FilippoGuerrieri26/timeseriesmomentum.ipynb

## timeseriesmomentum.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3dc0a5c6-80b1-49f3-bdb4-478d48eeba70",
   "metadata": {},
   "source": [
    "# Time Series Momentum"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6fdd933-048c-442e-8805-c146e952e68f",
   "metadata": {},
   "source": [
    "Time series momentum is related to, but different from, the phenomenon known as “momentum” in the finance literature, which is primarily cross-sectional in nature. The momentum literature focuses on the relative performance of securities in the cross-section, finding that securities that recently outperformed their peers over the past three to 12 months continue to outperform their peers on average over the next month. <br>\n",
    "Rather than focus on the relative returns of securities in the cross-section, time series momentum focuses purely on a security's own past return. <br>\n",
    "Here you can find the link to the original paper of Moskowitz: <br>\n",
    "https://www.sciencedirect.com/science/article/pii/S0304405X11002613"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa70eb95-0f6a-48e9-9ab1-91e33e245416",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6e7895c-ae1f-448f-8e24-26004e3bd455",
   "metadata": {},
   "source": [
    "# 1. Define constants and basic functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec1a7ca5-e8b3-4e62-8573-43e6c6fc73c0",
   "metadata": {},
   "source": [
    "On following, I am going to define some basic constants that allow us to to deal with data frequency and resampling, together with a few basic functions to run the backtest on the strategy and visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a23b0b5-74b9-4f56-97e7-cbad85e732b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define constants to handle data frequency\n",
    "days_per_month = 22\n",
    "days_per_year = 252\n",
    "weeks_per_year = 52\n",
    "months_per_year = 12\n",
    "quarters_per_year = 4\n",
    "\n",
    "daily = 'D'\n",
    "weekly = 'W'\n",
    "monthly = 'M'\n",
    "quarterly = 'Q'\n",
    "yearly = 'Y'\n",
    "\n",
    "frequencies = {\n",
    "    daily: days_per_year,\n",
    "    weekly: weeks_per_year,\n",
    "    monthly: months_per_year,\n",
    "    quarterly: quarters_per_year,\n",
    "    yearly: 1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f2a8b33-f424-4eec-859e-e40b828bfb01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to compute simple returns\n",
    "def simple_returns(series):\n",
    "    \"\"\"\n",
    "    Computes simple returns from prices\n",
    "    \"\"\"\n",
    "    return series.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9fb3c1d-93d6-4cd7-b6ee-a9d636e7b2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a simple strategy backtster\n",
    "def backtester(returns, weights):\n",
    "    \"\"\"\n",
    "    Runs a simple backtest from returns and weights series\n",
    "    \"\"\"\n",
    "    returns_df = returns.to_frame() if isinstance(returns, pd.Series) else returns.copy()\n",
    "    weights_df = weights.to_frame() if isinstance(returns, pd.Series) else weights.copy()\n",
    "    return (returns_df * weights_df.shift(1)).sum(axis=\"columns\",  min_count=1).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d90eb0f6-8e1c-49ac-ac4e-cc68be0332c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to plot the Equity Line\n",
    "def plot_equty_line(returns):\n",
    "    plt.figure(figsize=(18, 8))\n",
    "    plt.plot((returns + 1).cumprod())\n",
    "    plt.title(\"Equity Line\")\n",
    "    plt.grid()\n",
    "    plt.xlabel(\"Date\")\n",
    "    plt.ylabel(\"Wealth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f30459e-d41d-4992-98a5-57d68eceb65f",
   "metadata": {},
   "source": [
    "# 2. Load Sample Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2541565a-bf93-416e-b6c7-fa3213761a3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GE</th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>103.178185</td>\n",
       "      <td>113.086746</td>\n",
       "      <td>80.562042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>104.153717</td>\n",
       "      <td>116.195221</td>\n",
       "      <td>80.936966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>106.334343</td>\n",
       "      <td>118.548584</td>\n",
       "      <td>81.649330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>106.391731</td>\n",
       "      <td>119.127762</td>\n",
       "      <td>82.661644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>104.899727</td>\n",
       "      <td>119.846268</td>\n",
       "      <td>82.746010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    GE         IBM       MSFT\n",
       "Date                                         \n",
       "2018-01-02  103.178185  113.086746  80.562042\n",
       "2018-01-03  104.153717  116.195221  80.936966\n",
       "2018-01-04  106.334343  118.548584  81.649330\n",
       "2018-01-05  106.391731  119.127762  82.661644\n",
       "2018-01-08  104.899727  119.846268  82.746010"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read sample Adjusted Close price data for 3 stocks: GE, IBM, MSFT\n",
    "prices = pd.read_excel(\"../sample_data.xlsx\")\n",
    "prices.set_index(\"Date\", inplace=True)\n",
    "prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65d3f72-c31f-47be-9ae0-93559fa8168b",
   "metadata": {},
   "source": [
    "# 3. Define a class to run a Time Series Momentum Strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71d40fb6-0b18-429d-9ca7-99580efdfc87",
   "metadata": {},
   "source": [
    "The weights of a Time Series Momentum Startegy are sinmply defined as: <br>\n",
    "<br>\n",
    "$ w_{i,t} = r_{i,t} / \\sigma_{i,t} $ <br>\n",
    "<br>\n",
    "Where: <br>\n",
    "<br>\n",
    "   $r_{i,t}$ is equal to the average return for each of the assets for the last year, excluding the last month\n",
    "   $\\sigma_{i,t}$ is equal to the volatility of the returns over the previous year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f99fdc3-fac5-45b5-a707-93e86406ab45",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TimeSeriesMomentum:\n",
    "    \"\"\"\n",
    "    Define a Time Series Momentum strategy based on Moskowitz, Pedersen\n",
    "    The weight are defined simply as std / mean_ret (annualized)\n",
    "    Frequency defines the rebalancing\n",
    "    \"\"\"\n",
    "    def __init__(self, prices, freq=weekly):\n",
    "        self.prices = prices\n",
    "        self.freq = freq\n",
    "        self.w_length = {\"D\": 230,\n",
    "                         \"W\": 48,\n",
    "                         \"M\": 11}\n",
    "        self.shift_ = {\"D\": 22,\n",
    "                       \"W\": 4,\n",
    "                       \"M\": 1}\n",
    "        self.weights, self.returns = self.strategy()\n",
    "\n",
    "    def strategy(self) -> (pd.DataFrame, pd.DataFrame):\n",
    "        # resampling prices according to desired frequency\n",
    "        p_resampled = self.prices.resample(self.freq).last()\n",
    "        # computing simple returns\n",
    "        returns = simple_returns(p_resampled)\n",
    "        # compute mean return over last year, discarding last month to avoid mean reversion effect\n",
    "        mean_ret = returns.shift(self.shift_[self.freq]).rolling(window=self.w_length[self.freq]).mean().dropna()\n",
    "        # compute std over last year, discarding last month\n",
    "        roll_std = returns.shift(self.shift_[self.freq]).rolling(window=self.w_length[self.freq]).std().dropna()\n",
    "        # compute weights\n",
    "        weights = mean_ret.div(roll_std)\n",
    "\n",
    "        return weights, returns\n",
    "\n",
    "    def backtest(self) -> pd.DataFrame:\n",
    "        # run backtest on strategy returns\n",
    "        return backtester(self.weights, self.returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22ce9714-a394-48d0-b4ce-14e724fba3fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "tsm = TimeSeriesMomentum(prices=prices.dropna(axis=1), freq=weekly)\n",
    "\n",
    "strategy_ret = tsm.backtest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "398e6143-224a-4ba9-8205-1c0249579866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-01-06   -0.015696\n",
       "2019-01-13   -0.034072\n",
       "2019-01-20   -0.029857\n",
       "2019-01-27   -0.003648\n",
       "2019-02-03   -0.013538\n",
       "Freq: W-SUN, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategy_ret.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ce0644c-7edd-4e90-8087-7b6fdf624c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_equty_line(strategy_ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "270ec03c-72b3-4a31-8ad0-24cd455a5b63",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}