TradingBacktesting Backtester
Runs parameterized backtests, sweeping tickers, intervals, and strategy parameters to find and rank the best configurations
PPredictive Labs·Trading
Library skill — the default version is maintained in GitHub; edits you make live in your own clone.
How does this work?
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ChatGPT opens a new chat with the skill loaded. If it's too long for a link, it's copied to your clipboard — just paste.
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Claude works the same way. To install it permanently, download the .zip and upload it under Claude → Settings → Capabilities → Skills (Pro/Team/Enterprise).
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Copy prompt copies the skill so you can paste it into any assistant, including Grok.
Backtester
Turns a strategy concept into a systematic historical backtest. It sweeps portfolios, intervals, and strategy parameters, then ranks the resulting configurations by performance.
For research and education only. This is not investment advice; you execute any real trades yourself through your own broker.
When to use
- You want to test a rules-based strategy against historical price data.
- You want to sweep parameters (thresholds, hold periods, sizing) to compare configurations.
- You need reproducible, ranked results before considering paper or live trading.
What to provide
- Strategy concept or explicit rules (entry, exit, take-profit, stop-loss).
- Universe or ticker list, and the date range plus data interval (e.g. daily, hourly).
- Initial capital, position sizing, and any risk limits.
- Which parameter values to vary, and which metric to optimize (e.g. Sharpe, total return, win rate).
- Historical price data, or a market-data source you will pull it from yourself.
How to work through it
- Restate the strategy as precise rules and confirm any assumptions before running.
- Build the parameter grid from the values to vary (e.g. dip threshold, take-profit, hold days, stop-loss, position size).
- For each combination, simulate entries and exits bar by bar over the date range, keeping signal timing separate from fill timing.
- Record per-trade fills and per-configuration metrics: total return, annualized return, max drawdown, Sharpe, win rate, and trade count.
- Rank configurations by the chosen objective and flag the best one, noting overfitting risk when many variants are tested.
- Recommend out-of-sample or walk-forward checks before trusting a tuned configuration.
Presenting results
- Present every result as one or more clear Markdown tables — one per section, each with a short heading (e.g. trades, per-parameter results, P&L, metrics).
- Keep prose minimal; put the substance in the tables.
- Offer the user a downloadable PDF (formatted) and CSV (the underlying rows/trades), and generate them when asked.
- Never invent figures or fills. If a required input is missing, list exactly what you need and ask for it first.