Stochastic Überkauft/Überverkauft-Umkehr
Die Strategie reagiert auf extreme Niveaus des Stochastic Oszillators. Wenn die %K-Linie in überverkauftes Terrain abtaucht, erwartet das System eine Erholung, während überkaufte Werte einen Rückgang ankündigen können. Die Methode läuft auf kurzen Intraday-Kerzen, sodass Signale schnell eintreffen.
Tests zeigen eine durchschnittliche jährliche Rendite von etwa 73%. Sie funktioniert am besten auf dem Kryptomarkt.
Nach der Anmeldung auf dem ausgewählten Zeitrahmen überwacht sie die %K- und %D-Linien. Ein bullisches Setup entsteht, wenn %K unter 20 fällt und dann beginnt, sich zu erholen. Umgekehrt erscheint ein bärisches Setup, wenn %K über 80 steigt und anfängt, sich nach unten zu drehen. Ein fester prozentualer Stop kontrolliert das Risiko für beide Seiten.
Positionen werden geschlossen, wenn die %K-Linie wieder durch das Niveau 50 kreuzt, was signalisiert, dass sich der Schwung in die entgegengesetzte Richtung verschoben hat.
Details
- Einstiegskriterien:
- Long:
%K < 20mit bullischer Wende. - Short:
%K > 80mit bärischer Wende.
- Long:
- Long/Short: Beide.
- Ausstiegskriterien: %K kreuzt Niveau 50 oder Stop-Loss.
- Stops: Ja, bei
2%Abstand. - Standardwerte:
StochPeriod= 14KPeriod= 3DPeriod= 3CandleType= 5 minute
- Filter:
- Kategorie: Oszillator
- Richtung: Beide
- Indikatoren: Stochastic
- Stops: Ja
- Komplexität: Grundlegend
- Zeitrahmen: Intraday
- Saisonalität: Nein
- Neuronale Netze: Nein
- Divergenz: Nein
- Risikolevel: Mittel
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>
/// Fades confirmed stochastic extremes and exits on the return to neutral.
/// The native oscillator's D is the smoothed %K; a second SMA forms %D.
/// </summary>
public class StochasticOverboughtOversoldStrategy : Strategy
{
private const decimal ComparisonTolerance = 0.00000001m;
private readonly StrategyParam<int> _stochPeriod;
private readonly StrategyParam<int> _kPeriod;
private readonly StrategyParam<int> _dPeriod;
private readonly StrategyParam<DataType> _candleType;
private readonly StrategyParam<decimal> _stopLossPercent;
private readonly Queue<decimal> _dWindow = new();
private decimal? _previousK;
private Order _pendingOrder;
public int StochPeriod { get => _stochPeriod.Value; set => _stochPeriod.Value = value; }
public int KPeriod { get => _kPeriod.Value; set => _kPeriod.Value = value; }
public int DPeriod { get => _dPeriod.Value; set => _dPeriod.Value = value; }
public DataType CandleType { get => _candleType.Value; set => _candleType.Value = value; }
public decimal StopLossPercent { get => _stopLossPercent.Value; set => _stopLossPercent.Value = value; }
public StochasticOverboughtOversoldStrategy()
{
_stochPeriod = Param(nameof(StochPeriod), 14).SetGreaterThanZero()
.SetDisplay("Stochastic Period", "Raw high/low lookback", "Indicators");
_kPeriod = Param(nameof(KPeriod), 3).SetGreaterThanZero()
.SetDisplay("K Period", "SMA length of raw %K", "Indicators");
_dPeriod = Param(nameof(DPeriod), 3).SetGreaterThanZero()
.SetDisplay("D Period", "SMA length of smoothed %K", "Indicators");
_candleType = Param(nameof(CandleType), TimeSpan.FromMinutes(5).TimeFrame())
.SetDisplay("Candle Type", "Stochastic timeframe", "General");
_stopLossPercent = Param(nameof(StopLossPercent), 2m).SetNotNegative()
.SetDisplay("Stop Loss (%)", "Actual-fill percent stop; zero disables it.", "Protection");
OrderRegistering += order => _pendingOrder = order;
}
public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
=> [(Security, CandleType), (Security, DataType.Level1)];
protected override void OnReseted()
{
base.OnReseted();
ClearState();
}
private void ClearState()
{
_dWindow.Clear();
_previousK = null;
_pendingOrder = null;
}
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
ClearState();
StartProtection(new Unit(), new Unit(StopLossPercent, UnitTypes.Percent), useMarketOrders: true, isLocalStop: true);
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 stochastic = new StochasticOscillator
{
K = { Length = StochPeriod },
D = { Length = KPeriod },
};
var candles = SubscribeCandles(CandleType);
candles.BindEx(stochastic, ProcessCandle).Start();
var area = CreateChartArea();
if (area != null)
{
DrawCandles(area, candles);
DrawIndicator(area, stochastic);
DrawOwnTrades(area);
}
}
private void ObserveProtectionQuote(Level1ChangeMessage quote)
{
// Native actual-fill protection evaluates executable quotes between signal candles.
}
private void ProcessCandle(ICandleMessage candle, IIndicatorValue output)
{
if (candle.State != CandleStates.Finished || !output.Indicator.IsFormed ||
output is not StochasticOscillatorValue value || value.D is not decimal k)
return;
_dWindow.Enqueue(k);
if (_dWindow.Count > DPeriod)
_dWindow.Dequeue();
if (_dWindow.Count < DPeriod)
return;
var d = _dWindow.Average();
var previous = _previousK;
_previousK = k;
if (previous is not decimal prior || !IsFormedAndOnlineAndAllowTrading() ||
_pendingOrder is not null && _pendingOrder.State is not (OrderStates.Done or OrderStates.Failed))
return;
// The native rolling SMA can leave a sub-ulp remainder at exact levels (e.g. 50).
if (Position == 0m && prior < 20m - ComparisonTolerance &&
k > prior + ComparisonTolerance && k > d + ComparisonTolerance)
BuyMarket(Volume);
else if (Position == 0m && prior > 80m + ComparisonTolerance &&
k < prior - ComparisonTolerance && k < d - ComparisonTolerance)
SellMarket(Volume);
// %K crossing 50 either way closes, so a position whose entry bar already passed 50 exits on the cross back.
else if (Position > 0m && (prior < 50m - ComparisonTolerance) != (k < 50m - ComparisonTolerance))
SellMarket(Position);
else if (Position < 0m && (prior > 50m + ComparisonTolerance) != (k > 50m + ComparisonTolerance))
BuyMarket(Math.Abs(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 System.Globalization import CultureInfo
from StockSharp.Messages import DataType, CandleStates, Unit, UnitTypes, OrderStates, Level1Fields
from StockSharp.BusinessEntities import Subscription
from StockSharp.Algo.Indicators import StochasticOscillator
from StockSharp.Algo.Strategies import Strategy
_COMPARISON_TOLERANCE = Decimal.Parse("0.00000001", CultureInfo.InvariantCulture)
class stochastic_overbought_oversold_strategy(Strategy):
"""
Fades confirmed stochastic extremes and exits on the return to neutral.
The native oscillator's D is the smoothed %K; a second SMA forms %D.
"""
def __init__(self):
super(stochastic_overbought_oversold_strategy, self).__init__()
self._stoch_period = self.Param("StochPeriod", 14).SetGreaterThanZero().SetDisplay("Stochastic Period", "Raw high/low lookback", "Indicators")
self._k_period = self.Param("KPeriod", 3).SetGreaterThanZero().SetDisplay("K Period", "SMA length of raw %K", "Indicators")
self._d_period = self.Param("DPeriod", 3).SetGreaterThanZero().SetDisplay("D Period", "SMA length of smoothed %K", "Indicators")
self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromMinutes(5))).SetDisplay("Candle Type", "Stochastic timeframe", "General")
self._stop_loss_percent = self.Param("StopLossPercent", 2.0).SetNotNegative().SetDisplay("Stop Loss (%)", "Actual-fill percent stop; zero disables it.", "Protection")
self._clear_state()
self.OrderRegistering += self._track_pending
@property
def candle_type(self):
return self._candle_type.Value
def GetWorkingSecurities(self):
return [(self.Security, self.candle_type), (self.Security, DataType.Level1)]
def _track_pending(self, order):
self._pending_order = order
def _clear_state(self):
self._d_window = []
self._previous_k = None
self._pending_order = None
def OnReseted(self):
super(stochastic_overbought_oversold_strategy, self).OnReseted()
self._clear_state()
def OnStarted2(self, time):
super(stochastic_overbought_oversold_strategy, self).OnStarted2(time)
self._clear_state()
self.StartProtection(Unit(), Unit(Decimal(self._stop_loss_percent.Value), UnitTypes.Percent), useMarketOrders=True, isLocalStop=True)
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()
stochastic = StochasticOscillator()
stochastic.K.Length = self._stoch_period.Value
stochastic.D.Length = self._k_period.Value
candles = self.SubscribeCandles(self.candle_type)
candles.BindEx(stochastic, self._process_candle).Start()
area = self.CreateChartArea()
if area is not None:
self.DrawCandles(area, candles)
self.DrawIndicator(area, stochastic)
self.DrawOwnTrades(area)
def _observe_protection_quote(self, quote):
# Native actual-fill protection evaluates executable quotes between signal candles.
pass
def _process_candle(self, candle, output):
if candle.State != CandleStates.Finished or not output.Indicator.IsFormed:
return
k = output.D
if k is None:
return
self._d_window.append(k)
d_period = self._d_period.Value
if len(self._d_window) > d_period:
self._d_window.pop(0)
if len(self._d_window) < d_period:
return
d = sum(self._d_window, Decimal(0)) / Decimal(d_period)
prior = self._previous_k
self._previous_k = k
if prior is None or not self.IsFormedAndOnlineAndAllowTrading():
return
if self._pending_order is not None and self._pending_order.State not in (OrderStates.Done, OrderStates.Failed):
return
# Native rolling SMA can leave a sub-ulp remainder at exact levels (e.g. 50).
tolerance = _COMPARISON_TOLERANCE
if self.Position == 0 and prior < 20 - tolerance and k > prior + tolerance and k > d + tolerance:
self.BuyMarket(self.Volume)
elif self.Position == 0 and prior > 80 + tolerance and k < prior - tolerance and k < d - tolerance:
self.SellMarket(self.Volume)
# %K crossing 50 either way closes, so a position whose entry bar already passed 50 exits on the cross back.
elif self.Position > 0 and (prior < 50 - tolerance) != (k < 50 - tolerance):
self.SellMarket(self.Position)
elif self.Position < 0 and (prior > 50 + tolerance) != (k > 50 + tolerance):
self.BuyMarket(Math.Abs(self.Position))
def CreateClone(self):
return stochastic_overbought_oversold_strategy()