diff --git a/PortFolio Optimaization Using Genetic ALgorithm/Portfolio_Optimization_Using_Genetic_Algorithm.ipynb b/PortFolio Optimaization Using Genetic ALgorithm/Portfolio_Optimization_Using_Genetic_Algorithm.ipynb new file mode 100644 index 0000000..292f4a3 --- /dev/null +++ b/PortFolio Optimaization Using Genetic ALgorithm/Portfolio_Optimization_Using_Genetic_Algorithm.ipynb @@ -0,0 +1,1090 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# **Portfolio Optimization Using Genetic Algorithm**" + ], + "metadata": { + "id": "FgneRLb5xWON" + } + }, + { + "cell_type": "code", + "source": [ + "Load the data" + ], + "metadata": { + "id": "HTGK4uLYMf5A" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 324 + }, + "id": "CCgHhKp6FHbT", + "outputId": "f358ffad-1db0-4203-ee75-37ce1f5c1234" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Asset_1 Asset_2 Asset_3 Portfolio_Value Market_Index\n", + "timestamp \n", + "01-01-2020 544.883183 423.654799 645.894113 525.051834 1034.932512\n", + "02-01-2020 437.587211 891.773001 963.662760 792.579124 1026.081261\n", + "03-01-2020 383.441519 791.725038 528.894920 600.939199 1024.844657\n", + "06-01-2020 568.044561 925.596638 71.036058 564.754139 966.747919\n", + "07-01-2020 87.129300 20.218397 832.619845 290.226071 1036.577579" + ], + "text/html": [ + "\n", + "
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Asset_1Asset_2Asset_3Portfolio_ValueMarket_Index
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02-01-2020-0.1969161.1049520.4919830.509525-0.008552
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\"samples\": [\n 0.2878634293587754,\n -0.8129127864207362,\n -0.8511612594885148\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Asset_3\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 17.748320739953726,\n \"min\": -0.9993120588883697,\n \"max\": 380.4407359233574,\n \"num_unique_values\": 1042,\n \"samples\": [\n -0.5503353506007611,\n -0.06812604800404243,\n -0.4344354157245046\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Portfolio_Value\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.78378699798799,\n \"min\": -0.8891291434918596,\n \"max\": 6.697990119731567,\n \"num_unique_values\": 1042,\n \"samples\": [\n -0.21442045447586378,\n -0.2965740287022468,\n -0.6318635767325322\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Market_Index\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06793369258923397,\n \"min\": -0.2066701830021066,\n \"max\": 0.20590153875399797,\n \"num_unique_values\": 1042,\n \"samples\": [\n -0.0447209020183893,\n -0.12488407279002756,\n -0.045631757458398425\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Define Fitness Function" + ], + "metadata": { + "id": "7MLPZBswwuTL" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "def fitness_function(weights, returns):\n", + " portfolio_return = np.sum(returns.mean() * weights) * 252\n", + " portfolio_stddev = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))\n", + " sharpe_ratio = portfolio_return / portfolio_stddev\n", + " return sharpe_ratio\n" + ], + "metadata": { + "id": "ADnAYwDKFdu7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Initial Population" + ], + "metadata": { + "id": "tN7T8oixw0ZL" + } + }, + { + "cell_type": "code", + "source": [ + "def initialize_population(pop_size, num_assets):\n", + " population = []\n", + " for _ in range(pop_size):\n", + " weights = np.random.random(num_assets)\n", + " weights /= np.sum(weights)\n", + " population.append(weights)\n", + " return np.array(population)\n", + "\n", + "pop_size = 100\n", + "num_assets = len(df.columns)\n", + "population = initialize_population(pop_size, num_assets)\n" + ], + "metadata": { + "id": "newRizz2FhTL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Selection" + ], + "metadata": { + "id": "xfwajDq9w4AH" + } + }, + { + "cell_type": "code", + "source": [ + "def selection(population, fitness_scores, num_parents):\n", + " parents = np.empty((num_parents, population.shape[1]))\n", + " for i in range(num_parents):\n", + " max_fitness_idx = np.where(fitness_scores == np.max(fitness_scores))\n", + " max_fitness_idx = max_fitness_idx[0][0]\n", + " parents[i, :] = population[max_fitness_idx, :]\n", + " fitness_scores[max_fitness_idx] = -999999\n", + " return parents\n", + "\n", + "num_parents = 20\n", + "fitness_scores = np.array([fitness_function(weights, returns) for weights in population])\n", + "parents = selection(population, fitness_scores, num_parents)\n" + ], + "metadata": { + "id": "JVhMT8DbFjMo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Crossover" + ], + "metadata": { + "id": "jqEhbITsw9t1" + } + }, + { + "cell_type": "code", + "source": [ + "def crossover(parents, offspring_size):\n", + " offspring = np.empty(offspring_size)\n", + " crossover_point = np.uint8(offspring_size[1]/2)\n", + "\n", + " for k in range(offspring_size[0]):\n", + " parent1_idx = k % parents.shape[0]\n", + " parent2_idx = (k + 1) % parents.shape[0]\n", + " offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]\n", + " offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]\n", + " return offspring\n", + "\n", + "offspring_size = (pop_size - parents.shape[0], num_assets)\n", + "offspring = crossover(parents, offspring_size)\n" + ], + "metadata": { + "id": "1kGod21wFldT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Mutation" + ], + "metadata": { + "id": "AjnCQOIrxBbe" + } + }, + { + "cell_type": "code", + "source": [ + "def mutation(offspring, mutation_rate=0.01):\n", + " for idx in range(offspring.shape[0]):\n", + " for gene_idx in range(offspring.shape[1]):\n", + " if np.random.rand() < mutation_rate:\n", + " random_value = np.random.random()\n", + " offspring[idx, gene_idx] = offspring[idx, gene_idx] + random_value\n", + " offspring[idx, gene_idx] /= np.sum(offspring[idx, :])\n", + " return offspring\n", + "\n", + "mutated_offspring = mutation(offspring)\n" + ], + "metadata": { + "id": "fCZuohWDFnyT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Termination" + ], + "metadata": { + "id": "xEDvzIMbxGTc" + } + }, + { + "cell_type": "code", + "source": [ + "def mutation(offspring, mutation_rate=0.01):\n", + " for idx in range(offspring.shape[0]):\n", + " for gene_idx in range(offspring.shape[1]):\n", + " if np.random.rand() < mutation_rate:\n", + " random_value = np.random.random()\n", + " offspring[idx, gene_idx] = offspring[idx, gene_idx] + random_value\n", + " offspring[idx, gene_idx] /= np.sum(offspring[idx, :])\n", + " return offspring\n", + "\n", + "mutated_offspring = mutation(offspring)\n" + ], + "metadata": { + "id": "WzsJq1NSFpf3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "s-2eE9IWxIxw" + } + }, + { + "cell_type": "code", + "source": [ + "def genetic_algorithm(returns, num_generations, pop_size, num_parents, mutation_rate):\n", + " num_assets = returns.shape[1]\n", + " population = initialize_population(pop_size, num_assets)\n", + " for generation in range(num_generations):\n", + " fitness_scores = np.array([fitness_function(weights, returns) for weights in population])\n", + " parents = selection(population, fitness_scores, num_parents)\n", + " offspring_size = (pop_size - parents.shape[0], num_assets)\n", + " offspring = crossover(parents, offspring_size)\n", + " offspring = mutation(offspring, mutation_rate)\n", + " population[0:parents.shape[0], :] = parents\n", + " population[parents.shape[0]:, :] = offspring\n", + " best_fitness = np.max(fitness_scores)\n", + " print(f\"Generation {generation}: Best Fitness = {best_fitness}\")\n", + " return population, fitness_scores\n", + "\n", + "num_generations = 100\n", + "population, fitness_scores = genetic_algorithm(returns, num_generations, pop_size, num_parents, mutation_rate=0.01)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rkW-7vLYFrRq", + "outputId": "45fbe294-405e-491d-9b41-312512d0b006" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 0: Best Fitness = 1.417253614680323\n", + "Generation 1: Best Fitness = 1.8779466185434148\n", + "Generation 2: Best Fitness = 2.0430037580572336\n", + "Generation 3: Best Fitness = 2.0920158355234864\n", + "Generation 4: Best Fitness = 2.4902477236462275\n", + "Generation 5: Best Fitness = 2.4902477236462275\n", + "Generation 6: Best Fitness = 2.4902477236462275\n", + "Generation 7: Best Fitness = 2.521225040481554\n", + "Generation 8: Best Fitness = 2.521225040481554\n", + "Generation 9: Best Fitness = 2.5409096684344568\n", + "Generation 10: Best Fitness = 2.5409096684344568\n", + "Generation 11: Best Fitness = 2.5414701981903742\n", + "Generation 12: Best Fitness = 2.5414701981903742\n", + "Generation 13: Best Fitness = 2.575155889229864\n", + "Generation 14: Best Fitness = 2.575155889229864\n", + "Generation 15: Best Fitness = 2.575155889229864\n", + "Generation 16: Best Fitness = 2.5758970013396527\n", + "Generation 17: Best Fitness = 2.5758970013396527\n", + "Generation 18: Best Fitness = 2.5759063088633067\n", + "Generation 19: Best Fitness = 2.5760569272179454\n", + "Generation 20: Best Fitness = 2.5760569272179454\n", + "Generation 21: Best Fitness = 2.580023239229417\n", + "Generation 22: Best Fitness = 2.580023239229417\n", + "Generation 23: Best Fitness = 2.5861124541129388\n", + "Generation 24: Best Fitness = 2.5861124541129388\n", + "Generation 25: Best Fitness = 2.586185095785435\n", + "Generation 26: Best Fitness = 2.586185095785435\n", + "Generation 27: Best Fitness = 2.586185095785435\n", + "Generation 28: Best Fitness = 2.586185095785435\n", + "Generation 29: Best Fitness = 2.586185095785435\n", + "Generation 30: Best Fitness = 2.586185095785435\n", + "Generation 31: Best Fitness = 2.586185095785435\n", + "Generation 32: Best Fitness = 2.586185095785435\n", + "Generation 33: Best Fitness = 2.586185095785435\n", + "Generation 34: Best Fitness = 2.586185095785435\n", + "Generation 35: Best Fitness = 2.586185095785435\n", + "Generation 36: Best Fitness = 2.586185095785435\n", + "Generation 37: Best Fitness = 2.586185095785435\n", + "Generation 38: Best Fitness = 2.588992264842197\n", + "Generation 39: Best Fitness = 2.596323300370395\n", + "Generation 40: Best Fitness = 2.596323300370395\n", + "Generation 41: Best Fitness = 2.596323300370395\n", + "Generation 42: Best Fitness = 2.596323300370395\n", + "Generation 43: Best Fitness = 2.596323300370395\n", + "Generation 44: Best Fitness = 2.596323300370395\n", + "Generation 45: Best Fitness = 2.596323300370395\n", + "Generation 46: Best Fitness = 2.596323300370395\n", + "Generation 47: Best Fitness = 2.596331571848502\n", + "Generation 48: Best Fitness = 2.596331571848502\n", + "Generation 49: Best Fitness = 2.596331571848502\n", + "Generation 50: Best Fitness = 2.596331571848502\n", + "Generation 51: Best Fitness = 2.596331571848502\n", + "Generation 52: Best Fitness = 2.596331571848502\n", + "Generation 53: Best Fitness = 2.596331571848502\n", + "Generation 54: Best Fitness = 2.596331571848502\n", + "Generation 55: Best Fitness = 2.596331571848502\n", + "Generation 56: Best Fitness = 2.596331571848502\n", + "Generation 57: Best Fitness = 2.596331571848502\n", + "Generation 58: Best Fitness = 2.596331571848502\n", + "Generation 59: Best Fitness = 2.596331571848502\n", + "Generation 60: Best Fitness = 2.596331571848502\n", + "Generation 61: Best Fitness = 2.596331571848502\n", + "Generation 62: Best Fitness = 2.596331571848502\n", + "Generation 63: Best Fitness = 2.596331571848502\n", + "Generation 64: Best Fitness = 2.596331571848502\n", + "Generation 65: Best Fitness = 2.596331571848502\n", + "Generation 66: Best Fitness = 2.596331571848502\n", + "Generation 67: Best Fitness = 2.5963368694291065\n", + "Generation 68: Best Fitness = 2.5963368694291065\n", + "Generation 69: Best Fitness = 2.5963368694291065\n", + "Generation 70: Best Fitness = 2.5963368694291065\n", + "Generation 71: Best Fitness = 2.5963368694291065\n", + "Generation 72: Best Fitness = 2.5963368694291065\n", + "Generation 73: Best Fitness = 2.5963368694291065\n", + "Generation 74: Best Fitness = 2.5963368694291065\n", + "Generation 75: Best Fitness = 2.5963368694291065\n", + "Generation 76: Best Fitness = 2.5963368694291065\n", + "Generation 77: Best Fitness = 2.5963368694291065\n", + "Generation 78: Best Fitness = 2.5963368694291065\n", + "Generation 79: Best Fitness = 2.5963498076018925\n", + "Generation 80: Best Fitness = 2.5963498076018925\n", + "Generation 81: Best Fitness = 2.5963498076018925\n", + "Generation 82: Best Fitness = 2.5963498076018925\n", + "Generation 83: Best Fitness = 2.5963498076018925\n", + "Generation 84: Best Fitness = 2.5963498076018925\n", + "Generation 85: Best Fitness = 2.5963498076018925\n", + "Generation 86: Best Fitness = 2.5963498076018925\n", + "Generation 87: Best Fitness = 2.5963498076018925\n", + "Generation 88: Best Fitness = 2.5963498076018925\n", + "Generation 89: Best Fitness = 2.5963498076018925\n", + "Generation 90: Best Fitness = 2.5963498076018925\n", + "Generation 91: Best Fitness = 2.5963498076018925\n", + "Generation 92: Best Fitness = 2.5963498076018925\n", + "Generation 93: Best Fitness = 2.5963498076018925\n", + "Generation 94: Best Fitness = 2.5963498076018925\n", + "Generation 95: Best Fitness = 2.5963498076018925\n", + "Generation 96: Best Fitness = 2.5963498076018925\n", + "Generation 97: Best Fitness = 2.5963498076018925\n", + "Generation 98: Best Fitness = 2.5963498076018925\n", + "Generation 99: Best Fitness = 2.5963502910456278\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Evaluate Best portfolio" + ], + "metadata": { + "id": "YFQ1uXj5xNuu" + } + }, + { + "cell_type": "code", + "source": [ + "best_idx = np.argmax(fitness_scores)\n", + "best_portfolio = population[best_idx, :]\n", + "best_portfolio_return = np.sum(returns.mean() * best_portfolio) * 252\n", + "best_portfolio_stddev = np.sqrt(np.dot(best_portfolio.T, np.dot(returns.cov() * 252, best_portfolio)))\n", + "best_portfolio_sharpe = best_portfolio_return / best_portfolio_stddev\n", + "\n", + "print(f\"Best Portfolio Weights: {best_portfolio}\")\n", + "print(f\"Expected Annual Return: {best_portfolio_return}\")\n", + "print(f\"Annual Volatility: {best_portfolio_stddev}\")\n", + "print(f\"Sharpe Ratio: {best_portfolio_sharpe}\")\n" + ], + "metadata": { + "id": "6vLI0G12Fxfc", + "outputId": "061304f9-ef21-447f-87c7-f1782a0e732f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Best Portfolio Weights: [0.04189262 0.13620183 0.72651027 0.56140873 0.49904594]\n", + "Expected Annual Return: 737.635018426684\n", + "Annual Volatility: 284.1045836421465\n", + "Sharpe Ratio: 2.5963502910456278\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "tybZDt2YxUIU" + } + } + ] +} \ No newline at end of file diff --git a/PortFolio Optimaization Using Genetic ALgorithm/portfolio_data.csv b/PortFolio Optimaization Using Genetic ALgorithm/portfolio_data.csv new file mode 100644 index 0000000..95769f0 --- /dev/null +++ b/PortFolio Optimaization Using Genetic ALgorithm/portfolio_data.csv @@ -0,0 +1,1044 @@ +timestamp,Asset_1,Asset_2,Asset_3,Portfolio_Value,Market_Index +01-01-2020,544.883183,423.6547993,645.8941131,525.0518337,1034.932512 +02-01-2020,437.5872113,891.7730008,963.6627605,792.5791242,1026.081261 +03-01-2020,383.4415188,791.7250381,528.8949198,600.9391987,1024.844657 +06-01-2020,568.0445611,925.5966383,71.0360582,564.7541386,966.7479191 +07-01-2020,87.1292997,20.21839744,832.6198455,290.2260711,1036.577579 +08-01-2020,778.1567509,870.0121482,978.6183422,879.1422738,1015.982489 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b/PortFolio Optimaization Using Genetic ALgorithm/readme.md new file mode 100644 index 0000000..2d6817b --- /dev/null +++ b/PortFolio Optimaization Using Genetic ALgorithm/readme.md @@ -0,0 +1,36 @@ +# Portfolio Optimization Using Genetic Algorithms + +## Overview +This project implements portfolio optimization using genetic algorithms to find the optimal asset allocation that maximizes returns and minimizes risk. + +## Objectives +- **Data Collection:** Gather historical price data for a set of assets. +- **Data Preprocessing:** Clean and prepare the data for analysis. +- **Feature Engineering:** Calculate returns, volatility, and correlations. +- **Genetic Algorithm Implementation:** Develop and apply a genetic algorithm to optimize the portfolio. +- **Evaluation:** Assess the performance of the optimized portfolio. + +## Methodology +1. **Data Collection:** + - Collect historical price data for various assets from sources like Yahoo Finance, Quandl, or Bloomberg. + +2. **Data Preprocessing:** + - Handle missing values and outliers. + - Calculate daily, monthly, or annual returns. + +3. **Feature Engineering:** + - Compute metrics such as average return, standard deviation, and correlation matrix. + +4. **Genetic Algorithm Implementation:** + - Define the chromosome representation, fitness function, and genetic operators (selection, crossover, mutation). + - Optimize the portfolio by maximizing the Sharpe ratio or minimizing risk. + +5. **Evaluation:** + - Evaluate the optimized portfolio's performance against a benchmark. + - Use metrics such as Sharpe ratio, Sortino ratio, and Maximum Drawdown. + +## Tools and Technologies +- **Programming Languages:** Python +- **Libraries:** pandas, numpy, scipy, matplotlib, seaborn, DEAP (for genetic algorithms) + +