From e194c16b31c79083caa90b59307d94aed0f751f3 Mon Sep 17 00:00:00 2001 From: "ap550083@gmail.com" Date: Fri, 19 Jul 2024 18:22:14 +0530 Subject: [PATCH] Blockchain_and_Cryptography_Investment_Strategy added --- ...Cryptocurrency_Investment_Strategies.ipynb | 661 +++++++++++ .../crypto_investment_data.csv | 1001 +++++++++++++++++ .../readme.md | 25 + 3 files changed, 1687 insertions(+) create mode 100644 Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/Blockchain_and_Cryptocurrency_Investment_Strategies.ipynb create mode 100644 Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/crypto_investment_data.csv create mode 100644 Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/readme.md diff --git a/Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/Blockchain_and_Cryptocurrency_Investment_Strategies.ipynb b/Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/Blockchain_and_Cryptocurrency_Investment_Strategies.ipynb new file mode 100644 index 00000000..f2f73095 --- /dev/null +++ b/Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/Blockchain_and_Cryptocurrency_Investment_Strategies.ipynb @@ -0,0 +1,661 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "\n", + "# Load the dataset\n", + "data = pd.read_csv('crypto_investment_data.csv')\n", + "data['Date'] = pd.to_datetime(data['Date'])\n", + "\n", + "# Display the first few rows of the dataset\n", + "data.head()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "ru8FIoG7S0P7", + "outputId": "db3bf1f2-1c4e-4ec8-d6e9-68a7f14245b6" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Date Open High Low Close \\\n", + "0 2021-01-01 33745.401188 34671.065833 32436.872770 34436.213165 \n", + "1 2021-01-02 39507.143064 42216.647801 38272.249069 40693.868653 \n", + "2 2021-01-03 37319.939418 41684.668597 32788.666516 36321.811013 \n", + "3 2021-01-04 35986.584842 39647.709274 34738.853843 36486.081240 \n", + "4 2021-01-05 31560.186404 35592.992144 30200.437774 31847.170337 \n", + "\n", + " Volume Market_Cap Sentiment_Score \n", + "0 6147.962905 2.117126e+08 -0.212729 \n", + "1 8248.890964 3.356793e+08 -0.053129 \n", + "2 7841.448368 2.848156e+08 0.709095 \n", + "3 2385.099142 8.702292e+07 -0.319991 \n", + "4 2343.245228 7.462573e+07 0.739299 " + ], + "text/html": [ + "\n", + "
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30265.68711941783,\n \"max\": 44637.43856160318,\n \"num_unique_values\": 1000,\n \"samples\": [\n 38481.08870033779,\n 39139.1069538196,\n 38764.48733453044\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3243.983836410401,\n \"min\": 25501.58775517054,\n \"max\": 39451.7427254499,\n \"num_unique_values\": 1000,\n \"samples\": [\n 33136.732919269605,\n 35443.58273071446,\n 32798.39411538921\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3106.5468786187566,\n \"min\": 28176.3282091152,\n \"max\": 41439.02279468347,\n \"num_unique_values\": 1000,\n \"samples\": [\n 33685.33741050053,\n 39170.70294253194,\n 36244.23695667149\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2581.2894898608392,\n \"min\": 1000.2764696084416,\n \"max\": 9979.744501091804,\n \"num_unique_values\": 1000,\n \"samples\": [\n 6194.053741491159,\n 4139.487167127671,\n 7468.463019359821\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Market_Cap\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 91892772.08648586,\n \"min\": 31397831.48857624,\n \"max\": 405041663.1998673,\n \"num_unique_values\": 1000,\n \"samples\": [\n 208648790.2209029,\n 162146622.15798107,\n 270688743.3758156\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Sentiment_Score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5783896131424128,\n \"min\": -0.9876323547182068,\n \"max\": 0.9987006009444004,\n \"num_unique_values\": 1000,\n \"samples\": [\n 0.1547068024393354,\n -0.4677972629253828,\n 0.7819477693814076\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Create features and labels\n", + "data['Future_Close'] = data['Close'].shift(-1) # Predicting the next day's closing price\n", + "data = data.dropna() # Drop the last row with NaN value\n", + "\n", + "features = data[['Open', 'High', 'Low', 'Close', 'Volume', 'Market_Cap', 'Sentiment_Score']]\n", + "labels = data['Future_Close']\n", + "\n", + "# Split the data into training and testing sets\n", + "train_size = int(0.8 * len(data))\n", + "X_train, X_test = features[:train_size], features[train_size:]\n", + "y_train, y_test = labels[:train_size], labels[train_size:]\n", + "\n", + "print(f'Training samples: {len(X_train)}, Testing samples: {len(X_test)}')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1qwEyU96S2N5", + "outputId": "bdeb3e9a-e70d-4c0a-95ed-c4a7442986b7" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Training samples: 799, Testing samples: 200\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Define Genetic Algorithm\n" + ], + "metadata": { + "id": "odujTdOSS6tu" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "def evaluate_model(params, X_train, y_train, X_test, y_test):\n", + " # Create and train the model\n", + " model = LinearRegression()\n", + " model.fit(X_train, y_train)\n", + "\n", + " # Make predictions\n", + " predictions = model.predict(X_test)\n", + "\n", + " # Calculate mean squared error\n", + " mse = mean_squared_error(y_test, predictions)\n", + "\n", + " return -mse # We want to minimize MSE, so we return its negative value\n" + ], + "metadata": { + "id": "TD0bhp8uS5Ra" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Population initialization" + ], + "metadata": { + "id": "nYYJ4RioTAUZ" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "XnJHRBqcUgPY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "def initialize_population(pop_size, param_bounds):\n", + " return [np.random.uniform(low, high, size=(len(param_bounds),)) for low, high in param_bounds]\n" + ], + "metadata": { + "id": "AYAt1gw5S_ti" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Selection function" + ], + "metadata": { + "id": "AhM_SItDTFIo" + } + }, + { + "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": "0oOEqSPFTHGQ" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "bzsxFyavTKab" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Crossover" + ], + "metadata": { + "id": "4uZiD_SyTI7I" + } + }, + { + "cell_type": "code", + "source": [ + "def crossover(parents, offspring_size):\n", + " offspring = []\n", + " for _ in range(offspring_size):\n", + " parent1, parent2 = np.random.choice(len(parents), size=2, replace=False)\n", + " crossover_point = np.random.randint(1, len(parents[0]) - 1)\n", + " child = np.concatenate((parents[parent1][:crossover_point], parents[parent2][crossover_point:]))\n", + " offspring.append(child)\n", + " return offspring\n" + ], + "metadata": { + "id": "S2amvxhSTL_Q" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Mutation" + ], + "metadata": { + "id": "HzBIxb3cTOAr" + } + }, + { + "cell_type": "code", + "source": [ + "def mutation(offspring, mutation_rate=0.01):\n", + " for individual in offspring:\n", + " if np.random.rand() < mutation_rate:\n", + " mutation_idx = np.random.randint(len(individual))\n", + " individual[mutation_idx] += np.random.uniform(-0.1, 0.1)\n", + " return offspring\n" + ], + "metadata": { + "id": "ktjOme19TPcF" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Imeplement Genetics ALgorithm" + ], + "metadata": { + "id": "W4uozlQnTRpC" + } + }, + { + "cell_type": "code", + "source": [ + "def genetic_algorithm(X_train, y_train, X_test, y_test, num_generations, pop_size, num_parents, mutation_rate):\n", + " param_bounds = [(0, 1) for _ in range(X_train.shape[1])] # Example bounds for feature weights\n", + " population = initialize_population(pop_size, param_bounds)\n", + "\n", + " for generation in range(num_generations):\n", + " fitness_scores = [evaluate_model(individual, X_train, y_train, X_test, y_test) 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_params = population[np.argmax(fitness_scores)]\n", + " return best_params\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(X_train, y_train, X_test, y_test, num_generations, pop_size, num_parents, mutation_rate)\n", + "print(f\"Best Parameters: {best_params}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NH4a09mZTUrv", + "outputId": "26f972ee-4724-4781-c3ac-4776b5a2f77d" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 0: Best Fitness = -9990491.192329021\n", + "Generation 1: Best Fitness = -9990491.192329021\n", + "Generation 2: Best Fitness = -9990491.192329021\n", + "Generation 3: Best Fitness = -9990491.192329021\n", + "Generation 4: Best Fitness = -9990491.192329021\n", + "Generation 5: Best Fitness = -9990491.192329021\n", + "Generation 6: Best Fitness = -9990491.192329021\n", + "Generation 7: Best Fitness = -9990491.192329021\n", + "Generation 8: Best Fitness = -9990491.192329021\n", + "Generation 9: Best Fitness = -9990491.192329021\n", + "Generation 10: Best Fitness = -9990491.192329021\n", + "Generation 11: Best Fitness = -9990491.192329021\n", + "Generation 12: Best Fitness = -9990491.192329021\n", + "Generation 13: Best Fitness = -9990491.192329021\n", + "Generation 14: Best Fitness = -9990491.192329021\n", + "Generation 15: Best Fitness = -9990491.192329021\n", + "Generation 16: Best Fitness = -9990491.192329021\n", + "Generation 17: Best Fitness = -9990491.192329021\n", + "Generation 18: Best Fitness = -9990491.192329021\n", + "Generation 19: Best Fitness = -9990491.192329021\n", + "Generation 20: Best Fitness = -9990491.192329021\n", + "Generation 21: Best Fitness = -9990491.192329021\n", + "Generation 22: Best Fitness = -9990491.192329021\n", + "Generation 23: Best Fitness = -9990491.192329021\n", + "Generation 24: Best Fitness = -9990491.192329021\n", + "Generation 25: Best Fitness = -9990491.192329021\n", + "Generation 26: Best Fitness = -9990491.192329021\n", + "Generation 27: Best Fitness = -9990491.192329021\n", + "Generation 28: Best Fitness = -9990491.192329021\n", + "Generation 29: Best Fitness = -9990491.192329021\n", + "Generation 30: Best Fitness = -9990491.192329021\n", + "Generation 31: Best Fitness = -9990491.192329021\n", + "Generation 32: Best Fitness = -9990491.192329021\n", + "Generation 33: Best Fitness = -9990491.192329021\n", + "Generation 34: Best Fitness = -9990491.192329021\n", + "Generation 35: Best Fitness = -9990491.192329021\n", + "Generation 36: Best Fitness = -9990491.192329021\n", + "Generation 37: Best Fitness = -9990491.192329021\n", + "Generation 38: Best Fitness = -9990491.192329021\n", + "Generation 39: Best Fitness = -9990491.192329021\n", + "Generation 40: Best Fitness = -9990491.192329021\n", + "Generation 41: Best Fitness = -9990491.192329021\n", + "Generation 42: Best Fitness = -9990491.192329021\n", + "Generation 43: Best Fitness = -9990491.192329021\n", + "Generation 44: Best Fitness = -9990491.192329021\n", + "Generation 45: Best Fitness = -9990491.192329021\n", + "Generation 46: Best Fitness = -9990491.192329021\n", + "Generation 47: Best Fitness = -9990491.192329021\n", + "Generation 48: Best Fitness = -9990491.192329021\n", + "Generation 49: Best Fitness = -9990491.192329021\n", + "Best Parameters: [0.07748363 0.87439803 0.26974247 0.28467474 0.99946068 0.8022737\n", + " 0.78672301]\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/crypto_investment_data.csv b/Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/crypto_investment_data.csv new file mode 100644 index 00000000..e196ea86 --- /dev/null +++ b/Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/crypto_investment_data.csv @@ -0,0 +1,1001 @@ +Date,Open,High,Low,Close,Volume,Market_Cap,Sentiment_Score +2021-01-01,33745.401188473625,34671.065832666725,32436.872769794096,34436.213165309215,6147.962904552335,211712561.1135778,-0.21272895944511583 +2021-01-02,39507.14306409916,42216.64780099095,38272.24906873916,40693.86865286465,8248.890963699876,335679285.40860486,-0.05312868124995718 +2021-01-03,37319.93941811405,41684.66859749609,32788.66651550887,36321.811013284045,7841.448368111432,284815605.696968,0.7090947864043606 +2021-01-04,35986.58484197037,39647.709274018176,34738.85384272284,36486.081240398365,2385.099142243963,87022921.07031769,-0.3199912278802157 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Domain/Blockchain_and_Cryptography_Investment_Strategy/readme.md new file mode 100644 index 00000000..049f7f36 --- /dev/null +++ b/Finacial Domain/Blockchain_and_Cryptography_Investment_Strategy/readme.md @@ -0,0 +1,25 @@ +##Objective +Develop an investment strategy for blockchain and cryptographic assets using Genetic Algorithms (GA). + +##Description +This project utilizes Genetic Algorithms to optimize investment strategies for blockchain and cryptographic assets by evolving investment parameters. + +Steps +1. Collect and preprocess blockchain and cryptographic asset data. +2. Define investment parameters for optimization. +3. Initialize a population of investment strategies. +4. Evaluate the fitness of each strategy based on historical performance metrics. +5. Select the best-performing strategies. +6. Apply crossover and mutation to generate new strategies. +7. Repeat the process for a defined number of generations. +## Requirements +Python +NumPy +Pandas +matplotlib + +##Usage +1. Collect and preprocess blockchain and cryptographic asset data. +2. Define the investment parameters and GA settings. +3. Run the genetic algorithm to optimize the investment strategy. +4. Evaluate the performance of the optimized strategy on a test dataset. \ No newline at end of file