From 276be89e7f0b53e1272360d13a3b2a6e06fe521b Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 7 Oct 2024 16:25:03 +0530 Subject: [PATCH 1/9] Create README.md --- Financial Environment Segmentation/README.md | 65 ++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 Financial Environment Segmentation/README.md diff --git a/Financial Environment Segmentation/README.md b/Financial Environment Segmentation/README.md new file mode 100644 index 0000000..c0c5c07 --- /dev/null +++ b/Financial Environment Segmentation/README.md @@ -0,0 +1,65 @@ +# 📈 Financial Environment Segmentation + +## 📚 Table of Contents +1. [📖 Overview](#-overview) +2. [🚀 Problem Statement](#-problem-statement) +3. [💡 Proposed Solution](#-proposed-solution) +4. [📦 Installation & Usage](#-installation--usage) +5. [⚙️ Alternatives Considered](#-alternatives-considered) +6. [📊 Results](#-results) +7. [🔍 Conclusion](#-conclusion) +8. [🤝 Acknowledgments](#-acknowledgments) +9. [📧 Contact](#-contact) + +## 📖 Overview +The **Financial Environment Segmentation** project focuses on identifying and classifying different market regimes using historical stock price data. This approach aids in understanding market dynamics, helping traders and investors make informed decisions. + +## 🚀 Problem Statement +Recognizing distinct market regimes (bull, bear, neutral) is vital for effective investment strategies. Variability in market conditions necessitates a robust framework to identify and respond to these changes promptly. + +## 💡 Proposed Solution +This project employs clustering techniques to segment financial environments, providing insights into market behavior based on historical data. + +| Key Components | Description | +|-----------------------|------------------------------------------------------------------| +| Data Collection | Historical stock price data gathered from Yahoo Finance. | +| Data Preprocessing | Calculation of daily returns, moving averages, and volatility. | +| Feature Engineering | Normalization and selection of relevant features for analysis. | +| Clustering | K-means clustering to classify market regimes. | +| Analysis & Validation | Evaluation of regimes and their characteristics through backtesting. | + +## 📦 Installation & Usage +To get started, ensure you have the necessary libraries installed: + +| Library | Purpose | +|----------------|-------------------------------------------| +| pandas | Data manipulation and analysis | +| numpy | Numerical computing | +| matplotlib | Data visualization | +| scikit-learn | Machine learning algorithms | +| yfinance | Financial data retrieval | + +## ⚙️ Alternatives Considered +Several alternative approaches were evaluated for market regime detection: + +| Alternative Approach | Description | +|---------------------------------|----------------------------------------------------------------------| +| Traditional Machine Learning | Techniques like SVM and k-NN; effective for smaller datasets. | +| Advanced Clustering Algorithms | Exploring DBSCAN and Hierarchical Clustering for better segmentation.| + +## 📊 Results +The model aims to achieve accurate segmentation of market regimes, facilitating better investment strategies and risk management. + +## 🔍 Conclusion +The project demonstrates the importance of identifying financial market regimes, showcasing how clustering techniques can provide valuable insights for traders and investors. + +## 🤝 Acknowledgments +- **Dataset**: Historical stock price data from Yahoo Finance. +- **Frameworks**: Built using Python libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, and yfinance. + +## 📧 Contact +For any inquiries or contributions, feel free to reach out: + +| Name | Email | GitHub | +|---------------------|--------------------------------|----------------------| +| Alolika Bhowmik | alolikabhowmik72@gmail.com | [alo7lika](https://github.com/alo7lika) | From 6a6d3c6b4ac443d74134764ae6332f4ea34da7a3 Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 7 Oct 2024 16:29:16 +0530 Subject: [PATCH 2/9] Add files via upload --- .../Financial Environment Segmentation.ipynb | 463 ++++++++++++++++++ ...nancial Insights - Market Segmentation.png | Bin 0 -> 1576592 bytes 2 files changed, 463 insertions(+) create mode 100644 Financial Environment Segmentation/Financial Environment Segmentation.ipynb create mode 100644 Financial Environment Segmentation/Financial Insights - Market Segmentation.png diff --git a/Financial Environment Segmentation/Financial Environment Segmentation.ipynb b/Financial Environment Segmentation/Financial Environment Segmentation.ipynb new file mode 100644 index 0000000..cc54a11 --- /dev/null +++ b/Financial Environment Segmentation/Financial Environment Segmentation.ipynb @@ -0,0 +1,463 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "141dc98c-e1f3-40fd-98b5-83299444940d", + "metadata": {}, + "source": [ + "# Financial Environment Segmentation\n", + "\n", + "## Overview\n", + "The Financial Environment Segmentation project aims to identify different market conditions (regimes) in historical stock price data using clustering techniques. By classifying these regimes, the project provides insights into periods of market behavior, such as bull, bear, or neutral phases, helping investors or financial analysts understand market trends and develop effective strategies.\n" + ] + }, + { + "cell_type": "markdown", + "id": "49439176-3972-44de-9788-4a389d912e40", + "metadata": {}, + "source": [ + "# Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cbbda166-cb81-431e-acc7-f8866f9aae7e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in c:\\users\\asus\\anaconda3\\lib\\site-packages (2.1.4)\n", + "Requirement already satisfied: numpy in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.26.4)\n", + "Requirement already satisfied: matplotlib in c:\\users\\asus\\anaconda3\\lib\\site-packages (3.8.0)\n", + "Requirement already satisfied: scikit-learn in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.2.2)\n", + 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"Requirement already satisfied: six>=1.9 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n", + "Requirement already satisfied: webencodings in c:\\users\\asus\\anaconda3\\lib\\site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2.0.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (3.4)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2024.2.2)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install pandas numpy matplotlib scikit-learn yfinance\n" + ] + }, + { + "cell_type": "markdown", + "id": "76abe973-63cc-4a65-ab3e-b3a1d2e32fe8", + "metadata": {}, + "source": [ + "## Steps Involved\n", + "### 1: **Import Libraries**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f07cd4fc-d510-4ce2-ad19-78c369a7aad4", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import yfinance as yf\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d2a2991-9ffe-4cdb-88e2-fb74eb3e66c9", + "metadata": {}, + "source": [ + "### 2. **Data Collection**\n", + "Historical stock price data is collected using Yahoo Finance (via the `yfinance` library). In this example, the data of the S&P 500 index is fetched to observe trends and detect market regimes over time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cbf3670a-ca22-41a2-91c0-c9f4a2f92869", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[*********************100%***********************] 1 of 1 completed\n" + ] + } + ], + "source": [ + "# Download historical stock data for a selected stock (e.g., S&P 500: ^GSPC)\n", + "stock_data = yf.download('^GSPC', start='2010-01-01', end='2024-01-01')" + ] + }, + { + "cell_type": "markdown", + "id": "3b2ea549-dab7-4d37-b9f4-6ff74e7746e3", + "metadata": {}, + "source": [ + "### 3. **Data Preprocessing**\n", + "- **Daily Returns**: Calculate the daily percentage change in stock prices to observe the day-to-day performance.\n", + "- **Feature Engineering**: Compute additional features such as moving averages (50-day and 200-day) and volatility (standard deviation of returns) to capture market behavior over time.\n", + "- **Normalization**: Standardize these features using the `StandardScaler` for uniform scaling before applying clustering algorithms.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8b09ddac-6d12-4adc-857d-86a17e6a0d6d", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate daily returns\n", + "stock_data['Return'] = stock_data['Adj Close'].pct_change()\n", + "\n", + "# Calculate moving averages and volatility as features\n", + "stock_data['MA_50'] = stock_data['Adj Close'].rolling(window=50).mean()\n", + "stock_data['MA_200'] = stock_data['Adj Close'].rolling(window=200).mean()\n", + "stock_data['Volatility'] = stock_data['Return'].rolling(window=50).std()\n", + "\n", + "# Drop NaN values created by rolling calculations\n", + "stock_data.dropna(inplace=True)\n", + "\n", + "# Selecting relevant features for clustering\n", + "features = stock_data[['Return', 'MA_50', 'MA_200', 'Volatility']]\n", + "\n", + "# Normalize the features\n", + "scaler = StandardScaler()\n", + "features_scaled = scaler.fit_transform(features)" + ] + }, + { + "cell_type": "markdown", + "id": "a731b0f3-4cac-4c85-afcd-a1e952d95f21", + "metadata": {}, + "source": [ + "### 4. **Clustering with K-Means**\n", + "The K-means clustering algorithm is applied to classify the market data into distinct regimes:\n", + "- **Optimal Number of Clusters**: The Elbow Method is used to determine the ideal number of clusters (`k`). By plotting the Sum of Squared Errors (SSE) against the number of clusters, the elbow point helps identify the best value of `k`.\n", + "- **Clustering**: The market data is then clustered based on the selected features, and each data point is assigned a regime label.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1ea3aeb3-5d68-40aa-a29f-eef45197f9c7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. 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Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Determine the optimal number of clusters using the elbow method\n", + "sse = []\n", + "for k in range(2, 10):\n", + " kmeans = KMeans(n_clusters=k, random_state=42)\n", + " kmeans.fit(features_scaled)\n", + " sse.append(kmeans.inertia_)\n", + "\n", + "# Plot the elbow curve\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(range(2, 10), sse, marker='o')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('SSE (Sum of Squared Errors)')\n", + "plt.title('Elbow Method for Optimal K')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3f25d290-7b74-4028-a51e-41fa9e14a0fb", + "metadata": {}, + "source": [ + "### 5 **Applying K-Means and Analyzing Clusters**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2bd5ec18-a000-4401-81f2-5414c8f604db", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ASUS\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Choosing the optimal K (e.g., based on the elbow method)\n", + "optimal_k = 3\n", + "kmeans = KMeans(n_clusters=optimal_k, random_state=42)\n", + "stock_data['Regime'] = kmeans.fit_predict(features_scaled)\n", + "\n", + "# Visualizing the regimes\n", + "plt.figure(figsize=(14, 7))\n", + "plt.plot(stock_data['Adj Close'], label='S&P 500 Price')\n", + "for i in range(optimal_k):\n", + " plt.scatter(stock_data[stock_data['Regime'] == i].index, \n", + " stock_data[stock_data['Regime'] == i]['Adj Close'], label=f'Regime {i}', alpha=0.6)\n", + "\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Adjusted Close Price')\n", + "plt.title('Market Regimes Detected by Clustering')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cf4d659c-1ccc-4fed-bcea-acdcd8bb219e", + "metadata": {}, + "source": [ + "### 6. **Analyzing and Labeling Clusters**\n", + "- **Average Metrics**: For each cluster, the average returns and volatility are calculated to understand the characteristics of each regime.\n", + "- **Manual Labeling**: Based on the analysis, regimes are labeled manually (e.g., high return and low volatility might indicate a Bull Market, while high volatility and low returns could indicate a Bear Market).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5cbdacce-1a40-4da8-bb69-d8154f40292a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Regime 0: Avg Return = 0.0002, Avg Volatility = 0.0109\n", + "Regime 1: Avg Return = 0.0005, Avg Volatility = 0.0083\n", + "Regime 2: Avg Return = 0.0046, Avg Volatility = 0.0389\n" + ] + } + ], + "source": [ + "# Calculate average returns and volatility for each regime\n", + "for i in range(optimal_k):\n", + " regime_data = stock_data[stock_data['Regime'] == i]\n", + " avg_return = regime_data['Return'].mean()\n", + " avg_volatility = regime_data['Volatility'].mean()\n", + " print(f'Regime {i}: Avg Return = {avg_return:.4f}, Avg Volatility = {avg_volatility:.4f}')\n", + "\n", + "# Manually label the regimes based on characteristics (e.g., high return, low volatility = Bull Market)\n", + "regime_labels = {0: 'Bull', 1: 'Bear', 2: 'Neutral'} # Example labels; adjust based on analysis\n", + "stock_data['Regime_Label'] = stock_data['Regime'].map(regime_labels)" + ] + }, + { + "cell_type": "markdown", + "id": "59dd5e82-308e-42f8-a30e-5d043691dc72", + "metadata": {}, + "source": [ + "### 7. **Model Validation**\n", + "- **Cumulative Returns**: The cumulative returns of each regime are calculated and plotted to validate the accuracy and behavior of the detected regimes over time.\n", + "- **Backtesting**: By examining the performance of each regime, the reliability of the clustering model can be assessed.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "aae5a8bc-fdb1-438b-8035-ad4b13b98b51", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate cumulative returns for each regime\n", + "stock_data['Cumulative_Return'] = (1 + stock_data['Return']).cumprod()\n", + "\n", + "# Plot cumulative returns per regime\n", + "plt.figure(figsize=(14, 7))\n", + "for label in stock_data['Regime_Label'].unique():\n", + " regime_returns = stock_data[stock_data['Regime_Label'] == label]['Cumulative_Return']\n", + " plt.plot(regime_returns, label=f'Regime: {label}')\n", + "\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Cumulative Return')\n", + "plt.title('Cumulative Returns by Market Regime')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "46f80bdf-f871-4b34-b8e0-877418d0578e", + "metadata": {}, + "source": [ + "### 8. **Predicting Market Regimes**\n", + "To extend the project, a supervised learning model (e.g., Logistic Regression) can be implemented to predict future market regimes based on current market features:\n", + "- **Model Training**: The Logistic Regression model is trained using historical features and the identified regimes.\n", + "- **Evaluation**: Model performance is evaluated using metrics like precision, recall, and F1-score, helping to assess its effectiveness in predicting future market behavior." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e3f54c17-5411-43d2-bb56-4f6edd091eb1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 276\n", + " 1 1.00 1.00 1.00 701\n", + " 2 1.00 0.95 0.97 20\n", + "\n", + " accuracy 1.00 997\n", + " macro avg 1.00 0.98 0.99 997\n", + "weighted avg 1.00 1.00 1.00 997\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import classification_report\n", + "\n", + "# Prepare the dataset for supervised learning\n", + "X = features_scaled\n", + "y = stock_data['Regime']\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "\n", + "# Train the Logistic Regression model\n", + "model = LogisticRegression(random_state=42)\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Evaluate the model\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "id": "1abd5342-d02a-4aff-8809-34adf5d3bb05", + "metadata": {}, + "source": [ + "## Conclusion\n", + "This project implements a basic market regime detection model using clustering techniques and evaluates it with historical stock data. The approach can be expanded by using different clustering algorithms, incorporating additional market features, or even integrating reinforcement learning for predictive trading strategies based on identified regimes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b04c95f2-526a-4e0c-a2d6-6f82df4e598c", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Financial Environment Segmentation/Financial Insights - Market Segmentation.png b/Financial Environment Segmentation/Financial Insights - Market Segmentation.png new file 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+ +

+ + ## 📚 Table of Contents 1. [📖 Overview](#-overview) 2. [🚀 Problem Statement](#-problem-statement) From 4f21b595f9c5da198b31d08fde134296d0b0887d Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 14 Oct 2024 01:42:11 +0530 Subject: [PATCH 4/9] Update README.md --- Financial Environment Segmentation/README.md | 26 ++++++++++++++++---- 1 file changed, 21 insertions(+), 5 deletions(-) diff --git a/Financial Environment Segmentation/README.md b/Financial Environment Segmentation/README.md index 4df6d2a..da17711 100644 --- a/Financial Environment Segmentation/README.md +++ b/Financial Environment Segmentation/README.md @@ -1,10 +1,5 @@ # 📈 Financial Environment Segmentation -

- -

- - ## 📚 Table of Contents 1. [📖 Overview](#-overview) 2. [🚀 Problem Statement](#-problem-statement) @@ -44,6 +39,27 @@ To get started, ensure you have the necessary libraries installed: | scikit-learn | Machine learning algorithms | | yfinance | Financial data retrieval | +### Clone the Repository + +1. Clone this repository to your local machine using the following command: + ```bash + git clone https://github.com/alo7lika/Stock-Price-Prediction.git + ``` +2. Navigate to the project directory + ```bash + cd Stock-Price-Prediction/Financial\ Environment\ Segmentation + ``` +3. It is recommended to create a virtual environment to manage dependencies: + ``` + python -m venv env + source env/bin/activate # On Windows use `env\Scripts\activate` + ``` +4. Install the necessary libraries using pip: + ``` + pip install -r requirements.txt + ``` + + ## ⚙️ Alternatives Considered Several alternative approaches were evaluated for market regime detection: From e7e9e34c63dc7b03d4abcd2ee06fc96d1bdddc89 Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 14 Oct 2024 20:43:45 +0530 Subject: [PATCH 5/9] Create README.md --- Multi-Asset Portfolio Modeling/README.md | 63 ++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 Multi-Asset Portfolio Modeling/README.md diff --git a/Multi-Asset Portfolio Modeling/README.md b/Multi-Asset Portfolio Modeling/README.md new file mode 100644 index 0000000..bb73cfe --- /dev/null +++ b/Multi-Asset Portfolio Modeling/README.md @@ -0,0 +1,63 @@ +# 📈 Multi-Asset Portfolio Modeling + +Welcome to the **Multi-Asset Portfolio Modeling** project! This tool leverages advanced financial models to help optimize portfolio allocation across multiple assets using real-time data, sentiment analysis, and scenario analysis. This README provides an overview of the project, its functionality, and how to get started. + +## 🌟 Features + +This project enhances traditional portfolio modeling with several powerful features: + +| Feature | Description | +|----------------------------------|-----------------------------------------------------------------------------| +| 📊 **Real-Time Data Integration** | Automatically adjust portfolios based on real-time market data via APIs | +| 📰 **Sentiment Analysis** | Incorporates news and social media sentiment to provide better decision-making| +| 🌍 **Scenario Analysis** | Simulate different economic scenarios (e.g., recessions, booms) | +| 🖥️ **Interactive Dashboard** | User-friendly interface to visualize portfolio performance and results | + +## 🚀 Getting Started + +To run the **Multi-Asset Portfolio Modeling** system locally, follow these steps: + +### Prerequisites +Ensure you have the following installed: +- Python 3.x +- Pip package manager +- Virtual environment (optional but recommended) + +### Installation + +1. **Clone the repository:** + ```bash + git clone https://github.com/your-username/multi-asset-portfolio-modeling.git + cd multi-asset-portfolio-modeling + ``` +2. **Install the required dependencies**: + ```bash + pip install -r requirements.txt + ``` +3.**Set up your API keys for real-time data integration**: +Sign up for an API key from Alpha Vantage or Yahoo Finance. +Add your API keys to the environment file or directly to the Python script. + +4**Run the dashboard**: +```bash +python app.py +``` +### 🎯 To-Do List +- Incorporate more alternative data sources (e.g., geopolitics, interest rates). +- Improve sentiment analysis by integrating additional sentiment models. +- Expand stress testing for various asset classes (bonds, crypto). + +### 🤝 Contributing +Contributions are welcome! To contribute: +1. Fork the repository. +2. Create a new branch for your feature or bugfix. +3. Submit a pull request for review. + +Please ensure that your code follows the existing style and includes appropriate tests. + +### 📜 License +This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. + +### 💬 Questions? +If you have any questions or feedback, feel free to reach out via [GitHub Issues](https://github.com/alo7lika/multi-asset-portfolio-modeling/issues) or email me at [alolikabhowmik72@g +mail.com] From 3eaa07f91abcca7c406ec5c79721a0da513b6f2e Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 14 Oct 2024 20:46:05 +0530 Subject: [PATCH 6/9] Update README.md --- Multi-Asset Portfolio Modeling/README.md | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/Multi-Asset Portfolio Modeling/README.md b/Multi-Asset Portfolio Modeling/README.md index bb73cfe..066ceab 100644 --- a/Multi-Asset Portfolio Modeling/README.md +++ b/Multi-Asset Portfolio Modeling/README.md @@ -2,6 +2,19 @@ Welcome to the **Multi-Asset Portfolio Modeling** project! This tool leverages advanced financial models to help optimize portfolio allocation across multiple assets using real-time data, sentiment analysis, and scenario analysis. This README provides an overview of the project, its functionality, and how to get started. +## 📚 Table of Contents + +1. [🌟 Features](#-features) +2. [🚀 Getting Started](#-getting-started) + - [Prerequisites](#prerequisites) + - [Installation](#installation) + - [Set up API Keys](#set-up-your-api-keys-for-real-time-data-integration) +3. [🛠️ Usage](#-usage) +4. [🎯 To-Do List](#-to-do-list) +5. [🤝 Contributing](#-contributing) +6. [📜 License](#-license) +7. [💬 Questions?](#-questions) + ## 🌟 Features This project enhances traditional portfolio modeling with several powerful features: @@ -34,11 +47,13 @@ Ensure you have the following installed: ```bash pip install -r requirements.txt ``` -3.**Set up your API keys for real-time data integration**: +3. **Set up your API keys for real-time data integration**: + Sign up for an API key from Alpha Vantage or Yahoo Finance. + Add your API keys to the environment file or directly to the Python script. -4**Run the dashboard**: +4. **Run the dashboard**: ```bash python app.py ``` From fbdaa669b5fa260075e37255482a479a0e4131f5 Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 14 Oct 2024 20:48:39 +0530 Subject: [PATCH 7/9] Update README.md --- Multi-Asset Portfolio Modeling/README.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/Multi-Asset Portfolio Modeling/README.md b/Multi-Asset Portfolio Modeling/README.md index 066ceab..05e12eb 100644 --- a/Multi-Asset Portfolio Modeling/README.md +++ b/Multi-Asset Portfolio Modeling/README.md @@ -57,12 +57,20 @@ Add your API keys to the environment file or directly to the Python script. ```bash python app.py ``` -### 🎯 To-Do List +## 🛠️ Usage +Once the dashboard is running, you can: + +- **Upload your dataset**: Import your asset data in `.csv` format to get started. +- **Set portfolio parameters**: Adjust risk preferences, asset allocations, and other settings via the dashboard. +- **Visualize results**: See optimized portfolio weights, performance metrics, and scenario simulations. +- **Analyze scenarios**: Use the scenario analysis tool to model different market conditions. + +## 🎯 To-Do List - Incorporate more alternative data sources (e.g., geopolitics, interest rates). - Improve sentiment analysis by integrating additional sentiment models. - Expand stress testing for various asset classes (bonds, crypto). -### 🤝 Contributing +## 🤝 Contributing Contributions are welcome! To contribute: 1. Fork the repository. 2. Create a new branch for your feature or bugfix. @@ -70,9 +78,9 @@ Contributions are welcome! To contribute: Please ensure that your code follows the existing style and includes appropriate tests. -### 📜 License +## 📜 License This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. -### 💬 Questions? +## 💬 Questions? If you have any questions or feedback, feel free to reach out via [GitHub Issues](https://github.com/alo7lika/multi-asset-portfolio-modeling/issues) or email me at [alolikabhowmik72@g mail.com] From a68cec35e57027e17d8b658d4de5b169f5881c3e Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 14 Oct 2024 20:50:28 +0530 Subject: [PATCH 8/9] Update README.md --- Multi-Asset Portfolio Modeling/README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Multi-Asset Portfolio Modeling/README.md b/Multi-Asset Portfolio Modeling/README.md index 05e12eb..a2a99aa 100644 --- a/Multi-Asset Portfolio Modeling/README.md +++ b/Multi-Asset Portfolio Modeling/README.md @@ -82,5 +82,4 @@ Please ensure that your code follows the existing style and includes appropriate This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. ## 💬 Questions? -If you have any questions or feedback, feel free to reach out via [GitHub Issues](https://github.com/alo7lika/multi-asset-portfolio-modeling/issues) or email me at [alolikabhowmik72@g -mail.com] +If you have any questions or feedback, feel free to reach out via [GitHub Issues](https://github.com/alo7lika/multi-asset-portfolio-modeling/issues) or email me at [alolikabhowmik72@gmail.com] From cccdaf204a19c16b67a937eec8b66677c6db1560 Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 14 Oct 2024 20:50:58 +0530 Subject: [PATCH 9/9] Add files via upload --- .../Multi-Asset Portfolio Modeling.ipynb | 264 ++++++++++++++++++ 1 file changed, 264 insertions(+) create mode 100644 Multi-Asset Portfolio Modeling/Multi-Asset Portfolio Modeling.ipynb diff --git a/Multi-Asset Portfolio Modeling/Multi-Asset Portfolio Modeling.ipynb b/Multi-Asset Portfolio Modeling/Multi-Asset Portfolio Modeling.ipynb new file mode 100644 index 0000000..8c9ab99 --- /dev/null +++ b/Multi-Asset Portfolio Modeling/Multi-Asset Portfolio Modeling.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "id": "cca0db65-8eaf-41f5-a36c-d8af5b19c884", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy in c:\\users\\asus\\anaconda3\\lib\\site-packages (1.26.4)Note: you may need to restart the kernel to use updated packages.\n", + "\n", + "Requirement already satisfied: pandas in c:\\users\\asus\\anaconda3\\lib\\site-packages (2.1.4)\n", + "Requirement already satisfied: matplotlib in c:\\users\\asus\\anaconda3\\lib\\site-packages (3.8.0)\n", + "Requirement already satisfied: yfinance in c:\\users\\asus\\anaconda3\\lib\\site-packages (0.2.44)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n", 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urllib3<3,>=1.21.1 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\asus\\anaconda3\\lib\\site-packages (from requests>=2.31->yfinance) (2024.2.2)\n" + ] + } + ], + "source": [ + "pip install numpy pandas matplotlib yfinance" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "27f0a1fc-c8fb-47e9-8456-875a2e229e9a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[*********************100%***********************] 3 of 3 completed" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ticker AAPL GOOGL MSFT\n", + "Date \n", + "2023-01-03 00:00:00+00:00 123.904625 88.899872 236.183517\n", + "2023-01-04 00:00:00+00:00 125.182610 87.862434 225.852112\n", + "2023-01-05 00:00:00+00:00 123.855095 85.987083 219.158356\n", + "2023-01-06 00:00:00+00:00 128.412216 87.124260 221.741196\n", + "2023-01-09 00:00:00+00:00 128.937286 87.802582 223.900177\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "import yfinance as yf\n", + "import pandas as pd\n", + "\n", + "# Define the assets\n", + "assets = ['AAPL', 'MSFT', 'GOOGL']\n", + "\n", + "# Fetch historical price data for the last 1 year\n", + "data = yf.download(assets, start='2023-01-01', end='2024-01-01')['Adj Close']\n", + "print(data.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8d06c2ba-c0b4-42a8-a2d3-21d05fdfd6f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ticker AAPL GOOGL MSFT\n", + "Date \n", + "2023-01-04 00:00:00+00:00 0.010314 -0.011670 -0.043743\n", + "2023-01-05 00:00:00+00:00 -0.010605 -0.021344 -0.029638\n", + "2023-01-06 00:00:00+00:00 0.036794 0.013225 0.011785\n", + "2023-01-09 00:00:00+00:00 0.004089 0.007786 0.009736\n", + "2023-01-10 00:00:00+00:00 0.004456 0.004544 0.007617\n" + ] + } + ], + "source": [ + "# Calculate daily returns\n", + "returns = data.pct_change().dropna()\n", + "print(returns.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ca2e4ca6-b750-44e8-a58a-40ec9d7e6b21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected Returns:\n", + " Ticker\n", + "AAPL 0.462402\n", + "GOOGL 0.501407\n", + "MSFT 0.496859\n", + "dtype: float64\n", + "\n", + "Covariance Matrix:\n", + " Ticker AAPL GOOGL MSFT\n", + "Ticker \n", + "AAPL 0.039818 0.032044 0.027468\n", + "GOOGL 0.032044 0.092405 0.038936\n", + "MSFT 0.027468 0.038936 0.063103\n" + ] + } + ], + "source": [ + "# Calculate expected returns and covariance matrix\n", + "expected_returns = returns.mean() * 252 # Annualize the mean returns\n", + "cov_matrix = returns.cov() * 252 # Annualize the covariance matrix\n", + "print(\"Expected Returns:\\n\", expected_returns)\n", + "print(\"\\nCovariance Matrix:\\n\", cov_matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "099547bd-aee5-448c-a78f-5143fed0a918", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Optimal Weights:\n", + " [0.63821833 0.07981002 0.28197165]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "def portfolio_performance(weights, expected_returns, cov_matrix):\n", + " portfolio_return = np.sum(expected_returns * weights)\n", + " portfolio_std_dev = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))\n", + " return portfolio_return, portfolio_std_dev\n", + "\n", + "def negative_sharpe(weights, expected_returns, cov_matrix, risk_free_rate=0.01):\n", + " portfolio_return, portfolio_std_dev = portfolio_performance(weights, expected_returns, cov_matrix)\n", + " return -(portfolio_return - risk_free_rate) / portfolio_std_dev # Negative Sharpe ratio\n", + "\n", + "# Optimize portfolio weights\n", + "from scipy.optimize import minimize\n", + "\n", + "num_assets = len(assets)\n", + "args = (expected_returns, cov_matrix)\n", + "\n", + "constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) # Sum of weights must be 1\n", + "bounds = tuple((0, 1) for _ in range(num_assets)) # No short selling\n", + "\n", + "# Initial guess (equal distribution)\n", + "initial_weights = num_assets * [1. / num_assets,]\n", + "\n", + "optimal_solution = minimize(negative_sharpe, initial_weights, args=args, \n", + " method='SLSQP', bounds=bounds, constraints=constraints)\n", + "\n", + "optimal_weights = optimal_solution.x\n", + "print(\"\\nOptimal Weights:\\n\", optimal_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "96d95260-4592-4e77-88d0-5f62b160704e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_portfolio_weights(weights, assets):\n", + " plt.figure(figsize=(8, 6))\n", + " plt.pie(weights, labels=assets, autopct='%1.1f%%', startangle=140)\n", + " plt.title('Optimal Portfolio Allocation')\n", + " plt.axis('equal') # Equal aspect ratio ensures that pie chart is a circle\n", + " plt.show()\n", + "\n", + "# Plot the optimal portfolio weights\n", + "plot_portfolio_weights(optimal_weights, assets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06bfb489-43dc-49d8-929d-46a3d831c7c7", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}

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zQhvhNc43Hl(y{g3?qaV?sr(e_P->W&?qvKR@_XoNNKEa<;YfYax=`jik>HsY zZ5j8d6z?QW-5N?vKmdFf850iKCq`M^A8Si-APEvjJ+_YTAXFapToE$8TxCO9&ytw+ zSNJW?^Vsx!;N<76JDT_6cx z5BHHzAor<99>aJvLvni1%18qTS&?Ds(O5AgmC!{k1LkTfQ4aPKmP?+U2?L4gOwy&d z|8?5_%Qk}~J}$6VLS&_2c$}m@@fcI<(Qx~C;P%`JZQ9J{bX=q9!Db{4$}y=hDk5q8 zsVTuctw;ra1Z+Lqpwhz~JV$u_l7z?%7g(Ifsmv=B33<-dDa(C@^AlKiPdow&Ma+%Z z%Gbxk{C(cg{}(#`y@r*<1$J-ZfsP1#|6tO9hUUP|Luj?}{p;7g0DAe~SNvbr{4X#5 eKNPJq0!}(`P8{B(24Ggvp2{i77D>N)|9=3wgXF9L literal 0 HcmV?d00001 From 17ad83cbde1d7bb1ff8ee096f43a705a157a8f6d Mon Sep 17 00:00:00 2001 From: alolika bhowmik <152315710+alo7lika@users.noreply.github.com> Date: Mon, 7 Oct 2024 16:30:33 +0530 Subject: [PATCH 3/9] Update README.md --- Financial Environment Segmentation/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/Financial Environment Segmentation/README.md b/Financial Environment Segmentation/README.md index c0c5c07..4df6d2a 100644 --- a/Financial Environment Segmentation/README.md +++ b/Financial Environment Segmentation/README.md @@ -1,5 +1,10 @@ # 📈 Financial Environment Segmentation +

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