What is an AI backtesting tool?

An AI backtesting tool turns a trading idea written in plain language into strategy code, runs that code against historical market data, and reports how it would have performed. The useful ones also check for lookahead bias and overfitting so the result is evidence rather than a story.

Is there a free AI backtesting tool?

Yes. Scout's Free plan costs $0 and includes the CLI with unlimited local strategies and backtests. You bring your own model key from Anthropic, OpenAI, or Google, or run a local model through Ollama or LM Studio.

Can ChatGPT or Claude backtest a trading strategy on their own?

A chat assistant can write backtest code, but numbers in a chat reply are only results if code produced them from market data you can identify. Scout uses the model you choose to write the strategy, then runs it locally on historical data with lookahead and robustness checks.

Which markets can Scout backtest?

Scout backtests equities through yfinance data and crypto through Binance data, with yfinance as the crypto fallback. Alpaca, Binance, and Interactive Brokers connections are documented for moving a strategy toward paper or live trading.

Is a good backtest a reason to trade?

No. A backtest is research, not investment advice. Historical results do not guarantee future returns, and Scout's verdicts are designed to keep weak strategies in research rather than promote them.

Scout by Polarline

AI backtesting tool

Describe a trading strategy in plain English. Scout writes inspectable Python, backtests it on historical data, and then tries to break it with walk-forward, Monte Carlo, and parameter checks before you trust the result.

Install the free CLI Open Scout Cloud

How AI backtesting works in Scout

Describe the idea
State the market, timeframe, entry and exit rules, and risk limits in plain English. Scout can research the market first so the premise is grounded in the data you will test on.
Get Python you can read
Scout writes the strategy as a Python class with an on_bar method into a versioned workspace folder. Nothing is hidden behind a chat reply: you can open, edit, and diff the file.
Validate before it runs
The strategy contract is checked before a backtest counts. Orders fill on the next bar, and only the current bar's open is safe to use at decision time, so indicators are built from prior closes.
Backtest and stress it
The engine reports returns, risk, drawdown, trade, and exposure metrics, then optional walk-forward, Monte Carlo, regime, benchmark, and per-symbol blocks show whether the edge survives.

What a trustworthy backtest has to survive

Generating strategy code is the easy part. Most backtests fail later, quietly, when the code peeks at data it could not have had or when one parameter set happens to fit the past. Scout's engine is built around those failure modes.

Lookahead bias

A strategy that reads today's close to trade at today's close is not tradable. Scout fills orders on the next bar and treats only the bar's open as known at decision time.

Overfitting

Walk-forward analysis splits history into folds (70/30 in-sample to out-of-sample by default) and reports deflated and probabilistic Sharpe ratios. A strategy that only works in-sample is flagged as overfit.

Luck in the trade order

Monte Carlo resampling shuffles or block-bootstraps trades and reports p5, p50, and p95 outcomes, so one lucky sequence does not pass for a durable result.

Fragile parameters

Parameter sweeps test up to 27 combinations read through self.params.get, so you can see whether neighbouring settings hold up or the result depends on one magic number.

Bad data

A data-quality block reports coverage, gaps, and OHLC violations, so a missing month or a broken bar does not quietly become performance.

Regime dependence

A regime breakdown reports return, Sharpe, drawdown, and win rate separately for low, mid, and high volatility periods instead of one blended number.

What you get from each run

Every run reports more than 60 metrics across return, risk, ratio, drawdown, trade, exposure, and stability categories, with optional benchmark, Monte Carlo, walk-forward, regime, data-quality, and per-symbol attribution blocks. The full trade log and a JSON report are written into the strategy's versioned workspace folder, so results stay attached to the exact code that produced them.

Readable code

Strategies are plain Python using a small broker API: buy, sell, position, cash, equity, and price.

Versioned workspace

Each meaningful change gets its own version folder, so you can compare runs instead of overwriting them.

Build, Research, Chat

Switch modes with Tab: build a strategy, research a market, or ask questions without touching code.

Your model, your key

Use Claude, OpenAI, Gemini, or a local model. Switch providers with /init.

Free to start, room to grow

The Free plan is $0 and covers the CLI with unlimited local strategies and backtests on macOS and Linux. Plus ($20 per month) adds Scout Research, scheduled watchers, and three cloud-hosted live runs. Pro ($50 per month) adds more live runs, TradingView webhooks, a Telegram bot, and a metered model allowance. Teams that need a private deployment can talk to us about Scout Private or Scout Managed.

Compare plans See ScoutBench results

AI backtesting guides

Go deeper on the engine, the data, and how Scout compares with other tools.

Backtesting referenceEvery metric, report block, and limitation in Scout's backtest engine.
Best AI backtesting tools in 2026Scout, QuantConnect, BacktestLoop, TradeTest.ai, Composer, LumiBot, and Jesse compared.
Backtest a strategy in the terminalA step-by-step run from idea to metrics on the command line.
yfinance backtesting guideWhat free Yahoo Finance data is good for, and the pitfalls to check.
Scout vs QuantConnectAn AI terminal assistant compared with a code-first quant platform.
Plain English to trading strategyHow to phrase an idea so it becomes testable rules.
Backtest a strategy with ClaudePrompts, lookahead checks, and running Claude-written code on real data.
ChatGPT trading strategy backtestWhy chat numbers are not results, and a workflow that produces real ones.

Questions

What is an AI backtesting tool?: An AI backtesting tool turns a trading idea written in plain language into strategy code, runs that code against historical market data, and reports how it would have performed. The useful ones also check for lookahead bias and overfitting so the result is evidence rather than a story.
Is there a free AI backtesting tool?: Yes. Scout's Free plan costs $0 and includes the CLI with unlimited local strategies and backtests. You bring your own model key from Anthropic, OpenAI, or Google, or run a local model through Ollama or LM Studio.
Can ChatGPT or Claude backtest a trading strategy on their own?: A chat assistant can write backtest code, but numbers in a chat reply are only results if code produced them from market data you can identify. Scout uses the model you choose to write the strategy, then runs it locally on historical data with lookahead and robustness checks.
Which markets can Scout backtest?: Scout backtests equities through yfinance data and crypto through Binance data, with yfinance as the crypto fallback. Alpaca, Binance, and Interactive Brokers connections are documented for moving a strategy toward paper or live trading.
Is a good backtest a reason to trade?: No. A backtest is research, not investment advice. Historical results do not guarantee future returns, and Scout's verdicts are designed to keep weak strategies in research rather than promote them.

Scout is research software. Backtests and verdicts are not investment advice, and past performance does not guarantee future results.