manifoldbt-backtester

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.

{
  "name": "sma_cross_costed",
  "indicators": {
    "fast": { "type": "sma", "period": 20 },
    "slow": { "type": "sma", "period": 60 }
  },
  "entry": { "left": "fast", "op": ">", "right": "slow" },
  "size": 1.0,
  "stop_loss_pct": 1.5,
  "fees_bps": 5.0,
  "slippage_bps": 2.0
}

Field reference: references/strategy_spec.md.

Set fees_bps and slippage_bps 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

python3 scripts/run_backtest.py \
  --spec strategy.json \
  --data bars.csv \
  --symbol BTCUSDT \
  --json-out result.json