Backtesting a Mean-Reversion Strategy with R and Bollinger Bands

Summary

The article describes a basic four-stage process for systematic strategy work: form a hypothesis, test it, refine it, and move toward production. Its example assumes mean reversion in NIFTY-Bees, an exchange-traded fund, and uses Bollinger Bands on closing prices to generate signals: crossing the upper band triggers a sell signal, while crossing the lower band triggers a buy signal. The described R workflow uses quantstrat and builds the strategy from indicators, signals, and rules, then reviews backtest output.

The article says the example supports its mean-reversion hypothesis, but the excerpt provides no performance figures, test dates, transaction costs, or comparison benchmark. It suggests varying thresholds, tightening entry rules, adding stops, using more data, and accounting for volatility, while acknowledging that live production is outside its scope. The example is therefore a starting point for experimentation, not evidence that the rule will remain profitable out of sample or in live trading.

Key ideas

• The strategy-development process proceeds from hypothesis to testing, refinement, and production.
• The example tests mean reversion in NIFTY-Bees using closing prices and Bollinger Bands.
• The upper-band crossing generates a sell signal, while a lower-band crossing generates a buy signal.
• The R implementation is organized around indicators, signals, and trading rules.
• The excerpt gives no quantitative performance evidence or live-trading evaluation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.