Introduction to Quantitative Trading

A comprehensive Python project demonstrating real-time and historical cryptocurrency data aggregation, analysis, and visualization for quantitative trading applications.

🚀 Features

• Real-time Data Streaming: Connect to BitMEX WebSocket for live tick and orderbook data
• Historical Data Analysis: Process CSV files with date range filtering
• Multiple Aggregation Methods:
  • OHLCV Candlesticks: Open, High, Low, Close, Volume data
  • VWAP: Volume Weighted Average Price calculations
  • Volume Buckets: Aggregated trades based on volume thresholds
  • Order Flow Analysis: Buy/sell volume, net flow, and imbalance ratios
  • Summary Statistics: Comprehensive tick data metrics
• Data Visualization: Beautiful charts using matplotlib and seaborn
• Modular Architecture: Clean, focused classes for each aggregation type
• Teaching Materials: Simple examples for learning quantitative concepts

📁 Project Structure

Intro to quant/
├── aggregations_examples/     # Example scripts for each aggregator
├── data/                      # Historical CSV data files
├── data_aggregator/           # Individual aggregation classes
├── exchange/                  # Exchange connectivity modules
├── tick_queue_module/         # Real-time tick data queue
├── visualization/             # Data visualization functions
├── main.py                    # Main entry point
└── requirements.txt           # Python dependencies

🛠️ Installation

1. Clone the repository:

   git clone git@github.com:FarhanKardan/intoToQuant.git
   cd intoToQuant

2. Install dependencies:

   pip install -r requirements.txt

📊 Data Aggregation Types

1. OHLCV Aggregator

Generates traditional candlestick data with open, high, low, close, and volume information.

from data_aggregator.ohlcv_aggregator import OHLCVAggregator

agg = OHLCVAggregator("BTCUSDT")
agg.add_ticks(tick_data)
ohlcv_data = agg.generate_ohlcv('5min')

2. VWAP Aggregator

Calculates Volume Weighted Average Price for accurate price analysis.

from data_aggregator.vwap_aggregator import VWAPAggregator

agg = VWAPAggregator("BTCUSDT")
agg.add_ticks(tick_data)
vwap_data = agg.generate_vwap('5min')

3. Volume Bucket Aggregator

Groups trades into volume-based buckets for large trade analysis.

from data_aggregator.volume_bucket_aggregator import VolumeBucketAggregator

agg = VolumeBucketAggregator("BTCUSDT")
agg.add_ticks(tick_data)
buckets = agg.generate_volume_buckets(bucket_size=1000.0)

4. Order Flow Aggregator

Analyzes buy/sell pressure and market microstructure.

from data_aggregator.order_flow_aggregator import OrderFlowAggregator

agg = OrderFlowAggregator("BTCUSDT")
agg.add_ticks(tick_data)
orderflow = agg.generate_order_flow('5min')

5. Statistics Aggregator

Provides comprehensive summary statistics for tick data.

from data_aggregator.stats_aggregator import StatsAggregator

agg = StatsAggregator("BTCUSDT")
agg.add_ticks(tick_data)
stats = agg.get_summary_stats()

🎯 Usage Examples

Real-time Data Streaming

python main.py

Run Individual Aggregation Examples

# OHLCV Example
python aggregations_examples/ohlcv_example.py

# VWAP Example
python aggregations_examples/vwap_example.py

# Volume Buckets Example
python aggregations_examples/volume_buckets_example.py

# Order Flow Example
python aggregations_examples/order_flow_example.py

# Order Flow / Volume Profile Example
python aggregations_examples/volume_profile_example.py --timeframe 1H --limit 10000 --style classic

# Order Flow / Footprint Example
python aggregations_examples\footprint_example.py --limit 100000
python aggregations_examples\footprint_example.py --mode range --range_levels 10 --bin_size 10 --limit 50000

Run All Examples

python aggregations_examples/run_all_examples.py

Real-time Tick Queue

python tick_queue_module/main.py

📈 Visualization

The project includes comprehensive visualization capabilities:

from visualization.visualization import plot_ohlcv, plot_vwap, plot_volume_buckets

# Plot OHLCV candlesticks
plot_ohlcv(ohlcv_data, "BTCUSDT OHLCV Analysis")

# Plot VWAP analysis
plot_vwap(vwap_data, "BTCUSDT VWAP Analysis")

# Plot volume buckets
plot_volume_buckets(bucket_data, "BTCUSDT Volume Buckets")

🔧 Configuration

WebSocket Configuration

• Testnet: Set testnet=True in BitmexWebSocket for testing
• Production: Use testnet=False for live data

Data Sources

• Real-time: BitMEX WebSocket API
• Historical: CSV files in the data/ directory

📋 Requirements

• Python 3.8+
• pandas==2.0.3
• pyarrow==12.0.0
• websockets==12.0
• matplotlib==3.7.2
• seaborn==0.12.2

🎓 Learning Objectives

This project is designed to teach:

1. Real-time Data Processing: Handling live market data streams
2. Data Aggregation: Converting tick data into meaningful time series
3. Market Microstructure: Understanding order flow and volume analysis
4. Technical Analysis: OHLCV and VWAP calculations
5. Data Visualization: Creating professional trading charts
6. Modular Design: Building maintainable, focused components

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

This project is for educational purposes. Please ensure compliance with BitMEX API terms of service when using live data.

⚠️ Disclaimer

This software is for educational purposes only. It is not financial advice. Trading cryptocurrencies involves substantial risk of loss. Always do your own research and consider consulting with a financial advisor.

🔗 Links

• BitMEX API Documentation
• GitHub Repository