Rainbow Oscillator Trading Strategy

Pine ScriptMachine learning Mean reversion Trend following RSI EMA SMA Stochastic CCI

Author: ChaoZhang
Complexity: Intermediate · 133 lines
Parameters: 16

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Overview

The Rainbow Oscillator trading strategy mainly uses multiple smoothed moving averages and oscillation indicators to build a multi-layer oscillation channel and generate clear long/short signals. It belongs to the trend-following strategy category. This strategy combines RSI, CCI, Stochastic and MA composite indicators to determine the overall market trend and overbought/oversold areas. It is a multi-factor rating strategy.

Principles

Calculate the weighted average of RSI, CCI and Stochastic to build a combined oscillation indicator called Magic;
Apply exponential smoothing to Magic multiple times to generate two lines called sampledMagicFast and sampledMagicSlow;
sampledMagicFast represents the fast EMA line, sampledMagicSlow represents the slow EMA line;
A buy signal is generated when sampledMagicFast crosses above sampledMagicSlow;
A sell signal is generated when sampledMagicFast crosses below sampledMagicSlow;
Calculate the direction of the last bar's sampledMagicFast relative to previous bar to determine the current trend;
Determine entry and exit points based on trend direction and crossing situation between sampledMagicFast and sampledMagicSlow.

Advantages

Combining multiple indicators to determine overall market trend improves signal accuracy;
Smoothed MA filters out noise effectively;
Multi-layer oscillation signals provide clear operation guidance;
Can be configured for trend-following or mean-reversion by enabling/disabling trend filter;
Customizable overbought/oversold strength for flexibility.

Risks

Over-smoothed curves may cause missing best entry points;
Improper overbought/oversold area setting may keep position closed for too long;
Failure of some factors in the rating model may weaken signals.

Solutions:

Optimize parameters for appropriate smoothing extent;
Adjust overbought/oversold area strength to reduce time in flat position;
Test and weight each indicator by its predictive power.

Optimization Directions

Dynamically adjust indicator parameters based on market conditions;
Introduce machine learning methods to auto optimize indicator combinations;
Add factors like volume and volatility filters to entry signals.

Conclusion

The Rainbow Oscillator strategy combines signals from multiple indicators and uses exponential smoothing to improve stability. It can be configured for both trending and sideways markets, or for specific products. Further improvements can be made by parameter tuning and indicator expansion. Overall this is a clear, easy-to-use strategy.

Parameters

Name	Default	What it controls
v_input_bool_1	true	Use trend filter
v_input_float_1	0.33	RSI Weight
v_input_float_2	0.33	CCI Weight
v_input_float_3	0.33	Stoch Weight
v_input_int_1	16	Ocillograph Fast Period
v_input_int_2	22	Ocillograph Slow Period
v_input_int_3	8	Oscillograph Samples Period
v_input_int_4	2	Oscillograph Samples Count
v_input_string_1	0	Oscillograph Samples Type: RMA \| SMA \| EMA \| WMA
v_input_int_5	26	Level Period
v_input_int_6	false	Level Offset
v_input_float_4	0.5	Level Redunant
v_input_int_7	2	Level Smooth Samples
v_input_string_2	0	Level MA type: RMA \| SMA \| EMA \| WMA
v_input_float_5	5	% Take profit
v_input_float_6	2	% Stop Loss

Source Pine Script

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © businessduck

//@version=5
strategy("Rainbow Oscillator [Strategy]", overlay=false, margin_long=100, margin_short=100, initial_capital = 2000)

bool trendFilter = input.bool(true, 'Use trend filter')
float w1 = input.float(0.33, 'RSI Weight', 0, 1, 0.01)
float w2 = input.float(0.33, 'CCI Weight', 0, 1, 0.01)
float w3 = input.float(0.33, 'Stoch Weight', 0, 1, 0.01)
int fastPeriod = input.int(16, 'Ocillograph Fast Period', 4, 60, 1)
int slowPeriod = input.int(22, 'Ocillograph Slow Period', 4, 60, 1)
int oscillographSamplePeriod = input.int(8, 'Oscillograph Samples Period', 1, 30, 1)
int oscillographSamplesCount = input.int(2, 'Oscillograph Samples Count', 0, 4, 1)
string oscillographMAType = input.string("RMA", "Oscillograph Samples Type", options = ["EMA", "SMA", "RMA", "WMA"])
int levelPeriod = input.int(26, 'Level Period', 2, 100)
int levelOffset = input.int(0, 'Level Offset', 0, 200, 10)
float redunant = input.float(0.5, 'Level Redunant', 0, 1, 0.01)
int levelSampleCount = input.int(2, 'Level Smooth Samples', 0, 4, 1)
string levelType = input.string("RMA", "Level MA type", options = ["EMA", "SMA", "RMA", "WMA"])

perc(current, prev) => ((current - prev) / prev) * 100

smooth(value, type, period) =>
    float ma = switch type
        "EMA" => ta.ema(value, period)
        "SMA" => ta.sma(value, period)
        "RMA" => ta.rma(value, period)
        "WMA" => ta.wma(value, period)
        =>
            runtime.error("No matching MA type found.")
            float(na)

getSample(value, samples, type, period) =>
    float ma = switch samples
        0 => value
        1 => smooth(value, type, period)
        2 => smooth(smooth(value, type, period), type, period)
        3 => smooth(smooth(smooth(value, type, period), type, period), type, period)
        4 => smooth(smooth(smooth(smooth(value, type, period), type, period), type, period), type, period)

float takeProfit = input.float(5, "% Take profit", 0.8, 100, step = 0.1)  / 100
float stopLoss = input.float(2, "% Stop Loss", 0.8, 100, step = 0.1) / 100
float magicFast = w2 * ta.cci(close, fastPeriod) + w1 * (ta.rsi(close, fastPeriod) - 50) + w3 * (ta.stoch(close, high, low, fastPeriod) - 50)
float magicSlow = w2 * ta.cci(close, slowPeriod) + w1 * (ta.rsi(close, slowPeriod) - 50) + w3 * (ta.stoch(close, high, low, slowPeriod) - 50)
float sampledMagicFast = getSample(magicFast, oscillographSamplesCount, oscillographMAType, oscillographSamplePeriod)
float sampledMagicSlow = getSample(magicSlow, oscillographSamplesCount, oscillographMAType, oscillographSamplePeriod)
float lastUpperValue = 0
float lastLowerValue = 0

if (magicFast > 0)
    lastUpperValue := math.max(magicFast, magicFast[1])
else 
    lastUpperValue := math.max(0, lastUpperValue[1]) * redunant

    
if (magicFast <= 0)
    lastLowerValue := math.min(magicFast, magicFast[1])
else
    lastLowerValue := math.min(0, lastLowerValue[1]) * redunant

float level1up = getSample( (magicFast >= 0 ? magicFast : lastUpperValue) / 4, levelSampleCount, levelType, levelPeriod) + levelOffset
float level2up = getSample( (magicFast >= 0 ? magicFast : lastUpperValue) / 2, levelSampleCount, levelType, levelPeriod) + levelOffset
float level3up = getSample( magicFast >= 0 ? magicFast : lastUpperValue, levelSampleCount, levelType, levelPeriod) + levelOffset
float level4up = getSample( (magicFast >= 0 ? magicFast : lastUpperValue) * 2, levelSampleCount, levelType, levelPeriod) + levelOffset

float level1low = getSample( (magicFast <= 0 ? magicFast : lastLowerValue) / 4, levelSampleCount, levelType, levelPeriod) - levelOffset
float level2low = getSample( (magicFast <= 0 ? magicFast : lastLowerValue) / 2, levelSampleCount, levelType, levelPeriod) - levelOffset
float level3low = getSample( magicFast <= 0 ? magicFast : lastLowerValue, levelSampleCount, levelType, levelPeriod) - levelOffset
float level4low = getSample( (magicFast <= 0 ? magicFast : lastLowerValue) * 2, levelSampleCount, levelType, levelPeriod) - levelOffset

var transparent = color.new(color.white, 100)
var overbough4Color = color.new(color.red, 75)
var overbough3Color = color.new(color.orange, 75)
var overbough2Color = color.new(color.yellow, 75)

var oversold4Color = color.new(color.teal, 75)
var oversold3Color = color.new(color.blue, 75)
var oversold2Color = color.new(color.aqua, 85)

upperPlotId1 = plot(level1up, 'Upper1', transparent)
upperPlotId2 = plot(level2up, 'Upper2', transparent)
upperPlotId3 = plot(level3up, 'Upper3', transparent)
upperPlotId4 = plot(level4up, 'Upper4', transparent)

fastColor = color.new(color.teal, 60)
slowColor = color.new(color.red, 60)
fastPlotId = plot(sampledMagicFast, 'fast', color = fastColor)
slowPlotId = plot(sampledMagicSlow, 'slow', color = slowColor)

lowerPlotId1 = plot(level1low, 'Lower1', transparent)
lowerPlotId2 = plot(level2low, 'Lower2', transparent)
lowerPlotId3 = plot(level3low, 'Lower3', transparent)
lowerPlotId4 = plot(level4low, 'Lower4', transparent)

fill(upperPlotId4, upperPlotId3, overbough4Color)
fill(upperPlotId3, upperPlotId2, overbough3Color)
fill(upperPlotId2, upperPlotId1, overbough2Color)

fill(lowerPlotId4, lowerPlotId3, oversold4Color)
fill(lowerPlotId3, lowerPlotId2, oversold3Color)
fill(lowerPlotId2, lowerPlotId1, oversold2Color)

upTrend = sampledMagicFast > sampledMagicFast[1]
buySignal = ((upTrend or not trendFilter) and ta.crossunder(sampledMagicSlow, sampledMagicFast)) ? sampledMagicSlow : na
sellSignal = ((not upTrend or not trendFilter) and ta.crossover(sampledMagicSlow, sampledMagicFast)) ? sampledMagicSlow : na
diff = sampledMagicSlow - sampledMagicFast

fill(fastPlotId, slowPlotId, upTrend ? fastColor : slowColor)
plot(buySignal, color = color.aqua, style = plot.style_circles, linewidth = 4)
plot(sellSignal, color = color.red, style = plot.style_circles, linewidth = 4)


// longCondition = upTrend != upTrend[1] and upTrend
long_take_level = strategy.position_avg_price * (1 + takeProfit)
long_stop_level = strategy.position_avg_price * (1 - stopLoss)

short_take_level = strategy.position_avg_price * (1 - takeProfit)
short_stop_level = strategy.position_avg_price * (1 + stopLoss)

strategy.close(id="Long", when=sellSignal, comment = "Exit")
strategy.close(id="Short", when=buySignal, comment = "Exit")

strategy.entry("Long", strategy.long, when=buySignal)
strategy.entry("Short", strategy.short, when=sellSignal)

strategy.exit("Take Profit/ Stop Loss","Long", stop=long_stop_level, limit=long_take_level)
strategy.exit("Take Profit/ Stop Loss","Short", stop=short_stop_level, limit=short_take_level)


// plot(long_stop_level, color=color.red, overlay=true)
// plot(long_take_level, color=color.green)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-23. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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