RSI Divergence Trading Strategy 439702

The RSI divergence trading strategy captures mispricing opportunities by analyzing the divergence between the RSI indicator and price. It goes long or short when the divergence appears.

Pine ScriptArbitrage Mean reversion Trend following RSI Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 166 lines
Parameters: 10
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The RSI divergence trading strategy captures mispricing opportunities by analyzing the divergence between the RSI indicator and price. It goes long or short when the divergence appears.

Strategy Logic

The strategy is based on the value-price divergence when RSI diverges from price. RSI reflects the strength while price reflects the supply-demand relationship. When the two diverge, it indicates market mispricing for arbitrage.

Specifically, a regular bullish divergence happens when RSI forms a higher low while price prints a lower low. This shows that although the market looks weak on the surface, it is actually garnering strength internally for a bounce. When RSI diverges from price and breaks above the 50-line, it presents an opportunity to catch the bounce.

A regular bearish divergence happens when RSI makes a lower high while price forms a higher high. This suggests that although the market looks strong externally, it is showing weakness signals internally. When RSI diverges from price and breaks below the 50-line, it allows profiting from the short side.

There are also hidden bullish and bearish divergences where the relationship between RSI and price is opposite of regular divergences, but the logic remains the same for taking profits.

Advantages

Captures market mispricing from value-price divergence
Improves win rate combining indicator and price divergence
Covers more opportunities distinguishing all types of divergences

Risk Analysis

Fake divergences can happen under special market conditions
Breaking 50-line has relatively low success rate, can optimize
Picking wrong direction could lead to big losses

Optimization Directions

Optimize RSI parameters for higher accuracy
Combine signals from other indicators to confirm divergences
Assess risk-reward for longs and shorts to control per trade loss

Summary

The RSI divergence strategy arbitrages market mispricing through analyzing divergence between value and price signals. Its advantage lies in timely catching trend reversal opportunities, while its risk comes from the accuracy of divergence recognition. With continuous optimization, steady gains can be achieved in live trading.

Parameters

Name	Default	What it controls
v_input_int_1	14	RSI Period
v_input_1_close	0	RSI Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_2	5	Pivot Lookback Right
v_input_3	5	Pivot Lookback Left
v_input_4	60	Max of Lookback Range
v_input_5	5	Min of Lookback Range
v_input_6	true	Plot Bullish
v_input_7	true	Plot Hidden Bullish
v_input_8	true	Plot Bearish
v_input_9	true	Plot Hidden Bearish

Source Pine Script

/*backtest
start: 2024-01-15 00:00:00
end: 2024-01-22 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy(title="Divergence Indicator")
len = input.int(title="RSI Period", minval=1, defval=14)
src = input(title="RSI Source", defval=close)
lbR = input(title="Pivot Lookback Right", defval=5)
lbL = input(title="Pivot Lookback Left", defval=5)
rangeUpper = input(title="Max of Lookback Range", defval=60)
rangeLower = input(title="Min of Lookback Range", defval=5)
plotBull = input(title="Plot Bullish", defval=true)
plotHiddenBull = input(title="Plot Hidden Bullish", defval=true)
plotBear = input(title="Plot Bearish", defval=true)
plotHiddenBear = input(title="Plot Hidden Bearish", defval=true)
bearColor = color.red
bullColor = color.green
hiddenBullColor = color.new(color.green, 80)
hiddenBearColor = color.new(color.red, 80)
textColor = color.white
noneColor = color.new(color.white, 100)
osc = ta.rsi(src, len)

plot(osc, title="RSI", linewidth=2, color=#2962FF)
hline(50, title="Middle Line", color=#787B86, linestyle=hline.style_dotted)
obLevel = hline(70, title="Overbought", color=#787B86, linestyle=hline.style_dotted)
osLevel = hline(30, title="Oversold", color=#787B86, linestyle=hline.style_dotted)
fill(obLevel, osLevel, title="Background", color=color.rgb(33, 150, 243, 90))

plFound = na(ta.pivotlow(osc, lbL, lbR)) ? false : true
phFound = na(ta.pivothigh(osc, lbL, lbR)) ? false : true
_inRange(cond) =>
	bars = ta.barssince(cond == true)
	rangeLower <= bars and bars <= rangeUpper

//------------------------------------------------------------------------------
// Regular Bullish
// Osc: Higher Low

oscHL = osc[lbR] > ta.valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[1])

// Price: Lower Low

priceLL = low[lbR] < ta.valuewhen(plFound, low[lbR], 1) 
// bull : 상승 Condition : 조건
bullCond = plotBull and priceLL and oscHL and plFound // 상승다이버전스?
strategy.entry("상승 다이버전스 진입", strategy.long, when = bullCond)
strategy.close("상승 다이버전스 진입", when = ta.crossover(osc, 50)) 
plot(
     plFound ? osc[lbR] : na,
     offset=-lbR,
     title="Regular Bullish",
     linewidth=2,
     color=(bullCond ? bullColor : noneColor)
     )

plotshape(
	 bullCond ? osc[lbR] : na,
	 offset=-lbR,
	 title="Regular Bullish Label",
	 text=" Bull ",
	 style=shape.labelup,
	 location=location.absolute,
	 color=bullColor,
	 textcolor=textColor
	 )

//------------------------------------------------------------------------------
// Hidden Bullish
// Osc: Lower Low

oscLL = osc[lbR] < ta.valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[1])

// Price: Higher Low

priceHL = low[lbR] > ta.valuewhen(plFound, low[lbR], 1)
hiddenBullCond = plotHiddenBull and priceHL and oscLL and plFound
// strategy.entry("히든 상승 다이버전스 진입", strategy.long, when = hiddenBullCond)
// strategy.close("히든 상승 다이버전스 진입", when = ta.crossover(osc, 50))
plot(
	 plFound ? osc[lbR] : na,
	 offset=-lbR,
	 title="Hidden Bullish",
	 linewidth=2,
	 color=(hiddenBullCond ? hiddenBullColor : noneColor)
	 )

plotshape(
	 hiddenBullCond ? osc[lbR] : na,
	 offset=-lbR,
	 title="Hidden Bullish Label",
	 text=" H Bull ",
	 style=shape.labelup,
	 location=location.absolute,
	 color=bullColor,
	 textcolor=textColor
	 )

//------------------------------------------------------------------------------
// Regular Bearish
// Osc: Lower High

oscLH = osc[lbR] < ta.valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1])

// Price: Higher High

priceHH = high[lbR] > ta.valuewhen(phFound, high[lbR], 1)
// bear : 하락 
bearCond = plotBear and priceHH and oscLH and phFound
// strategy.entry("하락 다이버전스 진입", strategy.short, when = bearCond)
// strategy.close("하락 다이버전스 진입", when = ta.crossunder(osc, 50)) 
plot(
	 phFound ? osc[lbR] : na,
	 offset=-lbR,
	 title="Regular Bearish",
	 linewidth=2,
	 color=(bearCond ? bearColor : noneColor)
	 )

plotshape(
	 bearCond ? osc[lbR] : na,
	 offset=-lbR,
	 title="Regular Bearish Label",
	 text=" Bear ",
	 style=shape.labeldown,
	 location=location.absolute,
	 color=bearColor,
	 textcolor=textColor
	 )

//------------------------------------------------------------------------------
// Hidden Bearish
// Osc: Higher High

oscHH = osc[lbR] > ta.valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1])

// Price: Lower High

priceLH = high[lbR] < ta.valuewhen(phFound, high[lbR], 1)

hiddenBearCond = plotHiddenBear and priceLH and oscHH and phFound
// strategy.entry("히든 하락 다이버전스 진입", strategy.short, when = hiddenBearCond)
// strategy.close("히든 하락 다이버전스 진입", when = ta.crossunder(osc, 50)) 
plot(
	 phFound ? osc[lbR] : na,
	 offset=-lbR,
	 title="Hidden Bearish",
	 linewidth=2,
	 color=(hiddenBearCond ? hiddenBearColor : noneColor)
	 )

plotshape(
	 hiddenBearCond ? osc[lbR] : na,
	 offset=-lbR,
	 title="Hidden Bearish Label",
	 text=" H Bear ",
	 style=shape.labeldown,
	 location=location.absolute,
	 color=bearColor,
	 textcolor=textColor
	 )

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-23. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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