Trading Strategy Backtesting

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, using equal-weight, momentum-proportional, or volatility-scaled weighting to research how capital rotates across assets over time. You set the pieces that define the model — the lookback window the ranking is measured over, how many holdings the rotation carries at a time, and which safe-haven security a dual-momentum rule falls back to — then re-run the test with different settings to see how sensitive the result is to each choice.

Measure what actually matters

Every backtest reports returns, drawdowns, volatility, and risk-adjusted metrics, plus the trade-level record behind them — how many trades the rules would have taken, how often they worked, and the size of the typical win and loss. A compare mode puts strategies head to head against each other or a benchmark, and any strategy can be saved to your library to refine and 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. Results are hypothetical, do not include every real-world cost or constraint, and do not predict or guarantee future performance — this is a research and education tool, not investment advice or a recommendation to trade.