Quantitative Trading Strategy Based on RSI Indicator Signals

This article explains in detail a quantitative trading strategy that utilizes RSI indicator to generate trading signals. It processes the RSI indicator and sets entry and exit criteria for long and short trades. I…

Pine ScriptMean reversion Trend following RSI EMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 81 lines
Parameters: 8
Backtest bar: 3h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

I. Strategy Logic

The main trading logic is as follows:

Compute the RSI(14) indicator and smooth it using EMA(28) to obtain the processed oscillator.
Calculate Bollinger Bands on the processed RSI to get upper/lower bands. Set overbought/oversold zones.
When the processed RSI crosses below the entry line, a buy signal is generated. When it crosses above, a sell signal is generated.
When the indicator enters the overbought/oversold zones, a close position signal is generated.

In this way, the characteristics of RSI can be utilized to capture reversal opportunities. The indicator processing also improves the signal quality and reference value.

II. Advantages of the Strategy

The biggest advantage is the increased parameter tuning space from indicator processing, which allows tighter control over trade frequency and prevents overtrading.

Another advantage is the intuitive entry criteria based on clear numeric values of the indicator.

Lastly, the overbought/oversold range also helps with timely profit taking and risk control per trade.

III. Potential Weaknesses

However, the strategy also has the following risks:

Firstly, RSI focuses on reversal trades, which can generate false signals during trends.

Secondly, improper parameter tuning can also lead to over-optimization and failure to adapt to changing market conditions.

Lastly, the relatively low win rate also exposes the strategy to drawdown risks.

IV. Summary

In summary, this article mainly introduces a quantitative trading strategy utilizing the RSI indicator. It controls trade frequency via parameter tuning and has clear entry/exit rules. While optimizing parameters, risks of reversal trading also need to be managed. Overall, it provides a simple and intuitive RSI strategy framework.

Parameters

Name	Default	What it controls
v_input_1	14	(?=== RSI ===)RSI
v_input_2	28	EMA
v_input_3	80	(?=== %b ===)Length
v_input_4	3	Multiplier
v_input_5	0.8	Overbought
v_input_6	0.2	Oversold
v_input_7	0.8	Entry Short
v_input_8	0.2	Entry Long

Source Pine Script

/*backtest
start: 2023-08-14 00:00:00
end: 2023-09-13 00:00:00
period: 3h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
//-----------------------------------------------------------------
//This simple strategy base on RSI, EMA, Bollinger Bands to get Buy and Sell Signal with detail as below:
//-----------------------------------------------------------------
//1.Define Oscillator Line
//+ Oscillator Line is smoothed by ema(28) of RSI(14) on H1 Timeframe
//2.Define Overbought and Oversold
//+ Apply Bollinger Bands BB(80,3) on Oscillator Line and calculate %b
//+ Overbought Zone marked above level 0.8
//+ Oversold Zone marked below level 0.2
//3.Buy Signal
//+ Entry Long Positon when %b crossover Point of Entry Long
//+ Deafault Point of Entry Long is 0.2
//+ Buy signal marked by Green dot
//4.Sell Signal
//+ Entry Short Position when %b crossunder Point of Entry Short
//+ Deafault Point of Entry Short is 0.8
//+ Sell signal marked by Red dot
//5.Exit Signal
//+ Exit Position (both Long and Short) when %b go into Overbought Zone or Oversold Zone
//+ Exit signal marked by Yellow dot
//-----------------------------------------------------------------
strategy(title="RSI %b Signal [H1 Backtesting]", overlay=false)

//RSI
rsi_gr="=== RSI ==="
rsi_len = input(14, title = "RSI",inline="set",group=rsi_gr)
smoothed_len = input(28, title = "EMA",inline="set",group=rsi_gr)
rsi=ta.ema(ta.rsi(close,rsi_len),smoothed_len)
//rsi's BOLLINGER BANDS
pb_gr="=== %b ==="
length = input(80, title = "Length",inline="set1",group=pb_gr)
rsimult = input(3.0, title = "Multiplier",inline="set1",group=pb_gr)
ovb = input(0.8, title = "Overbought",inline="set2",group=pb_gr)
ovs = input(0.2, title = "Oversold",inline="set2",group=pb_gr)
et_short = input(0.8, title = "Entry Short",inline="set3",group=pb_gr)
et_long = input(0.2, title = "Entry Long",inline="set3",group=pb_gr)
[rsibasis, rsiupper, rsilower] = ta.bb(rsi, length, rsimult)
//rsi's %B
rsipB = ((rsi - rsilower) / (rsiupper - rsilower))
plot(rsipB, title="rsi's %B", color=rsipB>math.min(ovb,et_short)?color.red:rsipB<math.max(ovs,et_long)?color.green:color.aqua, linewidth=1)

h1=hline(1,color=color.new(color.red,100))
h4=hline(ovb,color=color.new(color.red,100))
h0=hline(0,color=color.new(color.green,100))
h3=hline(ovs,color=color.new(color.green,100))
h5=hline(0.5,color=color.new(color.silver,0),linestyle=hline.style_dotted)

fill(h1,h4, title="Resistance", color=color.new(color.red,90))
fill(h0,h3, title="Support", color=color.new(color.green,90))

//Signal
rsi_buy=
           rsipB[1]<et_long
           and
           rsipB>et_long
rsi_sell=
           rsipB[1]>et_short
           and
           rsipB<et_short
rsi_exit=
           (rsipB[1]>ovs and rsipB<ovs)
           or
           (rsipB[1]<ovb and rsipB>ovb)
plotshape(rsi_buy?rsipB:na,title="Buy",style=shape.circle,color=color.new(color.green,0),location=location.absolute)
plotshape(rsi_sell?rsipB:na,title="Sell",style=shape.circle,color=color.new(color.red,0),location=location.absolute)
plotshape(rsi_exit?rsipB:na,title="Exit",style=shape.circle,color=color.new(color.yellow,0),location=location.absolute)
//Alert
strategy.entry("Long",strategy.long,when=rsi_buy)
strategy.close("Long",when=rsi_exit)
strategy.entry("Short",strategy.short,when=rsi_sell)
strategy.close("Short",when=rsi_exit)
//EOF

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-14. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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