Agent skill · tradermonty
manifoldbt-backtester
Runs a declarative strategy spec over OHLCV bars with the manifoldbt Rust engine, pairs the fill log into round trips, and emits the eight inputs the backtest-expert skill scores. Use when the user wants to execute a backtest, measure a rule they have described, obtain win rate / average win / average loss / max drawdown from real bars, or feed backtest-expert with measured numbers instead of estimates.
What it needs
About 4k tokens when loaded.
What this skill does
manifoldbt Backtester Skill Purpose Execute what backtest-expert teaches. That skill grades a backtest on five dimensions, and its prerequisites say "metrics are user-provided": it scores numbers it never produces. This skill produces them. It runs a strategy over real bars and returns the eight inputs its evaluator asks for. The two chain in one direction: spec, run, evaluate. When to Use This Skill A user describes a rule and wants it measured backtest-expert is about to run and the numbers do not exist yet A win rate, average winner, average loser or drawdown must come from bars A strategy's parameter count must be established for scoring Leave the verdict to backtest-expert. It owns the thresholds and the red flags, and this skill does not duplicate them. Prerequisites Python 3.9+ pip install manifoldbt (Apache 2.0 with Commons Clause; the free tier covers everything this skill does) OHLCV bars as CSV or Parquet with columns timestamp, open, high, low, close, volume No API key required Workflow 1. Write the strategy spec A spec names indicators and one entry condition. Keep it to the smallest rule that states the hypothesis. Every added knob makes an in-sample fit easier to reach by accident, and the evaluator penalises the count. Field reference: references/strategyspec.md. Set feesbps and slippagebps to realistic values before you read any result. A frictionless run scores 0 on execution realism, and over short holding periods costs decide whether an edge survives. 2. Run it The script validates the spec before it touches the data, so you see a spec mistake in a second instead of after a long load. 3. Read the warnings before the numbers The run prints warnings that change how you should read the result: a sample under 30 trades, a span under a year, no friction modelled, or a gap between the engine's win rate and the paired one. Each one is a reason to fix the setup and run again. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/manifoldbt-backtester