options strategy playground

Most options tools show one strategy at a time and assume you already know which one you want. The real question is the reverse: given a view like "$X in N days", which structure fits it, and what are you giving up with each one? Answering that by hand means re-pricing every leg for every scenario.

You set a target price, a horizon, and optionally an implied-volatility shift. The engine prices 19 strategy templates, from single legs and spreads to condors, butterflies, calendars, and covered positions, and shows them in one sortable table: P&L at the target, max profit and loss, breakevens, probability of profit, expected P&L under your view, and net Greeks. Every strike, quantity, and premium can be edited. Typing in your own premium back-solves the implied vol, so the Greeks and the curves before expiry stay consistent with the price you entered.

The playground is step one: build a strategy and see how it behaves. Next comes backtesting those strategies against historical prices and volatility, then deploying the ones that hold up as automated strategies. It's built for my own trading first, and for anyone who wants to test and compare options strategies before putting money behind them.

1. 01Price view + horizon
2. 02Strategy templates → legs
3. 03Black-Scholes pricing on a price × time grid
4. 04Side-by-side comparison + charts

• EnginePython · NumPy
• APIFastAPI
• AppReact + TypeScript · Plotly
• DeployVercel · Playwright + pytest in CI

• Every strategy re-priced in a single vectorized request
• Pricing checked against textbook values, put-call parity, and finite-difference Greeks
• Engine is a plain Python package the next stage (forecasting and strategy selection) can import directly