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Connors RSI Reversion
Blend short-horizon RSI, streak persistence, and return percentile rank into one short-term reversal score.
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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Pro Access
Connors RSI 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
Connors RSI is a deliberately short-horizon composite indicator. Instead of relying on one oscillator, it averages three pieces of evidence: a very fast RSI on price, an RSI on the current up/down streak length, and the percentile rank of the latest one-day return inside a longer sample.
The motivation is practical. Short-term reversals are usually not explained by one feature alone. You want to know whether price is stretched, whether the move has persisted for several consecutive days, and whether the latest return is unusually large relative to recent history. Connors RSI packages those three checks into one score.
That makes it more nuanced than a plain RSI threshold. A market can be oversold on price but not especially unusual in streak or return percentile terms. Connors RSI tends to fire only when several kinds of short-term pressure line up at once.
The weakness is fragility. Because it combines multiple components, the signal is more parameter-sensitive than a basic oscillator. It can also overtrade during stressed markets where "extreme" short-term moves are no longer rare but simply the new regime.
The Math
Read this as a compact model summary: what the signal sees, what it ignores, and where fragility can creep in.
RSI_price(t) = RSI(Close, rsi_period)
streak(t) = consecutive up/down closes with sign
RSI_streak(t) = RSI(streak, streak_period)
PctRank(t) = percentile rank of 1-day return within the last rank_window days
CRSI(t) = (RSI_price(t) + RSI_streak(t) + PctRank(t)) / 3
Signal(t) = +1 if CRSI(t) < oversold
= -1 if CRSI(t) > overbought
= 0 otherwise
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| rsi_period | int | 3 | RSI period applied to closing prices |
| streak_period | int | 2 | RSI period applied to up/down streak length |
| rank_window | int | 100 | Window used for one-day return percentile rank |
| oversold | float | 20.0 | Connors RSI threshold for long entries |
| overbought | float | 80.0 | Connors RSI threshold for short entries |
Source Code
Source access for this built-in strategy is included with Pro.
Further Reading
- Connors, L. & Alvarez, C. (2009). Short Term Trading Strategies That Work. TradingMarkets Publishing.
- Connors Research (2012). Connors RSI indicator notes.
- Chan, E. (2013). Algorithmic Trading. Wiley.
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