{ "cells": [ { "cell_type": "markdown", "id": "5f6ccd5b", "metadata": {}, "source": [ "# CapiPort - PORTFOLIO OPTIMISATION" ] }, { "cell_type": "markdown", "id": "b1962897", "metadata": {}, "source": [ " Two things to consider for Portfolio Optimisation:\n", " \n", " 1) Minimising Risk\n", " 2) Maximising Return" ] }, { "cell_type": "markdown", "id": "9d025126", "metadata": {}, "source": [ " Basic process of Portfolio Optimisation:\n", " \n", " 1) Select the Asset class to work on.\n", " 1.1) Asset Class choosen - Equity (Stocks)\n", " 2) Select the Companies which you want to use to build a Portfolio.\n", " 2.1) Companies choosen - \n", " 2.1.1) Tata Power - TATAPOWER.NS\n", " 2.1.2) Tata Motors - TATAMOTORS.NS\n", " 2.1.3) Tata Steel - TATASTEEL.NS\n", " 2.1.4) Zomato - ZOMATO.NS\n", " 2.1.5) NHPC - NHPC.NS\n", " 2.1.6) NCC - NCC.NS\n", " 2.1.7) IREDA - IREDA.NS\n", " 2.1.8) IRCON - IRCON.NS\n", " 3) To try various Statistical Methods relating to Portfolio Optimisation.\n", " 3.1) Method 1 - Result\n", " 3.2) Method 2 - Result\n", " 4) You will obtain Weigths or Percentages of Portfolio to invest.\n", " 4.1) Method 1 - Weights\n", " 4.2) Method 2 - Weights\n", " 5) Testing the Portfolio for the future.\n", " 5.1) Method 1 - Result\n", " 5.2) Method 2 - Result\n", " 6) Final Result" ] }, { "cell_type": "markdown", "id": "a80152f2", "metadata": {}, "source": [ "# Steps of Implementation\n", "\n", " 1) Importing Libraries\n", " 2) Select the Financial Instruments\n", " 3) Get the Adjacent Close prices of Last 5 Years\n", " 4) Calculating the Log-Return of Company Dataset\n", " 5) Calculating the Sharpe Ratio\n", " 6) Getting Started with Monte Carlo\n", " 7) Let's look closer at the Simulations" ] }, { "cell_type": "markdown", "id": "42c5d329", "metadata": {}, "source": [ "## Importing Libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "945bbd48", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:35.347796Z", "start_time": "2024-03-07T20:09:30.939936Z" } }, "outputs": [], "source": [ "import pathlib\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import scipy.optimize as sci_opt\n", "\n", "from pprint import pprint\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "import yfinance as yf\n", "\n", "# Set some display options for Pandas.\n", "pd.set_option('expand_frame_repr', False)" ] }, { "cell_type": "markdown", "id": "8dbc2573", "metadata": {}, "source": [ "## Select the Financial Instruments" ] }, { "cell_type": "code", "execution_count": 2, "id": "08345846", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:37.966003Z", "start_time": "2024-03-07T20:09:37.963670Z" } }, "outputs": [], "source": [ "## Have Choosen Stocks\n", "\n", "## The Companies selected to build a Optimal Portfolio\n", "com_sel = [\"TATAPOWER.NS\", \"TATAMOTORS.NS\", \"TATASTEEL.NS\", \"RELIANCE.NS\", \"ADANIENT.NS\", \"ADANIPORTS.NS\"]\n", "\n", "## We will need Number of Tickers for future\n", "num_tick = len(com_sel)" ] }, { "cell_type": "markdown", "id": "2376a747", "metadata": {}, "source": [ "## Get the Adjacent Close prices of Last 5 Years" ] }, { "cell_type": "code", "execution_count": 3, "id": "cb64a4c0", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:39.680530Z", "start_time": "2024-03-07T20:09:38.995900Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[*********************100%%**********************] 6 of 6 completed\n" ] }, { "data": { "text/html": [ "
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TickerADANIENT.NSADANIPORTS.NSRELIANCE.NSTATAMOTORS.NSTATAPOWER.NSTATASTEEL.NS
Date
2019-03-01132.264633322.6734311098.479736179.73980761.86130943.332832
2019-03-05140.120483328.5322881108.872925193.44708363.79879044.540791
2019-03-06137.600677326.4017641133.197998188.21339465.78241744.463959
2019-03-07135.278519330.1301271138.080933188.71185365.09046244.489574
2019-03-08136.563110331.3406981135.258667180.63700964.58302343.354172
\n", "
" ], "text/plain": [ "Ticker ADANIENT.NS ADANIPORTS.NS RELIANCE.NS TATAMOTORS.NS TATAPOWER.NS TATASTEEL.NS\n", "Date \n", "2019-03-01 132.264633 322.673431 1098.479736 179.739807 61.861309 43.332832\n", "2019-03-05 140.120483 328.532288 1108.872925 193.447083 63.798790 44.540791\n", "2019-03-06 137.600677 326.401764 1133.197998 188.213394 65.782417 44.463959\n", "2019-03-07 135.278519 330.130127 1138.080933 188.711853 65.090462 44.489574\n", "2019-03-08 136.563110 331.340698 1135.258667 180.637009 64.583023 43.354172" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "com_data = yf.download(com_sel, start=\"2019-03-01\", end=\"2024-03-01\")['Adj Close']\n", "\n", "com_data.head()" ] }, { "cell_type": "markdown", "id": "fbc4ccf9", "metadata": {}, "source": [ "## Calculating the Log-Return of Company Dataset" ] }, { "cell_type": "code", "execution_count": 4, "id": "49aadb44", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:40.540798Z", "start_time": "2024-03-07T20:09:40.536374Z" } }, "outputs": [], "source": [ "## Log-Return of Company Dataset\n", "log_return = np.log(1 + com_data.pct_change())" ] }, { "cell_type": "markdown", "id": "8a3b8f50", "metadata": {}, "source": [ "## Calculating the Sharpe Ratio" ] }, { "cell_type": "code", "execution_count": 5, "id": "f91abb2c", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:41.663993Z", "start_time": "2024-03-07T20:09:41.653810Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==========================================================================================\n", "PORTFOLIO WEIGHTS:\n", "------------------------------------------------------------------------------------------\n", " random_weights rebalance_weights\n", "0 0.180926 0.069165\n", "1 0.010467 0.004001\n", "2 0.442599 0.169198\n", "3 0.811715 0.310305\n", "4 0.409706 0.156624\n", "5 0.760449 0.290707\n", "------------------------------------------------------------------------------------------\n", "\n", "==========================================================================================\n", "PORTFOLIO METRICS:\n", "------------------------------------------------------------------------------------------\n", " Expected Portfolio Returns Expected Portfolio Volatility Portfolio Sharpe Ratio\n", "0 0.306969 0.307192 0.999276\n", "------------------------------------------------------------------------------------------\n" ] } ], "source": [ "## Generate Random Weights\n", "rand_weig = np.array(np.random.random(num_tick))\n", "\n", "## Rebalancing Random Weights\n", "rebal_weig = rand_weig / np.sum(rand_weig)\n", "\n", "## Calculate the Expected Returns, Annualize it by * 247.0\n", "exp_ret = np.sum((log_return.mean() * rebal_weig) * 247)\n", "\n", "## Calculate the Expected Volatility, Annualize it by * 247.0\n", "exp_vol = np.sqrt(\n", "np.dot(\n", " rebal_weig.T,\n", " np.dot(\n", " log_return.cov() * 247,\n", " rebal_weig\n", " )\n", ")\n", ")\n", "\n", "## Calculate the Sharpe Ratio.\n", "sharpe_ratio = exp_ret / exp_vol\n", "\n", "# Put the weights into a data frame to see them better.\n", "weights_df = pd.DataFrame(data={\n", "'random_weights': rand_weig,\n", "'rebalance_weights': rebal_weig\n", "})\n", "\n", "print('')\n", "print('='*90)\n", "print('PORTFOLIO WEIGHTS:')\n", "print('-'*90)\n", "print(weights_df)\n", "print('-'*90)\n", "\n", "# Do the same with the other metrics.\n", "metrics_df = pd.DataFrame(data={\n", " 'Expected Portfolio Returns': exp_ret,\n", " 'Expected Portfolio Volatility': exp_vol,\n", " 'Portfolio Sharpe Ratio': sharpe_ratio\n", "}, index=[0])\n", "\n", "print('')\n", "print('='*90)\n", "print('PORTFOLIO METRICS:')\n", "print('-'*90)\n", "print(metrics_df)\n", "print('-'*90)" ] }, { "cell_type": "markdown", "id": "22925bff", "metadata": {}, "source": [ "## Getting Started with Monte Carlo" ] }, { "cell_type": "code", "execution_count": 6, "id": "7abe8654", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:42.830884Z", "start_time": "2024-03-07T20:09:42.827059Z" } }, "outputs": [], "source": [ "## Let's get started with Monte Carlo Simulations\n", "\n", "## How many times should we run Monte Carlo\n", "num_of_port = 20000\n", "\n", "## Create an Array to store the weights as they are generated\n", "all_weights = np.zeros((num_of_port, num_tick))\n", "\n", "## Create an Array to store the returns as they are generated\n", "ret_arr = np.zeros(num_of_port)\n", "\n", "## Create an Array to store the volatilities as they are generated\n", "vol_arr = np.zeros(num_of_port)\n", "\n", "## Create an Array to store the Sharpe Ratios as they are generated\n", "sharpe_arr = np.zeros(num_of_port)" ] }, { "cell_type": "markdown", "id": "d81282dd", "metadata": {}, "source": [ "## Monte Carlo Simulations" ] }, { "cell_type": "code", "execution_count": 7, "id": "9150b622", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:52.035492Z", "start_time": "2024-03-07T20:09:43.833813Z" } }, "outputs": [], "source": [ "## Let's start the Monte Carlo Simulation\n", "\n", "for ind in range(num_of_port):\n", " \n", " ## Let's first Calculate the Weights\n", " weig = np.array(np.random.random(num_tick))\n", " weig = weig / np.sum(weig)\n", " \n", " ## Append the Weights to Weigths array\n", " all_weights[ind, :] = weig\n", " \n", " ## Calculate and Append the Expected Log Returns to Returns Array\n", " ret_arr[ind] = np.sum((log_return.mean() * weig) * 247)\n", " \n", " ## Calculate and Append the Volatility to the Volatitlity Array\n", " vol_arr[ind] = np.sqrt(\n", " np.dot(weig.T, np.dot(log_return.cov() * 247, weig))\n", " )\n", " \n", " ## Calculate and Append the Sharpe Ratio to Sharpe Ratio Array\n", " sharpe_arr[ind] = ret_arr[ind] / vol_arr[ind]" ] }, { "cell_type": "markdown", "id": "d496de6c", "metadata": {}, "source": [ "## Let's look closer at the Simulations" ] }, { "cell_type": "code", "execution_count": 8, "id": "c6b2b637", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:52.335274Z", "start_time": "2024-03-07T20:09:52.040485Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==========================================================================================\n", "SIMULATIONS RESULT:\n", "------------------------------------------------------------------------------------------\n", " Returns Volatility Sharpe Ratio Portfolio Weights\n", "0 0.386904 0.333399 1.160483 [0.3150312209419956, 0.2238901925845945, 0.055...\n", "1 0.324310 0.282726 1.147080 [0.13585396400045283, 0.21898629748712684, 0.2...\n", "2 0.274413 0.283271 0.968728 [0.040532773086664156, 0.21976943153480988, 0....\n", "3 0.333915 0.286389 1.165947 [0.18490446424930748, 0.1374780866867471, 0.27...\n", "4 0.344173 0.303290 1.134796 [0.16840813424759166, 0.27132535034095534, 0.0...\n", "------------------------------------------------------------------------------------------\n" ] } ], "source": [ "## Let's create a Data Frame with Weights, Returns, Volatitlity, and the Sharpe Ratio\n", "sim_data = [ret_arr, vol_arr, sharpe_arr, all_weights]\n", "\n", "## Create a Data Frame using above, then Transpose it\n", "sim_df = pd.DataFrame(data = sim_data).T\n", "\n", "## Give the columns in Simulation Data Proper Names\n", "sim_df.columns = [\n", " 'Returns',\n", " 'Volatility',\n", " 'Sharpe Ratio',\n", " 'Portfolio Weights'\n", "]\n", "\n", "## Make sure the Data Types are correct in the Data Frame\n", "sim_df = sim_df.infer_objects()\n", "\n", "# Print out the results.\n", "print('')\n", "print('='*90)\n", "print('SIMULATIONS RESULT:')\n", "print('-'*90)\n", "print(sim_df.head())\n", "print('-'*90)" ] }, { "cell_type": "markdown", "id": "836a92d8", "metadata": {}, "source": [ "## Look at Important Metrics" ] }, { "cell_type": "code", "execution_count": 9, "id": "190b2de9", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:52.346009Z", "start_time": "2024-03-07T20:09:52.337508Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "================================================================================\n", "MAX SHARPE RATIO:\n", "--------------------------------------------------------------------------------\n", "Returns 0.432582\n", "Volatility 0.328832\n", "Sharpe Ratio 1.315513\n", "Portfolio Weights [0.4113690848985404, 0.004429081705741692, 0.2...\n", "Name: 15537, dtype: object\n", "--------------------------------------------------------------------------------\n", "\n", "================================================================================\n", "MIN VOLATILITY:\n", "--------------------------------------------------------------------------------\n", "Returns 0.260189\n", "Volatility 0.2604\n", "Sharpe Ratio 0.999189\n", "Portfolio Weights [0.009702368306188258, 0.15720111499006897, 0....\n", "Name: 5364, dtype: object\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ "# Return the Max Sharpe Ratio from the run.\n", "max_sharpe_ratio = sim_df.loc[sim_df['Sharpe Ratio'].idxmax()]\n", "\n", "# Return the Min Volatility from the run.\n", "min_volatility = sim_df.loc[sim_df['Volatility'].idxmin()]\n", "\n", "print('')\n", "print('='*80)\n", "print('MAX SHARPE RATIO:')\n", "print('-'*80)\n", "print(max_sharpe_ratio)\n", "print('-'*80)\n", "\n", "print('')\n", "print('='*80)\n", "print('MIN VOLATILITY:')\n", "print('-'*80)\n", "print(min_volatility)\n", "print('-'*80)" ] }, { "cell_type": "markdown", "id": "ad243990", "metadata": {}, "source": [ "## Let's Visualize the Monte Carlo Simulation" ] }, { "cell_type": "code", "execution_count": 10, "id": "751eab2f", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:52.835850Z", "start_time": "2024-03-07T20:09:52.351612Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/3549902619.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " max_sharpe_ratio[1],\n", "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/3549902619.py:21: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " max_sharpe_ratio[0],\n", "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/3549902619.py:29: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " min_volatility[1],\n", "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/3549902619.py:30: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", " min_volatility[0],\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# This is so I can see the plot inside of my Jupyter Notebook.\n", "%matplotlib inline\n", "\n", "# Plot the data on a Scatter plot.\n", "plt.scatter(\n", " y=sim_df['Returns'],\n", " x=sim_df['Volatility'],\n", " c=sim_df['Sharpe Ratio'],\n", " cmap='RdYlBu'\n", ")\n", "\n", "# Give the Plot some labels, and titles.\n", "plt.title('Portfolio Returns Vs. Risk')\n", "plt.colorbar(label='Sharpe Ratio')\n", "plt.xlabel('Standard Deviation')\n", "plt.ylabel('Returns')\n", "\n", "# Plot the Max Sharpe Ratio, using a `Red Star`.\n", "plt.scatter(\n", " max_sharpe_ratio[1],\n", " max_sharpe_ratio[0],\n", " marker=(5, 1, 0),\n", " color='r',\n", " s=600\n", ")\n", "\n", "# Plot the Min Volatility, using a `Blue Star`.\n", "plt.scatter(\n", " min_volatility[1],\n", " min_volatility[0],\n", " marker=(5, 1, 0),\n", " color='b',\n", " s=600\n", ")\n", "\n", "# Finally, show the plot.\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "759a52dd", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:52.848946Z", "start_time": "2024-03-07T20:09:52.840460Z" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "id": "728a0752", "metadata": { "ExecuteTime": { "end_time": "2024-03-07T20:09:52.860738Z", "start_time": "2024-03-07T20:09:52.853368Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/2037253725.py:31: FutureWarning:\n", "\n", "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", "\n", "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/2037253725.py:32: FutureWarning:\n", "\n", "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", "\n", "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/2037253725.py:44: FutureWarning:\n", "\n", "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", "\n", "/var/folders/jx/_r4pg95j3pzdd581p_wql9pc0000gn/T/ipykernel_7250/2037253725.py:45: FutureWarning:\n", "\n", "Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", "\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": [ 1.1604834938077349, 1.1470804170109308, 0.9687278310796746, 1.1659469193051266, 1.1347962830831468, 1.155734864704956, 1.1019706492953907, 1.194550053297057, 1.0551564624752197, 1.2585042777914268, 1.181432805579782, 1.123457239911254, 1.190207322930829, 1.1514562844649905, 1.2114934123137695, 1.139331083449568, 1.1124338569892402, 1.0224106162276827, 1.0300835047920094, 1.0661172725066468, 1.0733519899233106, 1.0230798121279339, 0.9422684165886241, 1.1436748584059029, 0.9844638025117354, 1.1541404536941278, 1.0363082377303925, 1.112292306707109, 1.1284799231714948, 1.2081397989020335, 1.1568146484861668, 1.161867576432618, 1.129379593131362, 1.1977256721118759, 1.041296260527832, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objects as go\n", "\n", "# Plot the data on a Scatter plot.\n", "fig = go.Figure(data=go.Scatter(\n", " x=sim_df['Volatility'],\n", " y=sim_df['Returns'],\n", " mode='markers',\n", " marker=dict(\n", " color=sim_df['Sharpe Ratio'],\n", " colorscale='RdYlBu',\n", " size=10\n", " )\n", "))\n", "\n", "# Add color bar\n", "fig.update_layout(\n", " coloraxis_colorbar=dict(\n", " title='Sharpe Ratio'\n", " )\n", ")\n", "\n", "# Add title and axis labels\n", "fig.update_layout(\n", " title='Portfolio Returns Vs. Risk',\n", " xaxis=dict(title='Standard Deviation'),\n", " yaxis=dict(title='Returns')\n", ")\n", "\n", "# Plot the Max Sharpe Ratio, using a `Red Star`.\n", "fig.add_trace(go.Scatter(\n", " x=[max_sharpe_ratio[1]],\n", " y=[max_sharpe_ratio[0]],\n", " mode='markers',\n", " marker=dict(\n", " color='red',\n", " symbol='star',\n", " size=20\n", " ),\n", " name='Max Sharpe Ratio'\n", "))\n", "\n", "# Plot the Min Volatility, using a `Blue Star`.\n", "fig.add_trace(go.Scatter(\n", " x=[min_volatility[1]],\n", " y=[min_volatility[0]],\n", " mode='markers',\n", " marker=dict(\n", " color='blue',\n", " symbol='star',\n", " size=20\n", " ),\n", " name='Min Volatility'\n", "))\n", "\n", "# Show the plot\n", "fig.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "bc534b3e", "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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 5 }