beta-neutral-pair-trading-manual.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "0e43aa20",
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   "source": [
    "# Pairs-Trading (Beta Neutral) Strategy Analysis\n",
    "\n",
    "The beta neutral trading strategy is a market-neutral approach designed to eliminate systematic market risk by constructing a portfolio that has a net beta of zero. This strategy aims to generate alpha (excess returns above the market) by taking both long and short positions in securities such that the overall portfolio is insulated from broad market movements.\n",
    "\n",
    "Key Concepts:\n",
    "- **Beta**: A measure of a security's volatility in relation to the overall market. A beta of 1 indicates that the security moves with the market, a beta greater than 1 indicates higher volatility than the market, and a beta less than 1 indicates lower volatility.\n",
    "- **Market Neutral**: A strategy that seeks to minimize exposure to market risk, focusing on generating returns from the individual performance of securities rather than market movements.\n",
    "\n",
    "Here's how it works:\n",
    "\n",
    "- **Identifying a Pair / Basket of Tokens**: Identify a set of tokens with varying betas.\n",
    "- **Calculating Beta**: Use historical price data to calculate the beta of each token relative to a benchmark token (e.g., BTC and ETH). This can be done using regression analysis where the token's returns are regressed against the benchmark's returns.\n",
    "- **Constructing the Portfolio**: \n",
    "    - **Long Positions**: Select tokens with positive alpha (expected to outperform their beta-implied returns) and go long on these.\n",
    "    - **Short Positions**: Select tokens with negative alpha (expected to underperform their beta-implied returns) and short these.\n",
    "    - Ensure the weighted average beta of the long positions is equal to the weighted average beta of the short positions, resulting in a net beta of zero.\n",
    "- **Position Sizing**: Calculate the dollar amount for each position to ensure that the portfolio remains beta neutral. This involves balancing the dollar value of long and short positions based on their respective betas.\n",
    "- **Monitoring and Rebalancing**: Regularly monitor the portfolio to ensure it remains beta neutral. Adjust positions as necessary to maintain the desired beta neutrality, especially if there are significant changes in the market or individual tokens.\n",
    "\n",
    "In this notebook, the pair trading negative / low correlation strategy is demonstrated using cryptocurrency price data sourced from the Binance, OKX, and Bybit APIs.\n",
    "\n",
    "Special Note:\n",
    "There are **two notebooks** for Pairs-Trading (Beta Neutral) Strategy Analysis. **This notebook allow users to manually set the portfolio weights and plot the returns of portfolio compared to market. The other notebook uses CVXPY package to optimise portfolio weights given a set of data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6485699-1817-44c9-9c53-08bfb86511e9",
   "metadata": {},
   "source": [
    "## Credits\n",
    "\n",
    "This notebook references [quant_playground](https://github.com/rukawa917/quant_playground) repository by [rukawa917](https://github.com/rukawa917) and [mkt_neutral.ipynb](https://gist.github.com/FilippoGuerrieri26/8086c42db442835234dd61ea6c07decb) by [FilippoGuerrieri26](https://gist.github.com/FilippoGuerrieri26). I want to extend my sincere gratitude to them for creating this valuable resource.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab73ff6-fa1f-46cc-900b-8268a0c14305",
   "metadata": {},
   "source": [
    "## Prepare your Environment\n",
    "\n",
    "Ensure that the correct kernel is selected for this notebook. If you are following the instruction in *README.md*, the kernel is 'crypto-trading-analysis'. To check, click on 'Kernel' at the top bar, select 'Change Kernel...' and select the correct kernel. For convenience, ensure that 'Always start the preferred kernel' is ticked. Click 'Select' to confirm the setting.\n",
    "\n",
    "Install the environment's dependencies using the command below. After installation, restart the kernel to use the updated packages. To restart, click on 'Kernel' at the top bar and select 'Restart Kernel' and click on 'Restart'. Please skip this step if you have already done it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8d92236-023d-4e09-94c8-a65fcecd9c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90ad883-ce63-4504-8af6-52998aedb87d",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1f89e4a",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "papermill": {
     "duration": 29.034008,
     "end_time": "2023-11-01T02:04:18.534845",
     "exception": false,
     "start_time": "2023-11-01T02:03:49.500837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gordon.oh/miniconda3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "/Users/gordon.oh/miniconda3/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import os\n",
    "import sys\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from statsmodels.tsa.stattools import coint\n",
    "from itertools import combinations\n",
    "from statsmodels.tsa.stattools import coint\n",
    "from utils import calculate_profit, plot_strategy\n",
    "from data_manager import load_ts_df, process_data, sanitize_data\n",
    "from typing import List\n",
    "import cvxpy as cvx\n",
    "import warnings\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf0de13",
   "metadata": {},
   "source": [
    "## Process Price Dataframe\n",
    "\n",
    "- Before proceeding, ensure that the price data has been downloaded using ***'data_manager.py'***.\n",
    "- Enter the ***cex*** (Centralized Exchange) and ***interval*** values used for data download to load the relevant *.pkl* files and retrieve the dataframe.\n",
    "- You can specify a batch of pairs to load using the ***selected_pairs*** variable. The key represents the pair and its value represents the direction it must take (1: Long, -1: Short, 0: Does not matter). Unlike other notebooks, no pairs will be selected if the dictionary is empty.\n",
    "- Note that some pairs might be new and may lack sufficient data within the downloaded timeframe. Such pairs will be removed based on the ***nan_remove_threshold*** setting, which defines the maximum percentage of NaN values allowed relative to the total data points. For example, with a ***nan_remove_threshold*** of 0.1, if a pair has 100 data points and 15 are NaN, the pair will be excluded.\n",
    "- From the remaining pairs, you can filter the top N rolling or mean volume pairs using the ***top_n_volume_pairs*** and ***volume_filter_mode*** parameter. \n",
    "- This part of the code will also ensure that all timeseries columns have the same number of data points.\n",
    "- The earliest and latest dates for all pairs will be recorded. These dates can then be used to determine the timeframe for slicing the data in the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13057dad-759b-4606-b673-6b699765f2c1",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9ffa57f5-0ef6-49ca-847f-b439c1c63bdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### INPUTS #####\n",
    "cex = 'binance'\n",
    "interval = '1d'\n",
    "nan_remove_threshold = 0.0\n",
    "\n",
    "# Select only the pairs below to analyse. The key represents the pair and its value represents the pair's contribution to the portfolio \n",
    "# weight. The sum of the absolute contributions must be equal to 1. Eg. selected_pairs = {'SOLUSDT': 0.8, 'ALTUSDT': -0.2}. This means \n",
    "# 0.8 of the portfolio is set to long SOLUSDT and 0.2 of the portfolio is set to short ALTUSDT. Furthermore, abs(0.8) + abs(-0.2) = \n",
    "# 0.8 + 0.2 = 1.\n",
    "selected_pairs = {'SOLUSDT': 0.7, 'ETHUSDT': -0.3}\n",
    "\n",
    "# Select the benchmark token.\n",
    "benchmark_token = 'BTCUSDT'\n",
    "\n",
    "# Select only the top N mean volume pairs from the selected pairs to analyse.\n",
    "top_n_volume_pairs = 100\n",
    "\n",
    "# Select volume filter mode. Options: ['rolling', 'mean'].\n",
    "volume_filter_mode = 'rolling'\n",
    "\n",
    "# Rolling Window\n",
    "rolling_window = 3\n",
    "##################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17b5ffa9-8c7e-487a-8656-31e6b5e9bf2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_pairs[benchmark_token] = 0\n",
    "absolute_weight_list = [abs(weight) for weight in selected_pairs.values()]\n",
    "portfolio_weights_sum = sum(absolute_weight_list)\n",
    "\n",
    "if portfolio_weights_sum != 1:\n",
    "    print(\"\\n\")\n",
    "    print(\"The sum of the absolute contributions must be equal to 1. Please change before proceeding.\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa468df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mode: Pairs-Trading (Beta Neutral) Strategy\n",
      "CEX: Binance\n",
      "Interval: 1d\n",
      "NaN Remove Threshold: 0.0\n",
      "Selected pairs to analyse: ['SOLUSDT', 'ETHUSDT', 'BTCUSDT']\n",
      "Benchmark Token: BTCUSDT\n",
      "Top N Volume Pairs: 100\n",
      "Volume Filter Mode: Rolling\n",
      "Rolling Window: 3\n",
      "Successfully loaded candlestick dataframe for all available pairs.\n",
      "\n",
      "Earliest time series start date: 2023-08-19\n",
      "Latest time series end date: 2024-08-17\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nMode: Pairs-Trading (Beta Neutral) Strategy\")\n",
    "print(\"CEX: {}\".format(str(cex).capitalize()))\n",
    "print(\"Interval: {}\".format(interval))\n",
    "print(\"NaN Remove Threshold: {}\".format(nan_remove_threshold))\n",
    "print(\"Selected pairs to analyse: {}\".format(list(selected_pairs.keys())))\n",
    "print(\"Benchmark Token: {}\".format(benchmark_token))\n",
    "print(\"Top N Volume Pairs: {}\".format(top_n_volume_pairs))\n",
    "print(\"Volume Filter Mode: {}\".format(str(volume_filter_mode).capitalize()))\n",
    "print(\"Rolling Window: {}\".format(rolling_window))\n",
    "\n",
    "merged_df = process_data('beta_neutral', cex, interval, nan_remove_threshold, selected_pairs,\n",
    "                 top_n_volume_pairs, volume_filter_mode)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66614d32-8464-4265-b709-8a2d7a681acf",
   "metadata": {},
   "source": [
    "## Sanitize the dataframe\n",
    "\n",
    "- Slice the dataframe according to the specified ***start_date*** and ***end_date***. Choose ***start_date*** and ***end_date*** within the timeframe shown by the output of the previous cell.\n",
    "- Interpolate any missing values in the dataframe.\n",
    "- If the interpolation fails, just backfill with the latest valid value.\n",
    "- Verify that all is as expected with an `assert` and check the shapes of 2 random pairs, which should have the same dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1705815e-af19-428e-b1b2-2f1ffd123c1b",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57b0551a-033f-4b9a-8ac0-d82ceef0861b",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### INPUTS #####\n",
    "start_date = '2024-07-01' # refer to the previous output to set\n",
    "end_date = '2024-08-17' # refer to the previous output to set\n",
    "##################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1c499c5-4313-40e1-bb52-ae3462912b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "BTCUSDT's Data Shape: (48, 1)\n",
      "ETHUSDT's Data Shape: (48, 1)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "data_sanitized, sorted_available_pairs = sanitize_data(merged_df, start_date, end_date)\n",
    "\n",
    "if data_sanitized:\n",
    "    print(\"-Data Check-\")\n",
    "    keys = list(data_sanitized.keys())\n",
    "    count = 0\n",
    "\n",
    "    for key in keys:\n",
    "        print(\"{}'s Data Shape: {}\".format(key, data_sanitized[key].shape))\n",
    "        count+=1\n",
    "\n",
    "        if count == 2:\n",
    "            break\n",
    "            \n",
    "else:\n",
    "    print(\"No data found.\")\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87a62a5",
   "metadata": {},
   "source": [
    "## Compute Percentage Returns\n",
    "\n",
    "To compute percentage returns with a time series of close data, you first need to calculate the daily returns by finding the percentage change between consecutive prices. This can be done using the formula below:\n",
    "$$Percentage \\space Return = \\frac{Price_{t} - Price_{t-1}}{Price_{t-1}} \\times 100$$\n",
    "1. ${Price_{t}}$ is the calculated price on day t.\n",
    "2. ${Price_{t-1}}$ is the calculated price on the previous candlestick.\n",
    "\n",
    "In a pandas DataFrame, this can be efficiently computed using the pct_change() method on the price column. The resulting series represents the daily percentage returns, reflecting the day-to-day performance of the asset. Summarizing or compounding these daily returns over longer periods can provide insights into the overall performance of the asset over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e53c419-c425-46da-8e69-c9087ef98586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Percentage Returns' Data Shape (should be 1 row lesser than printed above): (47, 3)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "returns = pd.DataFrame()\n",
    "\n",
    "for pair, df in data_sanitized.items():\n",
    "    tmp_return_df = df['Close'].pct_change().dropna().to_frame(name=pair)\n",
    "    returns = pd.merge(returns, tmp_return_df, how='outer', left_index=True, right_index=True)\n",
    "returns = returns.T.sort_index().T\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Percentage Returns' Data Shape (should be 1 row lesser than printed above): {}\".format(returns.shape))\n",
    "print(\"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f2ebbce-f593-42c2-8401-c2b050a0c1de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Full Returns' Data Shape: (47, 3)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "full_return_df = returns.reset_index().set_index('Open Time')\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Full Returns' Data Shape: {}\".format(full_return_df.shape))\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd4abaa-25d4-43be-b67c-738fe6603497",
   "metadata": {},
   "source": [
    "## Full Covariance Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a16ece79-b2bb-4755-a64f-49e06cb859c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>0.000522</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHUSDT</th>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>0.000689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.000690</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BTCUSDT   ETHUSDT   SOLUSDT\n",
       "BTCUSDT  0.000522  0.000554  0.000690\n",
       "ETHUSDT  0.000554  0.000781  0.000689\n",
       "SOLUSDT  0.000690  0.000689  0.001305"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sigma = full_return_df.cov()\n",
    "Sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ec059a-a3e9-45d8-aa61-5071fcb8da46",
   "metadata": {},
   "source": [
    "## Full Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a0141d3-8821-4b83-b297-88cdb393686f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.868070</td>\n",
       "      <td>0.836726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHUSDT</th>\n",
       "      <td>0.868070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.682446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.836726</td>\n",
       "      <td>0.682446</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BTCUSDT   ETHUSDT   SOLUSDT\n",
       "BTCUSDT  1.000000  0.868070  0.836726\n",
       "ETHUSDT  0.868070  1.000000  0.682446\n",
       "SOLUSDT  0.836726  0.682446  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev = np.sqrt(np.diag(Sigma))\n",
    "std_dev_outer = np.outer(std_dev, std_dev)\n",
    "correlation_matrix = Sigma / std_dev_outer\n",
    "np.fill_diagonal(correlation_matrix.values, 1)\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb0559b-0a8d-424c-aaf6-51bd93cd7415",
   "metadata": {},
   "source": [
    "## Calculate Betas given the Datetime Period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b84588b9-faad-4b0d-a6a0-f0e4a7e14c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Betas:\n",
      "SOLUSDT    1.323599\n",
      "ETHUSDT    1.061989\n",
      "Name: BTCUSDT, dtype: float64\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "betas = Sigma[benchmark_token] / Sigma.loc[benchmark_token, benchmark_token]\n",
    "betas_without_benchmark = betas.drop(benchmark_token)\n",
    "betas_without_benchmark = betas_without_benchmark.sort_values(ascending=False)\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Betas:\")\n",
    "print(betas_without_benchmark)\n",
    "\n",
    "betas_without_benchmark = betas_without_benchmark.sort_index()\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be775818-37b0-4687-896c-97ae1411ced7",
   "metadata": {},
   "source": [
    "## Covariance Matrix without Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2a8423c-a2b8-4c14-8912-cb69837dee98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ETHUSDT</th>\n",
       "      <td>0.000781</td>\n",
       "      <td>0.000689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ETHUSDT   SOLUSDT\n",
       "ETHUSDT  0.000781  0.000689\n",
       "SOLUSDT  0.000689  0.001305"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_return_df_without_benchmark = full_return_df.drop(benchmark_token, axis=1)  # since we are using BTC as the benchmark\n",
    "final_Sigma = full_return_df_without_benchmark.cov()\n",
    "final_Sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a614d6-9e0d-43c4-93ac-4589ab9a74a8",
   "metadata": {},
   "source": [
    "## Setting Portfolio Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf0a683c-31ab-4b21-97b7-8c3b966ccb8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = []\n",
    "\n",
    "for col in final_Sigma.columns:\n",
    "    weights.append(selected_pairs[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c15440-dceb-40ce-8084-d9723b555df2",
   "metadata": {},
   "source": [
    "## Plot Portfolio Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4edcb202-0b18-48df-85ce-3f57333a5410",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = pd.Series(weights, index=final_Sigma.columns)\n",
    "\n",
    "index = list(w.index)\n",
    "values = list(w.values)\n",
    "\n",
    "plt.figure(figsize=(5, 6))\n",
    "\n",
    "long_patch = mpatches.Patch(color='green', label='Long')\n",
    "short_patch = mpatches.Patch(color='red', label='Short')\n",
    "\n",
    "colors = ['green' if val >= 0 else 'red' for val in values]\n",
    "plt.bar(w.index, w.values, color=colors)\n",
    "plt.title('Porfolio 1')\n",
    "plt.xlabel('Pairs', fontsize=14)\n",
    "plt.ylabel('Weights', fontsize=14)\n",
    "plt.legend(handles=[long_patch, short_patch])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca6ef439-6ccd-4464-bae7-3ff0a6192aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Portfolio:\n",
      "         Weights Direction\n",
      "SOLUSDT      0.7      Long\n",
      "ETHUSDT     -0.3     Short\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "weights_df = pd.DataFrame(w, columns=['Weights'], index=w.index)\n",
    "weights_df = weights_df.sort_values(by='Weights', ascending=False)\n",
    "weights_df['Direction'] = weights_df['Weights'].apply(lambda x: 'Long' if x > 0 else 'Short')\n",
    "\n",
    "print(\"Portfolio:\")\n",
    "print(weights_df)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ae03b1-0322-4c22-b841-aabee7e5aac6",
   "metadata": {},
   "source": [
    "## Using Fixed Portfolio Weights with Rolling Portfolio Beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8a0444cb-297d-4d90-886a-81ace9fc0c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <th>ETHUSDT</th>\n",
       "      <th>SOLUSDT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2024-07-04</th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.001046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHUSDT</th>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>0.001495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOLUSDT</th>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.001495</td>\n",
       "      <td>0.003307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2024-07-05</th>\n",
       "      <th>BTCUSDT</th>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETHUSDT</th>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>0.000444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     BTCUSDT   ETHUSDT   SOLUSDT\n",
       "Open Time                                       \n",
       "2024-07-04 BTCUSDT  0.000332  0.000472  0.001046\n",
       "           ETHUSDT  0.000472  0.000676  0.001495\n",
       "           SOLUSDT  0.001046  0.001495  0.003307\n",
       "2024-07-05 BTCUSDT  0.000101  0.000161  0.000356\n",
       "           ETHUSDT  0.000161  0.000312  0.000444"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rolling covariance matrix\n",
    "rolling_cov = full_return_df.rolling(window=rolling_window).cov().dropna()\n",
    "rolling_cov.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "044bcfcd-ea75-4d26-a16c-eef63a1552a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = list(set(rolling_cov.index.get_level_values(0)))\n",
    "dates.sort()\n",
    "weights_df = pd.DataFrame(index=pd.Index(dates).sort_values(), columns = full_return_df.columns)\n",
    "weights_df = weights_df.drop(benchmark_token, axis=1)\n",
    "betas_df = pd.DataFrame(index=pd.Index(dates).sort_values(), columns = full_return_df.columns)\n",
    "betas_without_benchmark = betas_df.drop(benchmark_token, axis=1)\n",
    "\n",
    "for i, dt in enumerate(dates):\n",
    "    full_cov = rolling_cov.loc[dt]\n",
    "    final_cov = full_cov.drop(benchmark_token).drop(benchmark_token, axis=1)\n",
    "    final_beta = full_cov[benchmark_token].drop(benchmark_token) / full_cov.loc[benchmark_token, benchmark_token]\n",
    "\n",
    "    weights_df.loc[dt, :] = weights\n",
    "    betas_without_benchmark.loc[dt, :] = final_beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf8c0dcc-e21a-4c19-a7e1-760394d12e49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Rolling Betas:\n",
      "             ETHUSDT   SOLUSDT\n",
      "2024-07-04  1.420482  3.148235\n",
      "2024-07-05  1.587179   3.51535\n",
      "2024-07-06  1.105592  2.609173\n",
      "2024-07-07  1.262412  1.917633\n",
      "2024-07-08  0.413193  3.151853\n",
      "             ETHUSDT   SOLUSDT\n",
      "2024-08-13  0.767819  1.799617\n",
      "2024-08-14  0.565586    1.4533\n",
      "2024-08-15   1.27809  0.770141\n",
      "2024-08-16  0.476857 -0.017165\n",
      "2024-08-17   0.70999  0.054142\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Rolling Betas:\")\n",
    "print(betas_without_benchmark.head())\n",
    "print(betas_without_benchmark.tail())\n",
    "# print(betas_without_benchmark)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c4eef7ec-86d5-4294-a068-2df09c54acb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Porfolio 1 Rolling Weights:\n",
      "           ETHUSDT SOLUSDT\n",
      "2024-07-04    -0.3     0.7\n",
      "2024-07-05    -0.3     0.7\n",
      "2024-07-06    -0.3     0.7\n",
      "2024-07-07    -0.3     0.7\n",
      "2024-07-08    -0.3     0.7\n",
      "           ETHUSDT SOLUSDT\n",
      "2024-08-13    -0.3     0.7\n",
      "2024-08-14    -0.3     0.7\n",
      "2024-08-15    -0.3     0.7\n",
      "2024-08-16    -0.3     0.7\n",
      "2024-08-17    -0.3     0.7\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Porfolio 1 Rolling Weights:\")\n",
    "print(weights_df.head())\n",
    "print(weights_df.tail())\n",
    "# print(weights_df)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "89e42a07-d659-488b-ba61-bd733da05cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-Data Check-\n",
      "Porfolio Beta:\n",
      "2024-07-05    1.984591\n",
      "2024-07-06    1.494744\n",
      "2024-07-07     0.96362\n",
      "2024-07-08    2.082339\n",
      "2024-07-09    0.774497\n",
      "dtype: object\n",
      "2024-08-13    1.029386\n",
      "2024-08-14    0.847634\n",
      "2024-08-15    0.155672\n",
      "2024-08-16   -0.155073\n",
      "2024-08-17   -0.175097\n",
      "dtype: object\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "print(\"-Data Check-\")\n",
    "print(\"Porfolio Beta:\")\n",
    "portfolio_beta = (weights_df.shift(1) * betas_without_benchmark).dropna().sum(axis=1)\n",
    "print(portfolio_beta.head())\n",
    "print(portfolio_beta.tail())\n",
    "# print(portfolio_beta)\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99377ff6-979c-4cca-9c25-b2bc6f975005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mkt Neutral Portfolio Rolling Beta to Market')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "portfolio_beta.plot(figsize=(18, 8))\n",
    "plt.axhline(portfolio_beta.mean(), color=\"red\")\n",
    "plt.title(\"Mkt Neutral Portfolio Rolling Beta to Market\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc627b90-8188-4167-82f9-1679022a1b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Backtest of Market Neutral Portfolio Return')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_return_df_copy = full_return_df.copy()\n",
    "\n",
    "weights_df = weights_df.T.sort_index().T\n",
    "full_return_df = full_return_df.T.sort_index().T.drop(benchmark_token, axis=1)\n",
    "\n",
    "portfolio_ret = (weights_df.shift(1) * full_return_df).dropna().sum(axis=1)\n",
    "(1 + portfolio_ret).cumprod().plot(figsize=(18, 8), label=\"Mkt Neutral Portfolio\")\n",
    "(1 + full_return_df_copy.loc[portfolio_ret.index][benchmark_token]).cumprod().plot()\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.title(\"Backtest of Market Neutral Portfolio Return\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50fe200e-18d7-4529-a7f6-363745a00395",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "crypto-trading-analysis",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 34.659839,
   "end_time": "2023-11-01T02:04:21.309361",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-11-01T02:03:46.649522",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}