index.html ADVANCED_FEATURES.md app.cpython-313.pyc README.md

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    Stock Market Time Series Dataset - Demo Page
    
    Author: Molla Samser
    Organization: RSK World
    Designer & Tester: Rima Khatun
    Website: https://rskworld.in/
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<body>
    <div class="container">
        <div class="main-container">
            <!-- Header -->
            <div class="header">
                <div class="icon-box">
                    <i class="fas fa-chart-line"></i>
                </div>
                <h1>Stock Market Time Series Dataset</h1>
                <p class="subtitle">Historical OHLCV Data with Technical Indicators for Multiple Stocks</p>
                <div class="mt-3">
                    <span class="badge badge-custom me-2"><i class="fas fa-calendar"></i> 2020 Data</span>
                    <span class="badge badge-custom me-2"><i class="fas fa-database"></i> 5 Stocks</span>
                    <span class="badge badge-custom"><i class="fas fa-chart-bar"></i> Technical Indicators</span>
                </div>
            </div>
            
            <!-- Overview -->
            <section>
                <h2 class="section-title"><i class="fas fa-info-circle"></i> Overview</h2>
                <p>This comprehensive dataset contains historical stock market data with OHLCV (Open, High, Low, Close, Volume) prices, trading volumes, and technical indicators for multiple stocks. Perfect for time series forecasting, technical analysis, portfolio optimization, and financial modeling.</p>
            </section>
            
            <!-- Features -->
            <section>
                <h2 class="section-title"><i class="fas fa-star"></i> Key Features</h2>
                <div class="row">
                    <div class="col-md-4 mb-3">
                        <div class="card feature-card">
                            <div class="card-body text-center">
                                <div class="feature-icon"><i class="fas fa-chart-candlestick"></i></div>
                                <h5 class="card-title">OHLCV Price Data</h5>
                                <p class="card-text">Complete open, high, low, close prices and trading volume for comprehensive analysis.</p>
                            </div>
                        </div>
                    </div>
                    <div class="col-md-4 mb-3">
                        <div class="card feature-card">
                            <div class="card-body text-center">
                                <div class="feature-icon"><i class="fas fa-chart-line"></i></div>
                                <h5 class="card-title">Technical Indicators</h5>
                                <p class="card-text">Pre-calculated MA_20, MA_50, RSI, and MACD indicators for quick analysis.</p>
                            </div>
                        </div>
                    </div>
                    <div class="col-md-4 mb-3">
                        <div class="card feature-card">
                            <div class="card-body text-center">
                                <div class="feature-icon"><i class="fas fa-building"></i></div>
                                <h5 class="card-title">Multiple Stocks</h5>
                                <p class="card-text">Data for AAPL, GOOGL, MSFT, AMZN, and TSLA covering the entire 2020.</p>
                            </div>
                        </div>
                    </div>
                    <div class="col-md-4 mb-3">
                        <div class="card feature-card">
                            <div class="card-body text-center">
                                <div class="feature-icon"><i class="fas fa-robot"></i></div>
                                <h5 class="card-title">ML Ready</h5>
                                <p class="card-text">Time series formatted data ready for machine learning and forecasting models.</p>
                            </div>
                        </div>
                    </div>
                    <div class="col-md-4 mb-3">
                        <div class="card feature-card">
                            <div class="card-body text-center">
                                <div class="feature-icon"><i class="fas fa-python"></i></div>
                                <h5 class="card-title">Python Scripts</h5>
                                <p class="card-text">Complete analysis, forecasting, and visualization scripts included.</p>
                            </div>
                        </div>
                    </div>
                    <div class="col-md-4 mb-3">
                        <div class="card feature-card">
                            <div class="card-body text-center">
                                <div class="feature-icon"><i class="fas fa-book"></i></div>
                                <h5 class="card-title">Documentation</h5>
                                <p class="card-text">Comprehensive documentation and metadata for all indicators and stocks.</p>
                            </div>
                        </div>
                    </div>
                </div>
            </section>
            
            <!-- Stocks Included -->
            <section>
                <h2 class="section-title"><i class="fas fa-building"></i> Stocks Included</h2>
                <div class="row">
                    <div class="col-md-6">
                        <div class="stock-card">
                            <div class="stock-symbol"><i class="fab fa-apple"></i> AAPL</div>
                            <strong>Apple Inc.</strong><br>
                            <small class="text-muted">Technology • Consumer Electronics</small>
                            <p class="mt-2 mb-0">Leading technology company known for iPhones, Mac, and innovative products.</p>
                        </div>
                    </div>
                    <div class="col-md-6">
                        <div class="stock-card">
                            <div class="stock-symbol"><i class="fab fa-google"></i> GOOGL</div>
                            <strong>Alphabet Inc.</strong><br>
                            <small class="text-muted">Technology • Internet Services</small>
                            <p class="mt-2 mb-0">Parent company of Google, leader in search, advertising, and cloud services.</p>
                        </div>
                    </div>
                    <div class="col-md-6">
                        <div class="stock-card">
                            <div class="stock-symbol"><i class="fab fa-microsoft"></i> MSFT</div>
                            <strong>Microsoft Corporation</strong><br>
                            <small class="text-muted">Technology • Software Infrastructure</small>
                            <p class="mt-2 mb-0">Software giant with Windows, Office, Azure, and enterprise solutions.</p>
                        </div>
                    </div>
                    <div class="col-md-6">
                        <div class="stock-card">
                            <div class="stock-symbol"><i class="fab fa-amazon"></i> AMZN</div>
                            <strong>Amazon.com Inc.</strong><br>
                            <small class="text-muted">Consumer Cyclical • Internet Retail</small>
                            <p class="mt-2 mb-0">E-commerce leader with AWS cloud services and diverse business portfolio.</p>
                        </div>
                    </div>
                    <div class="col-md-6">
                        <div class="stock-card">
                            <div class="stock-symbol"><i class="fas fa-car"></i> TSLA</div>
                            <strong>Tesla Inc.</strong><br>
                            <small class="text-muted">Consumer Cyclical • Auto Manufacturers</small>
                            <p class="mt-2 mb-0">Electric vehicle pioneer and clean energy company led by Elon Musk.</p>
                        </div>
                    </div>
                </div>
            </section>
            
            <!-- Data Format -->
            <section>
                <h2 class="section-title"><i class="fas fa-table"></i> Data Format</h2>
                <div class="table-responsive">
                    <table class="table table-striped table-hover">
                        <thead class="table-dark">
                            <tr>
                                <th>Column</th>
                                <th>Description</th>
                                <th>Type</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td><strong>Date</strong></td>
                                <td>Trading date (YYYY-MM-DD format)</td>
                                <td>Date</td>
                            </tr>
                            <tr>
                                <td><strong>Open</strong></td>
                                <td>Opening price of the trading day</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>High</strong></td>
                                <td>Highest price during the trading day</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>Low</strong></td>
                                <td>Lowest price during the trading day</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>Close</strong></td>
                                <td>Closing price of the trading day</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>Volume</strong></td>
                                <td>Total number of shares traded</td>
                                <td>Integer</td>
                            </tr>
                            <tr>
                                <td><strong>Adj Close</strong></td>
                                <td>Adjusted closing price (splits/dividends)</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>MA_20</strong></td>
                                <td>20-day Simple Moving Average</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>MA_50</strong></td>
                                <td>50-day Simple Moving Average</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>RSI</strong></td>
                                <td>Relative Strength Index (momentum indicator)</td>
                                <td>Float</td>
                            </tr>
                            <tr>
                                <td><strong>MACD</strong></td>
                                <td>Moving Average Convergence Divergence</td>
                                <td>Float</td>
                            </tr>
                        </tbody>
                    </table>
                </div>
            </section>
            
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            <section>
                <h2 class="section-title"><i class="fas fa-lightbulb"></i> Use Cases</h2>
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                        <ul class="list-group list-group-flush">
                            <li class="list-group-item"><i class="fas fa-check text-success me-2"></i> Time Series Forecasting (ARIMA, LSTM, Prophet)</li>
                            <li class="list-group-item"><i class="fas fa-check text-success me-2"></i> Technical Analysis & Trading Strategies</li>
                            <li class="list-group-item"><i class="fas fa-check text-success me-2"></i> Portfolio Optimization & Asset Allocation</li>
                        </ul>
                    </div>
                    <div class="col-md-6">
                        <ul class="list-group list-group-flush">
                            <li class="list-group-item"><i class="fas fa-check text-success me-2"></i> Risk Assessment & Volatility Analysis</li>
                            <li class="list-group-item"><i class="fas fa-check text-success me-2"></i> Machine Learning Model Training</li>
                            <li class="list-group-item"><i class="fas fa-check text-success me-2"></i> Algorithmic Trading & Backtesting</li>
                        </ul>
                    </div>
                </div>
            </section>
            
            <!-- Quick Start -->
            <section>
                <h2 class="section-title"><i class="fas fa-rocket"></i> Quick Start</h2>
                <div class="card">
                    <div class="card-body">
                        <h5 class="card-title">Python Example</h5>
                        <pre class="bg-dark text-light p-3 rounded"><code>import pandas as pd

# Load stock data
df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date')

# Display first few rows
print(df.head())

# Calculate daily returns
df['Returns'] = df['Close'].pct_change()

# Calculate volatility
volatility = df['Returns'].std() * (252 ** 0.5)
print(f"Annualized Volatility: {volatility:.2%}")</code></pre>
                        
                        <h5 class="card-title mt-4">Using Provided Scripts</h5>
                        <pre class="bg-dark text-light p-3 rounded"><code># Load and analyze data
from scripts.load_data import StockDataLoader
from scripts.analyze import StockAnalyzer

loader = StockDataLoader()
aapl = loader.load_stock('AAPL')

analyzer = StockAnalyzer('data/AAPL.csv')
analyzer.summary_statistics()
analyzer.plot_price_history()</code></pre>
                    </div>
                </div>
            </section>
            
            <!-- Download -->
            <section class="text-center mt-5">
                <h2 class="section-title"><i class="fas fa-download"></i> Download Dataset</h2>
                <p class="mb-4">Get the complete dataset with all stock data, metadata, and Python scripts.</p>
                <a href="stock-time-series.zip" class="btn btn-custom btn-lg me-2">
                    <i class="fas fa-download me-2"></i>Download Dataset
                </a>
                <a href="https://github.com/rskworld" target="_blank" class="btn btn-outline-secondary btn-lg">
                    <i class="fab fa-github me-2"></i>View on GitHub
                </a>
            </section>
            
            <!-- Contact Information -->
            <section>
                <div class="contact-info">
                    <h3 class="mb-4"><i class="fas fa-envelope"></i> Contact Information</h3>
                    <div class="row">
                        <div class="col-md-6">
                            <p><strong><i class="fas fa-user"></i> Author:</strong> Molla Samser</p>
                            <p><strong><i class="fas fa-building"></i> Organization:</strong> RSK World</p>
                            <p><strong><i class="fas fa-user-check"></i> Designer & Tester:</strong> Rima Khatun</p>
                        </div>
                        <div class="col-md-6">
                            <p><strong><i class="fas fa-globe"></i> Website:</strong> <a href="https://rskworld.in/" target="_blank">rskworld.in</a></p>
                            <p><strong><i class="fas fa-envelope"></i> Email:</strong> <a href="mailto:help@rskworld.in">help@rskworld.in</a></p>
                            <p><strong><i class="fas fa-phone"></i> Phone:</strong> +91 93305 39277</p>
                        </div>
                    </div>
                    <p class="mt-3 mb-0"><strong><i class="fas fa-map-marker-alt"></i> Address:</strong> Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147</p>
                </div>
            </section>
            
            <!-- Footer -->
            <div class="footer">
                <p class="mb-2">
                    <strong>Stock Market Time Series Dataset</strong> | Created by <a href="https://rskworld.in/" target="_blank" class="text-decoration-none">RSK World</a>
                </p>
                <p class="mb-2">
                    <a href="https://rskworld.in/" target="_blank" class="text-decoration-none me-3">
                        <i class="fas fa-globe"></i> Website
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                    <a href="mailto:help@rskworld.in" class="text-decoration-none me-3">
                        <i class="fas fa-envelope"></i> Email
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                        <i class="fab fa-github"></i> GitHub
                    </a>
                </p>
                <p class="small mt-3">
                    © 2024 RSK World. All rights reserved. | For educational and research purposes.
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ADVANCED_FEATURES.md

# Stock Market Time Series Dataset - Advanced Features



## 🚀 Advanced Features Overview

This dataset now includes **powerful advanced features** for professional traders, data scientists, and researchers!

---

## 1. 🤖 Machine Learning Models (`ml_models.py`)

### Features
- **LSTM Neural Networks** for time series prediction
- **Feature Engineering** with 20+ advanced features
- **Model Training & Evaluation** with comprehensive metrics
- **Future Price Prediction** capabilities
- **Fallback to Random Forest** if TensorFlow unavailable

### Quick Start
```python
from scripts.ml_models import StockMLModels, FeatureEngineering

# Initialize ML model
ml_model = StockMLModels('data/AAPL.csv')

# Train LSTM
results = ml_model.train_lstm(epochs=50, batch_size=32)

# Plot predictions
ml_model.plot_predictions(results)

# Predict future
future_forecast = ml_model.predict_future(days=30)
```

### Available Features
- LSTM with 3 layers + Dropout
- Custom sequence length
- Multiple input features (OHLCV + indicators)
- Training history visualization
- RMSE, MAE, R² metrics
- Future price forecasting

### Advanced Feature Engineering
```python
# Add 20+ technical features
df_enhanced = FeatureEngineering.add_technical_features(df)

# New features include:
# - Returns & Log Returns
# - Volatility (20 & 50 day)
# - Momentum indicators
# - Rate of Change (ROC)
# - Bollinger Bands
# - ATR, OBV, Price Position
# - Lag features (1, 3, 7, 14 days)
```

---

## 2. 📊 Backtesting Framework (`backtesting.py`)

### Features
- **Complete Backtesting Engine** with commission & slippage
- **Pre-built Strategies**: MA Crossover, RSI, MACD
- **Custom Strategy Development** support
- **Performance Metrics**: Sharpe Ratio, Max Drawdown, Win Rate
- **Strategy Comparison** tools

### Quick Start
```python
from scripts.backtesting import Backtester, MovingAverageCrossover, RSIStrategy, MACDStrategy

# Initialize backtester
backtester = Backtester('data/AAPL.csv', initial_capital=100000)

# Create strategies
strategies = [
MovingAverageCrossover(short_window=20, long_window=50),
RSIStrategy(oversold=30, overbought=70),
MACDStrategy()
]

# Compare strategies
comparison = backtester.compare_strategies(strategies)

# Plot individual strategy
backtester.plot_results('MA_Crossover_20_50')
```

### Available Strategies
1. **Moving Average Crossover**: Classic trend-following
2. **RSI Strategy**: Mean-reversion based on oversold/overbought
3. **MACD Strategy**: Momentum-based crossover signals

### Performance Metrics
- Initial & Final Capital
- Total Return & CAGR
- Annualized Volatility
- Sharpe Ratio
- Maximum Drawdown
- Win Rate
- Total number of trades

### Create Custom Strategies
```python
from scripts.backtesting import TradingStrategy

class MyStrategy(TradingStrategy):
def __init__(self):
super().__init__("My Custom Strategy")

def generate_signals(self, df):
signals = pd.Series(0, index=df.index)
# Your logic here
signals[condition] = 1 # Buy
signals[condition] = -1 # Sell
return signals

# Test your strategy
my_strategy = MyStrategy()
results = backtester.run_backtest(my_strategy)
```

---

## 3. 💼 Portfolio Optimization (`portfolio.py`)

### Features
- **Modern Portfolio Theory** implementation
- **Efficient Frontier** generation
- **Maximum Sharpe Ratio** optimization
- **Minimum Volatility** optimization
- **Monte Carlo Simulation** for risk analysis
- **Correlation Analysis**

### Quick Start
```python
from scripts.portfolio import PortfolioOptimizer

# Initialize with multiple stocks
stock_paths = [
'data/AAPL.csv',
'data/GOOGL.csv',
'data/MSFT.csv',
'data/AMZN.csv',
'data/TSLA.csv'
]

optimizer = PortfolioOptimizer(stock_paths)

# Find optimal portfolio (max Sharpe ratio)
optimal = optimizer.optimize_max_sharpe()

print(f"Optimal Weights: {optimal['weights']}")
print(f"Expected Return: {optimal['return']:.2%}")
print(f"Sharpe Ratio: {optimal['sharpe_ratio']:.4f}")

# Plot efficient frontier
optimizer.plot_efficient_frontier()

# Run Monte Carlo simulation
optimizer.plot_monte_carlo(optimal['weights'], days=252, simulations=1000)
```

### Optimization Methods
1. **Maximum Sharpe Ratio**: Best risk-adjusted returns
2. **Minimum Volatility**: Lowest risk portfolio
3. **Efficient Frontier**: Risk-return tradeoff visualization
4. **Monte Carlo**: Probabilistic future scenarios

### Features
- 5,000 portfolio simulations
- Risk-return visualization
- Correlation heatmaps
- Value at Risk (VaR) calculation
- Best/worst case scenarios

---

## 4. 📈 Interactive Dashboards (`interactive_dashboard.py`)

### Features
- **Interactive Candlestick Charts** with Plotly
- **Technical Indicators Dashboard** (4-panel view)
- **Returns Analysis** with distributions
- **Volume Profile** analysis
- **Multi-Stock Comparison** tools

### Quick Start
```python
from scripts.interactive_dashboard import InteractiveDashboard, create_multi_stock_comparison

# Create interactive dashboard
dashboard = InteractiveDashboard('data/AAPL.csv')

# Interactive candlestick chart
dashboard.create_candlestick_chart()

# Complete technical dashboard
dashboard.create_technical_indicators_dashboard()

# Returns analysis
dashboard.create_returns_analysis()

# Volume profile
dashboard.create_volume_profile()

# Compare multiple stocks
create_multi_stock_comparison([
'data/AAPL.csv',
'data/GOOGL.csv',
'data/MSFT.csv'
], normalize=True)
```

### Interactive Features
- Zoom & Pan
- Hover tooltips with data
- Toggle series visibility
- Export to PNG
- Responsive design
- Real-time updates (with streaming data)

### Dashboard Types
1. **Candlestick Chart**: OHLC + Moving Averages
2. **Technical Dashboard**: 4-panel (Price, Volume, RSI, MACD)
3. **Returns Analysis**: Daily returns, cumulative, distribution
4. **Volume Profile**: Price levels with volume
5. **Multi-Stock**: Normalized comparison

---

## 5. 📊 Advanced Technical Indicators (`advanced_indicators.py`)

### Features
- **10+ Advanced Indicators** beyond basic MA/RSI/MACD
- **Complete Implementation** ready to use
- **Customizable Parameters**
- **Ichimoku Cloud** for trend analysis

### Available Indicators

#### 1. **Bollinger Bands**
- Upper, Middle, Lower bands
- Band width
- %B (price position)

#### 2. **Average True Range (ATR)**
- Volatility measurement
- 14-period default

#### 3. **Stochastic Oscillator**
- %K and %D lines
- Overbought/oversold detection

#### 4. **Commodity Channel Index (CCI)**
- Mean reversion indicator
- +100/-100 thresholds

#### 5. **On-Balance Volume (OBV)**
- Volume-based momentum
- Cumulative indicator

#### 6. **Money Flow Index (MFI)**
- Volume-weighted RSI
- 14-period calculation

#### 7. **Williams %R**
- Momentum indicator
- -20/-80 levels

#### 8. **Parabolic SAR**
- Trend-following stop and reverse
- Acceleration factor based

#### 9. **Ichimoku Cloud**
- Tenkan-sen (Conversion Line)
- Kijun-sen (Base Line)
- Senkou Span A & B (Cloud)
- Chikou Span (Lagging)

### Quick Start
```python
from scripts.advanced_indicators import AdvancedIndicators

# Load data
df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date')

# Add all indicators at once
df_enhanced = AdvancedIndicators.add_all_indicators(df)

# Or add individually
df = AdvancedIndicators.bollinger_bands(df, window=20, num_std=2)
df['ATR'] = AdvancedIndicators.average_true_range(df, window=14)
df['Stoch_K'], df['Stoch_D'] = AdvancedIndicators.stochastic_oscillator(df)
df['CCI'] = AdvancedIndicators.commodity_channel_index(df)
df = AdvancedIndicators.ichimoku_cloud(df)

# Save enhanced dataset
df_enhanced.to_csv('data/AAPL_enhanced.csv')
```

---

## 6. 📓 Jupyter Notebook Tutorial

### Location: `tutorials/getting_started.ipynb`

### Topics Covered
1. Setup and Data Loading
2. Basic Data Exploration
3. Price Visualization
4. Technical Indicator Analysis
5. Using Analysis Module
6. Time Series Forecasting
7. Comparing Multiple Stocks
8. Correlation Analysis
9. Portfolio Optimization
10. Backtesting Trading Strategies

### How to Use
```bash
# Install Jupyter
pip install jupyter

# Navigate to tutorials directory
cd tutorials

# Launch Jupyter
jupyter notebook getting_started.ipynb
```

---

## 🎯 Complete Feature Matrix

| Feature | Basic | Advanced | Description |
|---------|-------|----------|-------------|
| **Data Loading** | ✅ | ✅ | CSV with OHLCV + indicators |
| **Basic Analysis** | ✅ | ✅ | Statistics, returns, volatility |
| **Visualization** | ✅ | ✅ | Matplotlib & Seaborn charts |
| **Technical Indicators** | ✅ | ✅ | MA, RSI, MACD + 10 more |
| **Forecasting** | ✅ | ✅ | SMA, ES, ARIMA |
| **LSTM Models** | ❌ | ✅ | Deep learning prediction |
| **Backtesting** | ❌ | ✅ | Complete framework |
| **Portfolio Optimization** | ❌ | ✅ | MPT, efficient frontier |
| **Interactive Dashboards** | ❌ | ✅ | Plotly visualizations |
| **Monte Carlo** | ❌ | ✅ | Risk simulations |
| **Feature Engineering** | ❌ | ✅ | 20+ automated features |
| **Jupyter Tutorials** | ❌ | ✅ | Complete walkthrough |

---

## 💻 Installation

### Basic Installation
```bash
pip install -r requirements.txt
```

### For Deep Learning (LSTM)
```bash
pip install tensorflow>=2.13.0 keras>=2.13.0
```

### For Interactive Dashboards
```bash
pip install plotly>=5.14.0 dash>=2.11.0
```

### Complete Installation
```bash
# All features at once
pip install pandas numpy matplotlib seaborn scikit-learn \
tensorflow keras statsmodels prophet plotly dash \
jupyter notebook scipy tqdm
```

---

## 📚 Usage Examples

### 1. Complete Analysis Pipeline
```python
# Load data
from scripts.load_data import StockDataLoader
loader = StockDataLoader()
aapl = loader.load_stock('AAPL')

# Analyze
from scripts.analyze import StockAnalyzer
analyzer = StockAnalyzer('data/AAPL.csv')
analyzer.summary_statistics()
analyzer.plot_price_history()

# Forecast
from scripts.forecast import StockForecaster
forecaster = StockForecaster('data/AAPL.csv')
forecast = forecaster.exponential_smoothing_forecast(days=30)

# ML Prediction
from scripts.ml_models import StockMLModels
ml_model = StockMLModels('data/AAPL.csv')
results = ml_model.train_lstm(epochs=50)

# Backtest Strategy
from scripts.backtesting import Backtester, MovingAverageCrossover
backtester = Backtester('data/AAPL.csv')
strategy = MovingAverageCrossover(20, 50)
results = backtester.run_backtest(strategy)

# Portfolio Optimization
from scripts.portfolio import PortfolioOptimizer
optimizer = PortfolioOptimizer(['data/AAPL.csv', 'data/GOOGL.csv'])
optimal = optimizer.optimize_max_sharpe()
```

### 2. Advanced Indicators
```python
from scripts.advanced_indicators import AdvancedIndicators

df = pd.read_csv('data/AAPL.csv')
df = AdvancedIndicators.add_all_indicators(df)

# Now df has 30+ columns with all indicators!
```

### 3. Interactive Dashboard
```python
from scripts.interactive_dashboard import InteractiveDashboard

dashboard = InteractiveDashboard('data/AAPL.csv')
dashboard.create_technical_indicators_dashboard()
```

---

## 🎓 Learning Resources

1. **Getting Started Notebook**: `tutorials/getting_started.ipynb`
2. **README.md**: Complete documentation
3. **Script Documentation**: Docstrings in all modules
4. **Example Usage**: Run any script with `python scripts/filename.py`

---

## 🔧 System Requirements

- **Python**: 3.8+
- **RAM**: 4GB minimum (8GB recommended for ML)
- **Storage**: 500MB for dataset + dependencies
- **GPU**: Optional (speeds up LSTM training)

---

## 📞 Support & Contact

**Author:** Molla Samser
**Organization:** RSK World
**Designer & Tester:** Rima Khatun
**Website:** [https://rskworld.in/](https://rskworld.in/)
**Email:** help@rskworld.in
**Phone:** +91 93305 39277
**Address:** Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147

---

## 🌟 What's Next?

We're constantly improving! Planned features:
- Real-time data integration
- More ML models (GRU, Transformer)
- Sentiment analysis
- Options data
- Web API
- Mobile app

Stay tuned at [rskworld.in](https://rskworld.in/)!

---

© 2024 RSK World. All rights reserved.

README.md

Raw Download

README.md

# Stock Market Time Series Dataset



![Stock Market Time Series](./stock-time-series.png)

## Overview

This dataset contains historical stock market data with OHLCV (Open, High, Low, Close, Volume) prices, trading volumes, and technical indicators for multiple stocks. Perfect for time series forecasting, technical analysis, portfolio optimization, and financial modeling.

## Features

- **OHLCV Price Data**: Complete open, high, low, close prices and volume
- **Trading Volumes**: Historical trading volumes for liquidity analysis
- **Multiple Stocks**: Data for various stocks across different sectors
- **Technical Indicators**: Pre-calculated indicators for quick analysis
- **Time Series Ready Format**: Data formatted for immediate time series analysis

## Technologies

- CSV
- JSON
- Pandas
- Time Series Analysis
- Python

## Difficulty Level

**Intermediate** - Suitable for data scientists with basic knowledge of time series analysis and financial markets.

## Dataset Structure

```
stock-time-series/
├── data/
│ ├── AAPL.csv # Apple Inc. stock data
│ ├── GOOGL.csv # Alphabet Inc. stock data
│ ├── MSFT.csv # Microsoft Corp. stock data
│ ├── AMZN.csv # Amazon.com Inc. stock data
│ └── TSLA.csv # Tesla Inc. stock data
├── metadata/
│ ├── stock_info.json # Stock metadata and information
│ └── indicators.json # Technical indicators description
├── scripts/
│ ├── load_data.py # Data loading utilities
│ ├── analyze.py # Basic analysis scripts
│ ├── forecast.py # Time series forecasting
│ └── visualize.py # Visualization utilities
├── index.html # Demo page
├── README.md # This file
└── requirements.txt # Python dependencies
```

## Data Format

Each CSV file contains the following columns:

| Column | Description |
|--------|-------------|
| Date | Trading date (YYYY-MM-DD) |
| Open | Opening price |
| High | Highest price |
| Low | Lowest price |
| Close | Closing price |
| Volume | Trading volume |
| Adj Close | Adjusted closing price |
| MA_20 | 20-day Moving Average |
| MA_50 | 50-day Moving Average |
| RSI | Relative Strength Index |
| MACD | Moving Average Convergence Divergence |

## Use Cases

1. **Time Series Forecasting**: Predict future stock prices using ARIMA, LSTM, Prophet
2. **Technical Analysis**: Analyze trading patterns and indicators
3. **Portfolio Optimization**: Build and optimize investment portfolios
4. **Risk Assessment**: Calculate volatility, VaR, and other risk metrics
5. **Machine Learning**: Train ML models for price prediction
6. **Algorithmic Trading**: Develop and backtest trading strategies

## Getting Started

### Prerequisites

```bash
pip install -r requirements.txt
```

### Loading Data

```python
import pandas as pd

# Load stock data
df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date')
print(df.head())
```

### Quick Analysis

```python
from scripts.analyze import StockAnalyzer

analyzer = StockAnalyzer('data/AAPL.csv')
analyzer.summary_statistics()
analyzer.plot_price_history()
```

### Time Series Forecasting

```python
from scripts.forecast import StockForecaster

forecaster = StockForecaster('data/AAPL.csv')
predictions = forecaster.arima_forecast(days=30)
forecaster.plot_forecast(predictions)
```

## Sample Analysis

```python
import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('data/AAPL.csv', parse_dates=['Date'], index_col='Date')

# Calculate returns
df['Returns'] = df['Close'].pct_change()

# Plot closing prices
df['Close'].plot(figsize=(12, 6), title='AAPL Closing Prices')
plt.ylabel('Price ($)')
plt.show()

# Calculate volatility
volatility = df['Returns'].std() * (252 ** 0.5) # Annualized
print(f"Annualized Volatility: {volatility:.2%}")
```

## Data Period

- **Start Date**: January 1, 2020
- **End Date**: December 31, 2024
- **Frequency**: Daily
- **Total Trading Days**: ~1260 per stock

## Technical Indicators Included

1. **Moving Averages (MA)**: 20-day and 50-day
2. **Relative Strength Index (RSI)**: Momentum oscillator
3. **MACD**: Trend-following momentum indicator
4. **Bollinger Bands**: Volatility indicator (calculable)

## License

This dataset is provided for educational and research purposes.

## Contact

- **Author**: Molla Samser
- **Organization**: RSK World
- **Designer & Tester**: Rima Khatun
- **Email**: help@rskworld.in
- **Phone**: +91 93305 39277
- **Website**: [https://rskworld.in/](https://rskworld.in/)
- **Address**: Nutanhat, Mongolkote, Purba Burdwan, West Bengal, India, 713147

## Acknowledgments

Data sourced from public financial markets. All prices are in USD.

---

**Visit [rskworld.in](https://rskworld.in/) for more datasets and data science projects!**

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