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.

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

manifoldbt Backtester Skill

2. When to Use

  • 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.

3. 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

4. Quick Start 

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

5. 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

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.

Three conditions stop the handoff instead of producing a score: no completed round trips, missing or non-finite maximum drawdown, and scratch trades. The evaluator has no scratch input, so passing a population that contains them would make its derived expectancy disagree with the completed trades.

4. Hand off to backtest-expert

The run ends with a command you can paste. Run it, or invoke the backtest-expert skill with the same figures:

python3 skills/backtest-expert/scripts/evaluate_backtest.py \
  --total-trades 3854 --win-rate 20.24 \
  --avg-win-pct 0.2917 --avg-loss-pct 0.2342 \
  --max-drawdown-pct 99.2893 --years-tested 0 \
  --num-parameters 3 --slippage-tested

6. Resources

References:

  • skills/manifoldbt-backtester/references/metric_bridge.md
  • skills/manifoldbt-backtester/references/strategy_spec.md

Scripts:

  • skills/manifoldbt-backtester/scripts/bridge.py
  • skills/manifoldbt-backtester/scripts/round_trips.py
  • skills/manifoldbt-backtester/scripts/run_backtest.py
  • skills/manifoldbt-backtester/scripts/spec.py