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algo-trading-setup.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
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"source": [
"<a href=\"https://colab.research.google.com/gist/mohitkh7/499ec2f9959d05298925c0f876ae9f37/algo-trading-setup.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3WY_CvoJBRFH"
},
"source": [
"## Algo Trading Setup\n",
"\n",
"The Aim of the notebook is to verify different trade setup based on technical analysis tools such as candlestick pattern, indicators etc. To backtest and identify best trade setup."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W0FLaMs1BmV6"
},
"source": [
"**Todo**\n",
"\n",
"1. [x] Load NSE stock prices\n",
"2. [x] Plot stock price as candlestick on chart\n",
"3. [x] Backtest marobazu trade setup.\n",
"4. [ ] Replicate equity delievery charges as commision\n",
"5. [ ] Create HangingMan / Hammer Trade setup\n",
"6. [ ] compute S&R\n",
"7. [ ] fix yahoo finance for date calculation\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kw6Cob55MeCa"
},
"source": [
"### Load Necessary Libraries & Packages"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UQ6LBh0ho9vi"
},
"source": [
"TA-Lib package is a technical analysis library with 150+ technical indicator implementations.\n",
"\n",
"Ta-Lib package needs a dependent C module installation. Command to install C module required for talib.\n",
"\n",
"Ref - [stackoverflow answer](https://stackoverflow.com/questions/49648391/how-to-install-ta-lib-in-google-colab)\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kkG8wIqppgtC",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c07fb164-2cc1-4c36-ac3e-d6c46913607e"
},
"source": [
"url = 'https://launchpad.net/~mario-mariomedina/+archive/ubuntu/talib/+files'\n",
"!wget $url/libta-lib0_0.4.0-oneiric1_amd64.deb -qO libta.deb\n",
"!wget $url/ta-lib0-dev_0.4.0-oneiric1_amd64.deb -qO ta.deb\n",
"!dpkg -i libta.deb ta.deb\n",
"!pip install TA-Lib\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Selecting previously unselected package libta-lib0.\n",
"(Reading database ... 155113 files and directories currently installed.)\n",
"Preparing to unpack libta.deb ...\n",
"Unpacking libta-lib0 (0.4.0-oneiric1) ...\n",
"Selecting previously unselected package ta-lib0-dev.\n",
"Preparing to unpack ta.deb ...\n",
"Unpacking ta-lib0-dev (0.4.0-oneiric1) ...\n",
"Setting up libta-lib0 (0.4.0-oneiric1) ...\n",
"Setting up ta-lib0-dev (0.4.0-oneiric1) ...\n",
"Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
"Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
"/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
"\n",
"Collecting TA-Lib\n",
" Downloading TA-Lib-0.4.24.tar.gz (269 kB)\n",
"\u001b[K |████████████████████████████████| 269 kB 5.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from TA-Lib) (1.21.5)\n",
"Building wheels for collected packages: TA-Lib\n",
" Building wheel for TA-Lib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for TA-Lib: filename=TA_Lib-0.4.24-cp37-cp37m-linux_x86_64.whl size=1465745 sha256=82b86a91aad309653683fa20ea9491d279ab8c645e4b2df0fd741040e9aa167c\n",
" Stored in directory: /root/.cache/pip/wheels/02/b1/a5/aca31e8cdd0137d7a83bf72237056b8705663dd1f9b5eac11e\n",
"Successfully built TA-Lib\n",
"Installing collected packages: TA-Lib\n",
"Successfully installed TA-Lib-0.4.24\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "eoa-H9Cge0s_",
"colab": {
"base_uri": "https://localhost:8080/"
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"outputId": "8812b6f2-b8f2-4e3f-b077-bf5a8982d642"
},
"source": [
"!pip install yfinance"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting yfinance\n",
" Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
"Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
"Collecting lxml>=4.5.1\n",
" Downloading lxml-4.7.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
"\u001b[K |████████████████████████████████| 6.4 MB 7.7 MB/s \n",
"\u001b[?25hRequirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
"Collecting requests>=2.26\n",
" Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
"\u001b[K |████████████████████████████████| 63 kB 1.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.5)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.11)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
"Installing collected packages: requests, lxml, yfinance\n",
" Attempting uninstall: requests\n",
" Found existing installation: requests 2.23.0\n",
" Uninstalling requests-2.23.0:\n",
" Successfully uninstalled requests-2.23.0\n",
" Attempting uninstall: lxml\n",
" Found existing installation: lxml 4.2.6\n",
" Uninstalling lxml-4.2.6:\n",
" Successfully uninstalled lxml-4.2.6\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
"datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
"Successfully installed lxml-4.7.1 requests-2.27.1 yfinance-0.1.70\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "k19AWcsnm6z2",
"outputId": "6e716192-d60e-4fcb-b8d1-db6aa5c2f34c"
},
"source": [
"%pip install backtrader"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting backtrader\n",
" Downloading backtrader-1.9.76.123-py2.py3-none-any.whl (410 kB)\n",
"\u001b[?25l\r\u001b[K |▉ | 10 kB 20.6 MB/s eta 0:00:01\r\u001b[K |█▋ | 20 kB 13.1 MB/s eta 0:00:01\r\u001b[K |██▍ | 30 kB 9.3 MB/s eta 0:00:01\r\u001b[K |███▏ | 40 kB 8.3 MB/s eta 0:00:01\r\u001b[K |████ | 51 kB 5.8 MB/s eta 0:00:01\r\u001b[K |████▉ | 61 kB 5.9 MB/s eta 0:00:01\r\u001b[K |█████▋ | 71 kB 5.7 MB/s eta 0:00:01\r\u001b[K |██████▍ | 81 kB 6.3 MB/s eta 0:00:01\r\u001b[K |███████▏ | 92 kB 6.2 MB/s eta 0:00:01\r\u001b[K |████████ | 102 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████▉ | 112 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████▋ | 122 kB 5.4 MB/s eta 0:00:01\r\u001b[K |██████████▍ | 133 kB 5.4 MB/s eta 0:00:01\r\u001b[K |███████████▏ | 143 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████ | 153 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████▉ | 163 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████████▋ | 174 kB 5.4 MB/s eta 0:00:01\r\u001b[K |██████████████▍ | 184 kB 5.4 MB/s eta 0:00:01\r\u001b[K |███████████████▏ | 194 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████ | 204 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████▉ | 215 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████████████▋ | 225 kB 5.4 MB/s eta 0:00:01\r\u001b[K |██████████████████▍ | 235 kB 5.4 MB/s eta 0:00:01\r\u001b[K |███████████████████▏ | 245 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████ | 256 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████▊ | 266 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████████████████▋ | 276 kB 5.4 MB/s eta 0:00:01\r\u001b[K |██████████████████████▍ | 286 kB 5.4 MB/s eta 0:00:01\r\u001b[K |███████████████████████▏ | 296 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████ | 307 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████▊ | 317 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▋ | 327 kB 5.4 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▍ | 337 kB 5.4 MB/s eta 0:00:01\r\u001b[K |███████████████████████████▏ | 348 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████████ | 358 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████████▊ | 368 kB 5.4 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▋ | 378 kB 5.4 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▍ | 389 kB 5.4 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▏| 399 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 409 kB 5.4 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 410 kB 5.4 MB/s \n",
"\u001b[?25hInstalling collected packages: backtrader\n",
"Successfully installed backtrader-1.9.76.123\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8BYy_V_hBNWN"
},
"source": [
"import talib\n",
"import yfinance as yf\n",
"import backtrader as bt\n",
"import requests\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objects as go\n",
"\n",
"from datetime import datetime"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OPlSvliyCL-e"
},
"source": [
"# matplotlib configurations\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "dLlGY5geCFmL"
},
"source": [
"### Load stock prices"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kUX7_23mfJHe"
},
"source": [
"def get_stock_data(symbol, start, end):\n",
" ticker = yf.Ticker(symbol)\n",
" df = ticker.history(start=start, end=end, auto_adjust=False, rounding=True, interval=\"15m\")\n",
" return df"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YqH1AgdDKSXr"
},
"source": [
"def get_stock_data_old(symbol, from_date, end_date):\n",
" from_date_epoch = int(datetime.strptime(from_date, '%Y-%m-%d').timestamp())\n",
" end_date_epoch = int(datetime.strptime(end_date, '%Y-%m-%d').timestamp())\n",
" # print(from_date_epoch, end_date_epoch)\n",
" data = pd.read_json(f'https://query1.finance.yahoo.com/v8/finance/chart/{symbol}.NS?period1={from_date_epoch}&period2={end_date_epoch}&interval=1d&frequency=1d&filter=history')\n",
" result = data['chart']['result'][0]\n",
" quotes = result['indicators']['quote'][0]\n",
" df = pd.DataFrame(quotes)\n",
" df['datetime'] = pd.to_datetime(result['timestamp'], unit='s') # convert epoch to datetime\n",
" df['date'] = df['datetime'].dt.strftime('%d-%m-%Y') # convert datetime to dd-mm-yyyy format\n",
" df.set_index('datetime', inplace=True)\n",
" df = df.dropna()\n",
" return df"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
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"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"id": "JaIus-LyLOfS",
"outputId": "50099247-a382-4a8a-de48-c691e1a43c61"
},
"source": [
"stock_data = get_stock_data('TCS.NS', '2022-01-01', '2022-02-15')\n",
"stock_data.head()"
],
"execution_count": null,
"outputs": [
{
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"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-ad739ae1-26e8-46b4-b6e0-e26f12a362d1 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-ad739ae1-26e8-46b4-b6e0-e26f12a362d1');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
" Open High ... Dividends Stock Splits\n",
"Datetime ... \n",
"2022-01-03 09:15:00+05:30 3748.35 3785.00 ... 0.0 0\n",
"2022-01-03 09:30:00+05:30 3782.90 3809.85 ... 0.0 0\n",
"2022-01-03 09:45:00+05:30 3804.10 3808.35 ... 0.0 0\n",
"2022-01-03 10:00:00+05:30 3802.40 3803.95 ... 0.0 0\n",
"2022-01-03 10:15:00+05:30 3801.45 3815.90 ... 0.0 0\n",
"\n",
"[5 rows x 8 columns]"
]
},
"metadata": {},
"execution_count": 50
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kFymZMQtVrFG"
},
"source": [
"### View charts"
]
},
{
"cell_type": "code",
"metadata": {
"id": "LrhJpJAONJGW"
},
"source": [
"import plotly.express as px\n",
"\n",
"def display_line_chart(data):\n",
" fig = px.line(data, x=data.index, y=\"Close\")\n",
" fig.show()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PyuYyMZbU2L0"
},
"source": [
"def display_candlestick_chart(data):\n",
" candlestick_data = go.Candlestick(x=data.index,\n",
" open=data['Open'],\n",
" high=data['High'],\n",
" low=data['Low'],\n",
" close=data['Close'])\n",
" fig = go.Figure(data=[candlestick_data])\n",
" fig.show()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "S3Us6JD2M6Ty"
},
"source": [
"### Strategies"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gKta70YRdknO"
},
"source": [
"#### Base Strategy\n",
"Base strategy class which implements core functionalities which child classes may override if required"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7bxKpvwzcdKW"
},
"source": [
"class MyStrategy(bt.Strategy):\n",
" \"\"\"\n",
" Overridden form of Backtrader Strategy to implement common functionalities\n",
" \"\"\"\n",
" def __init__(self):\n",
" # datas\n",
" self.data_high = self.datas[0].high\n",
" self.data_low = self.datas[0].low\n",
" self.data_open = self.datas[0].open\n",
" self.data_close = self.datas[0].close\n",
"\n",
" # ongoing order details\n",
" self.order = None\n",
" self.orefs = []\n",
"\n",
" def log(self, txt, dt=None):\n",
" dt = dt or self.datas[0].datetime.date(0)\n",
" print(f'{dt.isoformat()} {txt}')\n",
"\n",
" def notify_trade(self, trade):\n",
" if not trade.isclosed:\n",
" return\n",
" self.log(\"Trade Summary, gross: %.2f, net: %.2f, duration: %d\\n\" % (trade.pnl, trade.pnlcomm, trade.barlen))\n",
"\n",
" def notify_order(self, order):\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status in [order.Completed]:\n",
" if order.isbuy():\n",
" # self.log(f'BUY EXECUTED, {order.executed.price:.2f}')\n",
" pass\n",
" elif order.issell():\n",
" # self.log(f'SELL EXECUTED, {order.executed.price:.2f}')\n",
" pass\n",
"\n",
" elif order.status in [order.Margin, order.Rejected]:\n",
" self.log(f'Order failed({order.getstatusname()})')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kh-3Ju9V32ra"
},
"source": [
"#### Sample Strategy to print close price"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lJA6l9H8p5Ch"
},
"source": [
"class PrintClose(bt.Strategy):\n",
" def __init__(self):\n",
" #Keep a reference to the \"close\" line in the data[0] dataseries\n",
" self.dataclose = self.datas[0].close\n",
" self.datadate = self.datas[0].datetime\n",
"\n",
" def log(self, txt, close, dt):\n",
" print(f'{txt} {dt.isoformat()} {close:.2f}') #Print date and close\n",
"\n",
" def next(self):\n",
" # print(self.datas[0].datetime.date(0), end=\" \")\n",
" self.log('Close: ', self.dataclose[0], self.datadate.date(0))\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "X0WZa_Wv38Dc"
},
"source": [
"#### Moving average crossover strategy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QPF8gDdv3-z3"
},
"source": [
"class MACrossover(bt.Strategy):\n",
" # moving average params\n",
" params = (('pfast', 10), ('pslow', 20))\n",
"\n",
" def log(self, txt, dt=None):\n",
" dt = dt or self.datas[0].datetime.date(0)\n",
" # print(f'{dt.isoformat()} {txt}')\n",
"\n",
" def __init__(self):\n",
" self.dataclose = self.datas[0].close\n",
"\n",
" # ongoing order details\n",
" self.order = None\n",
"\n",
" self.slow_sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.pslow)\n",
" self.fast_sma = bt.indicators.MovingAverageSimple(self.datas[0], period=self.params.pfast)\n",
"\n",
" def notify_order(self, order):\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status in [order.Completed]:\n",
" if order.isbuy():\n",
" self.log(f'BUY EXECUTED, {order.executed.price:.2f}')\n",
" elif order.issell():\n",
" self.log(f'SELL EXECUTED, {order.executed.price:.2f}')\n",
" self.bar_executed = len(self)\n",
"\n",
" elif order.status in [order.Canceled, order.Margin, order.Rejected]:\n",
" self.log('Order Canceled/Margin/Rejected')\n",
"\n",
" # Reset orders\n",
" self.order = None\n",
"\n",
" def next(self):\n",
" # Check for open orders\n",
" if self.order:\n",
" return\n",
"\n",
" # Check if we are in the market\n",
" if not self.position:\n",
" # We are not in the market, look for a signal to OPEN trades\n",
"\n",
" #If the 20 SMA is above the 50 SMA\n",
" if self.fast_sma[0] > self.slow_sma[0] and self.fast_sma[-1] < self.slow_sma[-1]:\n",
" self.log(f'BUY Triggered, {self.dataclose[0]:2f}')\n",
" # Keep track of the created order to avoid a 2nd order\n",
" self.order = self.buy()\n",
" #Otherwise if the 20 SMA is below the 50 SMA\n",
" elif self.fast_sma[0] < self.slow_sma[0] and self.fast_sma[-1] > self.slow_sma[-1]:\n",
" self.log(f'SELL Triggered, {self.dataclose[0]:2f}')\n",
" # Keep track of the created order to avoid a 2nd order\n",
" self.order = self.sell()\n",
" else:\n",
" # We are already in the market, look for a signal to CLOSE trades\n",
" if len(self) >= (self.bar_executed + 5):\n",
" self.log(f'CLOSE CREATE {self.dataclose[0]:2f}')\n",
" self.order = self.close()\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "0s_zX5stN50b"
},
"source": [
"#### Test Strategy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "4iSCSkDRuL6P"
},
"source": [
"class TestStrategy(bt.Strategy):\n",
"\n",
" def log(self, txt, dt=None):\n",
" ''' Logging function fot this strategy'''\n",
" dt = dt or self.datas[0].datetime.date(0)\n",
" print('%s, %s' % (dt.isoformat(), txt))\n",
"\n",
" def __init__(self):\n",
" # Keep a reference to the \"close\" line in the data[0] dataseries\n",
" self.dataclose = self.datas[0].close\n",
"\n",
" def next(self):\n",
" # Simply log the closing price of the series from the reference\n",
" self.log('Close, %.2f' % self.dataclose[0])\n",
"\n",
" if self.dataclose[0] < self.dataclose[-1]:\n",
" # current close less than previous close\n",
"\n",
" if self.dataclose[-1] < self.dataclose[-2]:\n",
" # previous close less than the previous close\n",
"\n",
" # BUY, BUY, BUY!!! (with all possible default parameters)\n",
" self.log('BUY CREATE, %.2f' % self.dataclose[0])\n",
" self.buy()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2u7XPCRsN_e9"
},
"source": [
"#### Buy And Hold Strategy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "C8pbzJ-TvYMe"
},
"source": [
"class BuyAndHold(bt.Strategy):\n",
" def start(self):\n",
" self.val_start = self.broker.get_cash() # keep the starting cash\n",
"\n",
" def nextstart(self):\n",
" # Buy all the available cash\n",
" size = int(self.broker.get_cash() / self.data)\n",
" self.buy(size=size)\n",
"\n",
" def stop(self):\n",
" # calculate the actual returns\n",
" self.roi = (self.broker.get_value() / self.val_start) - 1.0\n",
" print('ROI: {:.2f}%'.format(100.0 * self.roi))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_9Tz2aGmUKde"
},
"source": [
"#### Marubozu Strategy\n",
"\n",
"This strategy works on Marubozu candlestick pattern. A marubozu pattern is formed when candle is formed with only body and no shadow. It represents strong directional momentum in market.\n",
"\n",
"* Result: Fails\n",
"* Strike rate: 26%\n",
"* ROI: -6.08%"
]
},
{
"cell_type": "code",
"metadata": {
"id": "w3bYkOMEUPHx"
},
"source": [
"from datetime import timedelta\n",
"\n",
"class MarubozuStrategy(MyStrategy):\n",
" params = (\n",
" ('rrr', 2), # risk reward ratio\n",
" )\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" # indicators\n",
" self.marubozu = bt.talib.CDLMARUBOZU(self.data.open, self.data.high, self.data.low, self.data.close)\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status == order.Completed:\n",
" self.holdstart = len(self)\n",
"\n",
" if not order.alive() and order.ref in self.orefs:\n",
" self.orefs.remove(order.ref)\n",
"\n",
" def next(self):\n",
" if self.orefs:\n",
" return #pending orders do nothing\n",
"\n",
" # Check if we are in the market\n",
" if not self.position:\n",
" # We are not in the market, look for a signal to OPEN trades\n",
" if self.marubozu[0] == 100:\n",
" # strong buy momentum, open long position\n",
" stoploss = self.data_low[0] # Low of marubozu will act as stoploss\n",
" target = (self.params.rrr * (self.data[0] - stoploss)) + self.data[0]\n",
" self.log(f'Long : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}', self.datas[0].datetime.datetime(0) + timedelta(hours=5, minutes=30))\n",
" os = self.buy_bracket(price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]\n",
" elif self.marubozu[0] == -100:\n",
" # strong buy momentum, open long position\n",
" stoploss = self.data_high[0] # High of marubozu will act as stoploss\n",
" target = self.data[0] - (self.params.rrr * (stoploss - self.data[0]))\n",
" self.log(f'Short : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}', self.datas[0].datetime.datetime(0) + timedelta(hours=5, minutes=30))\n",
" os = self.sell_bracket(price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "o4hqpkzTnQtc"
},
"source": [
"#### Hammer / Hanging Man strategy\n",
"This strategy is based on candlestick pattern formed with long lower shadow representing trend reversal.\n",
"\n",
"* Result: Successful\n",
"* Strike Rate: 70.83%\n",
"* ROI: 11.44%"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1gvn3jACnhjt"
},
"source": [
"class HammerStrategy(MyStrategy):\n",
" params = (\n",
" ('rrr', 1.5), # risk reward ratio\n",
" )\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" # indicators\n",
" self.hammer = bt.talib.CDLHAMMER(self.data.open, self.data.high, self.data.low, self.data.close)\n",
" self.hangingman = bt.talib.CDLHANGINGMAN(self.data.open, self.data.high, self.data.low, self.data.close)\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status == order.Completed:\n",
" self.holdstart = len(self)\n",
"\n",
" if not order.alive() and order.ref in self.orefs:\n",
" self.orefs.remove(order.ref)\n",
"\n",
" def get_support(self, price):\n",
" return levels.loc[levels < price].max()\n",
"\n",
" def get_resistance(self, price):\n",
" return levels.loc[levels > price].min()\n",
"\n",
" def is_close(self, price, level, range=5):\n",
" return abs(price - level) < range\n",
"\n",
"\n",
" def next(self):\n",
" if self.orefs:\n",
" return #pending orders do nothing\n",
"\n",
" # Check if we are in the market\n",
" if not self.position:\n",
" # We are not in the market, look for a signal to OPEN trades\n",
" if self.hammer[0] == 100 or self.hangingman[0] == 100:\n",
" # trend reversal marking entry of bulls, open long position\n",
" sp = self.get_support(self.data[0])\n",
" rs = self.get_resistance(self.data[0])\n",
" if self.is_close(self.data[0], sp):\n",
" stoploss = min(self.data_low[0], sp) # Low of hammer or support will act as stoploss\n",
" target = rs # (self.params.rrr * (self.data[0] - stoploss)) + self.data[0]\n",
" self.log(f'Long : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.buy_bracket(exectype=bt.Order.Market, price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]\n",
"\n",
" elif self.hammer[0] == -100 or self.hangingman[0] == -100:\n",
" # trend reversal marking entry of bears, open short position\n",
" sp = self.get_support(self.data[0])\n",
" rs = self.get_resistance(self.data[0])\n",
" if self.is_close(self.data[0], rs):\n",
" stoploss = max(self.data_high[0], rs) # High of hanging man will act as stoploss\n",
" target = sp # self.data[0] - (self.params.rrr * (stoploss - self.data[0]))\n",
" self.log(f'Short : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.sell_bracket(price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "JWOHawiFjV0G"
},
"source": [
"#### Engulfing Pattern\n",
"This candle is a trend reversal pattern.\n",
"1. Stock is in downtrend.\n",
"2. P1 candle is another red candle.\n",
"3. P2 candle is blue candle such that it completely engulfs previous candle.\n",
"\n",
"Vice-versa for bearish engulfing pattern."
]
},
{
"cell_type": "code",
"metadata": {
"id": "1GLgOfrakFNW"
},
"source": [
"class EngulfingStrategy(MyStrategy):\n",
" params = (\n",
" ('rrr', 1.5), # risk reward ratio\n",
" ('trend_period', 14),\n",
" ('strength_threshold', 25)\n",
" )\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" # indicators\n",
" self.engulfing = bt.talib.CDLENGULFING(self.data.open, self.data.high, self.data.low, self.data.close)\n",
" # Add directional indicator to identify trend\n",
" self.plus_di = bt.talib.PLUS_DI(self.data.high, self.data.low, self.data_close, timeperiod=self.params.trend_period)\n",
" self.minus_di = bt.talib.MINUS_DI(self.data.high, self.data.low, self.data_close, timeperiod=self.params.trend_period)\n",
" self.trend_strength = bt.talib.ADX(self.data_high, self.data_low, self.data_close, timeperiod=self.params.trend_period)\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status == order.Completed:\n",
" self.holdstart = len(self)\n",
"\n",
" if not order.alive() and order.ref in self.orefs:\n",
" self.orefs.remove(order.ref)\n",
"\n",
" def next(self):\n",
" if self.orefs:\n",
" return #pending orders do nothing\n",
"\n",
" # Check if we are in the market\n",
"\n",
" if self.engulfing[0] == 100:\n",
" self.log(f'bullish minus_di{self.minus_di[0]:.2f}, plus_di{self.plus_di[0]:.2f}, trend_strength{self.trend_strength[0]:.2f}')\n",
" if self.engulfing[0] == -100:\n",
" self.log(f'bearish minus_di{self.minus_di[0]:.2f}, plus_di{self.plus_di[0]:.2f}, trend_strength{self.trend_strength[0]:.2f}')\n",
" if not self.position:\n",
" # We are not in the market, look for a signal to OPEN trades\n",
" if self.minus_di[0] > self.plus_di[0] and self.trend_strength > self.params.strength_threshold and self.engulfing[0] == 100:\n",
" # trend reversal marking entry of bulls, open long position\n",
" stoploss = min(self.data_low[0], self.data_low[-1]) # Minumum low of p1 or p2 will act as stoploss\n",
" target = (self.params.rrr * (self.data[0] - stoploss)) + self.data[0]\n",
" self.log(f'Long : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.buy_bracket(exectype=bt.Order.Market, price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]\n",
"\n",
" elif self.plus_di[0] > self.minus_di[0] and self.trend_strength > self.params.strength_threshold and self.engulfing[0] == -100:\n",
" # trend reversal marking entry of bears, open short position\n",
" stoploss = max(self.data_high[0], self.data_high[-1]) # Max High of p1 or p2 man will act as stoploss\n",
" target = self.data[0] - (self.params.rrr * (stoploss - self.data[0]))\n",
" self.log(f'Short : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.sell_bracket(price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mwvf1Spmq2zl"
},
"source": [
"#### Morning Star Pattern\n",
"This candle is a trend reversal pattern.\n",
"1. Stock is in downtrend.\n",
"2. P1 candle is another red candle.\n",
"3. P2 candle is gap down opening forming doji or spinning top.\n",
"3. P3 candle has gap up opening forming blue candle.\n",
"\n",
"Vice-versa for evening star pattern."
]
},
{
"cell_type": "code",
"metadata": {
"id": "qpQcb584rYws"
},
"source": [
"class MorningStarPattern(MyStrategy):\n",
" params = (\n",
" ('rrr', 1.5), # risk reward ratio\n",
" ('trend_period', 7),\n",
" ('strength_threshold', 25)\n",
" )\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" # indicators\n",
" self.morning_star = bt.talib.CDLMORNINGSTAR(self.data.open, self.data.high, self.data.low, self.data.close)\n",
" self.evening_star = bt.talib.CDLEVENINGSTAR(self.data.open, self.data.high, self.data.low, self.data.close)\n",
" # Add directional indicator to identify trend\n",
" self.plus_di = bt.talib.PLUS_DI(self.data.high, self.data.low, self.data_close, timeperiod=self.params.trend_period)\n",
" self.minus_di = bt.talib.MINUS_DI(self.data.high, self.data.low, self.data_close, timeperiod=self.params.trend_period)\n",
" self.trend_strength = bt.talib.ADX(self.data_high, self.data_low, self.data_close, timeperiod=self.params.trend_period)\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status == order.Completed:\n",
" self.holdstart = len(self)\n",
"\n",
" if not order.alive() and order.ref in self.orefs:\n",
" self.orefs.remove(order.ref)\n",
"\n",
" def next(self):\n",
" if self.orefs:\n",
" return #pending orders do nothing\n",
"\n",
" # Check if we are in the market\n",
" if not self.position:\n",
" if self.morning_star[0] == 100 or self.morning_star[0] == -100:\n",
" self.log(f'morning star formed minus_di{self.minus_di[0]:.2f}, plus_di{self.plus_di[0]:.2f}, trend_strength{self.trend_strength[0]}')\n",
" if self.evening_star[0] == 100 or self.evening_star[0] == -100:\n",
" self.log(f'evening_star {self.evening_star[0]} formed minus_di{self.minus_di[0]:.2f}, plus_di{self.plus_di[0]:.2f}, trend_strength{self.trend_strength[0]}')\n",
" # We are not in the market, look for a signal to OPEN trades\n",
" if self.minus_di[0] > self.plus_di[0] and self.trend_strength[0] > self.params.strength_threshold and self.morning_star[0] == 100:\n",
" # trend reversal marking entry of bulls, open long position\n",
" stoploss = min(self.data_low[0], self.data_low[-1]) # Minumum low of p1 or p2 will act as stoploss\n",
" target = (self.params.rrr * (self.data[0] - stoploss)) + self.data[0]\n",
" self.log(f'Long : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.buy_bracket(exectype=bt.Order.Market, price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]\n",
"\n",
" elif self.plus_di[0] > self.minus_di[0] and self.trend_strength[0] > self.params.strength_threshold and self.evening_star[0] == -100:\n",
" # trend reversal marking entry of bears, open short position\n",
" stoploss = max(self.data_high[0], self.data_high[-1]) # Max High of p1 or p2 man will act as stoploss\n",
" target = self.data[0] - (self.params.rrr * (stoploss - self.data[0]))\n",
" self.log(f'Short : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.sell_bracket(price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "voenrvQ82QWR"
},
"source": [
"#### 3 Soldiers"
]
},
{
"cell_type": "code",
"metadata": {
"id": "se0N8a4Y2S8S"
},
"source": [
"class ThreeSoldiers(MyStrategy):\n",
" params = (\n",
" ('rrr', 1.5), # risk reward ratio\n",
" ('trend_period', 7),\n",
" ('strength_threshold', 25)\n",
" )\n",
" def __init__(self):\n",
" super().__init__()\n",
" # indicators\n",
" self.white_soldiers = bt.talib.CDL3WHITESOLDIERS(self.data.open, self.data.high, self.data.low, self.data.close)\n",
" # Add directional indicator to identify trend\n",
" self.plus_di = bt.talib.PLUS_DI(self.data.high, self.data.low, self.data_close, timeperiod=self.params.trend_period)\n",
" self.minus_di = bt.talib.MINUS_DI(self.data.high, self.data.low, self.data_close, timeperiod=self.params.trend_period)\n",
" self.trend_strength = bt.talib.ADX(self.data_high, self.data_low, self.data_close, timeperiod=self.params.trend_period)\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status == order.Completed:\n",
" self.holdstart = len(self)\n",
"\n",
" if not order.alive() and order.ref in self.orefs:\n",
" self.orefs.remove(order.ref)\n",
"\n",
" def next(self):\n",
" if self.orefs:\n",
" return #pending orders do nothing\n",
"\n",
" # Check if we are in the market\n",
" if not self.position:\n",
" # We are not in the market, look for a signal to OPEN trades\n",
" if self.white_soldiers[0] == 100:\n",
" # trend reversal marking entry of bulls, open long position\n",
" stoploss = min(self.data_low[0], self.data_low[-1]) # Minumum low of p1 or p2 will act as stoploss\n",
" target = (self.params.rrr * (self.data[0] - stoploss)) + self.data[0]\n",
" self.log(f'Long : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.buy_bracket(exectype=bt.Order.Market, price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]\n",
"\n",
" elif self.white_soldiers[0] == -100:\n",
" # trend reversal marking entry of bears, open short position\n",
" stoploss = max(self.data_high[0], self.data_high[-1]) # Max High of p1 or p2 man will act as stoploss\n",
" target = self.data[0] - (self.params.rrr * (stoploss - self.data[0]))\n",
" self.log(f'Short : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.sell_bracket(price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3jmTRgPtgH24"
},
"source": [
"# Support and resistance strategy\n",
"class SupportResistanceStrategy(MyStrategy):\n",
" params = (\n",
" ('rrr', 1.5), # risk reward ratio\n",
" ('trend_period', 7),\n",
" ('strength_threshold', 25)\n",
" )\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status == order.Completed:\n",
" self.holdstart = len(self)\n",
"\n",
" if not order.alive() and order.ref in self.orefs:\n",
" self.orefs.remove(order.ref)\n",
"\n",
" def get_support(self, price):\n",
" return levels.loc[levels < price].max()\n",
"\n",
" def get_resistance(self, price):\n",
" return levels.loc[levels > price].min()\n",
"\n",
" def is_close(self, price, level, range=2):\n",
" return abs(price - level) < range\n",
"\n",
" def next(self):\n",
" if self.orefs:\n",
" return #pending orders do nothing\n",
"\n",
" # Check if we are in the market\n",
" if not self.position:\n",
" # We are not in the market, look for a signal to OPEN trades\n",
" sp = self.get_support(self.data[0])\n",
" rs = self.get_resistance(self.data[0])\n",
" if self.is_close(self.data[0], sp):\n",
" # trend reversal marking entry of bulls, open long position\n",
" stoploss = sp # Minumum low of p1 or p2 will act as stoploss\n",
" target = rs\n",
" self.log(f'Long : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.buy_bracket(exectype=bt.Order.Market, price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]\n",
"\n",
" elif self.is_close(self.data[0], rs):\n",
" # trend reversal marking entry of bears, open short position\n",
" stoploss = rs # Max High of p1 or p2 man will act as stoploss\n",
" target = sp # self.data[0] - (self.params.rrr * (stoploss - self.data[0]))\n",
" self.log(f'Short : {self.data[0]:.2f}, Target: {target:.2f}, Stoploss: {stoploss:.2f}')\n",
" os = self.sell_bracket(price=self.data[0], limitprice=target, stopprice=stoploss)\n",
" self.orefs = [o.ref for o in os]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "P-fG-ovcu7wL"
},
"source": [
"#### Buy On Dip\n",
"Result with invest perc: [1,2,4,8,16,32,64,84,100]\n",
"Starting Portfolio Value: 2160000.00\n",
"Final Portfolio Value: 4538775.33\n",
"PnL: 2378775.33\n",
"ROI %: 110.13\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wJyOOnerp55b"
},
"source": [
"class BuyOnDipStrategy(MyStrategy):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.last_max_price = 0\n",
" self.levels = [29,39,49,59,69,109,149,20,30]\n",
" self.is_trade_initiated = [False for i in range(len(self.levels))]\n",
" self.invest_amount = [5000, 10000, 15000, 25000, 40000, 65000, 105000, 170000, 275000]\n",
" self.invest_perc = [5,8,13,17,21,34,55,80,100]\n",
"\n",
" def _reset(self):\n",
" self.is_trade_initiated = [False for i in range(len(self.levels))]\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
" if order.status in [order.Submitted, order.Accepted]:\n",
" return\n",
"\n",
" if order.status == order.Completed:\n",
" self.holdstart = len(self)\n",
"\n",
" if not order.alive() and order.ref in self.orefs:\n",
" self.orefs.remove(order.ref)\n",
"\n",
" def next(self):\n",
" self.last_max_price = max(self.last_max_price, self.data_close[0])\n",
" drawdown = ((self.last_max_price - self.data_close[0]) / self.last_max_price) * 100\n",
" if drawdown > 2:\n",
" for i in range(len(self.levels)):\n",
" if drawdown >= self.levels[i] and self.is_trade_initiated[i] is False:\n",
" # order = self.order_target_value(target=self.invest_amount[i] * 3)\n",
" # order = self.buy(size=1)\n",
" percent_of_portfolio_value = self.invest_perc[i] / 100\n",
" self.order_target_percent(target=percent_of_portfolio_value)\n",
" self.is_trade_initiated[i] = True\n",
" elif drawdown == 0 and self.is_trade_initiated[0] is True:\n",
" self.log(self.broker.get_cash())\n",
" self.close()\n",
" self._reset()\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2tOceZ5IchOS"
},
"source": [
"#### SIP Strategy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sYDh8AA8cgfz"
},
"source": [
"class SIPStrategy(MyStrategy):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.salary = 16000\n",
" self.invest_amount = 16000\n",
"\n",
" def notify_order(self, order):\n",
" super().notify_order(order)\n",
"\n",
" def next(self):\n",
" dt = self.data.datetime.date()\n",
" if dt.day == 10:\n",
" self.broker.add_cash(self.salary)\n",
" if dt.day == 15:\n",
" self.buy(size=1)\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "jH_317l1NlIK"
},
"source": [
"### Backtest"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kOKjqSuZQO8U"
},
"source": [
"def fetch_data(symbol, start_date, end_date):\n",
" \"\"\"\n",
" fetch stock data and display charts\n",
" \"\"\"\n",
" stock_data = get_stock_data(symbol, start_date, end_date)\n",
" stock_data.head()\n",
" display_line_chart(stock_data)\n",
" return stock_data"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "n66CuuM6Qp2S"
},
"source": [
"def init_stat():\n",
" # instantiate cerebero engine\n",
" cerebro = bt.Cerebro()\n",
"\n",
" # Default position size\n",
" cerebro.addsizer(bt.sizers.AllInSizerInt)\n",
"\n",
" # set broker commission as 11.16 per 10k (refer zerodha brokerage calculator)\n",
" # TODO: Add Rs. 15.93 (DP Charges on sell side)\n",
" # cerebro.broker.setcommission(commission=0.00116)\n",
"\n",
" # to allow short order with stoploss\n",
" cerebro.broker.set_checksubmit(checksubmit=False)\n",
"\n",
" # initial cash\n",
" cerebro.broker.setcash(50000)\n",
"\n",
" return cerebro"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LJpxbr-0p8bq"
},
"source": [
"def run_stat(df, strategy):\n",
" cerebro = init_stat()\n",
"\n",
" # add data feeds to data engine\n",
" data = bt.feeds.PandasData(dataname=stock_data, openinterest=None)\n",
" cerebro.adddata(data)\n",
"\n",
" # add strategy to engine\n",
" cerebro.broker.set_fundmode(True)\n",
" cerebro.addstrategy(strategy)\n",
"\n",
" # initial cash stats\n",
" start_portfolio_value = cerebro.broker.getvalue()\n",
"\n",
" # add analyzer\n",
"# cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name = \"ta\")\n",
" cerebro.addanalyzer(bt.analyzers.TimeReturn)\n",
"\n",
" # run engine\n",
" strats= cerebro.run()\n",
" cerebro.plot()\n",
"\n",
"# printTradeAnalysis(strats[0].analyzers.ta.get_analysis())\n",
"\n",
" # evaluating PnL\n",
" end_portfolio_value = cerebro.broker.getvalue()\n",
" pnl = end_portfolio_value - start_portfolio_value\n",
" print(f'Starting Portfolio Value: {start_portfolio_value:.2f}')\n",
" print(f'Final Portfolio Value: {end_portfolio_value:.2f}')\n",
" print(f'PnL: {pnl:.2f}')\n",
" print(f'ROI %: {((pnl / start_portfolio_value) * 100):.2f}')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"run_stat(stock_data, MarubozuStrategy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "L7uMGOYafvcy",
"outputId": "a3a18e0f-0af4-4cb1-e2af-7defe5fa512c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2022-01-03T12:00:00 Short : 3803.05, Target: 3785.45, Stoploss: 3811.85\n",
"2022-01-03 Trade Summary, gross: -114.40, net: -114.40, duration: 1\n",
"\n",
"2022-01-03T13:15:00 Short : 3812.90, Target: 3799.30, Stoploss: 3819.70\n",
"2022-01-03 Trade Summary, gross: -78.65, net: -78.65, duration: 1\n",
"\n",
"2022-01-04T10:00:00 Long : 3848.00, Target: 3873.10, Stoploss: 3835.45\n",
"2022-01-04 Trade Summary, gross: -150.60, net: -150.60, duration: 1\n",
"\n",
"2022-01-05T11:30:00 Short : 3824.70, Target: 3808.10, Stoploss: 3833.00\n",
"2022-01-05 Trade Summary, gross: -125.40, net: -125.40, duration: 1\n",
"\n",
"2022-01-05T13:30:00 Long : 3854.15, Target: 3865.85, Stoploss: 3848.30\n",
"2022-01-05 Trade Summary, gross: 141.00, net: 141.00, duration: 2\n",
"\n",
"2022-01-05T14:45:00 Short : 3850.20, Target: 3832.70, Stoploss: 3858.95\n",
"2022-01-05 Trade Summary, gross: -129.60, net: -129.60, duration: 1\n",
"\n",
"2022-01-06T10:15:00 Short : 3811.15, Target: 3795.15, Stoploss: 3819.15\n",
"2022-01-06 Trade Summary, gross: 192.00, net: 192.00, duration: 4\n",
"\n",
"2022-01-06T12:00:00 Short : 3782.30, Target: 3760.50, Stoploss: 3793.20\n",
"2022-01-06 Trade Summary, gross: -140.40, net: -140.40, duration: 2\n",
"\n",
"2022-01-07T09:30:00 Long : 3834.60, Target: 3889.80, Stoploss: 3807.00\n",
"2022-01-10 Trade Summary, gross: 1631.40, net: 1631.40, duration: 23\n",
"\n",
"2022-01-11T10:30:00 Long : 3888.90, Target: 3904.70, Stoploss: 3881.00\n",
"2022-01-11 Trade Summary, gross: -102.70, net: -102.70, duration: 5\n",
"\n",
"2022-01-12T10:00:00 Short : 3877.10, Target: 3854.50, Stoploss: 3888.40\n",
"2022-01-12 Trade Summary, gross: -146.90, net: -146.90, duration: 1\n",
"\n",
"2022-01-12T12:15:00 Short : 3876.45, Target: 3860.35, Stoploss: 3884.50\n",
"2022-01-12 Trade Summary, gross: 209.30, net: 209.30, duration: 2\n",
"\n",
"2022-01-13T11:45:00 Long : 3883.05, Target: 3902.35, Stoploss: 3873.40\n",
"2022-01-13 Trade Summary, gross: 250.90, net: 250.90, duration: 4\n",
"\n",
"2022-01-13T13:15:00 Short : 3894.90, Target: 3880.70, Stoploss: 3902.00\n",
"2022-01-13 Trade Summary, gross: -55.25, net: -55.25, duration: 1\n",
"\n",
"2022-01-13T15:00:00 Long : 3898.60, Target: 3913.10, Stoploss: 3891.35\n",
"2022-01-14 Trade Summary, gross: -267.80, net: -267.80, duration: 1\n",
"\n",
"2022-01-14T12:00:00 Long : 3916.25, Target: 3932.75, Stoploss: 3908.00\n",
"2022-01-14 Trade Summary, gross: 215.15, net: 215.15, duration: 2\n",
"\n",
"2022-01-17T10:00:00 Long : 3998.10, Target: 4040.30, Stoploss: 3977.00\n",
"2022-01-17 Trade Summary, gross: 506.40, net: 506.40, duration: 4\n",
"\n",
"2022-01-20T12:45:00 Short : 3848.40, Target: 3820.50, Stoploss: 3862.35\n",
"2022-01-20 Trade Summary, gross: 362.70, net: 362.70, duration: 3\n",
"\n",
"2022-01-24T11:15:00 Short : 3798.45, Target: 3769.35, Stoploss: 3813.00\n",
"2022-01-24 Trade Summary, gross: 378.30, net: 378.30, duration: 8\n",
"\n",
"2022-01-25T14:15:00 Short : 3750.50, Target: 3739.50, Stoploss: 3756.00\n",
"2022-01-25 Trade Summary, gross: -77.00, net: -77.00, duration: 1\n",
"\n",
"2022-01-27T12:00:00 Short : 3646.05, Target: 3620.75, Stoploss: 3658.70\n",
"2022-01-27 Trade Summary, gross: -169.40, net: -169.40, duration: 8\n",
"\n",
"2022-01-27T14:15:00 Long : 3673.20, Target: 3711.60, Stoploss: 3654.00\n",
"2022-01-27 Trade Summary, gross: -266.00, net: -266.00, duration: 2\n",
"\n",
"2022-01-31T15:00:00 Short : 3730.90, Target: 3684.60, Stoploss: 3754.05\n",
"2022-02-01 Trade Summary, gross: -495.30, net: -495.30, duration: 1\n",
"\n",
"2022-02-01T11:15:00 Long : 3788.70, Target: 3815.10, Stoploss: 3775.50\n",
"2022-02-01 Trade Summary, gross: -171.60, net: -171.60, duration: 6\n",
"\n",
"2022-02-03T15:15:00 Short : 3817.20, Target: 3797.90, Stoploss: 3826.85\n",
"2022-02-04 Trade Summary, gross: 270.40, net: 270.40, duration: 1\n",
"\n",
"2022-02-04T11:30:00 Short : 3802.00, Target: 3780.70, Stoploss: 3812.65\n",
"2022-02-04 Trade Summary, gross: -124.80, net: -124.80, duration: 9\n",
"\n",
"2022-02-07T12:45:00 Short : 3795.05, Target: 3779.45, Stoploss: 3802.85\n",
"2022-02-07 Trade Summary, gross: 206.70, net: 206.70, duration: 2\n",
"\n",
"2022-02-07T14:15:00 Long : 3773.90, Target: 3790.20, Stoploss: 3765.75\n",
"2022-02-07 Trade Summary, gross: -141.70, net: -141.70, duration: 1\n",
"\n",
"2022-02-08T10:30:00 Short : 3727.90, Target: 3705.90, Stoploss: 3738.90\n",
"2022-02-08 Trade Summary, gross: -143.00, net: -143.00, duration: 2\n",
"\n",
"2022-02-08T13:00:00 Short : 3726.05, Target: 3700.15, Stoploss: 3739.00\n",
"2022-02-08 Trade Summary, gross: -168.35, net: -168.35, duration: 6\n",
"\n",
"2022-02-10T10:00:00 Long : 3773.90, Target: 3793.70, Stoploss: 3764.00\n",
"2022-02-10 Trade Summary, gross: -128.70, net: -128.70, duration: 4\n",
"\n",
"2022-02-11T12:45:00 Short : 3707.45, Target: 3691.05, Stoploss: 3715.65\n",
"2022-02-11 Trade Summary, gross: -106.60, net: -106.60, duration: 2\n",
"\n",
"2022-02-14T13:15:00 Long : 3752.60, Target: 3791.60, Stoploss: 3733.10\n",
"2022-02-14 Trade Summary, gross: -253.50, net: -253.50, duration: 2\n",
"\n",
"Starting Portfolio Value: 50000.00\n",
"Final Portfolio Value: 50806.60\n",
"PnL: 806.60\n",
"ROI %: 1.61\n"
]
}
]
},
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"metadata": {
"id": "Nog8xcM9a76s"
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"source": [
"### Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QJbDV0SRa-Uk"
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"source": [
"#### Trade Analysis"
]
},
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"cell_type": "code",
"metadata": {
"id": "TBMbwPyLkMxE"
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"source": [
"def printTradeAnalysis(analyzer):\n",
" try:\n",
" total_open = analyzer.total.open\n",
" total_closed = analyzer.total.closed\n",
" total_won = analyzer.won.total\n",
" total_lost = analyzer.lost.total\n",
" win_streak = analyzer.streak.won.longest\n",
" lost_streak = analyzer.streak.lost.longest\n",
" pnl_net = round(analyzer.pnl.net.total,2)\n",
" strike_rate = round((total_won/total_closed)*100, 2) if total_closed > 0 else 0\n",
"\n",
" h1 = [\"Total Open\", \"Total Closed\", \"Total Won\", \"Total Lost\"]\n",
" h2 = [\"Strike Rate\", \"Win Streak\", \"Losing Streak\", \"PnL Net\"]\n",
" r1 = [total_open, total_closed, total_won, total_lost]\n",
" r2 = [strike_rate, win_streak, lost_streak, pnl_net]\n",
" if len(h1) > len(h2):\n",
" header_length = len(h1)\n",
" else:\n",
" header_length = len(h2)\n",
"\n",
" print_list = [h1,r1,h2,r2]\n",
" row_format = \"{:<15}\" * (header_length+1)\n",
" print(\"Trade Analysis Results:\")\n",
" for row in print_list:\n",
" print(row_format.format(\"\", *row))\n",
" except KeyError as e:\n",
" print(\"No Analysis as no trade is taken\")\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "trOzy6WESQBE"
},
"source": [
"### Evaluate"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CQWuy00jNnu_",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"outputId": "34860661-46ab-4e93-bc94-aac0fa4c58e6"
},
"source": [
"# Enter various trade parameters\n",
"symbol = '^NSEI' # refer yahoo finance for symbol\n",
"start = '2010-01-01'\n",
"end = '2021-12-20'\n",
"\n",
"# fetch stock data from yahoo finance API\n",
"stock_data = fetch_data(symbol, start, end)"
],
"execution_count": null,
"outputs": [
{
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" {\"responsive\": true}\n",
" ).then(function(){\n",
" \n",
"var gd = document.getElementById('d5af6330-0af3-4ed4-a995-a20108841efc');\n",
"var x = new MutationObserver(function (mutations, observer) {{\n",
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" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
"var notebookContainer = gd.closest('#notebook-container');\n",
"if (notebookContainer) {{\n",
" x.observe(notebookContainer, {childList: true});\n",
"}}\n",
"\n",
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"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" })\n",
" };\n",
" \n",
" </script>\n",
" </div>\n",
"</body>\n",
"</html>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VxUYuouoRoFn",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350
},
"outputId": "87c89041-18c2-4ea0-94ad-163c83b360fa"
},
"source": [
"# run_stat(stock_data, SIPStrategy)\n",
"cerebro = init_stat()\n",
"\n",
"# add data feeds to data engine\n",
"data = bt.feeds.PandasData(dataname=stock_data, openinterest=None)\n",
"cerebro.adddata(data)\n",
"\n",
"# add strategy to engine\n",
"cerebro.broker.set_fundmode(True)\n",
"cerebro.addstrategy(SIPStrategy)\n",
"\n",
"# initial cash stats\n",
"start_portfolio_value = cerebro.broker.getvalue()\n",
"\n",
"# add analyzer\n",
"# cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name = \"ta\")\n",
"cerebro.addanalyzer(bt.analyzers.TimeReturn)\n",
"\n",
"# run engine\n",
"strats= cerebro.run()\n",
"%matplotlib inline\n",
"cerebro.plot()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2010-01-18 Order failed(Margin)\n"
]
},
{
"output_type": "display_data",
"data": {
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" return MozWebSocket;\n",
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" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
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" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
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"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
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" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
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"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
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" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
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" this._resize_canvas = function(width, height) {\n",
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" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
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" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
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" canvas.focus();\n",
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"\n",
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"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
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" function toolbar_event(event) {\n",
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" }\n",
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" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
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" var name = mpl.toolbar_items[toolbar_ind][0];\n",
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" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
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"\n",
" var icon_img = $('<span/>');\n",
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" icon_img.addClass('ui-corner-all');\n",
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" var tooltip_span = $('<span/>');\n",
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" tooltip_span.html(tooltip);\n",
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" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
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" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fqtzatAwhIDB"
},
"source": [
"class SupportAndResistance:\n",
" def __init__(self):\n",
" self.period = 10\n",
" # self.data = data\n",
"\n",
" def identify_levels(self, data):\n",
" threshold = (data['High'] - data['Low']).mean()\n",
" result = []\n",
" levels = self.get_reversal_prices(data)\n",
" print(f\"Range==> : {threshold:.2f}, total pivots: {len(levels)}\")\n",
" i = 0\n",
" while i < len(levels):\n",
" price = levels[i]\n",
" j = i + 1\n",
" while j < len(levels):\n",
" if abs(levels[j] - price) <= threshold:\n",
" price = (price + levels[j]) / 2\n",
" else:\n",
" break\n",
" j += 1\n",
" result.append(round(price,1))\n",
" i = j\n",
" return pd.Series(result)\n",
"\n",
" def plot(self, data):\n",
" # line chart\n",
" fig_line = px.line(data, x=data.index, y=\"Close\")\n",
" levels = self.get_reversal_prices(data)\n",
" zones = self.identify_levels(data)\n",
"\n",
" fig = go.Figure(\n",
" [\n",
" go.Candlestick(\n",
" x=data.index,\n",
" open=data.Open,\n",
" high=data.High,\n",
" low=data.Low,\n",
" close=data.Close,\n",
" name='Chart'\n",
" ),\n",
" go.Scatter(\n",
" x=levels.index,\n",
" y=levels,\n",
" mode=\"markers\",\n",
" marker_color=\"blue\",\n",
" name=\"Pivot points\"\n",
" )\n",
" ]\n",
" )\n",
" threshold = (data['High'] - data['Low']).mean()\n",
" noise = threshold / 4\n",
" for z in zones:\n",
" col = \"green\" if z[0] < data['Close'][-1] else \"red\"\n",
" fig.add_hrect(y0=z[0]-noise, y1=z[0]+noise, line_width=0,fillcolor=col, opacity=(1 - .8 / z[1]))\n",
"\n",
" fig.show()\n",
"\n",
" def get_reversal_prices(self, data):\n",
" down = self.get_downtrend_reversal_prices(data)\n",
" up = self.get_uptrend_reversal_prices(data)\n",
" return pd.concat([down, up]).rename(\"Level\").sort_values()\n",
"\n",
" def get_downtrend_reversal_prices(self, data):\n",
" prev_low = data['Low'].rolling(self.period).min().shift(1)\n",
" next_low = data['Low'].rolling(self.period).min().shift(-self.period + 1)\n",
" mask = (data['Low'] <= prev_low) & (data['Low'] <= next_low)\n",
" return data['Low'].loc[mask]\n",
"\n",
" def get_uptrend_reversal_prices(self, data):\n",
" prev_high = data['High'].rolling(self.period).max().shift(1)\n",
" next_high = data['High'].rolling(self.period).max().shift(-self.period + 1)\n",
" mask = (data['High'] >= prev_high) & (data['High'] >= next_high)\n",
" return data['High'].loc[mask]\n",
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "w5ilXKviwa8W",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "1080b95f-6613-4b2b-9a84-744cf2d5f210"
},
"source": [
"snr = SupportAndResistance()\n",
"levels = snr.identify_levels(stock_data.loc[\"2016-01-01\": \"2018-12-31\"])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Range==> : 88.90, total pivots: 41\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
},
"id": "xS7T_9JawipF",
"outputId": "2768c3da-2c5a-4420-c34f-c77710ae01f6"
},
"source": [
"!pip install plotly --upgrade\n",
"import plotly\n",
"plotly.__version__"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.4.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
"Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n"
]
},
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'4.4.1'"
]
},
"metadata": {},
"execution_count": 100
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EupmYMiF4pMs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "46610e34-e4b4-4f14-e427-a02bb6ab0f84"
},
"source": [
"levels.loc[levels > 650].max()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"11760.2"
]
},
"metadata": {},
"execution_count": 101
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PLNJVBB7mjPq"
},
"source": [
"### Nifty Buy at Dip strategy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "eayJroWydG4k",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"outputId": "1de8908f-2247-40a7-a843-e52d9c62f730"
},
"source": [
"data = get_stock_data(\"^NSEI\", \"2010-1-1\", \"2021-12-31\")\n",
"data.describe()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" <th>Dividends</th>\n",
" <th>Stock Splits</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2920.000000</td>\n",
" <td>2920.000000</td>\n",
" <td>2920.000000</td>\n",
" <td>2920.000000</td>\n",
" <td>2920.000000</td>\n",
" <td>2.920000e+03</td>\n",
" <td>2920.0</td>\n",
" <td>2920.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8688.583185</td>\n",
" <td>8733.156524</td>\n",
" <td>8628.610120</td>\n",
" <td>8681.498716</td>\n",
" <td>8681.498716</td>\n",
" <td>2.239973e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3120.960429</td>\n",
" <td>3130.271741</td>\n",
" <td>3101.844054</td>\n",
" <td>3117.004648</td>\n",
" <td>3117.004648</td>\n",
" <td>2.254740e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4623.150000</td>\n",
" <td>4623.150000</td>\n",
" <td>4531.150000</td>\n",
" <td>4544.200000</td>\n",
" <td>4544.200000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5872.312500</td>\n",
" <td>5906.475000</td>\n",
" <td>5827.350000</td>\n",
" <td>5868.275000</td>\n",
" <td>5868.275000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>8260.275000</td>\n",
" <td>8307.425000</td>\n",
" <td>8215.500000</td>\n",
" <td>8261.700000</td>\n",
" <td>8261.700000</td>\n",
" <td>1.728000e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>10752.675000</td>\n",
" <td>10794.912500</td>\n",
" <td>10691.337500</td>\n",
" <td>10741.400000</td>\n",
" <td>10741.400000</td>\n",
" <td>2.958250e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>18602.350000</td>\n",
" <td>18604.450000</td>\n",
" <td>18445.300000</td>\n",
" <td>18477.050000</td>\n",
" <td>18477.050000</td>\n",
" <td>1.811000e+06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High ... Dividends Stock Splits\n",
"count 2920.000000 2920.000000 ... 2920.0 2920.0\n",
"mean 8688.583185 8733.156524 ... 0.0 0.0\n",
"std 3120.960429 3130.271741 ... 0.0 0.0\n",
"min 4623.150000 4623.150000 ... 0.0 0.0\n",
"25% 5872.312500 5906.475000 ... 0.0 0.0\n",
"50% 8260.275000 8307.425000 ... 0.0 0.0\n",
"75% 10752.675000 10794.912500 ... 0.0 0.0\n",
"max 18602.350000 18604.450000 ... 0.0 0.0\n",
"\n",
"[8 rows x 8 columns]"
]
},
"metadata": {},
"execution_count": 102
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lUkAGSbfrHoB"
},
"source": [
"cummax = data['Close'].cummax()\n",
"max_drawdown = data['Close'].cummax() - data['Close']\n",
"max_drawdown_perc = round(((data['Close'].cummax() - data['Close']) / data['Close'].cummax()) * 100, 2)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hBdN1NAMmt5Q"
},
"source": [
"max_drawdown = data['Close'].cummax() - data['Close']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6F6Hrb6enuOk"
},
"source": [
"max_drawdown_perc = round((max_drawdown / data['Close'].cummax()) * 100, 2)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HP84OZN_o0eJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "f9a62aed-d0fd-4d78-e8c2-ffdd6fdf9ff1"
},
"source": [
"result = []\n",
"mx_draw = 0\n",
"mx_draw_index = None\n",
"for i in range(len(data)):\n",
" if max_drawdown_perc[i] > mx_draw:\n",
" mx_draw = max(mx_draw, max_drawdown_perc[i])\n",
" mx_draw_index = i\n",
" if data['Close'][i] >= cummax[i] and mx_draw >= 1:\n",
" time_taken = i - mx_draw_index\n",
" result.append((mx_draw, data.index[mx_draw_index].strftime('%d-%m-%Y'), data.index[i].strftime('%d-%m-%Y'), (data.index[i] - data.index[mx_draw_index]).days))\n",
" mx_draw = 0\n",
" mx_draw_index = None\n",
"len(result)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"49"
]
},
"metadata": {},
"execution_count": 106
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ry90DOc6pEXv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 867
},
"outputId": "45fe136d-994a-4d83-936d-86fec2222196"
},
"source": [
"result.sort()\n",
"result"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[(1.0, '13-08-2018', '17-08-2018', 4),\n",
" (1.01, '31-03-2010', '05-04-2010', 5),\n",
" (1.18, '26-12-2019', '02-01-2020', 7),\n",
" (1.19, '01-07-2021', '07-07-2021', 6),\n",
" (1.2, '09-07-2021', '15-07-2021', 6),\n",
" (1.29, '19-11-2020', '24-11-2020', 5),\n",
" (1.31, '17-08-2010', '19-08-2010', 2),\n",
" (1.32, '20-09-2021', '23-09-2021', 3),\n",
" (1.41, '22-03-2017', '30-03-2017', 8),\n",
" (1.45, '23-05-2019', '24-05-2019', 1),\n",
" (1.5, '30-07-2010', '04-08-2010', 5),\n",
" (1.51, '25-11-2020', '01-12-2020', 6),\n",
" (1.54, '12-10-2010', '13-10-2010', 1),\n",
" (1.73, '24-05-2017', '26-05-2017', 2),\n",
" (1.74, '19-04-2017', '25-04-2017', 6),\n",
" (1.78, '16-04-2014', '21-04-2014', 5),\n",
" (1.81, '01-10-2021', '08-10-2021', 7),\n",
" (1.83, '20-07-2021', '03-08-2021', 14),\n",
" (1.86, '30-05-2014', '03-06-2014', 4),\n",
" (1.9, '28-06-2017', '10-07-2017', 12),\n",
" (2.13, '23-06-2014', '02-07-2014', 9),\n",
" (2.15, '18-01-2021', '20-01-2021', 2),\n",
" (2.35, '06-01-2020', '13-01-2020', 7),\n",
" (2.42, '10-12-2019', '17-12-2019', 7),\n",
" (2.55, '31-08-2010', '06-09-2010', 6),\n",
" (2.75, '07-05-2014', '09-05-2014', 2),\n",
" (3.14, '21-12-2020', '28-12-2020', 7),\n",
" (3.35, '08-08-2014', '18-08-2014', 10),\n",
" (3.91, '06-12-2017', '19-12-2017', 13),\n",
" (3.99, '11-08-2017', '18-09-2017', 38),\n",
" (4.04, '20-10-2010', '04-11-2010', 15),\n",
" (4.11, '27-09-2017', '13-10-2017', 16),\n",
" (4.28, '14-07-2014', '23-07-2014', 9),\n",
" (4.76, '09-02-2015', '02-03-2015', 21),\n",
" (5.04, '13-11-2013', '09-12-2013', 26),\n",
" (5.21, '16-10-2014', '31-10-2014', 15),\n",
" (5.42, '13-05-2019', '20-05-2019', 7),\n",
" (5.7, '04-02-2014', '06-03-2014', 30),\n",
" (6.5, '17-12-2014', '20-01-2015', 34),\n",
" (6.65, '20-04-2021', '27-05-2021', 37),\n",
" (6.9, '29-01-2021', '02-02-2021', 4),\n",
" (10.17, '23-03-2018', '24-07-2018', 123),\n",
" (10.57, '25-05-2010', '12-07-2010', 48),\n",
" (10.66, '05-02-2010', '26-03-2010', 49),\n",
" (11.45, '19-09-2019', '27-11-2019', 69),\n",
" (14.55, '26-10-2018', '16-04-2019', 172),\n",
" (22.52, '25-02-2016', '14-03-2017', 383),\n",
" (27.89, '20-12-2011', '01-11-2013', 682),\n",
" (38.44, '23-03-2020', '09-11-2020', 231)]"
]
},
"metadata": {},
"execution_count": 107
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DWvI7IG9rZyV"
},
"source": [
"buy_drawdown_levels = [2, 3, 4, 5, 6, 10, 14,20, 30]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "GS2R7nXNzbHq",
"outputId": "2131889d-4322-4f3f-96e5-c39eed55b48e"
},
"source": [
"invest_amount = [5000, 10000]\n",
"for i in range(len(buy_drawdown_levels) - 2):\n",
" invest_amount.append(invest_amount[-1] + invest_amount[-2])\n",
"invest_amount"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[5000, 10000, 15000, 25000, 40000, 65000, 105000, 170000, 275000]"
]
},
"metadata": {},
"execution_count": 109
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-Yq9nUIV0eCz"
},
"source": [
"lst = []\n",
"for i in range(len(buy_drawdown_levels)):\n",
" lst.append((buy_drawdown_levels[i], invest_amount[i]))\n",
"df = pd.DataFrame(lst, columns=['drawdown_perc', 'invest_amount'])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331
},
"id": "ol8ENQEW1L9n",
"outputId": "69fee592-3318-4296-e74c-1b3f7748cf2c"
},
"source": [
"df['sell_amount'] = df['invest_amount'] * 100 / (100 - df['drawdown_perc'])\n",
"df['profit'] = df['sell_amount'] - df['invest_amount']\n",
"df['profit_perc'] = (df['profit'] * 100) / df['invest_amount']\n",
"df['occurence'] = [6,4,4,4,3,4,1,2,2]\n",
"df['cum_occ'] = df.loc[::-1, 'occurence'].cumsum()[::-1]\n",
"df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>drawdown_perc</th>\n",
" <th>invest_amount</th>\n",
" <th>sell_amount</th>\n",
" <th>profit</th>\n",
" <th>profit_perc</th>\n",
" <th>occurence</th>\n",
" <th>cum_occ</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>5000</td>\n",
" <td>5102.040816</td>\n",
" <td>102.040816</td>\n",
" <td>2.040816</td>\n",
" <td>6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>10000</td>\n",
" <td>10309.278351</td>\n",
" <td>309.278351</td>\n",
" <td>3.092784</td>\n",
" <td>4</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>15000</td>\n",
" <td>15625.000000</td>\n",
" <td>625.000000</td>\n",
" <td>4.166667</td>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5</td>\n",
" <td>25000</td>\n",
" <td>26315.789474</td>\n",
" <td>1315.789474</td>\n",
" <td>5.263158</td>\n",
" <td>4</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" <td>40000</td>\n",
" <td>42553.191489</td>\n",
" <td>2553.191489</td>\n",
" <td>6.382979</td>\n",
" <td>3</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10</td>\n",
" <td>65000</td>\n",
" <td>72222.222222</td>\n",
" <td>7222.222222</td>\n",
" <td>11.111111</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>14</td>\n",
" <td>105000</td>\n",
" <td>122093.023256</td>\n",
" <td>17093.023256</td>\n",
" <td>16.279070</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>20</td>\n",
" <td>170000</td>\n",
" <td>212500.000000</td>\n",
" <td>42500.000000</td>\n",
" <td>25.000000</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>30</td>\n",
" <td>275000</td>\n",
" <td>392857.142857</td>\n",
" <td>117857.142857</td>\n",
" <td>42.857143</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" drawdown_perc invest_amount sell_amount ... profit_perc occurence cum_occ\n",
"0 2 5000 5102.040816 ... 2.040816 6 30\n",
"1 3 10000 10309.278351 ... 3.092784 4 24\n",
"2 4 15000 15625.000000 ... 4.166667 4 20\n",
"3 5 25000 26315.789474 ... 5.263158 4 16\n",
"4 6 40000 42553.191489 ... 6.382979 3 12\n",
"5 10 65000 72222.222222 ... 11.111111 4 9\n",
"6 14 105000 122093.023256 ... 16.279070 1 5\n",
"7 20 170000 212500.000000 ... 25.000000 2 4\n",
"8 30 275000 392857.142857 ... 42.857143 2 2\n",
"\n",
"[9 rows x 7 columns]"
]
},
"metadata": {},
"execution_count": 111
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "SgX1Aj5y1MeO",
"outputId": "4a133c71-8955-46a1-9211-8a3311375768"
},
"source": [
"((df['sell_amount'].sum() - df['invest_amount'].sum()) * 100) / df['invest_amount'].sum()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"26.701082882403796"
]
},
"metadata": {},
"execution_count": 112
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "dwH81hX84nE_",
"outputId": "bf6cc912-d105-44aa-a9bc-aa436ee780ef"
},
"source": [
"df['sell_amount'].sum() - df['invest_amount'].sum()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"189577.68846506695"
]
},
"metadata": {},
"execution_count": 113
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "oMnpBJIn5LVp",
"outputId": "64a65728-4cca-47b8-9ac9-41ec7f9d7e7f"
},
"source": [
"(df['profit'] * df['cum_occ']).sum()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"630854.2363468102"
]
},
"metadata": {},
"execution_count": 114
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "c99XTrY-6XqM",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "b7d5028a-d07f-49e2-e20f-21513af5a882"
},
"source": [
"list(df['invest_amount'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[5000, 10000, 15000, 25000, 40000, 65000, 105000, 170000, 275000]"
]
},
"metadata": {},
"execution_count": 115
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FCsw7H1J0wOO"
},
"source": [
"plt.plot([1,2,3])\n",
"plt.show()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "c7ZQoGlhjJ5h"
},
"source": [
"plt.plot([1, 2, 3, 4], [1, 4, 2, 3])\n",
"plt.show()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "hSXNlErylXIv"
},
"source": [
"### SIP Strategy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UqCm0VFSjpGq"
},
"source": [
"symbol = '^NSEI' # refer yahoo finance for symbol\n",
"start = '2010-01-01'\n",
"end = '2021-12-20'\n",
"\n",
"# fetch stock data from yahoo finance API\n",
"data = get_stock_data(symbol, start, end)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dhKukzP7ldFh",
"outputId": "0304635e-2ac2-43d3-b132-8e5b0e0003ee"
},
"source": [
"invested_amount = data['Close'].sum()\n",
"portfolio_value = data['Close'][-1] * data['Close'].count()\n",
"print(f\"Invested: {invested_amount}, Value: {portfolio_value}\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Invested: 25349976.25, Value: 50214364.0\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Z7z2nsf9mmpG",
"outputId": "6192be89-e03d-4bd9-c07c-8ce6c79979b7"
},
"source": [
"from datetime import datetime\n",
"then = datetime(2010, 1, 1) # Random date in the past\n",
"now = datetime(2019, 12, 31) # Now\n",
"duration = now - then # For build-in functions\n",
"duration_in_s = duration.total_seconds()\n",
"year = divmod(duration_in_s, 31536000)[0]\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"10.0"
]
},
"metadata": {},
"execution_count": 138
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "M7vA5jf-nwpD",
"outputId": "ff99ae08-d527-4c3f-9c0c-b485d640a3af"
},
"source": [
"cagr = round(((data['Close'][-1] / data['Close'][0]) ** (1 / year) - 1) * 100, 2)\n",
"abs_return = round((data['Close'][-1] / data['Close'][0]) * 100, 2)\n",
"abs_return"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"328.67"
]
},
"metadata": {},
"execution_count": 153
}
]
},
{
"cell_type": "markdown",
"source": [
"### 15 Minute Marubozu Strategy\n",
"\n",
"On a 15 Minute chart if a marubozu candle is formed than take a position in same direction."
],
"metadata": {
"id": "l7IYgFPlePqY"
}
},
{
"cell_type": "code",
"source": [
"display_line_chart(stock_data)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "FicovvBueO6G",
"outputId": "0f493f14-4f0f-4e05-cc48-105a3426acc8"
},
"execution_count": null,
"outputs": [
{
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" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-468c3d6f-7eab-4ac5-a2d6-6aca720b4760 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-468c3d6f-7eab-4ac5-a2d6-6aca720b4760');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
" Open High ... Dividends Stock Splits\n",
"Datetime ... \n",
"2022-02-14 14:30:00+05:30 1694.35 1697.25 ... 0 0\n",
"2022-02-14 14:45:00+05:30 1696.20 1698.00 ... 0 0\n",
"2022-02-14 15:00:00+05:30 1687.70 1688.80 ... 0 0\n",
"2022-02-14 15:15:00+05:30 1683.50 1687.00 ... 0 0\n",
"2022-02-15 15:30:00+05:30 1738.25 1738.25 ... 0 0\n",
"\n",
"[5 rows x 8 columns]"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"from datetime import datetime, timedelta\n",
"import time"
],
"metadata": {
"id": "EDQVcProew48"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"now_timestamp = time.time()\n",
"offset = datetime.fromtimestamp(now_timestamp) - datetime.utcfromtimestamp(now_timestamp)\n",
"print(datetime.now() + timedelta(hours=5, minutes=30))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "UcItf2Fum424",
"outputId": "583aa7c4-75b3-429c-a486-a8c6cc00e898"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2022-02-15 23:52:00.178711\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "45S6xjyom_PN"
},
"execution_count": null,
"outputs": []
}
]
}
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