Outline added for framework

Author: uberdeveloper

notebooks/OptionsBacktesting.ipynb

@@ -0,0 +1,123 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Options Backtesting Framework\n",
+    "\n",
+    "This is a simple backtesting framework for options. Given end of day data for both the capital and derivatives markets, we would be backtesting for different strategies.\n",
+    "\n",
+    "> All options are opened at the start of the period and closed by the end of the period or by a stop loss if provided\n",
+    "\n",
+    "### Implementation\n",
+    "\n",
+    "1. Load and transform necessary data\n",
+    "2. Create a list of option contracts to be entered into each day\n",
+    "3. Create a list of signals on, whether to enter into the contract, for each day\n",
+    "4. Extract the necessary data for the options contracts that would be executed\n",
+    "5. Analyze Profit and loss and other metrices\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load necessary libraries\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from fastbt.rapid import backtest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utility Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10225"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def itm(price, opt='C', step=100):\n",
+    "    \"\"\"\n",
+    "    Get the strike price of the in the money option\n",
+    "    price\n",
+    "        spot price\n",
+    "    opt\n",
+    "        option type - C for Call and P for Put\n",
+    "    step\n",
+    "        multiple of option strike price\n",
+    "    \"\"\"\n",
+    "    if opt == 'C':\n",
+    "        return int(price/step)*step\n",
+    "    elif opt == 'P':\n",
+    "        return (int(price/step)+1)*step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Preparation\n",
+    "-----------------\n",
+    "Load the raw data and do some transformations. All raw options data is loaded from the bhav copy of the NSE website. Index data is from [niftyindices.com](https://www.niftyindices.com/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nifty = pd.read_csv('/home/machine/Downloads/NIFTY 50_Data.csv',\n",
+    "                   parse_dates=['Date'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}