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AlphaProve Documentation

Build trading strategies. Test your ideas. Understand the results.

Write a strategy with AI, create your own in Python, or start with a built-in. Backtest it against historical crypto market data and watch the results stream into a complete performance tearsheet.

Getting started Run your first backtest and learn to read the results.

Write a strategy Turn a trading idea into a testable Python strategy.

SDK reference Explore the context, indicators, and signals in the SDK.

Build and backtest

  • Strategy authoring — write, validate, and submit custom strategies in the sandbox.
  • Examples — start with an EMA crossover or RSI mean-reversion strategy, then explore filters, cooldowns, and sizing.
  • Pine Script import & export — bring a TradingView strategy into your workflow.
  • Connect an AI agent — work with AlphaProve from an external AI assistant through MCP.

Evaluate and refine

Go beyond returns. Understand how a strategy behaves, what it risks, and how it performs as market conditions change.

  • Risk management — explore position sizing, stops, leverage, and portfolio risk.
  • Prop firms — replay a strategy against supported prop-firm rules.
  • Paper trading — follow a strategy on live market data with simulated execution.

How AlphaProve runs your strategies

Every strategy runs server-side in an isolated, no-credentials sandbox. The same backtesting engine serves every tier, with an equity curve, trade list, and risk-adjusted metrics in each completed tearsheet.

Built-in and AI-generated strategies are available on every tier, including free, subject to quota and lookback caps. Writing or editing Python by hand and forward-walk optimisation start at Trader; marketplace publishing is Quant only.