From 5aef0558f4b3f1fb07f826fdcae4b9ff26199934 Mon Sep 17 00:00:00 2001 From: "ap550083@gmail.com" Date: Thu, 8 Aug 2024 19:04:34 +0530 Subject: [PATCH] optimized the trading strategy based on sentiment analysis added --- ...strategy_based_on_sentiment_analysis.ipynb | 316 ++++ .../readme.md | 54 + .../sentiment_trading_data.csv | 1462 +++++++++++++++++ 3 files changed, 1832 insertions(+) create mode 100644 optimized the trading strategy based on sentiment analysis/optimized_the_trading_strategy_based_on_sentiment_analysis.ipynb create mode 100644 optimized the trading strategy based on sentiment analysis/readme.md create mode 100644 optimized the trading strategy based on sentiment analysis/sentiment_trading_data.csv diff --git a/optimized the trading strategy based on sentiment analysis/optimized_the_trading_strategy_based_on_sentiment_analysis.ipynb b/optimized the trading strategy based on sentiment analysis/optimized_the_trading_strategy_based_on_sentiment_analysis.ipynb new file mode 100644 index 0000000000..5b730631a8 --- /dev/null +++ b/optimized the trading strategy based on sentiment analysis/optimized_the_trading_strategy_based_on_sentiment_analysis.ipynb @@ -0,0 +1,316 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "optimized the trading strategy based on sentiment analysis" + ], + "metadata": { + "id": "S_I_QeRRg52m" + } + }, + { + "cell_type": "markdown", + "source": [ + "Loading the Dataset" + ], + "metadata": { + "id": "x14oHjR0g9Dk" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Load data\n", + "data_sentiment = pd.read_csv('sentiment_trading_data.csv')\n", + "print(data_sentiment.head())\n", + "\n", + "# Convert to NumPy array (excluding the 'Date' column)\n", + "data_sentiment = data_sentiment.drop(columns=['Date']).to_numpy()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vHerMIPte3d2", + "outputId": "6fd74df2-d792-4ef5-ac99-1e170e3c33dc" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Date Stock_Price Sentiment\n", + "0 2020-01-01 87.454012 0.302466\n", + "1 2020-01-02 145.071431 -0.786814\n", + "2 2020-01-03 123.199394 0.315691\n", + "3 2020-01-04 109.865848 0.998827\n", + "4 2020-01-05 65.601864 -0.903576\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Intialize the fitness function" + ], + "metadata": { + "id": "ObuEsypzhAGI" + } + }, + { + "cell_type": "code", + "source": [ + "def fitness_function(individual, data):\n", + " stock_prices = data[:, 0]\n", + " sentiments = data[:, 1]\n", + "\n", + " capital = 100000 # Starting capital\n", + " position = 0 # Initial position (0 means no stock held)\n", + "\n", + " for i in range(len(data)):\n", + " if sentiments[i] > individual[0]: # Buy signal based on sentiment threshold\n", + " position += capital // stock_prices[i] # Buy as many stocks as possible\n", + " capital -= position * stock_prices[i] # Deduct spent capital\n", + " elif sentiments[i] < individual[1]: # Sell signal based on sentiment threshold\n", + " capital += position * stock_prices[i] # Sell all stocks\n", + " position = 0 # Reset position\n", + "\n", + " return capital\n", + "\n" + ], + "metadata": { + "id": "M5oMXowze6Od" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Intialize Population" + ], + "metadata": { + "id": "PH2klZcvhEct" + } + }, + { + "cell_type": "code", + "source": [ + "def initialize_population(pop_size):\n", + " population = []\n", + " for _ in range(pop_size):\n", + " buy_threshold = np.random.uniform(-1, 1) # Random buy threshold\n", + " sell_threshold = np.random.uniform(-1, 1) # Random sell threshold\n", + " individual = [buy_threshold, sell_threshold]\n", + " population.append(individual)\n", + " return population\n" + ], + "metadata": { + "id": "uYNfoXAxe7-m" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Perform Selection" + ], + "metadata": { + "id": "M9Wr94fnhHaZ" + } + }, + { + "cell_type": "code", + "source": [ + "def selection(population, fitness_scores, num_parents):\n", + " parents = [population[idx] for idx in np.argsort(fitness_scores)[-num_parents:]]\n", + " return parents\n" + ], + "metadata": { + "id": "BIzNDPbue9fc" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Perform crossover" + ], + "metadata": { + "id": "sTsXdkBRhJ4v" + } + }, + { + "cell_type": "code", + "source": [ + "def crossover(parents, offspring_size):\n", + " offspring = []\n", + " for _ in range(offspring_size):\n", + " parent1 = parents[np.random.randint(len(parents))]\n", + " parent2 = parents[np.random.randint(len(parents))]\n", + " crossover_point = np.random.randint(1, len(parent1))\n", + " child = parent1[:crossover_point] + parent2[crossover_point:]\n", + " offspring.append(child)\n", + " return offspring\n" + ], + "metadata": { + "id": "xOLySvsHe-5E" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Perform Mutation" + ], + "metadata": { + "id": "W_35vtRthMog" + } + }, + { + "cell_type": "code", + "source": [ + "def mutation(offspring, mutation_rate):\n", + " for individual in offspring:\n", + " if np.random.rand() < mutation_rate:\n", + " mutation_point = np.random.randint(len(individual))\n", + " individual[mutation_point] = np.random.uniform(-1, 1) # Mutate with new random threshold\n", + " return offspring\n" + ], + "metadata": { + "id": "oQV3Xgt2fAyk" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Perform Genetic algorithm" + ], + "metadata": { + "id": "ch0Uzd7OhOyc" + } + }, + { + "cell_type": "code", + "source": [ + "def genetic_algorithm(data, num_generations, pop_size, num_parents, mutation_rate):\n", + " population = initialize_population(pop_size)\n", + "\n", + " for generation in range(num_generations):\n", + " fitness_scores = [fitness_function(individual, data) for individual in population]\n", + " parents = selection(population, fitness_scores, num_parents)\n", + " offspring_size = pop_size - len(parents)\n", + " offspring = crossover(parents, offspring_size)\n", + " offspring = mutation(offspring, mutation_rate)\n", + " population = parents + offspring\n", + "\n", + " best_fitness = np.max(fitness_scores)\n", + " print(f\"Generation {generation}: Best Fitness = {best_fitness}\")\n", + "\n", + " best_individual = population[np.argmax(fitness_scores)]\n", + " return best_individual\n", + "\n", + "# Run the genetic algorithm\n", + "num_generations = 50\n", + "pop_size = 100\n", + "num_parents = 20\n", + "mutation_rate = 0.01\n", + "\n", + "best_params = genetic_algorithm(data_sentiment, num_generations, pop_size, num_parents, mutation_rate)\n", + "print(f\"Best Trading Strategy: Buy Threshold = {best_params[0]}, Sell Threshold = {best_params[1]}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hBM-j180fCPe", + "outputId": "fec15d5d-e03e-477d-eff7-4c87591f2edc" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 0: Best Fitness = 129785.02657707607\n", + "Generation 1: Best Fitness = 176212273.2138567\n", + "Generation 2: Best Fitness = 3.900617142553841e+32\n", + "Generation 3: Best Fitness = 3.900617142553841e+32\n", + "Generation 4: Best Fitness = 3.900617142553841e+32\n", + "Generation 5: Best Fitness = 3.900617142553841e+32\n", + "Generation 6: Best Fitness = 3.900617142553841e+32\n", + "Generation 7: Best Fitness = 3.900617142553841e+32\n", + "Generation 8: Best Fitness = 3.900617142553841e+32\n", + "Generation 9: Best Fitness = 3.900617142553841e+32\n", + "Generation 10: Best Fitness = 3.900617142553841e+32\n", + "Generation 11: Best Fitness = 3.900617142553841e+32\n", + "Generation 12: Best Fitness = 3.900617142553841e+32\n", + "Generation 13: Best Fitness = 3.900617142553841e+32\n", + "Generation 14: Best Fitness = 3.900617142553841e+32\n", + "Generation 15: Best Fitness = 3.900617142553841e+32\n", + "Generation 16: Best Fitness = 3.900617142553841e+32\n", + "Generation 17: Best Fitness = 3.900617142553841e+32\n", + "Generation 18: Best Fitness = 3.900617142553841e+32\n", + "Generation 19: Best Fitness = 3.900617142553841e+32\n", + "Generation 20: Best Fitness = 3.900617142553841e+32\n", + "Generation 21: Best Fitness = 3.900617142553841e+32\n", + "Generation 22: Best Fitness = 3.900617142553841e+32\n", + "Generation 23: Best Fitness = 3.900617142553841e+32\n", + "Generation 24: Best Fitness = 3.900617142553841e+32\n", + "Generation 25: Best Fitness = 3.900617142553841e+32\n", + "Generation 26: Best Fitness = 3.900617142553841e+32\n", + "Generation 27: Best Fitness = 3.900617142553841e+32\n", + "Generation 28: Best Fitness = 3.900617142553841e+32\n", + "Generation 29: Best Fitness = 3.900617142553841e+32\n", + "Generation 30: Best Fitness = 3.900617142553841e+32\n", + "Generation 31: Best Fitness = 3.900617142553841e+32\n", + "Generation 32: Best Fitness = 3.900617142553841e+32\n", + "Generation 33: Best Fitness = 3.900617142553841e+32\n", + "Generation 34: Best Fitness = 3.900617142553841e+32\n", + "Generation 35: Best Fitness = 3.900617142553841e+32\n", + "Generation 36: Best Fitness = 3.900617142553841e+32\n", + "Generation 37: Best Fitness = 3.900617142553841e+32\n", + "Generation 38: Best Fitness = 3.900617142553841e+32\n", + "Generation 39: Best Fitness = 3.900617142553841e+32\n", + "Generation 40: Best Fitness = 3.900617142553841e+32\n", + "Generation 41: Best Fitness = 3.900617142553841e+32\n", + "Generation 42: Best Fitness = 3.900617142553841e+32\n", + "Generation 43: Best Fitness = 3.900617142553841e+32\n", + "Generation 44: Best Fitness = 3.900617142553841e+32\n", + "Generation 45: Best Fitness = 3.900617142553841e+32\n", + "Generation 46: Best Fitness = 3.900617142553841e+32\n", + "Generation 47: Best Fitness = 3.900617142553841e+32\n", + "Generation 48: Best Fitness = 3.900617142553841e+32\n", + "Generation 49: Best Fitness = 3.900617142553841e+32\n", + "Best Trading Strategy: Buy Threshold = -0.7733929905450716, Sell Threshold = -0.9756864570452264\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/optimized the trading strategy based on sentiment analysis/readme.md b/optimized the trading strategy based on sentiment analysis/readme.md new file mode 100644 index 0000000000..0575a77be3 --- /dev/null +++ b/optimized the trading strategy based on sentiment analysis/readme.md @@ -0,0 +1,54 @@ +# Sentiment Analysis Driven Trading Strategy Optimization + +This project aims to optimize a trading strategy based on stock prices and sentiment data using a genetic algorithm. The goal is to find the best buy and sell thresholds that maximize the portfolio value over a given period. + +## Dataset + +The dataset used in this project is `sentiment_trading_data.csv`, which contains the following columns: +- `Date`: The date of the observation. +- `Stock_Price`: The stock price on the given date. +- `Sentiment`: The sentiment score for the given date, ranging from -1 to 1. + +## Genetic Algorithm Implementation + +### Step 1: Load the Data + +First, load the `sentiment_trading_data.csv` dataset. + +```python +import pandas as pd +import numpy as np + +# Load data +data_sentiment = pd.read_csv('sentiment_trading_data.csv') +print(data_sentiment.head()) + +# Convert to NumPy array (excluding the 'Date' column) +data_sentiment = data_sentiment.drop(columns=['Date']).to_numpy() + +# Step 2: Define the Fitness Function +The fitness function evaluates how well a given trading strategy (individual) performs based on stock prices and sentiment. + +# step 3: Initialize the Population +Generate an initial population of random trading strategies. + +# Step 4: Selection +Select the best-performing individuals to be parents for the next generation. + +# Step 5: Crossover +Create offspring by combining parts of two parents. + +# Step 6: Mutation +Randomly mutate some individuals to maintain genetic diversity. + +#Step 7: Run the Genetic Algorithm +Execute the genetic algorithm with the defined functions. + +#Results +The genetic algorithm was run for 50 generations with a population size of 100. The best trading strategy found is: + +Buy Threshold: -0.7733929905450716 +Sell Threshold: -0.9756864570452264 + +#Contributor +Ashish Kumar Patel diff --git a/optimized the trading strategy based on sentiment analysis/sentiment_trading_data.csv b/optimized the trading strategy based on sentiment analysis/sentiment_trading_data.csv new file mode 100644 index 0000000000..e4c062beb4 --- /dev/null +++ b/optimized the trading strategy based on sentiment analysis/sentiment_trading_data.csv @@ -0,0 +1,1462 @@ +Date,Stock_Price,Sentiment +2020-01-01,87.45401188473625,0.30246595408298416 +2020-01-02,145.07143064099162,-0.7868139393840186 +2020-01-03,123.1993941811405,0.31569060920607206 +2020-01-04,109.86584841970367,0.9988274515413331 +2020-01-05,65.60186404424365,-0.9035759222788879 +2020-01-06,65.59945203362027,0.9543483684426346 +2020-01-07,55.80836121681995,-0.18618407855425878 +2020-01-08,136.6176145774935,0.7415069006745183 +2020-01-09,110.11150117432088,0.5647709681165469 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