Inverse Fisher RSI Moving Average Multi Timeframe Strategy

Author: ChaoZhang, Date: 2023-11-21 14:45:28



The Inverse Fisher RSI Moving Average Multi Timeframe Strategy is a quantitative trading strategy that tries to identify potential market reversal points by calculating the moving average of the inversely adjusted RSI indicator on higher timeframes.

Strategy Logic

The strategy first calculates the regular RSI indicator, where the RSI_pm parameter represents the RSI calculation period. The original RSI is then inversely adjusted through a mathematical function IF(input)=>(exp(2input)-1)/(exp(2input)+1). The adjusted RSI is passed to the variable IF_RSI.

To filter out too much noise, the strategy further calculates the moving average of IF_RSI over the period RSI_ps, obtaining the final indicator wma_RSI used to determine buy and sell signals. This indicator is further mapped to a 0-100 range.

Finally, the strategy plots this indicator on a higher timeframe and sets threshold lines at 0.8 and -0.8. A buy signal is generated when the indicator line breaks above 0.8 from below. A sell signal is generated when the indicator line breaks below -0.8 from above.


The strategy processes the RSI trend through double smoothing, which can effectively filter out too much noise and lock in relatively clear reversal signals. Double smoothing is applied respectively to the original RSI indicator and the absolutely adjusted RSI indicator. This method can enhance the mean reversion characteristic of the indicator and produce relatively reliable trading signals.

In addition, the multi-timeframe analysis method adopted by the strategy identifies breakouts of the indicator on a higher level timeframe, which can capture long-term reversal opportunities and avoid interference from excessive short-term market noise.


The strategy relies on moving average indicators to determine buy and sell points, which has some lag. In long-term bull markets, the upside space of adjusted indicators may be limited, failing to fully capture trend opportunities.

On the other hand, the adjustments may also miss rebound opportunities after short-term corrections. If the indicator parameters are not properly optimized, certain strategy risks may be faced.


Try appropriately adjusting the indicator parameters to better adapt them to market conditions. For example, different RSI calculation cycles and smoothing period parameters can be tested to find the optimal parameter combination.

It is also worth considering combining other auxiliary indicators to verify signals and improve strategy stability. For example, volume indicators, Bollinger Bands etc. can be added to judge the strength of trend signals.


The Inverse Fisher RSI Moving Average Multi Timeframe Strategy has an overall robust logic, but still needs optimization to adapt to wider market situations. It is worth further testing and improving to make it a reliable quantitative trading strategy.

start: 2022-11-14 00:00:00
end: 2023-11-20 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]

strategy(title = "Inverse Fisher RSI-MTF2", shorttitle="INRSIM2",overlay=true)
RSI_pm = input(5, title="RSI Main Period",minval=2)
RSI_ps = input(1, title="RSI Smooth Period",minval=0)


//RSI Calculation
IF_RSI = IF(wma_RSI)

resCustom = input(title="Timeframe", defval="1440" ), resCustom,IF_RSI)

buy = crossover(v,z)
plotshape(sell, title="sell", style=shape.triangledown,location=location.abovebar, color=red, transp=0, size=size.small)
plotshape(buy,  title="buy", style=shape.triangleup,location=location.belowbar, color=green, transp=0, size=size.small)

golong =  crossover(v,z)
goshort =  crossunder(v,b)

strategy.entry("Buy",strategy.long,when = golong)
strategy.entry("Sell",strategy.short,when = goshort)