{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing Important and necessary Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import yfinance as yf\n",
"import plotly.express as exp\n",
"import datetime\n",
"from datetime import date\n",
"import numpy as np\n",
"from dataclasses import dataclass\n",
"from scipy.optimize import minimize\n",
"import numpy as np\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"np.set_printoptions(suppress=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparameters and Tickers List"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class data:\n",
" collectedData = pd.DataFrame()\n",
" commTickers: dict\n",
" years: int\n",
" upperBoundLimit: float\n",
"\n",
"\n",
"\n",
"# Define the commodities and their Yahoo Finance tickers\n",
"commodities = {\n",
" 'Gold': 'GC=F',\n",
" 'Silver': 'SI=F',\n",
" 'Crude Oil': 'CL=F',\n",
" 'Natural Gas': 'NG=F',\n",
" 'Copper': 'HG=F',\n",
" 'Platinum' : 'PL=F',\n",
" 'Paladium': 'PA=F',\n",
" 'RBOB Gasoliine': 'RB=F',\n",
" 'Brent Crude Oil': 'BZ=F',\n",
" 'Corn Futures': 'ZC=F',\n",
" 'Oat Futures': 'ZO=F',\n",
" 'KC HRW Wheat Futures': 'KE=F',\n",
" 'Soybean Oil Futures':'ZL=F',\n",
" 'Soybean Futures': 'ZS=F',\n",
" 'Live Cattle Futures': 'LE=F',\n",
" 'Cocoa': 'CC=F',\n",
" 'Coffee' : 'KC=F',\n",
" 'Cotton': 'CT=F',\n",
" 'Sugar': 'SB=F'\n",
"}\n",
"\n",
"yearsOfData = 5\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Function to Loop Through all the tickers list passed as an argument and download their adjusted close prices\n",
"\n",
"- We use Adjusted Close Prices rather than Close Prices since they're more accurate for weighting the portfolio\n",
"\n",
"- Adjusted Closing Price is the price after dividend payouts, stock splits or other dilution factors have been taken into account\n",
"\n",
"- While Closing price is just the regular security prices at the end of the day"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def retrieveTickers():\n",
" global dt\n",
"\n",
" if len(dt.commTickers) <= 0:\n",
" return\n",
"\n",
" startDate = date.today() - datetime.timedelta(days = dt.years * 365)\n",
" endDate = date.today()\n",
" \n",
" for com, tick in dt.commTickers.items():\n",
" # Keep Adding new tickers to global dataset\n",
" # It will automatically map rows, since index for Global Data and retrieved series is the same\n",
" if com not in dt.collectedData.columns:\n",
" dt.collectedData[com] = yf.download(tickers= tick,start= startDate, end=endDate)['Adj Close']\n",
" dt.collectedData.dropna(inplace=True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Function to visualise all the stocks data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def visualize(data: pd.core.frame.DataFrame,\n",
" title: str, hoverLabel: dict, \n",
" xTitle: str, \n",
" yTitle: str, \n",
" legendTitle: str\n",
" ):\n",
" # Create interactive plots\n",
" fig = exp.line(data,\n",
" title=title,\n",
" labels=hoverLabel)\n",
" fig.update_layout(\n",
" xaxis_title=xTitle,\n",
" yaxis_title=yTitle,\n",
" legend_title_text=legendTitle,\n",
" template='plotly_dark',\n",
" hovermode='x unified'\n",
" )\n",
" fig.show()\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Class to calculate all the key metrics\n",
"- Daily Returns\n",
"- Covariance Matrix\n",
"- Variance\n",
"- Standard Deviation\n",
"- Expected Returns\n",
"- Sharpe Ratio"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"class keyMetrics:\n",
" def __init__(self, data, logNormal = False) -> None:\n",
" self.data = data\n",
" self.initialWeights = np.array([1/len(self.data.columns) for _ in range(len(self.data.columns))])\n",
" self.logNormal = logNormal\n",
" \n",
" self.dailyReturns = None\n",
" self.covariaceMatrix = None\n",
" self.variance = None\n",
" self.standardDeviation = None\n",
" self.expectedReturns = None\n",
" self.sharpeRatio = None\n",
" self.negSharpeRatio = None\n",
"\n",
" def calculateDailyReturns(self):\n",
" self.dailyReturns = np.log(self.data / self.data.shift(1)).dropna() if self.logNormal else self.data.pct_change().dropna() \n",
"\n",
" def calculateCovariance(self):\n",
" if self.dailyReturns is None:\n",
" return \"Please Calculate Daily Returns First\"\n",
" self.covariaceMatrix = self.dailyReturns.cov() * 252 # we use 252 becaues we want to annualize\n",
"\n",
" \"\"\"\n",
" Standard Deviation is the risk associated with portfolio (all securities combined)\n",
" \"\"\"\n",
" def calculateStandardDeviation(self):\n",
" if self.covariaceMatrix is None:\n",
" return \"Please Calculate Covariance Matrix First\"\n",
" self.variance = self.initialWeights.T @ self.covariaceMatrix @ self.initialWeights\n",
" self.standardDeviation = np.sqrt(self.variance)\n",
"\n",
" \"\"\"\n",
" We will base the expected returns on Historical Returns (the simplest assumption to start off with)\n",
" If we were to take last 5 years of Data and Calculate the average returns,\n",
" we assume that the future returns will mimick the behavior and will be much similar like the past retuns\n",
" \"\"\"\n",
" def calculateExpectedReturn(self):\n",
" if self.standardDeviation is None:\n",
" return \"Please Calculate Standard Deviation First\"\n",
" \n",
" self.expectedReturns = np.sum(self.dailyReturns.mean() * self.initialWeights) * 252\n",
"\n",
" def calculateSharpeRatio(self, riskFreeRate):\n",
" if self.expectedReturns is None:\n",
" return \"Please Calculate Expected Returns First\"\n",
" self.sharpeRatio = (self.expectedReturns - riskFreeRate) / self.standardDeviation\n",
"\n",
" def negativeSharpeRatio(self):\n",
" if self.sharpeRatio is None:\n",
" return \"Please Calculate Sharpe Ratio First\"\n",
" self.negSharpeRatio = - self.sharpeRatio\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
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"data": [
{
"hovertemplate": "Commmmmmodity=Gold
Date=%{x}
Price=%{y}",
"legendgroup": "Gold",
"line": {
"color": "#636efa",
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"source": [
"# define key parameters\n",
"dt = data(commTickers= commodities, \n",
" years=yearsOfData,\n",
" upperBoundLimit=0.3)\n",
"retrieveTickers()\n",
"\n",
"\n",
"visualize(data=dt.collectedData,\n",
" title='Historical Prices of Commodities (Futures) for Portfolio',\n",
" hoverLabel= {'value': 'Price', 'variable': 'Commmmmmodity'},\n",
" xTitle='Date',\n",
" yTitle='Price',\n",
" legendTitle='Commodities')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### FRED API to get the Risk Free Based on Economic Data Provided by the US Government"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Legit Risk Free Rate\n",
"from fredapi import Fred\n",
"f = Fred(api_key='385c2ddfc6d3c369a67ad83c63f6fd7d')\n",
"treasuryRate = f.get_series_latest_release('GS10')\n",
"treasuryRatePercentage = treasuryRate / 100\n",
"riskFreeRate = treasuryRatePercentage.iloc[-1]\n",
"\n",
"# Constraints and Bounds\n",
"constraints = {'type':'eq', 'fun': lambda weights : np.sum(weights) - 1}\n",
"bounds = [(0,dt.upperBoundLimit) for _ in range(len(dt.collectedData.columns))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate the Optimal Weights"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Create an instance of keyMetrics\n",
"metrics = keyMetrics(data= dt.collectedData, logNormal=True)\n",
"\n",
"# Calculate daily returns\n",
"metrics.calculateDailyReturns()\n",
"metrics.calculateCovariance()\n",
"\n",
"# Defining the objective function we want to minimize\n",
"def objective(weights):\n",
" metrics.initialWeights = weights\n",
" metrics.calculateStandardDeviation()\n",
" metrics.calculateExpectedReturn()\n",
" metrics.calculateSharpeRatio(riskFreeRate=riskFreeRate) \n",
" return -metrics.sharpeRatio \n",
"\n",
"\n",
"# Perform the optimization\n",
"result = minimize(fun=objective,\n",
" x0= metrics.initialWeights,\n",
" constraints=constraints,\n",
" bounds=bounds,\n",
" method='SLSQP')\n",
"\n",
"# # Optimal weights\n",
"# optimal_weights = result.x\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Verify If the Optimal Weights were successfully determined"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal Weights:\n",
" Gold Silver Crude Oil Natural Gas Copper Platinum Paladium \\\n",
"0 0.29 0.0 0.037 0.0 0.041 0.0 0.0 \n",
"\n",
" RBOB Gasoliine Brent Crude Oil Corn Futures Oat Futures \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"\n",
" KC HRW Wheat Futures Soybean Oil Futures Soybean Futures \\\n",
"0 0.0 0.02 0.0 \n",
"\n",
" Live Cattle Futures Cocoa Coffee Cotton Sugar \n",
"0 0.294 0.203 0.08 0.0 0.035 \n",
"Optimal Sharpe Ratio: 0.7805935520948829\n"
]
}
],
"source": [
"if result.success:\n",
" optimal_weights = result.x\n",
" optimal_weights = np.round(optimal_weights, 3)\n",
" \n",
" \n",
" if np.isclose(np.sum(optimal_weights), 1):\n",
" # Update the metrics with the optimal weights\n",
" metrics.initialWeights = optimal_weights\n",
" metrics.calculateCovariance()\n",
" metrics.calculateStandardDeviation()\n",
" metrics.calculateExpectedReturn()\n",
" metrics.calculateSharpeRatio(riskFreeRate=riskFreeRate)\n",
" optimal_weights = pd.DataFrame(data=optimal_weights.reshape(1,-1), columns=dt.collectedData.columns)\n",
"\n",
" # Display the optimal weights and the Sharpe ratio\n",
" print(\"Optimal Weights:\\n\", optimal_weights)\n",
" print(\"Optimal Sharpe Ratio:\", metrics.sharpeRatio)\n",
" else:\n",
" print(\"The sum of the optimal weights is not equal to 1.\")\n",
"else:\n",
" print(\"Optimization failed:\", result.message)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([['Gold'],\n",
" ['Silver'],\n",
" ['Crude Oil'],\n",
" ['Natural Gas'],\n",
" ['Copper'],\n",
" ['Platinum'],\n",
" ['Paladium'],\n",
" ['RBOB Gasoliine'],\n",
" ['Brent Crude Oil'],\n",
" ['Corn Futures'],\n",
" ['Oat Futures'],\n",
" ['KC HRW Wheat Futures'],\n",
" ['Soybean Oil Futures'],\n",
" ['Soybean Futures'],\n",
" ['Live Cattle Futures'],\n",
" ['Cocoa'],\n",
" ['Coffee'],\n",
" ['Cotton'],\n",
" ['Sugar']], dtype=object)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"optimal_weights\n",
"np.array(dt.collectedData.columns).reshape(-1,1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal Weights:\n",
" Tickers Weights\n",
"0 Gold 0.290\n",
"1 Silver 0.000\n",
"2 Crude Oil 0.037\n",
"3 Natural Gas 0.000\n",
"4 Copper 0.041\n",
"5 Platinum 0.000\n",
"6 Paladium 0.000\n",
"7 RBOB Gasoliine 0.000\n",
"8 Brent Crude Oil 0.000\n",
"9 Corn Futures 0.000\n",
"10 Oat Futures 0.000\n",
"11 KC HRW Wheat Futures 0.000\n",
"12 Soybean Oil Futures 0.020\n",
"13 Soybean Futures 0.000\n",
"14 Live Cattle Futures 0.294\n",
"15 Cocoa 0.203\n",
"16 Coffee 0.080\n",
"17 Cotton 0.000\n",
"18 Sugar 0.035\n",
"Optimal Sharpe Ratio: 0.7805935520948829\n"
]
}
],
"source": [
"if result.success:\n",
" optimal_weights = result.x\n",
" optimal_weights = np.round(optimal_weights, 3)\n",
" \n",
" \n",
" if np.isclose(np.sum(optimal_weights), 1):\n",
" # Update the metrics with the optimal weights\n",
" metrics.initialWeights = optimal_weights\n",
" metrics.calculateCovariance()\n",
" metrics.calculateStandardDeviation()\n",
" metrics.calculateExpectedReturn()\n",
" metrics.calculateSharpeRatio(riskFreeRate=riskFreeRate)\n",
" optimal_weights = pd.DataFrame({ 'Tickers': np.array(dt.collectedData.columns), 'Weights': optimal_weights })\n",
"\n",
" # Display the optimal weights and the Sharpe ratio\n",
" print(\"Optimal Weights:\\n\", optimal_weights)\n",
" print(\"Optimal Sharpe Ratio:\", metrics.sharpeRatio)\n",
" else:\n",
" print(\"The sum of the optimal weights is not equal to 1.\")\n",
"else:\n",
" print(\"Optimization failed:\", result.message)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Tickers | \n",
" Weights | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" Gold | \n",
" 0.290 | \n",
"
\n",
" \n",
" | 1 | \n",
" Silver | \n",
" 0.000 | \n",
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\n",
" \n",
" | 2 | \n",
" Crude Oil | \n",
" 0.037 | \n",
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\n",
" \n",
" | 3 | \n",
" Natural Gas | \n",
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\n",
" \n",
" | 4 | \n",
" Copper | \n",
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\n",
" \n",
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\n",
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\n",
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\n",
" \n",
" | 8 | \n",
" Brent Crude Oil | \n",
" 0.000 | \n",
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\n",
" \n",
" | 9 | \n",
" Corn Futures | \n",
" 0.000 | \n",
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\n",
" \n",
" | 10 | \n",
" Oat Futures | \n",
" 0.000 | \n",
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\n",
" \n",
" | 11 | \n",
" KC HRW Wheat Futures | \n",
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\n",
" \n",
" | 12 | \n",
" Soybean Oil Futures | \n",
" 0.020 | \n",
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\n",
" \n",
" | 13 | \n",
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\n",
" \n",
" | 14 | \n",
" Live Cattle Futures | \n",
" 0.294 | \n",
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\n",
" \n",
" | 15 | \n",
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" 0.203 | \n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
"\n",
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\n",
" \n",
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" Silver | \n",
" Crude Oil | \n",
" Natural Gas | \n",
" Copper | \n",
" Platinum | \n",
" Paladium | \n",
" RBOB Gasoliine | \n",
" Brent Crude Oil | \n",
" Corn Futures | \n",
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" Cocoa | \n",
" Coffee | \n",
" Cotton | \n",
" Sugar | \n",
"
\n",
" \n",
" \n",
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" 0.29 | \n",
" 0.0 | \n",
" 0.037 | \n",
" 0.0 | \n",
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" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.02 | \n",
" 0.0 | \n",
" 0.294 | \n",
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