174 |     "        emaFilter = False\n",
175 |     "        mult = 0\n",
176 |     "        buybreak = 0\n",
177 |     "        sellbreak = 0\n",
178 |     "        buy_barssince_var = 0\n",
179 |     "        sell_barssince_var = 0\n",
180 |     "        buybreak_barssince_var = 0\n",
181 |     "        sellbreak_barssince_var = 0\n",
182 |     "        barssince_lst = list()\n",
183 |     "        barssince_var = 0\n",
184 |     "        bar_count_var = 0\n",
185 |     "        buy1 = list()\n",
186 |     "        sell1 = list()\n",
187 |     "        buy2 = list()\n",
188 |     "        sell2 = list()\n",
189 |     "        buybreak1 = list()\n",
190 |     "        sellbreak1 = list()\n",
191 |     "        def barssince(b,barssince_var):\n",
192 |     "            barssince_lst = []\n",
193 |     "            barssince_var = 0 \n",
194 |     "            new_var = len(b)\n",
195 |     "            for i in b[::-1]:\n",
196 |     "                if i == 1:\n",
197 |     "                    break\n",
198 |     "                barssince_lst.append(i)\n",
199 |     "            barssince_var = len(barssince_lst)\n",
200 |     "            return barssince_var\n",
201 |     "            barssince_lst.clear()\n",
202 |     "        if interval < 1441 :\n",
203 |     "            if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):\n",
204 |     "                mult = 1\n",
205 |     "            else:\n",
206 |     "                if round(anchor/interval) > 1:\n",
207 |     "                    mult = round(anchor/interval)\n",
208 |     "                else:\n",
209 |     "                    mult = 1\n",
210 |     "        else:\n",
211 |     "            mult = 1\n",
212 |     "        #isIntraday Not\n",
213 |     "        if interval > 1441:\n",
214 |     "            if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):\n",
215 |     "                mult = mult\n",
216 |     "            else:\n",
217 |     "                if round(anchor/interval) > 1:\n",
218 |     "                    mult = round(anchor/interval)\n",
219 |     "                else:\n",
220 |     "                    mult = 1\n",
221 |     "        else:\n",
222 |     "            mult = mult\n",
223 |     "        mult = 1\n",
224 |     "        for i in range(len(df)):\n",
225 |     "            emaF1 = df.loc[i,'ema_3']\n",
226 |     "            emaF2 = df.loc[i,'ema_5']\n",
227 |     "            emaF3 = df.loc[i,'ema_7']\n",
228 |     "            emaF4 = df.loc[i,'ema_9']\n",
229 |     "            emaF5 = df.loc[i,'ema_11']\n",
230 |     "            emaF6 = df.loc[i,'ema_13']\n",
231 |     "            emaF7 = df.loc[i,'ema_15']\n",
232 |     "            emaF8 = df.loc[i,'ema_17']\n",
233 |     "            emaF9 = df.loc[i,'ema_19']\n",
234 |     "            emaF10 = df.loc[i,'ema_21']\n",
235 |     "            emaF11 = df.loc[i,'ema_23']\n",
236 |     "            emaS1 = df.loc[i,'ema_25']\n",
237 |     "            emaS2 = df.loc[i,'ema_28']\n",
238 |     "            emaS3 = df.loc[i,'ema_31']\n",
239 |     "            emaS4 = df.loc[i,'ema_34']\n",
240 |     "            emaS5 = df.loc[i,'ema_37']\n",
241 |     "            emaS6 = df.loc[i,'ema_40']\n",
242 |     "            emaS7 = df.loc[i,'ema_43']\n",
243 |     "            emaS8 = df.loc[i,'ema_46']\n",
244 |     "            emaS9 = df.loc[i,'ema_49']\n",
245 |     "            emaS10 = df.loc[i,'ema_52']\n",
246 |     "            emaS11 = df.loc[i,'ema_55']\n",
247 |     "            emaS12 = df.loc[i,'ema_58']\n",
248 |     "            emaS13 = df.loc[i,'ema_61']\n",
249 |     "            emaS14 = df.loc[i,'ema_64']\n",
250 |     "            emaS15 = df.loc[i,'ema_67']\n",
251 |     "            emaS16 = df.loc[i,'ema_70']\n",
252 |     "            ema200 = df.loc[i,'ema_200'] \n",
253 |     "            emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11\n",
254 |     "            emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16\n",
255 |     "            #Fast EMA Color Rules\n",
256 |     "            colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)\n",
257 |     "            colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)\n",
258 |     "            #Slow EMA Color Rules\n",
259 |     "            colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)\n",
260 |     "            colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)\n",
261 |     "            if  emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):\n",
262 |     "                if int(buy1[-1]) > 0:\n",
263 |     "                    buy = buy1[-1] + 1\n",
264 |     "                else:\n",
265 |     "                    buy = 1\n",
266 |     "            else:\n",
267 |     "                buy = 0\n",
268 |     "            buy1.append(buy)\n",
269 |     "            if  emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):\n",
270 |     "                if int(sell1[-1]) > 0:\n",
271 |     "                    sell = sell1[-1] + 1\n",
272 |     "                else:\n",
273 |     "                    sell = 1\n",
274 |     "            else:\n",
275 |     "                sell = 0\n",
276 |     "            sell1.append(sell)\n",
277 |     "            #buy\n",
278 |     "            if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):\n",
279 |     "                buy3 = 1\n",
280 |     "            else:\n",
281 |     "                buy3 = buy\n",
282 |     "            buy2.append(buy3)\n",
283 |     "            #sell    \n",
284 |     "            if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):\n",
285 |     "                sell3 = 1\n",
286 |     "            else:\n",
287 |     "                sell3 = sell\n",
288 |     "            sell2.append(sell3)\n",
289 |     "            #buybreak\n",
290 |     "            if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):\n",
291 |     "                if buybreak1[-1] > 0:\n",
292 |     "                    buybreak = buybreak1[-1] + 1\n",
293 |     "                else:\n",
294 |     "                    buybreak = 1\n",
295 |     "            else:\n",
296 |     "                buybreak = 0\n",
297 |     "            buybreak1.append(buybreak)\n",
298 |     "            if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):\n",
299 |     "                if sellbreak1[-1] > 0:\n",
300 |     "                    sellbreak = sellbreak1[-1]+1\n",
301 |     "                else:\n",
302 |     "                    sellbreak = 1\n",
303 |     "            else:\n",
304 |     "                sellbreak = 0\n",
305 |     "            sellbreak1.append(sellbreak)\n",
306 |     "            #arrow plotting\n",
307 |     "            #buy_arrow\n",
308 |     "            buy_barssince_var = barssince(buy2[:-1],barssince_var)\n",
309 |     "            if (ShowSwing and buy3==1)and buy_barssince_var > 6:\n",
310 |     "                buy_arrow = 1\n",
311 |     "            else:\n",
312 |     "                buy_arrow = 0\n",
313 |     "            #sell arrow\n",
314 |     "            sell_barssince_var = barssince(sell2[:-1],barssince_var)\n",
315 |     "            if ShowSwing and (sell3==1 and sell_barssince_var > 6):\n",
316 |     "                sell_arrow = 1\n",
317 |     "            else:\n",
318 |     "                sell_arrow = 0\n",
319 |     "            #buybreak_arrow\n",
320 |     "            buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)\n",
321 |     "            sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)\n",
322 |     "            if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):\n",
323 |     "                buybreak_arrow = 1\n",
324 |     "            else:\n",
325 |     "                buybreak_arrow = 0\n",
326 |     "            #sellbreak_arrow\n",
327 |     "            if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):\n",
328 |     "                sellbreak_arrow = 1\n",
329 |     "            else:\n",
330 |     "                sellbreak_arrow = 0\n",
331 |     "            if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:\n",
332 |     "                arrow_color = 'green'\n",
333 |     "            elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:\n",
334 |     "                arrow_color = 'red'\n",
335 |     "            elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:\n",
336 |     "                arrow_color = 'aqua'\n",
337 |     "            elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:\n",
338 |     "                arrow_color = 'blue'\n",
339 |     "            else:\n",
340 |     "                arrow_color = 'none'\n",
341 |     "            df.loc[i,'arrow_color'] = arrow_color\n",
342 |     "        df = df[['date','open','high','low','close','arrow_color']]\n",
343 |     "        return df\n",
344 |     "    df=super_guppy(15,df)\n",
345 |     "    gup=df\n",
346 |     "    def bidatrema(df,period):\n",
347 |     "        df['hl']=abs(df['high']-df['low'])\n",
348 |     "        df['hpc']=abs(df['high']-df['close'].shift())\n",
349 |     "        df['lpc']=abs(df['low']-df['close'].shift())\n",
350 |     "        df['tr']=df[['hl','hpc','lpc']].max(axis=1)\n",
351 |     "        df['ATR']=pd.DataFrame.ewm(df[\"tr\"], span=period,min_periods=period).mean()\n",
352 |     "        df.drop([\"hl\",\"hpc\",\"lpc\",\"tr\"],axis = 1 , inplace =True)\n",
353 |     "    bidatrema(gup,14)\n",
354 |     "    df['bid_value'] = 0\n",
355 |     "    df['executed_value'] = 0\n",
356 |     "    df['diff'] = 0\n",
357 |     "    df['pnl'] = 0\n",
358 |     "    qty = int ( qu/ (gup.iloc[-1,2]) )\n",
359 |     "    var = False\n",
360 |     "    var_2 = False\n",
361 |     "    var_1 = False\n",
362 |     "    for i in range(len(df)):\n",
363 |     "        zz=df.loc[i,\"date\"]\n",
364 |     "        za=str(zz)[11:13]\n",
365 |     "        if za!=\"15\":\n",
366 |     "            if var==True:\n",
367 |     "                df.loc[i,'diff'] =  df.loc[i,'close'] - bid_value\n",
368 |     "                df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']\n",
369 |     "                var=False\n",
370 |     "                var1=False \n",
371 |     "            var_2=True \n",
372 |     "        if za!=\"15\":\n",
373 |     "            var_2=False\n",
374 |     "        if var==False and var_2==False:\n",
375 |     "            if df.loc[i,'arrow_color']=='green':\n",
376 |     "                bid_value = (df.loc[i,'high']+df.loc[i,'ATR']*.25)+(df.loc[i,'ATR']*.1)\n",
377 |     "                df.loc[i,'bid_value'] =bid_value\n",
378 |     "                var=True\n",
379 |     "                var1=False\n",
380 |     "        if var==True and var1==False and var_2==False:\n",
381 |     "            if df.loc[i,'high'] > bid_value and df.loc[i,'low'] < bid_value:\n",
382 |     "                df.loc[i,'executed_value']=bid_value\n",
383 |     "                var1=True\n",
384 |     "        if var==True and var1==True and var_2==False:\n",
385 |     "            if df.loc[i,'arrow_color']=='red':\n",
386 |     "                df.loc[i,'diff'] =  df.loc[i,'close'] - bid_value\n",
387 |     "                df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']\n",
388 |     "                var=False\n",
389 |     "                var1=False\n",
390 |     "    list_1 = df['pnl']\n",
391 |     "    pos_count = len(list(filter(lambda x: (x>0),list_1)))\n",
392 |     "    neg_count = len(list(filter(lambda x: (x<0),list_1)))\n",
393 |     "    total_trade = pos_count + neg_count\n",
394 |     "    try :\n",
395 |     "        strike_rate = pos_count/total_trade\n",
396 |     "    except:\n",
397 |     "        strike_rate = 0\n",
398 |     "    pnl = df['pnl'].sum()\n",
399 |     "    pnl_list.append(pnl)\n",
400 |     "    timeframe_list.append(t)\n",
401 |     "    strike_rate_list.append(strike_rate)\n",
402 |     "main_df = pd.DataFrame({'timeframe':timeframe_list,'pnl':pnl_list,'strike_rate':strike_rate_list})\n",
403 |     "main_df.to_csv('pnlbuy.csv')\n",
404 |     "print(\"complete\")"
405 |    ]
406 |   },
407 |   {
408 |    "cell_type": "code",
409 |    "execution_count": 6,
410 |    "metadata": {},
411 |    "outputs": [
412 |     {
413 |      "data": {
414 |       "text/html": [
415 |        "<div>\n",
416 |        "<style scoped>\n",
417 |        "    .dataframe tbody tr th:only-of-type {\n",
418 |        "        vertical-align: middle;\n",
419 |        "    }\n",
420 |        "\n",
421 |        "    .dataframe tbody tr th {\n",
422 |        "        vertical-align: top;\n",
423 |        "    }\n",
424 |        "\n",
425 |        "    .dataframe thead th {\n",
426 |        "        text-align: right;\n",
427 |        "    }\n",
428 |        "</style>\n",
429 |        "<table border=\"1\" class=\"dataframe\">\n",
430 |        "  <thead>\n",
431 |        "    <tr style=\"text-align: right;\">\n",
432 |        "      <th></th>\n",
433 |        "      <th>timeframe</th>\n",
434 |        "      <th>pnl</th>\n",
435 |        "      <th>strike_rate</th>\n",
436 |        "    </tr>\n",
437 |        "  </thead>\n",
438 |        "  <tbody>\n",
439 |        "    <tr>\n",
440 |        "      <th>0</th>\n",
441 |        "      <td>1</td>\n",
442 |        "      <td>-73.269911</td>\n",
443 |        "      <td>0.214286</td>\n",
444 |        "    </tr>\n",
445 |        "  </tbody>\n",
446 |        "</table>\n",
447 |        "</div>"
448 |       ],
449 |       "text/plain": [
450 |        "   timeframe        pnl  strike_rate\n",
451 |        "0          1 -73.269911     0.214286"
452 |       ]
453 |      },
454 |      "metadata": {},
455 |      "output_type": "display_data"
456 |     }
457 |    ],
458 |    "source": [
459 |     "display(main_df)"
460 |    ]
461 |   },
462 |   {
463 |    "cell_type": "markdown",
464 |    "metadata": {},
465 |    "source": [
466 |     "* * * * "
467 |    ]
468 |   },
469 |   {
470 |    "cell_type": "markdown",
471 |    "metadata": {},
472 |    "source": [
473 |     "# BACKTESTING for SELL"
474 |    ]
475 |   },
476 |   {
477 |    "cell_type": "code",
478 |    "execution_count": 8,
479 |    "metadata": {},
480 |    "outputs": [
481 |     {
482 |      "name": "stdout",
483 |      "output_type": "stream",
484 |      "text": [
485 |       "SCANNING START\n",
486 |       "complete\n"
487 |      ]
488 |     }
489 |    ],
490 |    "source": [
491 |     "## fno\n",
492 |     "tokenall=[5633, 6401, 3861249, 4451329, 2760193, 20993, 325121, 2524673, 41729, 49409, 54273, 60417, 70401, 1510401, 1195009, 1214721, 94977, 108033, 2714625, 2911489, 2763265, 3812865, 160001, 160769, 163073, 177665, 5215745, 3876097, 197633, 3771393, 225537, 173057, 261889, 1207553, 3463169, 2796801, 315393, 3378433, 2513665, 1850625, 340481, 341249, 3789569, 345089, 2747905, 348929, 359937, 356865, 364545, 3699201, 1270529, 377857, 3677697, 3060993, 381697, 2883073, 387073, 387841, 1346049, 408065, 2393089, 415745, 3920129, 424961, 1723649, 2661633, 2933761, 3011329, 4574465, 3001089, 4632577, 492033, 2061825, 511233, 2939649, 2672641, 519937, 2815745, 2674433, 582913, 593665, 3924993, 2977281, 2748929, 633601, 2819073, 636673, 2730497, 3834113, 2906881, 3364353, 731905, 3375873, 3930881, 737793, 738561, 141569, 3078657, 779521, 780289, 1492737, 1522689, 1102337, 857857, 3431425, 3076609, 1837825, 871681, 952577, 878593, 884737, 4343041, 877057, 895745, 2953217, 3465729, 897537, 2873089, 2952193, 2752769, 920065, 951809, 3026177, 969473, 3050241, 112129, 134657, 3721473, 2800641, 3385857, 4454401, 1152769, 806401, 617473, 2905857, 3660545, 3906305, 758529, 975873]\n",
493 |     "ss=['ACC', 'ADANIENT', 'ADANIPORTS', 'ADANIPOWER', 'ALBK', 'ALOKTEXT', 'AMBUJACEM', 'ANDHRABANK', 'APOLLOTYRE', 'ARVIND', 'ASHOKLEY', 'ASIANPAINT', 'AUROPHARMA', 'AXISBANK', 'BANKBARODA', 'BANKINDIA', 'BATAINDIA', 'BHARATFORG', 'BHARTIARTL', 'BIOCON', 'CANBK', 'CENTRALBK', 'CENTURYTEX', 'CESC', 'CHAMBLFERT', 'CIPLA', 'COALINDIA', 'COLPAL', 'DABUR', 'DLF', 'DRREDDY', 'EXIDEIND', 'FEDERALBNK', 'GAIL', 'GMRINFRA', 'GODREJIND', 'GRASIM', 'GSPL', 'HAVELLS', 'HCLTECH', 'HDFC', 'HDFCBANK', 'HDIL', 'HEROMOTOCO', 'HEXAWARE', 'HINDALCO', 'HINDPETRO', 'HINDUNILVR', 'HINDZINC', 'IBREALEST', 'ICICIBANK', 'IDBI', 'IDEA', 'IDFC', 'IFCI', 'IGL', 'INDHOTEL', 'INDIACEM', 'INDUSINDBK', 'INFY', 'IOB', 'IOC', 'IRB', 'ITC', 'JINDALSTEL', 'JISLJALEQS', 'JPASSOCIAT', 'JPPOWER', 'JSWENERGY', 'JSWSTEEL', 'JUBLFOOD', 'KOTAKBANK', 'KTKBANK', 'LICHSGFIN', 'LT', 'LUPIN', 'M&M', 'MARUTI', 'MCDOWELL-N', 'MRF', 'NCC', 'NMDC', 'NTPC', 'OFSS', 'ONGC', 'OPTOCIRCUI', 'ORIENTBANK', 'PNB', 'POWERGRID', 'PTC', 'PUNJLLOYD', 'RAYMOND', 'RCOM', 'RECLTD', 'RELCAPITAL', 'RELIANCE', 'RELINFRA', 'RENUKA', 'SBIN', 'SCI', 'SINTEX', 'SOUTHBANK', 'SRTRANSFIN', 'SUNPHARMA', 'SUNTV', 'SUZLON', 'SYNDIBANK', 'TATACHEM', 'TATACOMM', 'TATAGLOBAL', 'TATAMOTORS', 'TATAMTRDVR', 'TATAPOWER', 'TATASTEEL', 'TCS', 'TECHM', 'TITAN', 'UCOBANK', 'ULTRACEMCO', 'UNIONBANK', 'UNITECH', 'VOLTAS', 'WELCORP', 'WIPRO', 'YESBANK', 'BHEL', 'BPCL', 'DISHTV', 'DIVISLAB', 'GVKPIL', 'NHPC', 'MPHASIS', 'SIEMENS', 'PEL', 'PETRONET', 'PFC', 'RPOWER', 'SAIL', 'ZEEL']\n",
494 |     "\n",
495 |     "\n",
496 |     "sdate           =\"2019-08-20 00:00:00\"\n",
497 |     "todate          =\"2019-09-16 09:16:00\"\n",
498 |     "#sdate          =\"2019-08-19\"\n",
499 |     "#todate         =\"2019-10-01\"\n",
500 |     "\n",
501 |     "# inputs for backtesting\n",
502 |     "\n",
503 |     "time_frame      =\"minute\"\n",
504 |     "quu=50000\n",
505 |     "eexchange=\"NSE\"\n",
506 |     "productt=\"MIS\"\n",
507 |     "qu=int(quu)\n",
508 |     "\n",
509 |     "# Backtesting Dates\n",
510 |     "sdate_backtest          =\"2019-08-14\"\n",
511 |     "todate_backtest         =\"2019-10-02\"\n",
512 |     "\n",
513 |     "print(\"SCANNING START\")\n",
514 |     "ttoken=5633\n",
515 |     "ttradingsymbol=\"ACC\"\n",
516 |     "\n",
517 |     "\n",
518 |     "def time_con(time,df):\n",
519 |     "    z = 0\n",
520 |     "    chg = []\n",
521 |     "    chg_index = 0\n",
522 |     "    timeframe = time\n",
523 |     "    date = np.array([])\n",
524 |     "    for i in range(len(df)):\n",
525 |     "        date = np.hstack((date,str(df.loc[i,\"date\"])))\n",
526 |     "    open_np = df.open.values\n",
527 |     "    high_np = df.high.values\n",
528 |     "    low_np = df.low.values\n",
529 |     "    close_np = df.close.values\n",
530 |     "#     volume_np = df.volume.values\n",
531 |     "#     print(date)\n",
532 |     "    for j in range(len(date)):\n",
533 |     "        if date[j][11:16] == \"09:15\":\n",
534 |     "            chg.append(j)\n",
535 |     "    np_date=np.array([])\n",
536 |     "    np_open = np.array([])\n",
537 |     "    np_high = np.array([])\n",
538 |     "    np_low = np.array([])\n",
539 |     "    np_close = np.array([])\n",
540 |     "#     np_volume = np.array([])\n",
541 |     "    for i in range(len(date)-timeframe+1):\n",
542 |     "        if i>=z:\n",
543 |     "            chg_index+=1\n",
544 |     "            if (chg_index < len(chg)):\n",
545 |     "                for k in range(i,chg[chg_index],timeframe):\n",
546 |     "#                     print(date[k])\n",
547 |     "#                     print(k,k+timeframe)\n",
548 |     "                    np_date = np.hstack((np_date,date[k]))\n",
549 |     "                    np_open = np.hstack((np_open,open_np[k]))\n",
550 |     "                    np_high = np.hstack((np_high,np.amax(high_np[k:k+timeframe])))\n",
551 |     "#                     print(high_np[k:k+timeframe])\n",
552 |     "                    np_low = np.hstack((np_low,np.amin(low_np[k:k+timeframe])))\n",
553 |     "#                     print(low_np[k:k+timeframe])\n",
554 |     "                    np_close = np.hstack((np_close,close_np[k+timeframe-1]))\n",
555 |     "#                     np_volume = np.hstack((np_volume,np.sum(volume_np[k:k+timeframe])))\n",
556 |     "                    z = i+timeframe\n",
557 |     "                    z=chg[chg_index]\n",
558 |     "    else:\n",
559 |     "        con_df = pd.DataFrame({\"date\":np_date,\"open\":np_open,\"high\":np_high,\"low\":np_low,\"close\":np_close})\n",
560 |     "        return con_df\n",
561 |     "\n",
562 |     "strike_rate = 0\n",
563 |     "strike_rate_list = []\n",
564 |     "timeframe = []\n",
565 |     "timeframe_list = []\n",
566 |     "pnl = 0\n",
567 |     "pnl_list = []\n",
568 |     "\n",
569 |     "for t in range(1,2):\n",
570 |     "    dfw=kite.historical_data(ttoken,sdate_backtest,todate_backtest,time_frame,0)\n",
571 |     "    dfw=pd.DataFrame(dfw)\n",
572 |     "    df=pd.DataFrame(dfw[['date','open','high','low','close']])\n",
573 |     "    df= time_con(t,df)\n",
574 |     "    slow_ema = [3,5,7,9,11,13,15,17,19,21,23]\n",
575 |     "    fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]\n",
576 |     "    def EMA(df, base, target, period, alpha=False):\n",
577 |     "        con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])\n",
578 |     "        if (alpha == True):\n",
579 |     "            # (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period\n",
580 |     "            df[target] = con.ewm(alpha=1 / period, adjust=False).mean()\n",
581 |     "        else:\n",
582 |     "            # ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)\n",
583 |     "            df[target] = con.ewm(span=period, adjust=False).mean()\n",
584 |     "        df.fillna(0,inplace = True)\n",
585 |     "    #     return df\n",
586 |     "    for j in slow_ema:\n",
587 |     "        val = \"ema\"+\"_\"+str(j)\n",
588 |     "        EMA(df,\"close\",val,j)\n",
589 |     "    for k in fast_ema:\n",
590 |     "        val = \"ema\"+\"_\"+str(k)\n",
591 |     "        EMA(df,\"close\",val,k)\n",
592 |     "    def super_guppy(interval,df,anchor=0):\n",
593 |     "        anchor = 0\n",
594 |     "        ShowBreak = True\n",
595 |     "        ShowSwing = True\n",
596 |     "        ShowCon = False\n",
597 |     "        uOCCswing = False\n",
598 |     "        Lookback = 6\n",
599 |     "        emaFilter = False\n",
600 |     "        mult = 0\n",
601 |     "        buybreak = 0\n",
602 |     "        sellbreak = 0\n",
603 |     "        buy_barssince_var = 0\n",
604 |     "        sell_barssince_var = 0\n",
605 |     "        buybreak_barssince_var = 0\n",
606 |     "        sellbreak_barssince_var = 0\n",
607 |     "        barssince_lst = list()\n",
608 |     "        barssince_var = 0\n",
609 |     "        bar_count_var = 0\n",
610 |     "        buy1 = list()\n",
611 |     "        sell1 = list()\n",
612 |     "        buy2 = list()\n",
613 |     "        sell2 = list()\n",
614 |     "        buybreak1 = list()\n",
615 |     "        sellbreak1 = list()\n",
616 |     "        def barssince(b,barssince_var):\n",
617 |     "            barssince_lst = []\n",
618 |     "            barssince_var = 0 \n",
619 |     "            new_var = len(b)\n",
620 |     "            for i in b[::-1]:\n",
621 |     "                if i == 1:\n",
622 |     "                    break\n",
623 |     "                barssince_lst.append(i)\n",
624 |     "            barssince_var = len(barssince_lst)\n",
625 |     "            return barssince_var\n",
626 |     "            barssince_lst.clear()\n",
627 |     "        if interval < 1441 :\n",
628 |     "            if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):\n",
629 |     "                mult = 1\n",
630 |     "            else:\n",
631 |     "                if round(anchor/interval) > 1:\n",
632 |     "                    mult = round(anchor/interval)\n",
633 |     "                else:\n",
634 |     "                    mult = 1\n",
635 |     "        else:\n",
636 |     "            mult = 1\n",
637 |     "        #isIntraday Not\n",
638 |     "        if interval > 1441:\n",
639 |     "            if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):\n",
640 |     "                mult = mult\n",
641 |     "            else:\n",
642 |     "                if round(anchor/interval) > 1:\n",
643 |     "                    mult = round(anchor/interval)\n",
644 |     "                else:\n",
645 |     "                    mult = 1\n",
646 |     "        else:\n",
647 |     "            mult = mult\n",
648 |     "        mult = 1\n",
649 |     "        for i in range(len(df)):\n",
650 |     "            emaF1 = df.loc[i,'ema_3']\n",
651 |     "            emaF2 = df.loc[i,'ema_5']\n",
652 |     "            emaF3 = df.loc[i,'ema_7']\n",
653 |     "            emaF4 = df.loc[i,'ema_9']\n",
654 |     "            emaF5 = df.loc[i,'ema_11']\n",
655 |     "            emaF6 = df.loc[i,'ema_13']\n",
656 |     "            emaF7 = df.loc[i,'ema_15']\n",
657 |     "            emaF8 = df.loc[i,'ema_17']\n",
658 |     "            emaF9 = df.loc[i,'ema_19']\n",
659 |     "            emaF10 = df.loc[i,'ema_21']\n",
660 |     "            emaF11 = df.loc[i,'ema_23']\n",
661 |     "            emaS1 = df.loc[i,'ema_25']\n",
662 |     "            emaS2 = df.loc[i,'ema_28']\n",
663 |     "            emaS3 = df.loc[i,'ema_31']\n",
664 |     "            emaS4 = df.loc[i,'ema_34']\n",
665 |     "            emaS5 = df.loc[i,'ema_37']\n",
666 |     "            emaS6 = df.loc[i,'ema_40']\n",
667 |     "            emaS7 = df.loc[i,'ema_43']\n",
668 |     "            emaS8 = df.loc[i,'ema_46']\n",
669 |     "            emaS9 = df.loc[i,'ema_49']\n",
670 |     "            emaS10 = df.loc[i,'ema_52']\n",
671 |     "            emaS11 = df.loc[i,'ema_55']\n",
672 |     "            emaS12 = df.loc[i,'ema_58']\n",
673 |     "            emaS13 = df.loc[i,'ema_61']\n",
674 |     "            emaS14 = df.loc[i,'ema_64']\n",
675 |     "            emaS15 = df.loc[i,'ema_67']\n",
676 |     "            emaS16 = df.loc[i,'ema_70']\n",
677 |     "            ema200 = df.loc[i,'ema_200'] \n",
678 |     "            emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11\n",
679 |     "            emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16\n",
680 |     "            #Fast EMA Color Rules\n",
681 |     "            colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)\n",
682 |     "            colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)\n",
683 |     "            #Slow EMA Color Rules\n",
684 |     "            colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)\n",
685 |     "            colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)\n",
686 |     "            if  emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):\n",
687 |     "                if int(buy1[-1]) > 0:\n",
688 |     "                    buy = buy1[-1] + 1\n",
689 |     "                else:\n",
690 |     "                    buy = 1\n",
691 |     "            else:\n",
692 |     "                buy = 0\n",
693 |     "            buy1.append(buy)\n",
694 |     "            if  emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):\n",
695 |     "                if int(sell1[-1]) > 0:\n",
696 |     "                    sell = sell1[-1] + 1\n",
697 |     "                else:\n",
698 |     "                    sell = 1\n",
699 |     "            else:\n",
700 |     "                sell = 0\n",
701 |     "            sell1.append(sell)\n",
702 |     "            #buy\n",
703 |     "            if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):\n",
704 |     "                buy3 = 1\n",
705 |     "            else:\n",
706 |     "                buy3 = buy\n",
707 |     "            buy2.append(buy3)\n",
708 |     "            #sell    \n",
709 |     "            if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):\n",
710 |     "                sell3 = 1\n",
711 |     "            else:\n",
712 |     "                sell3 = sell\n",
713 |     "            sell2.append(sell3)\n",
714 |     "            #buybreak\n",
715 |     "            if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):\n",
716 |     "                if buybreak1[-1] > 0:\n",
717 |     "                    buybreak = buybreak1[-1] + 1\n",
718 |     "                else:\n",
719 |     "                    buybreak = 1\n",
720 |     "            else:\n",
721 |     "                buybreak = 0\n",
722 |     "            buybreak1.append(buybreak)\n",
723 |     "            if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):\n",
724 |     "                if sellbreak1[-1] > 0:\n",
725 |     "                    sellbreak = sellbreak1[-1]+1\n",
726 |     "                else:\n",
727 |     "                    sellbreak = 1\n",
728 |     "            else:\n",
729 |     "                sellbreak = 0\n",
730 |     "            sellbreak1.append(sellbreak)\n",
731 |     "            #arrow plotting\n",
732 |     "            #buy_arrow\n",
733 |     "            buy_barssince_var = barssince(buy2[:-1],barssince_var)\n",
734 |     "            if (ShowSwing and buy3==1)and buy_barssince_var > 6:\n",
735 |     "                buy_arrow = 1\n",
736 |     "            else:\n",
737 |     "                buy_arrow = 0\n",
738 |     "            #sell arrow\n",
739 |     "            sell_barssince_var = barssince(sell2[:-1],barssince_var)\n",
740 |     "            if ShowSwing and (sell3==1 and sell_barssince_var > 6):\n",
741 |     "                sell_arrow = 1\n",
742 |     "            else:\n",
743 |     "                sell_arrow = 0\n",
744 |     "            #buybreak_arrow\n",
745 |     "            buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)\n",
746 |     "            sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)\n",
747 |     "            if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):\n",
748 |     "                buybreak_arrow = 1\n",
749 |     "            else:\n",
750 |     "                buybreak_arrow = 0\n",
751 |     "            #sellbreak_arrow\n",
752 |     "            if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):\n",
753 |     "                sellbreak_arrow = 1\n",
754 |     "            else:\n",
755 |     "                sellbreak_arrow = 0\n",
756 |     "            if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:\n",
757 |     "                arrow_color = 'green'\n",
758 |     "            elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:\n",
759 |     "                arrow_color = 'red'\n",
760 |     "            elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:\n",
761 |     "                arrow_color = 'aqua'\n",
762 |     "            elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:\n",
763 |     "                arrow_color = 'blue'\n",
764 |     "            else:\n",
765 |     "                arrow_color = 'none'\n",
766 |     "            df.loc[i,'arrow_color'] = arrow_color\n",
767 |     "        df = df[['date','open','high','low','close','arrow_color']]\n",
768 |     "        return df\n",
769 |     "    df=super_guppy(15,df)\n",
770 |     "    gup=df\n",
771 |     "    def bidatrema(df,period):\n",
772 |     "        df['hl']=abs(df['high']-df['low'])\n",
773 |     "        df['hpc']=abs(df['high']-df['close'].shift())\n",
774 |     "        df['lpc']=abs(df['low']-df['close'].shift())\n",
775 |     "        df['tr']=df[['hl','hpc','lpc']].max(axis=1)\n",
776 |     "        df['ATR']=pd.DataFrame.ewm(df[\"tr\"], span=period,min_periods=period).mean()\n",
777 |     "        df.drop([\"hl\",\"hpc\",\"lpc\",\"tr\"],axis = 1 , inplace =True)\n",
778 |     "    bidatrema(gup,14)\n",
779 |     "    df['bid_value'] = 0\n",
780 |     "    df['executed_value'] = 0\n",
781 |     "    df['diff'] = 0\n",
782 |     "    df['pnl'] = 0\n",
783 |     "    qty = int ( qu/ (gup.iloc[-1,2]) )\n",
784 |     "    var = False\n",
785 |     "    var_2 = False\n",
786 |     "    var_1 = False\n",
787 |     "    for i in range(len(df)):\n",
788 |     "        zz=df.loc[i,\"date\"]\n",
789 |     "        za=str(zz)[11:13]\n",
790 |     "        if za==\"15\":\n",
791 |     "            if var==True:\n",
792 |     "                df.loc[i,'diff'] =  bid_value - df.loc[i,'close']\n",
793 |     "                df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']\n",
794 |     "                ##\n",
795 |     "                var=False\n",
796 |     "                var1=False \n",
797 |     "            var_2=True\n",
798 |     "        if za!=\"15\":\n",
799 |     "            var_2=False\n",
800 |     "        if var==False and var_2==False:\n",
801 |     "            if df.loc[i,'arrow_color']=='red':\n",
802 |     "                bid_value = (df.loc[i,'low']-df.loc[i,'ATR']*.25)-(df.loc[i,'ATR']*.1)\n",
803 |     "                df.loc[i,'bid_value'] =bid_value\n",
804 |     "                var=True\n",
805 |     "                var1=False\n",
806 |     "        if var==True and var1==False and var_2==False:\n",
807 |     "            if df.loc[i,'high'] > bid_value and df.loc[i,'low'] < bid_value:\n",
808 |     "                df.loc[i,'executed_value']=bid_value\n",
809 |     "                var1=True\n",
810 |     "        if var==True and var1==True and var_2==False:\n",
811 |     "            if df.loc[i,'arrow_color']=='green':\n",
812 |     "                df.loc[i,'diff'] =  bid_value - df.loc[i,'close'] \n",
813 |     "                df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']\n",
814 |     "                var=False\n",
815 |     "                var1=False\n",
816 |     "    list_1 = df['pnl']\n",
817 |     "    pos_count = len(list(filter(lambda x: (x>0),list_1)))\n",
818 |     "    neg_count = len(list(filter(lambda x: (x<0),list_1)))\n",
819 |     "    total_trade = pos_count + neg_count\n",
820 |     "    try :\n",
821 |     "        strike_rate = pos_count/total_trade\n",
822 |     "    except:\n",
823 |     "        strike_rate = 0\n",
824 |     "    #print(strike_rate)\n",
825 |     "    pnl = df['pnl'].sum()\n",
826 |     "    pnl_list.append(pnl)\n",
827 |     "    timeframe_list.append(t)\n",
828 |     "    strike_rate_list.append(strike_rate)\n",
829 |     "main_df = pd.DataFrame({'timeframe':timeframe_list,'pnl':pnl_list,'strike_rate':strike_rate_list})\n",
830 |     "main_df.to_csv('pnlsell.csv')\n",
831 |     "print(\"complete\")"
832 |    ]
833 |   },
834 |   {
835 |    "cell_type": "code",
836 |    "execution_count": 9,
837 |    "metadata": {},
838 |    "outputs": [
839 |     {
840 |      "data": {
841 |       "text/html": [
842 |        "<div>\n",
843 |        "<style scoped>\n",
844 |        "    .dataframe tbody tr th:only-of-type {\n",
845 |        "        vertical-align: middle;\n",
846 |        "    }\n",
847 |        "\n",
848 |        "    .dataframe tbody tr th {\n",
849 |        "        vertical-align: top;\n",
850 |        "    }\n",
851 |        "\n",
852 |        "    .dataframe thead th {\n",
853 |        "        text-align: right;\n",
854 |        "    }\n",
855 |        "</style>\n",
856 |        "<table border=\"1\" class=\"dataframe\">\n",
857 |        "  <thead>\n",
858 |        "    <tr style=\"text-align: right;\">\n",
859 |        "      <th></th>\n",
860 |        "      <th>timeframe</th>\n",
861 |        "      <th>pnl</th>\n",
862 |        "      <th>strike_rate</th>\n",
863 |        "    </tr>\n",
864 |        "  </thead>\n",
865 |        "  <tbody>\n",
866 |        "    <tr>\n",
867 |        "      <th>0</th>\n",
868 |        "      <td>1</td>\n",
869 |        "      <td>-11.92732</td>\n",
870 |        "      <td>0.333333</td>\n",
871 |        "    </tr>\n",
872 |        "  </tbody>\n",
873 |        "</table>\n",
874 |        "</div>"
875 |       ],
876 |       "text/plain": [
877 |        "   timeframe       pnl  strike_rate\n",
878 |        "0          1 -11.92732     0.333333"
879 |       ]
880 |      },
881 |      "metadata": {},
882 |      "output_type": "display_data"
883 |     }
884 |    ],
885 |    "source": [
886 |     "display(main_df)"
887 |    ]
888 |   }
889 |  ],
890 |  "metadata": {
891 |   "kernelspec": {
892 |    "display_name": "Python 3 (ipykernel)",
893 |    "language": "python",
894 |    "name": "python3"
895 |   },
896 |   "language_info": {
897 |    "codemirror_mode": {
898 |     "name": "ipython",
899 |     "version": 3
900 |    },
901 |    "file_extension": ".py",
902 |    "mimetype": "text/x-python",
903 |    "name": "python",
904 |    "nbconvert_exporter": "python",
905 |    "pygments_lexer": "ipython3",
906 |    "version": "3.10.12"
907 |   }
908 |  },
909 |  "nbformat": 4,
910 |  "nbformat_minor": 2
911 | }
912 | 


--------------------------------------------------------------------------------
/README.md:
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 1 | # 🌟 Mastering AlgoTrading: A Beginner's Guide using KiteConnect API 🌟
 2 | 
 3 | > 🚀 Launch into the realm of algorithmic trading with this beginner's guide. Explore the intricacies of the Indian Stock Market through practical examples and hands-on experience. This guide is tailored for those looking to harness the power of Zerodha's KiteConnect API for effective and efficient trading strategies.
 4 | 
 5 | ## 🌐 Overview
 6 | 
 7 | - **Zerodha KiteConnect**
 8 |   - 🤖 Immerse yourself in the world of algorithmic trading with Zerodha's feature-rich trading platform, offering comprehensive functionalities for an enhanced trading experience.
 9 | - **Complete Course**
10 |   - 📘 Dive deeper into algorithmic trading by exploring the full course content [here](https://unofficed.com/courses/mastering-algotrading-a-beginners-guide-using-kiteconnect-api/).
11 | 
12 | ## 📚 Table of Contents
13 | 
14 | 1. **Getting Instrument Token of a Scrip Using Python and Zerodha API**
15 | 2. **Getting Instrument Token of a Scrip Using Python and Zerodha Kite LTP Method**
16 | 3. **Getting Historical Data of Reliance Using Python and Zerodha API**
17 | 4. **Getting Historical Data of Reliance In Pandas Using Python and Zerodha API**
18 | 5. **Organizing Expiry Dates of Financial Instruments with Python**
19 | 6. **Buy on RSI Strategy Coding in Python using Zerodha**
20 | 7. **Guppy Strategy Screener Using Python and Zerodha**
21 | 8. **Guppy Strategy Trading Bot Using Python and Zerodha**
22 | 9. **Guppy Indicator Trading Bot Using Python and Zerodha**
23 | 10. **Plotting OHLC to Candlestick Chart**
24 | 11. **Plotting Zerodha OHLC to Candlestick Chart**
25 | 12. **Converting Candles to Heikin Ashi Using Zerodha KiteConnect**
26 | 13. **Creating TimeFrames from Minute-Based Data Using Python**
27 | 14. **Multi-Stock Bot Using Guppy Strategy with Screener and Backtesting**
28 | 15. **RSI Based Trading Bot with Python using Zerodha API**
29 | 16. **Multi-Timeframe Bot Using Guppy Strategy and Screener**
30 | 17. **Backtesting Guppy Multiple Moving Average (GMMA) with Python using Zerodha API**
31 | 18. **Exploring Technical Indicators in the Indian Stock Market with Zerodha API and Python**
32 | 
33 | ## 📜 Descriptions
34 | 
35 | ### 1. Getting Instrument Token of a Scrip Using Python and Zerodha API
36 |    - Learn how to retrieve the instrument token for a specific scrip using Python in conjunction with the Zerodha API.
37 | 
38 | ### 2. Getting Instrument Token of a Scrip Using Python and Zerodha Kite LTP Method
39 |    - A guide to acquiring instrument tokens using the Kite LTP (Last Traded Price) method in Python with Zerodha's API.
40 | 
41 | ### 3. Getting Historical Data of Reliance Using Python and Zerodha API
42 |    - Discover the method to fetch historical data for Reliance stocks using Python through the Zerodha API.
43 | 
44 | ### 4. Getting Historical Data of Reliance In Pandas Using Python and Zerodha API
45 |    - Explore how to process and analyze Reliance's historical data using Pandas in Python, leveraging Zerodha's API.
46 | 
47 | ### 5. Organizing Expiry Dates of Financial Instruments with Python
48 |    - Learn to organize and manage the expiry dates of various financial instruments using Python.
49 | 
50 | ### 6. Buy on RSI Strategy Coding in Python using Zerodha
51 |    - Implement the RSI (Relative Strength Index) strategy in Python for trading decisions on the Zerodha platform.
52 | 
53 | ### 7. Guppy Strategy Screener Using Python and Zerodha
54 |    - A comprehensive guide to using the Guppy strategy for screening stocks with Python on Zerodha.
55 | 
56 | ### 8. Guppy Strategy Trading Bot Using Python and Zerodha
57 |    - Develop an automated trading bot that employs the Guppy strategy in Python with Zerodha's API.
58 | 
59 | ### 9. Guppy Indicator Trading Bot Using Python and Zerodha
60 |    - Create a trading bot focused on the Guppy indicator, utilizing Python and Zerodha's tools for automated trading.
61 | 
62 | ### 10. Plotting OHLC to Candlestick Chart
63 |    - Understand the process of transforming OHLC (Open, High, Low, Close) data into intuitive candlestick charts.
64 | 
65 | ### 11. Plotting Zerodha OHLC to Candlestick Chart
66 |    - Learn to convert Zerodha's OHLC data into candlestick charts for better visualization and analysis.
67 | 
68 | ### 12. Converting Candles to Heikin Ashi Using Zerodha KiteConnect
69 |    - Master the technique of converting candlestick charts to Heikin Ashi format using Zerodha KiteConnect.
70 | 
71 | ### 13. Creating TimeFrames from Minute-Based Data Using Python
72 |    - A guide to creating various timeframes from minute-based data for comprehensive market analysis using Python.
73 | 
74 | ### 14. Multi-Stock Bot Using Guppy Strategy with Screener and Backtesting
75 |    - Build a multi-stock trading bot that incorporates the Guppy strategy, complete with screening and backtesting functionalities.
76 | 
77 | ### 15. RSI Based Trading Bot with Python using Zerodha API
78 |    - Develop a trading bot in Python that operates based on the RSI strategy, integrated with Zerodha's API.
79 | 
80 | ### 16. Multi-Timeframe Bot Using Guppy Strategy and Screener
81 |    - Create an advanced bot that operates across multiple timeframes using the Guppy strategy, along with stock screening.
82 | 
83 | ### 17. Backtesting Guppy Multiple Moving Average (GMMA) with Python using Zerodha API
84 |    - Learn the nuances of backtesting the Guppy Multiple Moving Average strategy using Python with Zerodha's API.
85 | 
86 | ### 18. Exploring Technical Indicators in the Indian Stock Market with Zerodha API and Python
87 |    - Dive into various technical indicators and their application in the Indian stock market using Python and Zerodha's API.
88 | 
89 | 
90 | ## 🚀 Conclusion
91 | 
92 | Step into the exciting world of Algo Trading. This guide is your gateway to understanding and applying effective trading strategies in the Indian stock market, leveraging the technological prowess of Zerodha’s KiteConnect API.
93 | 


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