Stochasticブレイクアウト戦略
このブレイクアウトアプローチは、Stochasticオシレーターが最近の平均から急激に動くことを監視します。%Kラインがボラティリティ調整された閾値を上下にブレイクすると、トレンドを開始する可能性のあるモメンタムの爆発を示します。
テストでは平均年間リターンが約181%であることが示されています。暗号資産市場で最も良いパフォーマンスを発揮します。
%Kが収縮期間後に上部閾値を上回ったときにロングポジションが発動されます。%Kが下部閾値を下回ったときにショートポジションが取られます。オシレーターが平均値に向かって戻るか保護的なストップに達したら取引を決済します。
この戦略はモメンタムスイングへの早期エントリーを望むイントラデイトレーダー向けに設計されています。ボラティリティベースのバンドを使用することでノイズを排除し、決定的な動きのみがシグナルを生成します。
詳細
- エントリー条件:
- ロング: %K > Avg + DeviationMultiplier * StdDev
- ショート: %K < Avg - DeviationMultiplier * StdDev
- ロング/ショート: 両方。
- エグジット条件:
- ロング: %K < Avg のときに決済
- ショート: %K > Avg のときに決済
- ストップ: あり、パーセントストップロス。
- デフォルト値:
StochasticPeriod= 14KPeriod= 3LookbackPeriod= 20DeviationMultiplier= 2StopLossPercent= 2CandleType= TimeSpan.FromMinutes(5) AvgとStdDevは現在値を含む直近LookbackPeriod個の%Kの平均と標準偏差です。 ストップはエントリー価格の固定StopLossPercentで、ローソク足の間も監視されます。0で無効になります。 %Kは最初の期間のストキャスティクスをKPeriod本で平滑化した値で、%Dはルールに関与しないためDPeriodは削除しました。 保有ポジションと逆方向のエントリーシグナルはドテンになります。
- フィルター:
- カテゴリ: ブレイクアウト
- 方向: 両方
- インジケーター: Stochastic Oscillator
- ストップ: あり
- 複雑さ: 中級
- 時間軸: イントラデイ
- 季節性: いいえ
- ニューラルネットワーク: いいえ
- ダイバージェンス: いいえ
- リスクレベル: 中
using System;
using System.Collections.Generic;
using System.Linq;
using Ecng.Common;
using StockSharp.Algo.Indicators;
using StockSharp.Algo.Strategies;
using StockSharp.BusinessEntities;
using StockSharp.Messages;
namespace StockSharp.Samples.Strategies;
/// <summary>
/// Stochastic Breakout strategy.
/// The bands lie DeviationMultiplier standard deviations around the average of the last LookbackPeriod %K values, the current one included.
/// %K above the upper band goes long and %K below the lower band goes short,
/// reversing an opposite position. A long closes once %K is back below its average and a short once it is back above it, and a percent stop limits the loss.
/// </summary>
public class StochasticBreakoutStrategy : Strategy
{
private readonly StrategyParam<int> _stochasticPeriod;
private readonly StrategyParam<int> _kPeriod;
private readonly StrategyParam<int> _lookbackPeriod;
private readonly StrategyParam<decimal> _deviationMultiplier;
private readonly StrategyParam<decimal> _stopLossPercent;
private readonly StrategyParam<DataType> _candleType;
private readonly Queue<decimal> _values = [];
/// <summary>
/// Lookback period of the raw stochastic.
/// </summary>
public int StochasticPeriod
{
get => _stochasticPeriod.Value;
set => _stochasticPeriod.Value = value;
}
/// <summary>
/// Smoothing period of %K.
/// </summary>
public int KPeriod
{
get => _kPeriod.Value;
set => _kPeriod.Value = value;
}
/// <summary>
/// Values of %K the average and the standard deviation span.
/// </summary>
public int LookbackPeriod
{
get => _lookbackPeriod.Value;
set => _lookbackPeriod.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 StochasticBreakoutStrategy()
{
_stochasticPeriod = Param(nameof(StochasticPeriod), 14)
.SetGreaterThanZero()
.SetDisplay("Stochastic Period", "Lookback period of the raw stochastic", "Indicators");
_kPeriod = Param(nameof(KPeriod), 3)
.SetGreaterThanZero()
.SetDisplay("%K Period", "Smoothing period of %K", "Indicators");
_lookbackPeriod = Param(nameof(LookbackPeriod), 20)
.SetGreaterThanZero()
.SetDisplay("Average Period", "Values of %K the average and the standard deviation span", "Indicators");
_deviationMultiplier = Param(nameof(DeviationMultiplier), 2m)
.SetGreaterThanZero()
.SetDisplay("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 OnReseted()
{
base.OnReseted();
_values.Clear();
}
/// <inheritdoc />
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
_values.Clear();
var stochastic = new StochasticOscillator
{
K = { Length = StochasticPeriod },
D = { Length = KPeriod },
};
var subscription = SubscribeCandles(CandleType);
subscription
.BindEx(stochastic, 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);
DrawOwnTrades(area);
var oscillators = CreateChartArea();
if (oscillators != null)
{
DrawIndicator(oscillators, stochastic);
}
}
}
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 stochasticValue)
{
if (candle.State != CandleStates.Finished)
return;
// The smoothed %K is the moving average the core oscillator exposes as D.
if (!stochasticValue.IsFormed || stochasticValue is not IStochasticOscillatorValue { D: decimal value })
return;
_values.Enqueue(value);
if (_values.Count > LookbackPeriod)
_values.Dequeue();
if (_values.Count < LookbackPeriod)
return;
if (!IsFormedAndOnlineAndAllowTrading())
return;
var mean = _values.Average();
var deviation = (decimal)Math.Sqrt((double)_values.Average(v => (v - mean) * (v - mean)));
var upper = mean + DeviationMultiplier * deviation;
var lower = mean - DeviationMultiplier * deviation;
if (value > upper && Position <= 0)
BuyMarket(Volume + Math.Abs(Position));
else if (value < lower && Position >= 0)
SellMarket(Volume + Math.Abs(Position));
else if (Position > 0 && value < mean)
SellMarket(Position);
else if (Position < 0 && value > mean)
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, Math
from StockSharp.Messages import DataType, CandleStates, Unit, UnitTypes, Level1Fields
from StockSharp.BusinessEntities import Subscription
from StockSharp.Algo.Indicators import StochasticOscillator
from StockSharp.Algo.Strategies import Strategy
class stochastic_breakout_strategy(Strategy):
"""
Stochastic Breakout strategy.
The bands lie DeviationMultiplier standard deviations around the average of the last LookbackPeriod %K values, the current one included.
%K above the upper band goes long and %K below the lower band goes short,
reversing an opposite position. A long closes once %K is back below its average and a short once it is back above it, and a percent stop limits the loss.
"""
def __init__(self):
super(stochastic_breakout_strategy, self).__init__()
self._stochastic_period = self.Param("StochasticPeriod", 14).SetGreaterThanZero().SetDisplay("Stochastic Period", "Lookback period of the raw stochastic", "Indicators")
self._k_period = self.Param("KPeriod", 3).SetGreaterThanZero().SetDisplay("%K Period", "Smoothing period of %K", "Indicators")
self._lookback_period = self.Param("LookbackPeriod", 20).SetGreaterThanZero().SetDisplay("Average Period", "Values of %K the average and the standard deviation span", "Indicators")
self._deviation_multiplier = self.Param("DeviationMultiplier", 2.0).SetGreaterThanZero().SetDisplay("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")
self._reset_state()
@property
def candle_type(self):
return self._candle_type.Value
def _reset_state(self):
self._values = []
def OnReseted(self):
super(stochastic_breakout_strategy, self).OnReseted()
self._reset_state()
def OnStarted2(self, time):
super(stochastic_breakout_strategy, self).OnStarted2(time)
self._reset_state()
stochastic = StochasticOscillator()
stochastic.K.Length = self._stochastic_period.Value
stochastic.D.Length = self._k_period.Value
subscription = self.SubscribeCandles(self.candle_type)
subscription.BindEx(stochastic, 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.DrawOwnTrades(area)
oscillators = self.CreateChartArea()
if oscillators is not None:
self.DrawIndicator(oscillators, stochastic)
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, stochastic_value):
if candle.State != CandleStates.Finished:
return
# The smoothed %K is the moving average the core oscillator exposes as D.
if not stochastic_value.IsFormed or stochastic_value.D is None:
return
value = stochastic_value.D
period = self._lookback_period.Value
self._values.append(value)
if len(self._values) > period:
self._values.pop(0)
if len(self._values) < period:
return
if not self.IsFormedAndOnlineAndAllowTrading():
return
total = Decimal(0)
for item in self._values:
total += item
mean = total / Decimal(period)
squares = Decimal(0)
for item in self._values:
squares += (item - mean) * (item - mean)
deviation = Decimal(Math.Sqrt(Decimal.ToDouble(squares / Decimal(period))))
multiplier = Decimal(self._deviation_multiplier.Value)
upper = mean + multiplier * deviation
lower = mean - multiplier * deviation
if value > upper and self.Position <= 0:
self.BuyMarket(self.Volume + abs(self.Position))
elif value < lower and self.Position >= 0:
self.SellMarket(self.Volume + abs(self.Position))
elif self.Position > 0 and value < mean:
self.SellMarket(self.Position)
elif self.Position < 0 and value > mean:
self.BuyMarket(-self.Position)
def CreateClone(self):
return stochastic_breakout_strategy()