🎯 Project Overview

Developed an AI-based algorithmic trading workflow with ML prediction and RL optimization. The system includes real-time data ingestion, signal generation, automated execution, and continuous retraining. Complete with risk filters and backtesting system for strategy validation.

💼 Business Impact

15-35% improvement in average strategy performance
Zero emotional bias - automated execution eliminates human emotions
24/7 trading without manual intervention
Risk management prevents catastrophic losses
Backtesting validation ensures strategies work before live trading

🛠️ Technical Architecture

Workflow Components

1. Data Ingestion

Live market feed integration: candlestick data, Level 2 order book data, technical indicators. Real-time streaming from multiple exchanges and brokers (Zerodha, Fyers, Binance).

2. Feature Engineering

Technical indicators: SMA/EMA, RSI, MACD, Bollinger Bands, volatility measures, order book depth analysis, volume profile, support/resistance levels.

3. Model Training

ML models for price direction prediction (LSTM, Transformer networks). RL agent (PPO, DQN) for strategy optimization and position sizing. Ensemble methods combining multiple models for robustness.

4. Signal Generation

Buy/Sell/Hold predictions with confidence scores. Entry/exit signals with stop-loss and take-profit levels. Position sizing based on risk parameters and portfolio allocation.

5. Risk Management Layer

Stop-loss (SL) and take-profit (TP) automation. Maximum drawdown limits, volatility filters, position size limits, correlation checks to prevent over-concentration.

6. Execution Engine

Integrates with broker APIs (Zerodha Kite Connect, Fyers API, Binance API, MetaTrader). Order placement, modification, cancellation. Real-time position tracking and P&L monitoring.

7. Backtesting & Monitoring

Historical data backtesting with realistic slippage and commission modeling. Daily model retraining on latest data. Drift detection alerts when model performance degrades.

Core Technologies

Python - Core trading logic and ML pipeline
TensorFlow/PyTorch - Deep learning models for prediction
Scikit-Learn - Traditional ML algorithms and preprocessing
MetaTrader/Zerodha Kite Connect - Broker API integration
Airflow - Workflow orchestration and scheduled retraining
Docker - Containerized deployment for reliability

🔧 Technical Challenges Solved

Challenge 1: Real-Time Data Latency

Problem: Market data arrives with delays, causing stale signals and missed opportunities.

Solution: Implemented low-latency data pipeline with WebSocket connections, local caching, and predictive signal generation that accounts for expected delays.

Challenge 2: Model Overfitting

Problem: Models perform well on historical data but fail in live trading.

Solution: Walk-forward optimization, out-of-sample testing, regularization techniques, and ensemble methods that combine multiple models to reduce overfitting risk.

Challenge 3: Risk Management

Problem: Single bad trade can wipe out weeks of profits.

Solution: Multi-layer risk management: position sizing (Kelly Criterion), stop-loss automation, maximum drawdown limits, correlation checks, and daily loss limits.

📊 Performance Metrics

15-35%

Strategy Improvement

Emotional Bias

24/7

Automated Trading

<100ms

Signal Latency

💡 Key Features

Multi-timeframe analysis: Signals from 1-minute to daily charts
Multi-asset support: Stocks, futures, options, crypto
Portfolio optimization: Risk-adjusted position sizing across multiple positions
Backtesting engine: Test strategies on years of historical data
Real-time monitoring: Dashboard showing live positions, P&L, signals

🚀 Results

✅ 15-35% improvement in average strategy performance vs manual trading
✅ Zero emotional decisions - all trades based on data and rules
✅ 24/7 operation without manual intervention
✅ Risk-controlled - maximum drawdown limits prevent catastrophic losses
✅ Scalable - handles multiple strategies and assets simultaneously

AI-Driven Algo Trading Workflow (Predictive + Reinforcement Learning)