Project: Titan-X Prime (Quantitative Options Scalper) Target Environment: Python 3.13 (Free-Threaded/No-GIL) Primary Goal: To capture high-conviction "Gamma Bursts" in Nifty Options while neutralizing the 2026 0.15% STT tax drag through microstructure-based precision. 1. System Architecture (Concurrency Model) The system must utilize a Multi-Threaded Producer-Consumer model to leverage Python 3.13’s true parallelism. • Thread 1 (Ingestion): WebSocket feed for Nifty Spot, Futures, and ATM/Near-OTM Option Chain (±10 strikes). • Thread 2 (Microstructure): Real-time calculation of Weighted Order Book Imbalance (WOBI) every 100ms. • Thread 3 (GEX Engine): Recalculation of Dealer Gamma Exposure (GEX) and Zero-Gamma Level every 60 seconds. • Thread 4 (Execution): Signal validation (Quad-Gate) and Order Management (API calls). 2. The Quad-Gate Signal Logic No order shall be fired unless all four gates return TRUE. Gate 1: Physics (Regime Detection) • Calculation: Total Net Gamma per strike ($OI \times \text{Gamma} \times \text{Spot}$). • Logic: * Short Gamma Regime (Spot < Zero-Gamma): Trend-following/Breakout strategies ENABLED. o Long Gamma Regime (Spot > Zero-Gamma): Mean-reversion only. Breakout signals DISABLED (to avoid chop). Gate 2: Truth (Microstructure) • Calculation: $WOBI = \frac{\sum (BidVol_i \times w_i) - \sum (AskVol_i \times w_i)}{\sum (BidVol_i \times w_i) + \sum (AskVol_i \times w_i)}$ where $w = [1.0, 0.5, 0.25, 0.125, 0.0625]$. • Validation: * Long: $WOBI > 0.35$. o Short: $WOBI < -0.35$. Gate 3: Probability (ML Layer) • Model: Lightweight XGBoost (pre-trained). • Inputs: RSI Slope, Distance from VWAP, WOBI, Time of Day. • Threshold: Probability score $> 0.65$. Gate 4: Technical (The Trigger) • Timeframe: 1-Minute Candle. • Long Trigger: Close above Upper Bollinger Band (20, 2) + Candle Body $> 60\%$ of total range. ________________________________________ 3. Execution & Risk Specs • Instrument: Deep ITM Options ($\Delta \approx 0.70$) to maximize point capture vs. fixed STT. • Entry: Limit Order at $LTP + 3$ points (Aggressive Fill). • Sizing: Dynamic ATR-based sizing (Risk $\div$ (ATR $\times$ Lot Size)). • Mandatory Stop Loss: Max(10% Premium, 8 points). • Exit Protocol: 1. Fast Exit: Exit if $LTP < \text{Prev 1-min Low}$. 2. Predictive Exit: Exit if $WOBI$ flips sign for $> 3$ seconds. 3. Trail: Trigger 0.5x ATR trail once profit exceeds 1.5x ATR. 4. Technical Implementation Notes for Developer 1. Parallelism: Use threading.Thread with shared-memory objects; avoid multiprocessing to eliminate serialization latency. 2. Speed: Implement WOBI and GEX calculations using NumPy vectorization or Numba (JIT). 3. Connectivity: Use DhanHQ or Fyers API with a persistent WebSocket connection. 4. Tax Sensitivity: All "Break-Even" logic must be calculated as: $\text{Entry Price} + (\text{Total Statutory Charges} \div \text{Lot Size})$.