Trading Strategy Backtesting
Test an investment idea before you commit to it — no code required.
QuantiBot.ai lets you describe a rule-based strategy in plain language and backtest it across securities and portfolios using historical market data. You define the logic; the platform runs the test and reports how the strategy would have behaved, so you can study its characteristics instead of guessing. Nothing runs live and no orders are placed — the backtest is a study of how the rules would have behaved, so you can size up an idea before taking it further.
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- Conversational research
- No coding required
- For every investor
- Thousands of stocks & ETFs
- Company fundamentals
- Technical indicators
- Historical backtesting
Try it now — backtest a strategy
Pick a signal strategy, ticker and window, then run it on real historical data — no sign-up needed. Every metric is free; upgrade for more strategies, tickers and longer windows.
Six strategy families to research
Start from a family that fits your idea, or build your own:
- Technical — rules built on indicators like moving averages, RSI, MACD, and Bollinger Bands.
- Mean reversion — strategies that respond to statistical extremes.
- Factor / smart beta — value, quality, momentum, and low-volatility tilts.
- Earnings — logic anchored to the earnings calendar.
- Sentiment — rules that incorporate news-derived sentiment.
- My Algorithm — a fully custom, user-defined strategy you assemble and save.
Rotation and momentum strategies
Go beyond single-rule strategies with dual-momentum and sector-rotation models. You set the pieces:
- Weighting scheme — equal-weight, momentum-proportional, or volatility-scaled.
- Lookback window — the period the ranking is measured over.
- Holdings carried — how many positions the rotation holds at a time.
- Safe-haven fallback — which security a dual-momentum rule rotates into.
Measure what actually matters
Every backtest reports the headline metrics and the trades behind them:
- Returns & risk — returns, drawdowns, volatility, and risk-adjusted metrics.
- Trade-level record — how many trades, how often they worked, and the typical win and loss.
- Compare mode — strategies head to head against each other or a benchmark.
- Save and refine — keep any strategy in your library to re-run as your research evolves.
What a backtest can and cannot tell you
Backtesting shows how a strategy would have performed on historical data under a set of assumptions:
- Hypothetical results — they don't include every real-world cost or constraint.
- Not a prediction — past behavior does not guarantee future performance.
Start researching with QuantiBot.ai
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For informational and educational purposes only. QuantiBot.ai is not a broker-dealer or investment adviser and does not provide investment advice.