CMO Oscillator Trading Strategy

This strategy uses the Chande Momentum Oscillator (CMO) to determine overbought and oversold levels for trading signals. The absolute CMO values over 3 periods are averaged to smooth the oscillator for identifying…

Pine ScriptMachine learning Breakout Mean reversion Volume

Author: ChaoZhang
Complexity: Starter · 47 lines
Parameters: 6
Backtest bar: 30m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The key logic includes:

Calculating absolute CMO values over 3 different periods
Taking the average of 3-period absolute CMO values
Going short when average value exceeds upper threshold
Going long when average value drops below lower threshold
Closing positions when CMO returns to normal range

The CMO reflects the momentum of price changes. High absolute values represent price divergence entering overbought/oversold zones. The strategy utilizes this characteristic of CMO, using a multi-period average to smooth the curve for identifying extremes.

Advantages

Uses CMO to identify overbought/oversold regions
Multi-period averaging smooths curve and avoids false signals
Sound theoretical basis for overbought/oversold detection
Customizable parameter thresholds to adapt
Simple mean reversion implementation

Risks and Mitigations

Potential for false CMO signals
Requires ongoing threshold optimization
Sustained extremes during trends can cause losses

Mitigations:

Adding trend filter to avoid counter-trend trades
Parameter optimization for better CMO sensitivity
Using stops to limit losses

Enhancement Opportunities

The strategy can be enhanced through:

Volume confirmation to avoid false breakouts
Incorporating trailing stops for better risk management
Auto-optimization of parameters via machine learning
Volatility-based position sizing
Combining with other strategies to diversify and improve returns

Conclusion

This strategy uses CMO to identify overbought/oversold for mean reversion trading. Multi-period averaging helps avoid false signals. CMO itself has sound theoretical basis for gauging divergence. Enhancements through better parameters, stops, and filters can make it a stable oscillator trading strategy.

Parameters

Name	Default	What it controls
v_input_1	5	Length1
v_input_2	10	Length2
v_input_3	20	Length3
v_input_4	58	TopBand
v_input_5	5	LowBand
v_input_6	false	Trade reverse

Source Pine Script

/*backtest
start: 2023-09-11 00:00:00
end: 2023-09-14 07:00:00
period: 30m
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
////////////////////////////////////////////////7////////////
//  Copyright by HPotter v1.0 21/02/2017
//    This indicator plots the absolute value of CMO averaged over three 
//    different lengths. This indicator plots a classical-looking oscillator, 
//    which is really an averaged value based on three different periods.
//
// You can change long to short in the Input Settings
// Please, use it only for learning or paper trading. Do not for real trading.
////////////////////////////////////////////////////////////
strategy(title="CMOabsav", shorttitle="CMOabsav")
Length1 = input(5, minval=1)
Length2 = input(10, minval=1)
Length3 = input(20, minval=1)
TopBand = input(58, minval=1)
LowBand = input(5, minval=0)
reverse = input(false, title="Trade reverse")
hline(0, color=green, linestyle=hline.style_dashed)
hline(TopBand, color=purple, linestyle=hline.style_solid)
hline(LowBand, color=red, linestyle=hline.style_solid)
xMom = close - close[1]
xMomabs = abs(close - close[1])
nSum1 = sum(xMom, Length1)
nSumAbs1 = sum(xMomabs, Length1)
nSum2 = sum(xMom, Length2)
nSumAbs2 = sum(xMomabs, Length2)
nSum3 = sum(xMom, Length3)
nSumAbs3 = sum(xMomabs, Length3)
nRes = abs(100 * (nSum1 / nSumAbs1 + nSum2 / nSumAbs2 + nSum3 / nSumAbs3 ) / 3)
pos = iff(nRes > TopBand, 1,
	     iff(nRes < LowBand, -1, nz(pos[1], 0))) 
possig = iff(reverse and pos == 1, -1,
          iff(reverse and pos == -1, 1, pos))	   
if (possig == 1) 
    strategy.entry("Long", strategy.long)
if (possig == -1)
    strategy.entry("Short", strategy.short)	   	    
barcolor(possig == -1 ? red: possig == 1 ? green : blue )
plot(nRes, color=blue, title="CMOabsav")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-19. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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