Random Fisher Transform Temporary Stop Reverse STOCH Indicator Quantitative Strategy

The core idea of this strategy is to combine the Random Fisher Transform and the Temporary Stop Reverse STOCH indicator to make buy and sell decisions. This strategy is suitable for medium-term operations and can…

Pine ScriptMean reversion Trend following Stochastic

Author: ChaoZhang
Complexity: Intermediate · 48 lines
Parameters: 7
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Principle

This strategy first calculates the standard STOCH indicator, then performs a Fisher transform on it to obtain INVLine. When INVLine crosses above the lower threshold dl, a buy signal is generated. When INVLine crosses below the upper threshold ul, a sell signal is generated. At the same time, this strategy also sets a trailing stop mechanism to lock in profits and reduce losses.

Specifically, the core logic of this strategy is:

Calculate the STOCH indicator: Use the standard formula to calculate the fast STOCH value of stock
Fisher transform: Perform a Fisher transform on the STOCH value to obtain INVLine
Generate trading signals: Buy when INVLine crosses above dl, sell when crossing below ul
Trailing stop: Enable a temporary stop tracking mechanism for timely stop loss

Advantage Analysis

The main advantages of this strategy are:

The Fisher transform effectively improves the sensitivity of the STOCH indicator and can detect trend reversal opportunities earlier
The temporary trailing stop mechanism can effectively control risks and lock in profits
It is suitable for medium-term operations, especially the currently popular high-frequency quantitative trading
It performs well in stable market conditions with steady returns

Risk Analysis

This strategy also has some risks:

The STOCH indicator is prone to generating false signals, which may cause unnecessary trading
The Fisher transform also amplifies the noise of the STOCH indicator, resulting in more false signals
It is easy to stop loss in oscillating markets and fail to make sustainable profits
Relatively short holding periods are required to obtain Alpha. Long holdings are not suitable

To mitigate these risks, consider optimizing the following aspects:

Adjust STOCH parameters to smooth the curve and reduce noise
Optimize threshold line positions to reduce false trading probabilities
Add filter conditions to avoid trading in oscillating markets
Adjust holding time length to match operating cycle

Optimization Directions

The main directions for optimizing this strategy include:

Optimize parameters of Fisher transform to smooth INVLine curve
Optimize STOCH period length to find optimal parameter combinations
Optimize threshold line parameters to reduce false trading probabilities
Add volume price confirmation to avoid unnecessary trailing stop
Add intraday breakout filters to reduce false signals in oscillating markets
Incorporate trend indicators to avoid counter trend trading

Conclusion

This strategy combines the Random Fisher Transform and the STOCH indicator to implement a simple and practical short-term quantitative strategy. Its advantage lies in the high operation frequency, which is suitable for the currently popular high-frequency quantitative trading. At the same time, this strategy also has some common technical indicator strategy risks. Parameters and filter conditions need to be optimized to reduce risks and improve stability. In general, this strategy provides a good idea for simple quantitative trading and is worth further in-depth research.

Parameters

Name	Default	What it controls
v_input_1	19	STOCH Length
v_input_2	4	Smooth
v_input_3	0.64	UP line
v_input_4	-0.62	DOWN line
v_input_5	true	Use trailing stop
v_input_6	245	trailing stop actiation pips
v_input_7	20	trailing stop offset pips

Source Pine Script

/*backtest
start: 2022-12-26 00:00:00
end: 2024-01-01 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("IFT Stochastic + Trailing Stop", overlay=false, pyramiding = 0, calc_on_order_fills = false, commission_type =  strategy.commission.percent, commission_value = 0.0454, default_qty_type = strategy.percent_of_equity, default_qty_value = 100)

//INPUTS
stochlength=input(19, "STOCH Length")
wmalength=input(4, title="Smooth")
ul = input(0.64,step=0.01, title="UP line")
dl = input(-0.62,step=0.01, title="DOWN line")
uts = input(true, title="Use trailing stop")
tsi = input(title="trailing stop actiation pips",defval=245)                                                                       
tso = input(title="trailing stop offset pips",defval=20)

//CALCULATIONS
v1=0.1*(stoch(close, high, low, stochlength)-50)
v2=wma(v1, wmalength)
INVLine=(exp(2*v2)-1)/(exp(2*v2)+1)

//CONDITIONS
sell = crossunder(INVLine,ul)? 1 : 0
buy = crossover(INVLine,dl)? 1 : 0

//PLOTS
plot(INVLine, color=aqua, linewidth=1, title="STOCH")
hline(ul, color=red)
hline(dl, color=green)
bgcolor(sell==1? red : na, transp=30, title = "sell signal")
bgcolor(buy==1? lime : na, transp=30, title = "buy signal")
plotchar(buy==1, title="Buy Signal", char='B', location=location.bottom, color=white, transp=0, offset=0)
plotchar(sell==1, title="Sell Signal", char='S', location=location.top, color=white, transp=0, offset=0)

//STRATEGY
strategy.entry("BUY", strategy.long, when = buy==1)
strategy.entry("SELL", strategy.short, when = sell==1)

if  (uts)
    strategy.entry("BUY", strategy.long, when = buy)
    strategy.entry("SELL", strategy.short, when = sell)
    strategy.exit("Close BUY with TS","BUY", trail_points = tsi, trail_offset = tso)
    strategy.exit("Close SELL with TS","SELL", trail_points = tsi, trail_offset = tso)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-02. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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