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Momentum Indicator Aggregation Trading Strategy

Author: ChaoZhang, Date: 2024-02-21 11:59:22
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Overview

This strategy integrates multiple technical indicators including moving average, MACD, RSI and Bollinger bands to generate a variety of buy and sell signals, forming a relatively complete momentum indicator aggregation trading strategy.

Strategy Logic

The core logic of the strategy is to aggregate the buy and sell signals of multiple technical indicators, which mainly includes the following aspects:

  1. Moving average indicator: Calculate the fast and slow moving average lines and generate buy signals when the fast line crosses above the slow line, and sell signals when crossing below.

  2. MACD indicator: Calculate the MACD line and signal line, generating buy signals when the MACD line crosses above the signal line, and sell signals when crossing below.

  3. RSI indicator: Calculate RSI values to determine if it enters overbought or oversold zones, combined with the golden cross and death cross of RSI line and middle line 50 to generate trading signals.

  4. Bollinger bands indicator: Determine if prices break through upper and lower bands, combined with signals of returning to middle band to generate trading signals.

  5. Exit criteria: Set stop profit and stop loss standards, exit positions when reaching certain percentages.

The signals of each indicator modules work independently. The strategy monitors these signals in real time, goes long when buy signals triggered and goes short when sell signals triggered, to dynamically aggregate profitable positions.

Advantage Analysis

The advantages of this strategy includes:

  1. Rich signal sources with various indicator signals, not easy to miss opportunities.

  2. Reduce false signals by verifying signals with different indicators.

  3. Good adaptability to trends and reversals with both trending and mean-reverting indicators.

  4. Automatic stop profit and stop loss mechanism helps control risks.

Risk Analysis

There are also some risks existed in this strategy:

  1. Invalidity risk of indicators under certain market conditions.

  2. Oversimplification problem when aggregating too many signals, leads to insufficient resolution.

  3. Difficulties in parameter optimization with many indicators.

  4. High turnover rate and increased trading costs.

Optimization Directions

There are some rooms for further optimizations:

  1. Test and optimize the combinations of indicators and parameters.

  2. Use machine learning methods to find optimal parameters automatically.

  3. Test different weighting methods for signal aggregation.

  4. Add adaptive stop loss mechanisms based on market volatility.

  5. Add opening algorithms to control single opening proportions for better risk control.

Conclusion

In conclusion, this is a typical and universal momentum indicator aggregation trading strategy, which integrates trading signals from various common technical indicators and improves performance through signal aggregation. Compared with single indicator strategies, it has the advantages of more abundant signal sources and better identification of trends and reversals. Meanwhile, the difficulties in parameter optimization and increased invalidity risks should also be noticed. With further testing and optimization, this strategy can become a very practical quantitative trading tool.

/*backtest
start: 2024-01-01 00:00:00
end: 2024-01-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Kesin Etkili Analiz V1 - Artun Sinan", overlay=true)
//indicator("Kesin Etkili Analiz V1 - Artun Sinan", overlay=true)

//BackTest
yearin = input (2019, title="BackTestBaşlangıç Tarihi")

// Göstergelerin parametrelerini tanımlayın
emaShrtPeriod = input.int(title="EMA Kısa Periyodu", defval=50, minval=1)
emaLngPeriod = input.int(title="EMA Uzun Periyodu", defval=100, minval=1)

maPeriod = input.int(50, "Hareketli Ortalama Periyodu", minval=1)
fast = input.int(12, "MACD Hızlı Periyodu", minval=1)
slow = input.int(26, "MACD Yavaş Periyodu", minval=1)
signal = input.int(9, "MACD Sinyal Periyodu", minval=1)
rsiPeriod = input.int(14, "RSI Periyodu", minval=1)
rsiOverbought = input.int(70, "RSI Aşırı Alım Eşiği", minval=50, maxval=100)
rsiOversold = input.int(30, "RSI Aşırı Satım Eşiği", minval=0, maxval=50)
bbPeriod = input.int(20, "Bollinger Bantları Periyodu", minval=1)
bbStd = input.float(2, "Bollinger Bantları Standart Sapması", minval=0.1)

//EMA göstergesi ayarları
ema1 = ta.ema (close,emaShrtPeriod)
ema2 = ta.ema (close, emaLngPeriod)

emaCrossUp = ema1 >= ema2
emaCrossDown = ema2 < ema1

plot(ema1, title="EMAKısa", color=color.rgb(0, 255, 13))
plot(ema2, title="EMAUzun", color=color.rgb(255, 251, 1))



// Göstergeleri hesaplayın
ma = ta.sma(close, maPeriod) // Hareketli ortalama
[macd, macdsignal, macdhist] = ta.macd(close, fast, slow, signal) // MACD
rsi = ta.rsi(close, rsiPeriod) // RSI
[upper, middle, lower] = ta.bb(close, bbPeriod, bbStd) // Bollinger Bantları

// Alım veya satım sinyalleri üretin
buySignal = false
sellSignal = false

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Fibonacci seviyelerini tanımlayın
fibLevels = array.new_float(7) // Fibonacci seviyelerini tutacak bir dizi oluşturun
array.set(fibLevels, 0, 0.0) // %0 seviyesini ayarlayın
array.set(fibLevels, 1, 0.236) // %23.6 seviyesini ayarlayın
array.set(fibLevels, 2, 0.382) // %38.2 seviyesini ayarlayın
array.set(fibLevels, 3, 0.5) // %50 seviyesini ayarlayın
array.set(fibLevels, 4, 0.618) // %61.8 seviyesini ayarlayın
array.set(fibLevels, 5, 0.786) // %78.6 seviyesini ayarlayın
array.set(fibLevels, 6, 1.0) // %100 seviyesini ayarlayın

// Tepe ve dip noktasını belirleyin
highpoint = ta.highest (high, 20) // Son 30 mum çubuğunun en yüksek değerini alın
lowpoint = ta.lowest (low, 20) // Son 30 mum çubuğunun en düşük değerini alın
diff = highpoint - lowpoint // Tepe ve dip noktası arasındaki farkı hesaplayın

// Fibonacci seviyelerini hesaplayın
fib0 = lowpoint + diff * array.get(fibLevels, 0) // %0 seviyesini hesaplayın
fib1 = lowpoint + diff * array.get(fibLevels, 1) // %23.6 seviyesini hesaplayın
fib2 = lowpoint + diff * array.get(fibLevels, 2) // %38.2 seviyesini hesaplayın
fib3 = lowpoint + diff * array.get(fibLevels, 3) // %50 seviyesini hesaplayın
fib4 = lowpoint + diff * array.get(fibLevels, 4) // %61.8 seviyesini hesaplayın
fib5 = lowpoint + diff * array.get(fibLevels, 5) // %78.6 seviyesini hesaplayın
fib6 = lowpoint + diff * array.get(fibLevels, 6) // %100 seviyesini hesaplayın

// Alım sinyali: Fiyat %61,8 seviyesinden yukarı yönlü kırılırsa ve MACD çizgisi sinyal çizgisinin üzerine çıkarsa, alım pozisyonu açın
alSignal = close > fib4 and ta.crossover(macd, macdsignal)

// Satım sinyali: Fiyat %61,8 seviyesinden aşağı yönlü kırılırsa ve MACD çizgisi sinyal çizgisinin altına inerse, satım pozisyonu açın
satSignal = close < fib4 and ta.crossunder(macd, macdsignal)

// Çıkış sinyali: Fiyat %38,2 Fibonacci seviyesine ulaşırsa veya belirli bir yüzde oranında kar veya zarar elde ederseniz, pozisyonu kapatın
exitSignal = close >= fib2 or close <= strategy.position_avg_price * 0.95 // Kar oranı olarak %5, zarar oranı olarak %5 belirledik

plot(fib0, title="%0", color=color.rgb(25, 0, 255))
plot(fib1, title="%23.6", color=color.rgb(25, 0, 255))
plot(fib2, title="%38.2", color=color.rgb(25, 0, 255))
plot(fib3, title="%50", color=color.rgb(25, 0, 255))
plot(fib4, title="%61.8", color=color.rgb(25, 0, 255))
plot(fib5, title="%78.6", color=color.rgb(25, 0, 255))
plot(fib6, title="%100", color=color.rgb(25, 0, 255))
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

// Hareketli ortalama kesişimi sinyali
maCrossUp = ta.crossover(ma, close) // Fiyat hareketli ortalamanın üzerine çıkarsa
maCrossDown = ta.crossunder(ma, close) // Fiyat hareketli ortalamanın altına inerse

// MACD çizgisi ve sinyal çizgisi kesişimi sinyali // Histogram yerine çizgiler
macdCrossUp = ta.crossover(macd, macdsignal) // MACD çizgisi sinyal çizgisinin üzerine çıkarsa
macdCrossDown = ta.crossunder(macd, macdsignal) // MACD çizgisi sinyal çizgisinin altına inerse

// RSI aşırı alım veya aşırı satım sinyali ve 50 seviyesi kesişimi sinyali // Sinyalleri birleştir
// Eşik değerleri doğrudan kullanın
rsiOverboughtSignal = rsi > rsiOverbought and ta.crossover(rsi, 50) // RSI değeri aşırı alım eşiğinin üzerindeyse ve 50 seviyesini yukarı keserse
rsiOversoldSignal = rsi < rsiOversold and ta.crossunder(rsi, 50) // RSI değeri aşırı satım eşiğinin altındaysa ve 50 seviyesini aşağı keserse

// Bollinger Bantları kırılımı sinyali ve orta bant geri dönüşü sinyali // Sinyalleri birleştir
bbBreakUp = close > upper and ta.crossover(close, middle) // Fiyat üst banttan çıkarsa ve orta banta geri dönerse
bbBreakDown = close < lower and ta.crossunder(close, middle) // Fiyat alt banttan inerse ve orta banta geri dönerse

// Sinyalleri birleştirin
buySignal := maCrossUp or macdCrossUp or rsiOversoldSignal or bbBreakUp or emaCrossUp and yearin >= year
sellSignal := maCrossDown or macdCrossDown or rsiOverboughtSignal or bbBreakDown or emaCrossDown and yearin >= year

// Sinyalleri grafikte oklar ile gösterin
plotshape(buySignal, style=shape.triangleup, location=location.belowbar, color=color.green, size=size.small)
plotshape(sellSignal, style=shape.triangledown, location=location.abovebar, color=color.red, size=size.small)

plot(macd, title="MACD", color=color.blue) // MACD çizgisini mavi renkte çizin
plot(macdsignal, title="Sinyal", color=color.orange) // Sinyal çizgisini turuncu renkte çizin


if buySignal
    strategy.entry("Enter Long", strategy.long)
else if sellSignal
    strategy.entry("Enter Short", strategy.short)
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