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Stochastic Reversion
Fade price moves that push the close to the edge of its recent high-low range.
Strategy briefing
Understand what this strategy is actually betting on before you touch the parameter panel.
Use category and difficulty as context
Compare before optimizing
Map the strategy to a regime thesis
Read the math as a constraint system
Use parameters to test fragility, not creativity
Learning linkup
Open Learning Hub
Read the model brief like a skeptic
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Strategy families & selection
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Validation & skepticism
Pro Access
Stochastic Reversion is part of the expanded Pro strategy library. The guide stays public, but running it in backtest, compare, paper tracking, and source-code view is a Pro capability. Pro currently unlocks 8 additional pre-built strategies.
The Intuition
The stochastic oscillator measures where the close sits inside its recent trading range. A reading near 0 means price closed near the range low. A reading near 100 means it closed near the range high. Mean-reversion traders interpret those extremes as signs of short-term exhaustion.
The signal is not about valuation. It is about local positioning pressure. If the close repeatedly prints near the bottom of the recent range, the move may be stretched enough for a short rebound. If it repeatedly prints near the top, the opposite logic applies.
This is one of the classic short-horizon reversal tools because it is easy to interpret and naturally adapts to the recent high-low envelope. It is especially common in markets where prices oscillate inside broad ranges instead of trending persistently.
Its weakness is exactly that simplicity. In a true trend, "oversold" can stay oversold and "overbought" can stay overbought for far longer than the indicator implies. Traders usually pair it with regime filters or use it only on assets where pullbacks tend to snap back quickly.
The Math
Read this as a compact model summary: what the signal sees, what it ignores, and where fragility can creep in.
HighestHigh(t) = max(High[t-n : t])
LowestLow(t) = min(Low[t-n : t])
%K(t) = 100 × (Close(t) - LowestLow(t)) / (HighestHigh(t) - LowestLow(t))
Signal(t) = +1 if %K(t) < oversold
= -1 if %K(t) > overbought
= 0 otherwise
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| window | int | 14 | Lookback window for the range |
| oversold | float | 20.0 | Percent-K threshold for long entries |
| overbought | float | 80.0 | Percent-K threshold for short entries |
Source Code
Source access for this built-in strategy is included with Pro.
Further Reading
- Lane, G. (1984). Lane's Stochastics. Market Technicians Association Journal.
- Murphy, J. (1999). Technical Analysis of the Financial Markets. NYIF.
- Connors, L. & Alvarez, C. (2009). Short Term Trading Strategies That Work. TradingMarkets Publishing.
Related Mean Reversion Strategies
Use nearby strategies to compare the same market hypothesis under different signal constructions.