Strategie Mean Reversion
Dieser statistische Ansatz sucht nach kurzfristigen Extremen im Preis im Verhältnis zu seinem jüngsten Durchschnitt. Die Strategie verwendet einen gleitenden Durchschnitt zur Definition des fairen Werts und misst die Abweichung von diesem Mittelwert durch eine Standardabweichungsberechnung.
Tests zeigen eine durchschnittliche Jahresrendite von etwa 85%. Sie funktioniert am besten auf dem Kryptomarkt.
Trades werden geöffnet, wenn der Preis eine festgelegte Distanz vom Durchschnitt schiebt. Ein Einbruch unter das untere Band löst einen Long-Einstieg aus, der eine Erholung in Richtung des Mittelwerts antizipiert, während eine Rally über das obere Band einen Short veranlasst. Sobald der Preis den gleitenden Durchschnitt wieder berührt, wird eine offene Position geschlossen.
Die Methode spricht Trader mit einem konträren Stil an, die klar definierte Einstiegs- und Ausstiegszonen wünschen. Da sie auf volatilitätsbasierten Bändern basiert, passt sie sich ruhigeren oder aktiveren Märkten an, während Verluste durch einen festen Stop-Loss unter Kontrolle bleiben.
Details
- Einstiegskriterien:
- Long: Price < MA - k*StdDev (below lower band)
- Short: Price > MA + k*StdDev (above upper band)
- Long/Short: Beide Seiten.
- Ausstiegskriterien:
- Long: Ausstieg, wenn der Preis über den gleitenden Durchschnitt kreuzt
- Short: Ausstieg, wenn der Preis unter den gleitenden Durchschnitt kreuzt
- Stops: Ja.
- Standardwerte:
MovingAveragePeriod = 20
DeviationMultiplier = 2
StopLossPercent = 2
MA ist der einfache gleitende Durchschnitt und StdDev die Standardabweichung der Schlusskurse über dieselben MovingAveragePeriod Kerzen, k ist DeviationMultiplier. Der Stop liegt bei festen StopLossPercent vom Einstiegspreis und wird auch zwischen den Kerzen überwacht. Ein Einstiegssignal gegen eine offene Position dreht sie.
CandleType = TimeSpan.FromMinutes(5)
- Filter:
- Kategorie: Mean Reversion
- Richtung: Beide
- Indikatoren: Mean Reversion
- Stops: Ja
- Komplexität: Mittel
- Zeitrahmen: Intraday
- Saisonalität: Nein
- Neuronale Netze: Nein
- Divergenz: Nein
- Risikolevel: Mittel
using System;
using System.Collections.Generic;
using Ecng.Common;
using StockSharp.Algo.Indicators;
using StockSharp.Algo.Strategies;
using StockSharp.BusinessEntities;
using StockSharp.Messages;
namespace StockSharp.Samples.Strategies;
/// <summary>
/// Mean Reversion strategy.
/// The bands lie DeviationMultiplier standard deviations around the MovingAveragePeriod simple moving average, both measured over the same
/// candles. A close below the lower band goes long and a close above the upper band goes short, reversing an opposite position.
/// A long closes once price closes above the average and a short once it closes below, and a percent stop limits the loss.
/// </summary>
public class MeanReversionStrategy : Strategy
{
private readonly StrategyParam<int> _movingAveragePeriod;
private readonly StrategyParam<decimal> _deviationMultiplier;
private readonly StrategyParam<decimal> _stopLossPercent;
private readonly StrategyParam<DataType> _candleType;
/// <summary>
/// Period of the moving average and the standard deviation.
/// </summary>
public int MovingAveragePeriod
{
get => _movingAveragePeriod.Value;
set => _movingAveragePeriod.Value = value;
}
/// <summary>
/// Standard deviations between the average and a band.
/// </summary>
public decimal DeviationMultiplier
{
get => _deviationMultiplier.Value;
set => _deviationMultiplier.Value = value;
}
/// <summary>
/// Stop loss percentage from entry price.
/// </summary>
public decimal StopLossPercent
{
get => _stopLossPercent.Value;
set => _stopLossPercent.Value = value;
}
/// <summary>
/// Candle type.
/// </summary>
public DataType CandleType
{
get => _candleType.Value;
set => _candleType.Value = value;
}
/// <summary>
/// Constructor.
/// </summary>
public MeanReversionStrategy()
{
_movingAveragePeriod = Param(nameof(MovingAveragePeriod), 20)
.SetGreaterThanZero()
.SetDisplay("MA Period", "Period of the moving average and the standard deviation", "Indicators");
_deviationMultiplier = Param(nameof(DeviationMultiplier), 2m)
.SetGreaterThanZero()
.SetDisplay("Deviation Multiplier", "Standard deviations between the average and a band", "Indicators");
_stopLossPercent = Param(nameof(StopLossPercent), 2m)
.SetNotNegative()
.SetDisplay("Stop Loss %", "Stop loss percentage from entry price", "Risk");
_candleType = Param(nameof(CandleType), TimeSpan.FromMinutes(5).TimeFrame())
.SetDisplay("Candle Type", "Type of candles to use", "General");
}
/// <inheritdoc />
public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
{
return [(Security, CandleType)];
}
/// <inheritdoc />
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
var ma = new SimpleMovingAverage { Length = MovingAveragePeriod };
var stdev = new StandardDeviation { Length = MovingAveragePeriod };
var subscription = SubscribeCandles(CandleType);
subscription
.BindEx(ma, stdev, ProcessCandle)
.Start();
StartProtection(new Unit(), new Unit(StopLossPercent, UnitTypes.Percent), useMarketOrders: true, isLocalStop: true);
// The stop has to see prices between candles, not only at their close.
foreach (var field in new[] { Level1Fields.BestBidPrice, Level1Fields.BestAskPrice })
{
var quotes = new Subscription(DataType.Level1, Security);
quotes.MarketData.BuildField = field;
SubscribeLevel1(quotes).Bind(ObserveProtectionQuote).Start();
}
var area = CreateChartArea();
if (area != null)
{
DrawCandles(area, subscription);
DrawIndicator(area, ma);
DrawOwnTrades(area);
var oscillators = CreateChartArea();
if (oscillators != null)
{
DrawIndicator(oscillators, stdev);
}
}
}
private void ObserveProtectionQuote(Level1ChangeMessage quote)
{
// The high-level handler activates native protection before this callback, also between signal bars.
}
private void ProcessCandle(ICandleMessage candle, IIndicatorValue maValue, IIndicatorValue stdDevValue)
{
if (candle.State != CandleStates.Finished)
return;
if (!maValue.IsFormed || !stdDevValue.IsFormed)
return;
if (!IsFormedAndOnlineAndAllowTrading())
return;
var ma = maValue.GetValue<decimal>();
var deviation = stdDevValue.GetValue<decimal>() * DeviationMultiplier;
var upper = ma + deviation;
var lower = ma - deviation;
var close = candle.ClosePrice;
if (close < lower && Position <= 0)
BuyMarket(Volume + Math.Abs(Position));
else if (close > upper && Position >= 0)
SellMarket(Volume + Math.Abs(Position));
else if (Position > 0 && close > ma)
SellMarket(Position);
else if (Position < 0 && close < ma)
BuyMarket(-Position);
}
}
import clr
clr.AddReference("StockSharp.Messages")
clr.AddReference("StockSharp.Algo")
clr.AddReference("StockSharp.Algo.Indicators")
clr.AddReference("StockSharp.Algo.Strategies")
clr.AddReference("StockSharp.BusinessEntities")
from System import TimeSpan, Decimal
from StockSharp.Messages import DataType, CandleStates, Unit, UnitTypes, Level1Fields
from StockSharp.BusinessEntities import Subscription
from StockSharp.Algo.Indicators import SimpleMovingAverage, StandardDeviation
from StockSharp.Algo.Strategies import Strategy
class mean_reversion_strategy(Strategy):
"""
Mean Reversion strategy.
The bands lie DeviationMultiplier standard deviations around the MovingAveragePeriod simple moving average, both measured over the same
candles. A close below the lower band goes long and a close above the upper band goes short, reversing an opposite position.
A long closes once price closes above the average and a short once it closes below, and a percent stop limits the loss.
"""
def __init__(self):
super(mean_reversion_strategy, self).__init__()
self._moving_average_period = self.Param("MovingAveragePeriod", 20).SetGreaterThanZero().SetDisplay("MA Period", "Period of the moving average and the standard deviation", "Indicators")
self._deviation_multiplier = self.Param("DeviationMultiplier", 2.0).SetGreaterThanZero().SetDisplay("Deviation Multiplier", "Standard deviations between the average and a band", "Indicators")
self._stop_loss_percent = self.Param("StopLossPercent", 2.0).SetNotNegative().SetDisplay("Stop Loss %", "Stop loss percentage from entry price", "Risk")
self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromMinutes(5))).SetDisplay("Candle Type", "Type of candles to use", "General")
@property
def candle_type(self):
return self._candle_type.Value
def OnStarted2(self, time):
super(mean_reversion_strategy, self).OnStarted2(time)
ma = SimpleMovingAverage()
ma.Length = self._moving_average_period.Value
stdev = StandardDeviation()
stdev.Length = self._moving_average_period.Value
subscription = self.SubscribeCandles(self.candle_type)
subscription.BindEx(ma, stdev, self._process_candle).Start()
self.StartProtection(Unit(), Unit(Decimal(self._stop_loss_percent.Value), UnitTypes.Percent), useMarketOrders=True, isLocalStop=True)
# The stop has to see prices between candles, not only at their close.
for field in (Level1Fields.BestBidPrice, Level1Fields.BestAskPrice):
quotes = Subscription(DataType.Level1, self.Security)
quotes.MarketData.BuildField = field
self.SubscribeLevel1(quotes).Bind(self._observe_protection_quote).Start()
area = self.CreateChartArea()
if area is not None:
self.DrawCandles(area, subscription)
self.DrawIndicator(area, ma)
self.DrawOwnTrades(area)
oscillators = self.CreateChartArea()
if oscillators is not None:
self.DrawIndicator(oscillators, stdev)
def _observe_protection_quote(self, quote):
# The high-level handler activates native protection before the callback, including between bars.
pass
def _process_candle(self, candle, ma_value, std_dev_value):
if candle.State != CandleStates.Finished:
return
if not ma_value.IsFormed or not std_dev_value.IsFormed:
return
if not self.IsFormedAndOnlineAndAllowTrading():
return
ma = ma_value.GetValue[Decimal](None)
deviation = std_dev_value.GetValue[Decimal](None) * Decimal(self._deviation_multiplier.Value)
upper = ma + deviation
lower = ma - deviation
close = candle.ClosePrice
if close < lower and self.Position <= 0:
self.BuyMarket(self.Volume + abs(self.Position))
elif close > upper and self.Position >= 0:
self.SellMarket(self.Volume + abs(self.Position))
elif self.Position > 0 and close > ma:
self.SellMarket(self.Position)
elif self.Position < 0 and close < ma:
self.BuyMarket(-self.Position)
def CreateClone(self):
return mean_reversion_strategy()