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How to backtest A-share strategies with T+1, fees and slippage with Tick Stock Panel

Run factor, strategy and minute level backtests that follow A-share T+1 rules and real costs.

Short answer: Start the workbench as above; then build or import a strategy in the strategy engine, or let the AI generate one; run a backtest with T+1, fees and slippage switched on. All steps.

  • Money
  • Intermediate
  • 45 minutes
  • From the README
  • Any agent

Who it is forA quant learner whose simple backtests ignore A-share settlement rules and trading costs.

The problemBacktests that skip T+1, fees and slippage look far better than any real result.

The outcomeBacktests on the same data path as live strategy runs, with factor attribution and no future data leaking in.

What you need first

  • Docker (one container, no external database)
  • Optional: a TickFlow API key or another plugged in data source
  • Optional: an OpenAI compatible LLM endpoint for strategy generation and the chat assistant

Steps

  1. Start the workbench as above
  2. Build or import a strategy in the strategy engine, or let the AI generate one
  3. Run a backtest with T+1, fees and slippage switched on
  4. Review factor attribution and the backtest views before changing parameters

Prompt for your agent

In shy3130/tick-stock-panel, create a strategy for <rule> on A-shares, backtest it over <period> with T+1, fees and slippage, and report return, drawdown and the factors that explain it.

Caveat. Factor mining results are searched out of sample and published explicitly, but past fits can still fail live. Not financial advice: paper trade first, and remember you can lose money.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for shy3130/tick-stock-panel and "Backtest A-share strategies with T+1, fees and slippage"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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