Bugra Trading Strategy Based on Dual Kinetic Moving Average

The Bugra trading strategy is a strategy that combines the OTT indicator developed by my dear teacher Anıl Özekşi and the Wavetrend Oscillator indicator by lonestar108. It forms a successful trading indicator by…

Pine ScriptMean reversion Trend following Volatility EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 94 lines
Parameters: 7
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Principle

The Bugra trading strategy first calculates the midline of Bollinger Bands, which is the moving average line MAvg. Then, based on the percentage range and period set by the user, it calculates the long stop loss longStop and the short stop loss shortStop. When the price breaks through the upper rail, go long. When it breaks through the lower rail, go short. The closing signal is when the price returns to around the moving average.

Specifically, the core indicator of this strategy is the OTT indicator. The OTT indicator consists of a moving average and boundary lines. It adjusts the position of the boundary lines according to the volatility of the market based on certain algorithms. When the price breaks through the lower boundary line OTT, go short. When it breaks through the upper boundary line OTT, go long.

This strategy also uses the Wavetrend indicator to determine the direction of the price trend. If it is judged to be a downward trend, only go short, not long. If it is judged to be an upward trend, only go long, not short.

Advantage Analysis

The Bugra trading strategy combines the advantages of moving averages, Bollinger Bands and OTT indicators. It can automatically adjust stop loss positions and reduce the probability of stop loss being triggered. At the same time, by incorporating trend judgment indicators, it avoids being trapped in oscillating trends.

Specifically, the main advantages of this strategy are:

It can automatically adjust stop loss positions to effectively control risks.
The OTT indicator can relatively accurately determine reversal points.
By incorporating trend judgment indicators, it avoids being trapped in oscillating markets.
Its rules are relatively simple and clear, easy to understand and apply.

Risk Analysis

The Bugra trading strategy also has some risks, mainly in the following aspects:

In violent market conditions, stop loss may be broken, causing greater losses.
The reversal signals judged by the OTT indicator may not be accurate, and faulty signals may occur.
Trend judgments can also be wrong. Going long in an downward oscillation will cause losses.
Improper parameter settings will also affect strategy performance.

The countermeasures are mainly:

Appropriately loosen the stop loss range to ensure that stop loss lines are not easily activated.
Combine with other indicators to judge the reliability of OTT signals to avoid false signals.
Appropriately adjust parameters to make trend judgments more reliable.
Optimize parameters to find the best parameter combination.

Optimization Directions

There is still room for further optimization of the dual kinetic moving average trading strategy:

Consider combining with other indicators to improve the accuracy of signal judgment.
Study adaptive stop loss algorithms so that stop loss lines can be adjusted according to market volatility.
Add trading volume indicators to avoid false breakouts with low volume.
Test different types of moving averages to find the most suitable moving average.
Try machine learning and other methods to automatically optimize parameters.

Summary

The dual kinetic moving average trading strategy integrates the advantages of multiple indicators. It can automatically adjust stop loss positions, judge reversal signals, and identify trend directions. It has advantages such as strong risk control capabilities and easy to understand and use. But it also has risks like being trapped and inaccurate signals. This strategy can be further optimized by combining with other indicators, studying adaptive algorithms, etc. In general, the dual kinetic moving average trading strategy is a practical breakout trading strategy.

Parameters

Name	Default	What it controls
v_input_1	5	Period
v_input_2	true	Sihirli Yüzde
v_input_3	0	Hareketli Ortalama Türü: VAR \| EMA \| WMA \| TMA \| SMA \| WWMA \| ZLEMA \| TSF
v_input_4	10	Kanal Periyodu
v_input_5	21	Averaj Uzunluğu
v_input_6	20200101	Başlangıç Tarihi (YYYYMMDD)
v_input_7	20201231	Bitiş Tarihi (YYYYMMDD)

Source Pine Script

/*backtest
start: 2023-02-12 00:00:00
end: 2024-02-18 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy(title="Bugra trade strategy", shorttitle="Bugra trade strategy", overlay=true)

// Kullanıcı Girdileri
length = input(5, title="Period", minval=1)
percent = input(1, title="Sihirli Yüzde", type=input.float, step=0.1, minval=0)
mav = input(title="Hareketli Ortalama Türü", defval="VAR", options=["SMA", "EMA", "WMA", "TMA", "VAR", "WWMA", "ZLEMA", "TSF"])
wt_n1 = input(10, title="Kanal Periyodu")
wt_n2 = input(21, title="Averaj Uzunluğu")
src = close

// Tarih Aralığı Girdileri
startDate = input(20200101, title="Başlangıç Tarihi (YYYYMMDD)")
endDate = input(20201231, title="Bitiş Tarihi (YYYYMMDD)")

// Tarih Filtresi Fonksiyonu
isDateInRange() => true
// Özel Fonksiyonlar
Var_Func(src, length) =>
    valpha = 2 / (length + 1)
    vud1 = src > src[1] ? src - src[1] : 0
    vdd1 = src < src[1] ? src[1] - src : 0
    vUD = sum(vud1, length)
    vDD = sum(vdd1, length)
    vCMO = (vUD - vDD) / (vUD + vDD)
    varResult = 0.0
    varResult := nz(valpha * abs(vCMO) * src + (1 - valpha * abs(vCMO)) * nz(varResult[1]))
    varResult

Wwma_Func(src, length) =>
    wwalpha = 1 / length
    wwma = 0.0
    wwma := wwalpha * src + (1 - wwalpha) * nz(wwma[1])
    wwma

Zlema_Func(src, length) =>
    zxLag = floor(length / 2)
    zxEMAData = src + (src - src[zxLag])
    zlema = ema(zxEMAData, length)
    zlema

Tsf_Func(src, length) =>
    lrc = linreg(src, length, 0)
    lrs = lrc - linreg(src, length, 1)
    tsf = lrc + lrs
    tsf

getMA(src, length) =>
    ma = mav == "SMA" ? sma(src, length) :
         mav == "EMA" ? ema(src, length) :
         mav == "WMA" ? wma(src, length) :
         mav == "TMA" ? sma(sma(src, ceil(length / 2)), floor(length / 2) + 1) :
         mav == "VAR" ? Var_Func(src, length) :
         mav == "WWMA" ? Wwma_Func(src, length) :
         mav == "ZLEMA" ? Zlema_Func(src, length) :
         mav == "TSF" ? Tsf_Func(src, length) : na

// Strateji Hesaplamaları
MAvg = getMA(src, length)
fark = MAvg * percent * 0.01
longStop = MAvg - fark
longStopPrev = nz(longStop[1], longStop)
longStop := MAvg > longStopPrev ? max(longStop, longStopPrev) : longStop
shortStop = MAvg + fark
shortStopPrev = nz(shortStop[1], shortStop)
shortStop := MAvg < shortStopPrev ? min(shortStop, shortStopPrev) : shortStop

dir = 1
dir := nz(dir[1], dir)
dir := dir == -1 and MAvg > shortStopPrev ? 1 : dir == 1 and MAvg < longStopPrev ? -1 : dir
MT = dir==1 ? longStop: shortStop
OTT = MAvg > MT ? MT*(200+percent)/200 : MT*(200-percent)/200

plot(OTT, title="BugRA", color=color.rgb(251, 126, 9))

// Alım ve Satım Koşulları
longCondition = crossover(src, OTT) and isDateInRange()
shortCondition = crossunder(src, OTT) and isDateInRange()

// Strateji Giriş ve Çıkış Emirleri
if (longCondition)
    strategy.entry("Long", strategy.long)

if (shortCondition)
    strategy.close("Long")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-19. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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