Multi-Timeframe RSI and Stochastic Extreme Reversal Strategy

Summary

This strategy averages RSI and smoothed stochastic readings from four configurable timeframes, then uses the combined readings to identify extreme conditions. It opens a long when both averages are oversold and a short when both are overbought. The example closes longs after the stochastic average reaches its upper threshold, RSI is at least at its midpoint, and price is no lower than the position average; short exits use the opposite conditions. RSI and stochastic lengths, thresholds, and timeframes can be adjusted.

The document provides BTC/USDT futures backtest settings for a stated period, but no return, drawdown, or trade statistics, so it offers no evidence of profitability. The introductory description says positions close when the indicators return to the middle, while the code uses more specific thresholds and also checks price relative to the entry average. Averaging readings across different timeframes may smooth signals, but can also mix lagging horizons; repeated entries and exposure during persistent trends remain concerns. Costs, execution assumptions, and the behavior of higher-timeframe values are not analyzed.

Key ideas

• The strategy averages RSI and stochastic values across four configurable timeframes.
• It opens long positions when both indicator averages are oversold and shorts when both are overbought.
• The code adds price-versus-entry conditions to its indicator-based exit rules.
• The published BTC/USDT futures settings do not include reported performance statistics.
• Multi-timeframe averaging may smooth signals but does not prevent losses in persistent trends.

Tags

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