Mean Reversion RSI and Price Range Momentum Strategy

Strategy Overview
The Mean Reversion RSI and Price Range Momentum Strategy is a quantitative trading approach that combines the Relative Strength Index (RSI) with historical price range analysis. Based on mean reversion theory, this strategy enters long positions when the market is severely oversold and the price is in the lower range of its 52-week trading band. It exits positions when the price reverts to the mean level or when the RSI indicates overbought conditions. By simultaneously monitoring technical indicators and price position, this strategy aims to capture rebound opportunities after market overselling, seeking stable returns with relatively low risk.

Strategy Principles
The core logic of this strategy is built on cross-validation of two key conditions:

1. RSI Oversold Signal: The strategy uses a 14-period RSI indicator, considering the market oversold when the RSI value falls below 30, which typically signals a potential rebound.

2. Price in Lower Range: The strategy calculates the price range over the past 252 trading days (approximately 52 weeks) and identifies whether the current price is within the bottom 10% of this range.

The entry condition requires both signals to be met simultaneously - RSI below 30 and price within the bottom 10% of the 52-week range. This dual confirmation mechanism significantly increases the reliability of trading signals.

Exit conditions are based on either of the following:

• RSI rising above 70, indicating the market may have entered overbought territory
• Price recovering to the midpoint of the 52-week range (average of the highest and lowest points)

This exit mechanism ensures profits are secured when price completes its mean reversion or when the market becomes overheated.

Strategy Advantages

1. Dual Confirmation Mechanism: By combining RSI indicators with price position analysis, the strategy reduces the possibility of false signals and improves trading accuracy.

2. Built-in Risk Control: The strategy only enters when prices are at historical lows, theoretically reducing purchase costs and potential downside.

3. Clear Exit Conditions: It establishes definite exit points based on technical indicators and price levels, helping to avoid emotional trading and premature profit-taking.

4. Comprehensive Backtesting Metrics: The strategy incorporates comprehensive backtesting statistics, including net profit, number of trades, win rate, average trade profit, and maximum drawdown, facilitating quantitative evaluation of strategy performance.

5. Integrated Fund Management: The strategy adopts a percentage-of-equity position management method rather than fixed lots, helping to adapt to account size changes and achieve more scientific position control.

6. Visual Assistance: The strategy plots key price levels (52-week midpoint and bottom 10% threshold) on the chart, providing intuitive reference for trading decisions.

Strategy Risks

1. False Breakout Risk: In markets with long-term downward trends, prices may fall further before rebounding, leading to false signals and losing trades.

2. Slippage and Liquidity Risk: Under extreme market conditions, actual execution prices may differ significantly from signal prices, affecting strategy performance.

3. Parameter Sensitivity: Strategy effectiveness highly depends on RSI parameter settings and price range definitions; different market environments may require different parameter combinations.

4. Market Adaptability Limitations: This strategy performs best in oscillating markets but may underperform in strong trend markets (especially continuous downtrends).

5. Compound Risk: If the entire market simultaneously meets entry conditions, it may lead to excessive concentration of funds, increasing systemic risk.

Methods to mitigate these risks include: setting reasonable stop-losses, appropriate diversification, regular parameter optimization, cross-validation with other indicators, and avoiding forced trading under extreme market conditions.

Strategy Optimization Directions

1. Dynamic Parameter Adjustment: Introduce adaptive mechanisms to automatically adjust RSI thresholds and price range percentages based on market volatility to adapt to different market environments. For example, in high-volatility environments, the RSI oversold threshold could be lowered to 25 or 20.

2. Add Trend Filters: Incorporate trend indicators such as moving averages or MACD to filter signals during strong trends, avoiding premature entry in downtrends.

3. Optimize Fund Management: Dynamically adjust position sizes based on volatility or drawdown depth, automatically reducing positions in high-risk environments.

4. Multi-timeframe Confirmation: Introduce multi-timeframe analysis to ensure oversold signals are confirmed across different time frames, improving signal reliability.

5. Add Stop-Loss Mechanism: Automatically trigger stop-losses when prices break specific thresholds (such as creating new 52-week lows) to limit single-trade losses.

6. Optimize Exit Strategy: Consider implementing partial profit-taking strategies, closing positions in batches as prices rise, both securing partial profits and retaining upside potential.

7. Integrate Seasonal Analysis: Study whether seasonal patterns exist in historical data and adjust strategy parameters or pause trading during specific time periods.

These optimization directions aim to improve the strategy's robustness and adaptability, especially in market environments with increased uncertainty.

Summary
The Mean Reversion RSI and Price Range Momentum Strategy is a quantitative trading system combining technical indicators with price position analysis. It enters positions when the market is oversold and prices are at historical lows, exiting when prices revert to the mean or the market becomes overheated. The strategy has a solid theoretical foundation, clear execution rules, and built-in risk management mechanisms, making it suitable for investors seeking low-risk reversal trades.

However, no trading strategy has a 100% win rate. Investors should fully understand the strategy's characteristics before real-world application, conduct thorough historical backtesting and forward validation, and adjust parameters according to personal risk preferences. Through continuous optimization and risk management, this strategy has the potential to become an effective tool in investment portfolios, especially in oscillating market environments.

Strategy source code

/*backtest
start: 2024-07-10 00:00:00
end: 2025-07-08 08:00:00
period: 2h
basePeriod: 2h
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

//@version=6
strategy("Reversion to Mean - TLT [with Metrics]", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)

// === Inputs ===
rsiLength = input.int(14, title="RSI Length")
rsiOversold = input.float(30, title="RSI Oversold Threshold")
rsiOverbought = input.float(70, title="RSI Overbought Threshold")
lookback = input.int(252, title="52-Week Lookback (in bars)")

// === Price + RSI ===
rsi = ta.rsi(close, rsiLength)
lowest = ta.lowest(low, lookback)
highest = ta.highest(high, lookback)
rangeMid = (highest + lowest) / 2
bottom10 = lowest + 0.10 * (highest - lowest)

// === Entry Condition ===
inBottom10 = close <= bottom10
rsiLow = rsi < rsiOversold
longCondition = inBottom10 and rsiLow

// === Exit Condition ===
rsiHigh = rsi > rsiOverbought
priceRevert = close >= rangeMid
exitCondition = rsiHigh or priceRevert

// === Strategy Execution ===
if (longCondition)
    strategy.entry("Long", strategy.long)

if (exitCondition)
    strategy.close("Long")

// === Plotting ===
plot(rangeMid, title="52-Week Midpoint", color=color.gray, style=plot.style_line)
plot(bottom10, title="Bottom 10% Threshold", color=color.red, style=plot.style_line)

Strategy parameters

The original address: Mean Reversion RSI and Price Range Momentum Strategy

Comments

[Grace]

Nice blend of mean reversion and momentum concepts here. Using RSI to identify overbought/oversold zones while layering in price range filters adds a solid extra layer of confirmation. It’s a practical way to avoid false signals, especially in sideways markets. Appreciate the clarity of the explanation and the code example—definitely something I’ll try tweaking for different timeframes.