Dual Oscillation Reversal Signal to Noise Ratio Optimization Combo Strategy

This strategy combines the dual oscillation reversal strategy and the signal-to-noise ratio optimization strategy to form a more powerful and stable trading strategy. The strategy aims to generate more accurate…

Pine ScriptMean reversion Machine learning Trend following RSI MACD SMA Stochastic

Author: ChaoZhang
Complexity: Intermediate · 73 lines
Parameters: 7
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The dual oscillation reversal strategy calculates the fast and slow K values of the last 14 days to determine if there is a reversal over two consecutive trading days. If the reversal happens when the fast K is below 50, it is a buy signal. If the fast K is above 50, it is a sell signal.

The signal-to-noise ratio optimization strategy calculates the signal-to-noise ratio of the last 21 days and smooths it with a 29-day simple moving average. When the signal-to-noise ratio crosses above the moving average, it is a sell signal. When it crosses below, it is a buy signal.

Finally, this strategy only initiates buy or sell trades when both strategies issue the same signal.

Advantage Analysis

Combining multiple strategies can generate more accurate trading signals and avoid false signals from a single strategy.
The dual oscillation reversal strategy catches trend reversal points. The signal-to-noise ratio optimization filters out false signals. Working together, they can accurately trade at reversals.
Optimized parameters like 14-day fast/slow stochastics and 21-day signal-to-noise period capture recent trends without too much noise.
The dual confirmation signals significantly reduce trading risk and avoid unnecessary losses.

Risk Analysis

Reversal signals may lag and miss absolute bottoms or tops. Parameters can be adjusted to shorten the lag.
Dual signal confirmation may miss some trading opportunities. Confirmation conditions could be relaxed but also increase risk.
Signal-to-noise ratio parameters need optimization. Improper periods may cause missing or false signals.
Monitoring multiple indicators increases complexity. Code optimization and computing resources need consideration.

Optimization Directions

Test more indicator combinations to find better combo signals, like MACD, RSI etc.
Optimize parameters of the reversal strategy for more accurate and timely signals.
Optimize signal-to-noise ratio periods to find the optimal balance.
Add stop loss strategies to control potential loss for single trades.
Consider machine learning methods to auto optimize parameters for better adaptability.

Conclusion

This strategy combines dual oscillation reversal and signal-to-noise ratio strategies to provide stable signals at trend reversal points. Optimized parameters significantly reduce false signals, and dual confirmation lowers trading risks. Further optimizations like indicator parameters, stop loss can improve performance. Overall, this is a stable strategy with practical trading value.

Parameters

Name	Default	What it controls
v_input_1	14	Length
v_input_2	true	KSmoothing
v_input_3	3	DLength
v_input_4	50	Level
v_input_5	21	Days
v_input_6	29	Smooth
v_input_7	false	Trade reverse

Source Pine Script

/*backtest
start: 2023-10-01 00:00:00
end: 2023-10-31 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 196/01/2021
// This is combo strategies for get a cumulative signal. 
//
// First strategy
// This System was created from the Book "How I Tripled My Money In The 
// Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
// The strategy buys at market, if close price is higher than the previous close 
// during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. 
// The strategy sells at market, if close price is lower than the previous close price 
// during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
//
// Second strategy
// The signal-to-noise (S/N) ratio. 
// And Simple Moving Average.
//
// WARNING:
// - For purpose educate only
// - This script to change bars colors.
////////////////////////////////////////////////////////////
Reversal123(Length, KSmoothing, DLength, Level) =>
    vFast = sma(stoch(close, high, low, Length), KSmoothing) 
    vSlow = sma(vFast, DLength)
    pos = 0.0
    pos := iff(close[2] < close[1] and close > close[1] and vFast < vSlow and vFast > Level, 1,
	         iff(close[2] > close[1] and close < close[1] and vFast > vSlow and vFast < Level, -1, nz(pos[1], 0))) 
	pos

SignalToNoise(length) =>
    StN = 0.0
    for i = 1 to length-1
        StN := StN + (1/close[i])/length
    StN := -10*log(StN)

StN(length,Smooth) =>
    pos = 0.0
    StN = SignalToNoise(length)
    SMAStN = sma(StN, Smooth)
    pos := iff(SMAStN[0] > StN[0] , -1,
    	     iff(SMAStN[0] < StN[0], 1, 0)) 
    pos

strategy(title="Combo Backtest 123 Reversal & Signal To Noise", shorttitle="Combo", overlay = true)
Length = input(14, minval=1)
KSmoothing = input(1, minval=1)
DLength = input(3, minval=1)
Level = input(50, minval=1)
//-------------------------
lengthStN = input(title="Days", type=input.integer, defval=21, minval=2)
SmoothStN =  input(title="Smooth", type=input.integer, defval=29, minval=2)
reverse = input(false, title="Trade reverse")
posReversal123 = Reversal123(Length, KSmoothing, DLength, Level)
posStN = StN(lengthStN,SmoothStN)
pos = iff(posReversal123 == 1 and posStN == 1 , 1,
	   iff(posReversal123 == -1 and posStN == -1, -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)	 
if (possig == 0) 
    strategy.close_all()
barcolor(possig == -1 ? #b50404: possig == 1 ? #079605 : #0536b3 )

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-01. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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