Real-Time Financial Market Data Pipeline with Reinforcement Learning for Trading Optimization

Overview

Develop a real-time data pipeline that collects financial market data from open-source APIs (e.g., Alpha Vantage, Binance API, Yahoo Finance, Polygon.io), processes it using a message broker like Kafka or RabbitMQ, transforms it into a structured format, and uses reinforcement learning (RL) for trading decisions. The RL agent will operate in a simulated trading environment based on the processed real-time data.

Tech Stack

• Data Sources: Open APIs (e.g., Binance, Alpha Vantage, Yahoo Finance, Quandl)
• Message Broker: Apache Kafka / RabbitMQ (for streaming market data)
• Processing Framework: Apache Spark / Pandas (for data transformation)
• Storage: PostgreSQL / InfluxDB / MongoDB (for historical data storage)
• RL Algorithm: Deep Q-Learning (DQN) / Proximal Policy Optimization (PPO)
• Simulation Environment: Gymnasium / OpenAI Gym for market simulation
• Visualization: Plotly / Dash for real-time analytics dashboard
• Containerization: Docker + Kubernetes (for deployment)
• Infrastructure: AWS Lambda / Google Cloud Functions (for serverless execution)

Pipeline Architecture

Data Ingestion:

Collect real-time market price, order book, and trading volume from APIs. Stream data to Kafka topics (e.g., price-stream, order-book-stream).

Data Transformation:

Consume raw data from Kafka. Convert data into OHLCV format (Open, High, Low, Close, Volume). Perform feature engineering (e.g., technical indicators like RSI, MACD, Bollinger Bands). Store processed data in a time-series database (InfluxDB) or PostgreSQL.

Reinforcement Learning (RL) Trading Strategy:

Train an RL agent (DQN/PPO) on historical data. Deploy the agent to trade in real time based on the latest processed data. Execute buy/sell actions through paper trading or simulation.

Evaluation & Backtesting:

• Log RL agent decisions.
• Compare RL performance vs. baseline strategies (e.g., SMA crossover, momentum trading).
• Visualize results in a real-time dashboard.

Future Extensions

• Add multi-agent RL for market-making.
• Integrate sentiment analysis from news sources and Twitter.
• Extend to crypto, forex, or commodities trading.
• Deploy automated backtesting using backtrader.

Why This Project?

• ✅ Real-time data processing with Kafka and message brokers
• ✅ Reinforcement learning (RL) applied to trading decisions
• ✅ Scalable and modular architecture
• ✅ Showcases full-stack ML engineering skills