Institutional
Prop Trading
Backtesting systematic options strategies — delta-hedged straddles, calendar spreads, vol targeting — across years of calibrated historical volatility data. Test strategy performance across multiple market regimes before deploying capital.
Backtesting an options strategy on historical price data tells you what happened to the underlying. It doesn’t tell you what the options market thought was going to happen. A delta-hedged straddle that looks profitable on price data alone may have been unprofitable in practice because implied volatility was already elevated when the position was entered.
To backtest options strategies accurately, you need historical implied volatility surfaces — the full strike-expiry grid as it existed at each timestamp, not just ATM vol or a single delta bucket.
Real derivatives strategies don’t live in a single instrument type. A basis trade combines spot and futures. A hedged straddle combines options with perpetuals. A cross-asset momentum strategy might hold BTC options and ETH perpetuals simultaneously. The backtester supports composing strategies that mix instrument types in a single backtest.
Options (calls, puts, digital calls, digital puts, straddles, strangles), futures (fixed or floating tenor), perpetuals (with funding rate accrual), and spot. Each component specifies its own entry time, exit time, side, quantity, and asset — a single backtest can hold BTC options alongside ETH perpetuals alongside SOL spot.
A delta-hedged straddle that buys ATM options and hedges with perpetuals, rebalancing hourly with funding costs tracked. A calendar spread selling 7-day vol and buying 30-day vol on the same underlying. A cross-asset relative value trade that goes long BTC call skew and short ETH call skew simultaneously. All executable in a single backtest with consistent P&L attribution across legs.
Prices options using the SVI-calibrated implied volatility surface, spot prices, and forward curves from market data. This reflects what an option should have been worth at each timestamp based on the calibrated surface — useful for strategies that trade theoretical edge against the market.
Prices against actual exchange bid-ask quotes for listed contracts. The backtester snaps to the nearest available listed instrument matching the requested strike and tenor. If listed quotes are unavailable, it falls back to theoretical pricing with a warning — so the backtest never silently gaps.
A strategy that looks profitable on theoretical pricing may underperform on listed pricing due to bid-ask spread, slippage, and the difference between where the model says an option is worth and where it actually trades. Running both modes on the same strategy reveals the gap between theoretical edge and executable edge.
Hedge option delta with the spot underlying. One-shot at entry or periodic rebalancing at configurable intervals. The backtester tracks hedge P&L separately from strategy P&L for clean attribution.
Hedge with perpetual futures instead of spot. Funding rate costs are accrued and tracked — the backtest output separates strategy P&L, hedging P&L, and funding P&L so you can see exactly where returns come from and what the carry cost of the hedge is.
Each backtest returns per-trade records (entry price, exit price, Greeks at entry and exit, P&L, funding), per-instrument summaries, and an overall summary including net P&L, win rate, Sharpe ratio, and maximum drawdown. Warnings flag any data fallbacks — if a listed price was unavailable and theoretical was substituted, the backtest tells you.
The backtest_strategy MCP tool executes backtests directly from an AI agent. Define the strategy in structured parameters, receive results, iterate on entry thresholds or hedge frequency, and re-run — all within a single conversation. The bi-directional nature of MCP means the agent can inspect intermediate results and refine the strategy based on what the data reveals.
Historical data spans BTC, ETH, SOL, XRP, HYPE, ADA, SUI, and additional assets. Both composite and exchange-specific volatility surfaces are available for theoretical pricing. Listed pricing uses actual exchange quotes from Deribit, Bybit, OKX, GateIO, and additional venues. The REST API provides historical data for multi-year backtests. The MCP server provides the same data through backtest_strategy and data_retrieval tools.
