Dynamische Trendfolgestrategie mit drei gleitenden Durchschnitten
ChaoZhang
Überblick
Die Dynamische Drei-Gleichlinien-Trend-Tracking-Strategie verwendet dynamische Gleitende Durchschnitte über mehrere Zeiträume, um Markttrends zu identifizieren und eine Trendkonsistenz-Filterung zwischen verschiedenen Zeiträumen zu ermöglichen, wodurch die Zuverlässigkeit der Handelssignale erhöht wird.
Strategieprinzip
Die Strategie verwendet drei verschiedene Parameter, um einen dynamisch ausgeglichenen Moving Average zu berechnen. Der erste Moving Average berechnet die Trendrichtung des aktuellen Zeitzykluspreises, der zweite Moving Average berechnet die Trendrichtung des höheren Zeitzykluspreises, der dritte Moving Average berechnet die Trendrichtung des noch höheren Zeitzykluspreises.
Moving averages verwenden die dynamische glatte Funktion, die automatisch berechnet und den entsprechenden Glatteffekt zwischen den verschiedenen Zeitperioden anwendet, so dass der Moving Average für hohe Zeiträume eine glatte Trendlinie anstelle einer krummen Faltung auf einem niedrigen Zeitplan darstellt. Diese dynamische Glatte ermöglicht es der Strategie, die Richtung des Gesamttrends in hohen Zeiträumen zu bestimmen und gleichzeitig in niedrigen Zeiträumen zu handeln, was eine effiziente Trendverfolgung ermöglicht.
Strategische Vorteile
Der größte Vorteil dieser Strategie liegt in der Trendfiltermechanik für mehrere Zeiträume. Durch die Berechnung der durchschnittlichen Trendrichtung der Preise für verschiedene Zeiträume und die Anforderung der Konsistenz zwischen den verschiedenen Perioden kann die Interferenz von kurzfristigen Preisschwankungen von vieleity auf die Handelssignale effektiv gefiltert werden, um sicherzustellen, dass jedes Handelssignal in den großen Trends ist, was die Gewinnwahrscheinlichkeit erheblich erhöht.
Ein weiterer Vorteil ist die Anwendung der Dynamic Smoothing-Funktion, die es der Strategie ermöglicht, sowohl die Gesamttrends in hohen Zeiträumen als auch die spezifischen Handelsplätze in niedrigen Zeiträumen zu identifizieren. Die Strategie kann die Richtung der großen Trends in hohen Zeiträumen bestimmen und gleichzeitig spezifische Geschäfte in niedrigen Zeiträumen ausführen. Die Verwendung dieses mehrfachen Zeitrahmens hilft, die Handelsrisiken zu kontrollieren, während die Marktchancen erfasst werden.
Risiko und Optimierung
Das Hauptrisiko dieser Strategie besteht darin, dass es weniger Handelssignale gibt. Strenge Trendfilterbedingungen reduzieren die Anzahl der Handelsmöglichkeiten, was für einige Anleger, die sich mit Hochfrequenzhandel beschäftigen, nicht sehr geeignet sein kann. Mehr Handelsmöglichkeiten können durch eine geringere Strenge der Filterbedingungen erzielt werden.
Darüber hinaus müssen die Parameter-Einstellungen sorgfältig getestet und optimiert werden, insbesondere die Periodenlänge der Moving Averages. In verschiedenen Märkten müssen unterschiedliche Perioden-Parameter eingestellt werden, um die optimale Wirkung zu erzielen. Die optimale Kombination von Parametern kann durch Rückprüfungen gefunden werden.
In Zukunft können wir auch mehr technische Kennzahlen für die Filterung oder die automatische Optimierung von Machine Learning-Algorithmen berücksichtigen. Diese sind effektive Methoden, um die Effektivität der Strategie zu verbessern.
Zusammenfassen
Diese Strategie ist insgesamt eine sehr praktische Trend-Tracking-Strategie. Die Mechanismen der Mehrzeit-Frame Trend-Filter bietet eine gute Richtung Unterstützung für jede Handelsentscheidung, effektiv zu reduzieren, die Handelsrisiken. Die Hinzufügung der dynamischen Smoothing-Funktion macht auch diese Mehrzeit-Framework-Methode effizient zu realisieren.
/*backtest
start: 2024-01-23 00:00:00
end: 2024-02-22 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Harrocop
//@version=5
strategy(title = "Triple MA HTF strategy - Dynamic Smoothing", shorttitle = "Triple MA strategy", overlay=true,
pyramiding=5, initial_capital = 10000,
calc_on_order_fills=false,
slippage = 0,
commission_type=strategy.commission.percent, commission_value=0.05)
//////////////////////////////////////////////////////
////////// Risk Management ////////////
//////////////////////////////////////////////////////
RISKM = "-------------------- Risk Management --------------------"
InitialBalance = input.float(defval = 10000, title = "Initial Balance", minval = 1, maxval = 1000000, step = 1000, tooltip = "starting capital", group = RISKM)
LeverageEquity = input.bool(defval = true, title = "qty based on equity %", tooltip = "true turns on MarginFactor based on equity, false gives fixed qty for positionsize", group = RISKM)
MarginFactor = input.float(0, minval = - 0.9, maxval = 100, step = 0.1, tooltip = "Margin Factor, meaning that 0.5 will add 50% extra capital to determine ordersize quantity, 0.0 means 100% of equity is used to decide quantity of instrument", inline = "qty", group = RISKM)
QtyNr = input.float(defval = 3.5, title = "Quantity Contracts", minval = 0, maxval = 1000000, step = 0.01, tooltip = "Margin Factor, meaning that 0.5 will add 50% extra capital to determine ordersize quantity, 0.0 means 100% of equity is used to decide quantity of instrument", inline = "qty", group = RISKM)
EquityCurrent = InitialBalance + strategy.netprofit[1]
QtyEquity = EquityCurrent * (1 + MarginFactor) / close[1]
QtyTrade = LeverageEquity ? QtyEquity : QtyNr
/////////////////////////////////////////////////////
////////// MA Filter Trend ////////////
/////////////////////////////////////////////////////
TREND = "-------------------- Moving Average 1 --------------------"
Plot_MA = input.bool(true, title = "Plot MA trend?", inline = "Trend1", group = TREND)
TimeFrame_Trend = input.timeframe(title='Higher Time Frame', defval='15', inline = "Trend1", group = TREND)
length = input.int(21, title="Length MA", minval=1, tooltip = "Number of bars used to measure trend on higher timeframe chart", inline = "Trend2", group = TREND)
MA_Type = input.string(defval="McGinley" , options=["EMA","DEMA","TEMA","SMA","WMA", "HMA", "McGinley"], title="MA type:", inline = "Trend2", group = TREND)
ma(type, src, length) =>
float result = 0
if type == 'TMA' // Triangular Moving Average
result := ta.sma(ta.sma(src, math.ceil(length / 2)), math.floor(length / 2) + 1)
result
if type == 'LSMA' // Least Squares Moving Average
result := ta.linreg(src, length, 0)
result
if type == 'SMA' // Simple Moving Average
result := ta.sma(src, length)
result
if type == 'EMA' // Exponential Moving Average
result := ta.ema(src, length)
result
if type == 'DEMA' // Double Exponential Moving Average
e = ta.ema(src, length)
result := 2 * e - ta.ema(e, length)
result
if type == 'TEMA' // Triple Exponentiale
e = ta.ema(src, length)
result := 3 * (e - ta.ema(e, length)) + ta.ema(ta.ema(e, length), length)
result
if type == 'WMA' // Weighted Moving Average
result := ta.wma(src, length)
result
if type == 'HMA' // Hull Moving Average
result := ta.wma(2 * ta.wma(src, length / 2) - ta.wma(src, length), math.round(math.sqrt(length)))
result
if type == 'McGinley' // McGinley Dynamic Moving Average
mg = 0.0
mg := na(mg[1]) ? ta.ema(src, length) : mg[1] + (src - mg[1]) / (length * math.pow(src / mg[1], 4))
result := mg
result
result
// Moving Average
MAtrend = ma(MA_Type, close, length)
MA_Value_HTF = request.security(syminfo.tickerid, TimeFrame_Trend, MAtrend)
// Get minutes for current and higher timeframes
// Function to convert a timeframe string to its equivalent in minutes
timeframeToMinutes(tf) =>
multiplier = 1
if (str.endswith(tf, "D"))
multiplier := 1440
else if (str.endswith(tf, "W"))
multiplier := 10080
else if (str.endswith(tf, "M"))
multiplier := 43200
else if (str.endswith(tf, "H"))
multiplier := int(str.tonumber(str.replace(tf, "H", "")))
else
multiplier := int(str.tonumber(str.replace(tf, "m", "")))
multiplier
// Get minutes for current and higher timeframes
currentTFMinutes = timeframeToMinutes(timeframe.period)
higherTFMinutes = timeframeToMinutes(TimeFrame_Trend)
// Calculate the smoothing factor
dynamicSmoothing = math.round(higherTFMinutes / currentTFMinutes)
MA_Value_Smooth = ta.sma(MA_Value_HTF, dynamicSmoothing)
// Trend HTF
UP = MA_Value_Smooth > MA_Value_Smooth[1] // Use "UP" Function to use as filter in combination with other indicators
DOWN = MA_Value_Smooth < MA_Value_Smooth[1] // Use "Down" Function to use as filter in combination with other indicators
/////////////////////////////////////////////////////
////////// Second MA Filter Trend ///////////
/////////////////////////////////////////////////////
TREND2 = "-------------------- Moving Average 2 --------------------"
Plot_MA2 = input.bool(true, title = "Plot Second MA trend?", inline = "Trend3", group = TREND2)
TimeFrame_Trend2 = input.timeframe(title='HTF', defval='60', inline = "Trend3", group = TREND2)
length2 = input.int(21, title="Length Second MA", minval=1, tooltip = "Number of bars used to measure trend on higher timeframe chart", inline = "Trend4", group = TREND2)
MA_Type2 = input.string(defval="McGinley" , options=["EMA","DEMA","TEMA","SMA","WMA", "HMA", "McGinley"], title="MA type:", inline = "Trend4", group = TREND2)
// Second Moving Average
MAtrend2 = ma(MA_Type2, close, length2)
MA_Value_HTF2 = request.security(syminfo.tickerid, TimeFrame_Trend2, MAtrend2)
// Get minutes for current and higher timeframes
higherTFMinutes2 = timeframeToMinutes(TimeFrame_Trend2)
// Calculate the smoothing factor for the second moving average
dynamicSmoothing2 = math.round(higherTFMinutes2 / currentTFMinutes)
MA_Value_Smooth2 = ta.sma(MA_Value_HTF2, dynamicSmoothing2)
// Trend HTF for the second moving average
UP2 = MA_Value_Smooth2 > MA_Value_Smooth2[1]
DOWN2 = MA_Value_Smooth2 < MA_Value_Smooth2[1]
/////////////////////////////////////////////////////
////////// Third MA Filter Trend ///////////
/////////////////////////////////////////////////////
TREND3 = "-------------------- Moving Average 3 --------------------"
Plot_MA3 = input.bool(true, title = "Plot third MA trend?", inline = "Trend5", group = TREND3)
TimeFrame_Trend3 = input.timeframe(title='HTF', defval='240', inline = "Trend5", group = TREND3)
length3 = input.int(50, title="Length third MA", minval=1, tooltip = "Number of bars used to measure trend on higher timeframe chart", inline = "Trend6", group = TREND3)
MA_Type3 = input.string(defval="McGinley" , options=["EMA","DEMA","TEMA","SMA","WMA", "HMA", "McGinley"], title="MA type:", inline = "Trend6", group = TREND3)
// Second Moving Average
MAtrend3 = ma(MA_Type3, close, length3)
MA_Value_HTF3 = request.security(syminfo.tickerid, TimeFrame_Trend3, MAtrend3)
// Get minutes for current and higher timeframes
higherTFMinutes3 = timeframeToMinutes(TimeFrame_Trend3)
// Calculate the smoothing factor for the second moving average
dynamicSmoothing3 = math.round(higherTFMinutes3 / currentTFMinutes)
MA_Value_Smooth3 = ta.sma(MA_Value_HTF3, dynamicSmoothing3)
// Trend HTF for the second moving average
UP3 = MA_Value_Smooth3 > MA_Value_Smooth3[1]
DOWN3 = MA_Value_Smooth3 < MA_Value_Smooth3[1]
/////////////////////////////////////////////////////
////////// Entry Settings ////////////
/////////////////////////////////////////////////////
BuySignal = ta.crossover(MA_Value_HTF, MA_Value_HTF2) and UP3 == true
SellSignal = ta.crossunder(MA_Value_HTF, MA_Value_HTF2) and DOWN3 == true
ExitBuy = ta.crossunder(MA_Value_HTF, MA_Value_HTF2)
ExitSell = ta.crossover(MA_Value_HTF, MA_Value_HTF2)
/////////////////////////////////////////////////
/////////// Strategy ////////////////
/////////// Entry & Exit ////////////////
/////////// logic ////////////////
/////////////////////////////////////////////////
// Long
if BuySignal
strategy.entry("Long", strategy.long, qty = QtyTrade)
if (strategy.position_size > 0 and ExitBuy == true)
strategy.close(id = "Long", comment = "Close Long")
// Short
if SellSignal
strategy.entry("Short", strategy.short, qty = QtyTrade)
if (strategy.position_size < 0 and ExitSell == true)
strategy.close(id = "Short", comment = "Close Short")
/////////////////////////////////////////////////////
////////// Visuals Chart ////////////
/////////////////////////////////////////////////////
// Plot Moving Average HTF
p1 = plot(Plot_MA ? MA_Value_Smooth : na, "HTF Trend", color = UP ? color.rgb(238, 255, 0) : color.rgb(175, 173, 38), linewidth = 1, style = plot.style_line)
p2 = plot(Plot_MA2 ? MA_Value_Smooth2 : na, "HTF Trend", color = UP2 ? color.rgb(0, 132, 255) : color.rgb(0, 17, 255), linewidth = 1, style = plot.style_line)
plot(Plot_MA3 ? MA_Value_Smooth3 : na, "HTF Trend", color = UP3 ? color.rgb(0, 255, 8) : color.rgb(255, 0, 0), linewidth = 2, style = plot.style_line)
fill(p1, p2, color = color.rgb(255, 208, 0, 90), title="Fill")