A Candle High-Low Mean-Reversion Strategy in Quantiacs

Summary

The article walks through a simple rule-based strategy implemented with the Quantiacs Python toolbox. It describes configuring a backtest, loading stock or futures data, setting parameters such as the lookback, capital, and slippage, and examining results through the platform’s visualization and performance metrics. The example uses Apple and Amazon shares. It compares price changes across a chosen number of prior candles: when all changes are positive, it takes a short position in anticipation of mean reversion; when all are negative, it takes a long position.

The piece is primarily a platform tutorial and illustrates the mechanics of expressing signals and running a backtest. It provides no performance figures, robust validation, transaction-cost analysis beyond mentioning slippage, or evidence that the rule has an enduring edge. The example is explicitly simple, and the account does not describe position sizing, exits, or out-of-sample testing. Its main reusable lesson is the workflow from data and settings to signals and result inspection, not a validated trading recommendation.

Key ideas

• The Quantiacs toolbox supports loading market data, configuring strategy settings, running tests, and viewing results.
• The example generates signals from the signs of price changes across a selected candle lookback.
• It shorts after a run of positive changes and goes long after a run of negative changes, expecting mean reversion.
• The article does not report results or establish that the example strategy is profitable.
• The tutorial mentions slippage but does not detail broader validation, exits, or position sizing.

Tags

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