{"id":24792723,"url":"https://github.com/misaghmomenib/stock-momentum-analysis","last_synced_at":"2025-04-10T18:34:00.276Z","repository":{"id":265477069,"uuid":"896075149","full_name":"MisaghMomeniB/Stock-Momentum-Analysis","owner":"MisaghMomeniB","description":"A Python-based Data Analysis Tool Designed to Evaluate Stock Momentum. 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It integrates time series forecasting, technical indicators, portfolio analysis, and even Monte Carlo simulations to project future stock prices. Whether you're a trader, analyst, or just someone interested in data science, this project will help you understand stock price behavior and make informed decisions.\r\n\r\n### 🛠️ Features:\r\n- 📅 **Data Preprocessing**: Clean and format stock data, including handling missing values and ensuring the correct date format.\r\n- 📈 **Technical Indicators**: Calculate key indicators like MACD, RSI, Bollinger Bands, and Exponential Moving Averages (EMA).\r\n- 📉 **Portfolio Analysis**: Calculate important portfolio metrics such as the Sharpe ratio.\r\n- 🔮 **ARIMA Forecasting**: Use the ARIMA model for time series forecasting of future stock prices.\r\n- 🏝️ **Monte Carlo Simulation**: Simulate thousands of potential future stock price paths to understand price volatility.\r\n- 📊 **Backtesting Strategies**: Test the effectiveness of trading strategies, such as moving average crossovers, with backtesting.\r\n- 📊 **Interactive Visualizations**: Visualize stock prices, trading volumes, and technical indicators using **Plotly** and **Matplotlib**.\r\n- 💾 **Save Results**: Export the processed data and analysis to a new CSV file.\r\n\r\n---\r\n\r\n### 🧑‍💻 Getting Started:\r\nTo run this analysis, you'll need to have Python installed along with the necessary libraries.\r\n\r\n#### 📥 Prerequisites:\r\n1. **Python 3.x** installed\r\n2. Install the required libraries using `pip`:\r\n    ```bash\r\n    pip install pandas numpy matplotlib seaborn statsmodels plotly\r\n    ```\r\n\r\n#### 📂 How to Run:\r\n1. **Clone** or **Download** this repository to your local machine.\r\n2. **Update the File Path**:\r\n    - Replace `'File Path !'` in the code with the path to your stock data CSV file. The CSV should contain a **Date** column and a **Close** price column (other columns like 'Volume' can be included for additional analysis).\r\n3. **Run the Script**:\r\n    ```bash\r\n    python stock_analysis.py\r\n    ```\r\n4. **Results**: The script will output a variety of visualizations, statistics, and forecasts. The processed data will be saved as a new CSV file (`analyzed_stock_data.csv`).\r\n\r\n---\r\n\r\n### 🧐 Code Breakdown:\r\n\r\nHere’s a step-by-step explanation of what the script does:\r\n\r\n#### 1. **Data Loading \u0026 Preprocessing**:\r\n   - Load the dataset from a CSV file.\r\n   - Convert the **Date** column to a datetime format and remove any invalid or missing dates.\r\n   - Forward fill missing values in the dataset to ensure completeness.\r\n\r\n```python\r\ndf = pd.read_csv('File Path !')\r\ndf['Date'] = pd.to_datetime(df['Date'], errors='coerce')\r\ndf = df.dropna(subset=['Date'])\r\ndf.set_index('Date', inplace=True)\r\ndf = df[~df.index.duplicated(keep='last')]\r\ndf.sort_index(inplace=True)\r\ndf.fillna(method='ffill', inplace=True)\r\n```\r\n\r\n#### 2. **Descriptive Statistics**:\r\n   - The code prints the first few rows and descriptive statistics of the data, giving you a summary of the stock's historical performance.\r\n\r\n```python\r\nprint(df.head())\r\nprint(df.describe())\r\n```\r\n\r\n#### 3. **Technical Indicators**:\r\n   - Calculate popular technical indicators:\r\n     - **MACD (Moving Average Convergence Divergence)**.\r\n     - **RSI (Relative Strength Index)**.\r\n     - **Bollinger Bands** for volatility.\r\n     - **50-day and 200-day Exponential Moving Averages**.\r\n     \r\n```python\r\ndf['MACD'] = short_ema - long_ema\r\ndf['RSI'] = 100 - (100 / (1 + rs))\r\ndf['Upper Band'] = rolling_mean + (rolling_std * 2)\r\ndf['Lower Band'] = rolling_mean - (rolling_std * 2)\r\n```\r\n\r\n#### 4. **Portfolio Analysis**:\r\n   - Calculate the **Sharpe ratio** based on daily returns to assess the risk-adjusted return of the stock.\r\n\r\n```python\r\nsharpe_ratio = returns / portfolio_risk\r\nprint(f\"Portfolio Sharpe Ratio: {sharpe_ratio}\")\r\n```\r\n\r\n#### 5. **ARIMA Forecasting**:\r\n   - Use the ARIMA model to forecast the stock's price for the next 10 days.\r\n\r\n```python\r\nforecast = model_fit.forecast(steps=10)\r\nprint(forecast)\r\n```\r\n\r\n#### 6. **Monte Carlo Simulation**:\r\n   - Simulate 1000 possible future price paths based on historical returns. The simulation helps visualize the potential volatility and price movements.\r\n\r\n```python\r\nsimulated_prices[i] = price_series\r\n```\r\n\r\n#### 7. **Backtesting Moving Average Strategy**:\r\n   - Implement and backtest a **Moving Average Crossover** strategy.\r\n   - Generate buy and sell signals based on the 7-day and 30-day moving averages.\r\n\r\n```python\r\ndf['Signal'] = 0\r\ndf['Signal'][df['7-day MA'] \u003e df['30-day MA']] = 1  # Buy signal\r\ndf['Signal'][df['7-day MA'] \u003c df['30-day MA']] = -1  # Sell signal\r\n```\r\n\r\n#### 8. **Visualization**:\r\n   - Visualize the **Monte Carlo simulations**, **cumulative returns** of the strategy versus the market, and technical indicators using both **Matplotlib** and **Plotly**.\r\n\r\n```python\r\nplt.plot(simulated_prices.T, color='blue', alpha=0.1)\r\nsns.lineplot(x=df.index, y=df['Cumulative Strategy Return'])\r\nfig1 = px.line(df, x=df.index, y='Close', title='Stock Prices Over Time')\r\nfig2 = px.line(df, x=df.index, y='Volume', title='Trading Volume Over Time')\r\n```\r\n\r\n#### 9. **Export the Data**:\r\n   - Save the processed and analyzed data to a new CSV file (`analyzed_stock_data.csv`).\r\n\r\n```python\r\ndf.to_csv(\"analyzed_stock_data.csv\")\r\n```\r\n\r\n---\r\n\r\n### 📈 Visualizations:\r\n- **Stock Price Over Time**: Interactive plot of stock closing prices.\r\n- **Volume Over Time**: Interactive plot of trading volumes.\r\n- **Monte Carlo Simulation**: Visualize the possible future paths of the stock price.\r\n- **Cumulative Strategy Return vs. Market Return**: A comparison of your strategy's cumulative return versus the market.\r\n\r\n---\r\n\r\n### 🔧 Future Improvements:\r\n- **Sentiment Analysis**: Incorporate news and social media sentiment data to improve forecasting.\r\n- **Advanced Backtesting**: Implement other strategies, such as MACD crossovers or RSI-based strategies.\r\n- **Machine Learning Models**: Explore machine learning models for predicting stock prices beyond ARIMA.\r\n\r\n---\r\n\r\n### 💬 Feedback \u0026 Contributions:\r\n- Contributions are welcome! Feel free to fork this repository, submit issues, or make pull requests to improve the analysis or add new features.\r\n\r\n---\r\n\r\n### 🙏 Thank You:\r\nThank you for exploring the **Stock Data Analysis \u0026 Forecasting** project! We hope this tool helps you gain valuable insights and make better decisions in the stock market. Happy analyzing! 📊\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmisaghmomenib%2Fstock-momentum-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmisaghmomenib%2Fstock-momentum-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmisaghmomenib%2Fstock-momentum-analysis/lists"}