High-precision zero-DTE options analytics, second-order Greeks (Vanna, Charm, Volga), and autonomous strategy code synthesis built for quantitative funds, hedge desks, and intraday proprietary traders.
Dealer Hedging Pressure • Green = Calls, Red = Puts
| Call Delta (Δ) | Call OI | Gamma (Γ) | Strike Price | Vanna | Put OI | Put Delta (Δ) | IV (%) |
|---|---|---|---|---|---|---|---|
| +0.74 | 54,120 | 0.0018 | 24,900 | +0.012 | 118,900 | -0.26 | 13.8% |
| +0.62 | 71,840 | 0.0024 | 25,000 | +0.018 | 92,400 | -0.38 | 12.9% |
| +0.51 | 104,200 | 0.0031 | 25,100 (ATM) | +0.024 | 101,650 | -0.49 | 12.2% |
| +0.38 | 112,500 | 0.0026 | 25,200 | -0.015 | 64,200 | -0.62 | 12.6% |
| +0.22 | 142,400 | 0.0019 | 25,300 | -0.021 | 41,100 | -0.78 | 13.4% |
Select a quantitative workflow below to inspect Claude's real-time prompt reasoning, AST parse, and Python execution script.
→ Ingesting order book depth at 25,124.60 spot...
1. Net Gamma is positive (+$482.4M). Dealer counter-hedging will act as a stabilizing cushion between 25,050 and 25,250.
2. Vanna coefficient (+0.024) indicates implied vol crush post-14:00 IST will rapidly deflate 25,100 CE & PE premiums.
3. Generating dynamic gamma-scalping trigger with auto-delta boundary tolerance of ±0.05.
# TradeGex Autonomous Engine | Claude 3.5 Sonnet Integration import tradegex as tg from tradegex.greeks import Vanna, Charm, NetGEX @tg.strategy(name="DeltaNeutral_GammaShield", max_slip_bps=1.8) async def execute_dynamic_rebalance(orderbook, gex_engine): spot = orderbook.get_spot("NIFTY") net_gex = gex_engine.calculate_net_gamma(window="0DTE") if net_gex.dealer_regime == tg.Regime.POSITIVE_PINNING: # Harvest decay while delta drift remains within 5 bps await tg.orders.rebalance_straddle( underlying="NIFTY", center_strike=25100, hedge_instrument="FUTURES", tolerance_delta=0.05 )
Traditional retail platforms display static Greeks. TradeGex models full market maker dealer positioning, continuous gamma imbalance shifts, and automated hedging flows.
Calculate exact zero-gamma flip lines, major call/put walls, and dealer inventory positioning continuously streamed across OPRA and NSE tick feeds.
Map Vanna, Charm, and Volga dynamics in real-time. Detect intraday volatility crushes and charm decay ramps before they hit spot asset pricing.
Translate raw options chain hypotheses into production-ready Python backtests, execution scripts, and risk constraints with zero hallucinations.
Analyze bid-ask queue depth, institutional block prints, and cumulative delta divergence to pinpoint smart money absorption levels.
Low-latency REST and WebSocket streaming bridges designed for direct algorithmic ingestion into custom C++, Rust, and Python execution engines.
Run Monte Carlo simulations across sudden ±5σ spot gaps and implied volatility spikes to stress test portfolio margin requirements before market open.
A purpose-built quantitative pipeline pairing 200,000-token context ingestion with high-speed deterministic trading bridges.
TradeGex feeds full multi-strike option surfaces, tick histories, and Greeks matrices directly into Claude 3.5 Sonnet in a single continuous context frame without chunking loss.
Input: Raw OPRA Book + DDL Schema
Claude evaluates skew anomalies, computes second-order cross-derivatives, and leverages JSON tool-calling to emit strictly validated quantitative execution schemas.
Output: Deterministic Risk Parameters
Direct synthesis of backtest-ready Python strategies and automated post-trade slippage analysis. Iterative error feedback loops automatically refine order execution boundaries.
Execution: Sub-millisecond Broker Hooks
Early beta access is currently prioritized for quantitative researchers, proprietary trading desks, and registered financial institutions.