OBV 均值回归策略
OBV 指标跟踪累计成交量的流向,以判断买卖力量。本策略在 OBV 与其均值出现显著背离时寻找回归机会。
测试表明年均收益约为 79%,该策略在股票市场表现最佳。
当 OBV 低于均值减 Multiplier 倍标准差且价格在均线下方时买入;当 OBV 高于均值加同样倍数并且价格在均线上方时卖出。OBV 回到均值线附近即平仓。
该策略适合将成交量因素纳入考虑的交易者,百分比止损用于应对成交量持续扩大的情况。
详细信息
- 入场条件:
- 做多: OBV < Avg - Multiplier * StdDev && Close < MA
- 做空: OBV > Avg + Multiplier * StdDev && Close > MA
- 多空方向: 双向
- 退出条件:
- 做多: Exit when OBV > Avg
- 做空: Exit when OBV < Avg
- 止损: 是
- 默认值:
AveragePeriod= 20Multiplier= 2StopLossPercent= 2CandleType= TimeSpan.FromMinutes(5) Avg和StdDev为最近AveragePeriod个OBV值(含当前值)的平均值和标准差,MA为最近AveragePeriod个收盘价的简单移动平均。止损为入场价的固定StopLossPercent百分比,在K线之间同样监控;设为0则关闭。与持仓方向相反的入场信号会反转持仓。
- 筛选条件:
- 类别: 均值回归
- 方向: 双向
- 指标: OBV
- 止损: 是
- 复杂度: 中等
- 时间框架: 日内
- 季节性: 否
- 神经网络: 否
- 背离: 否
- 风险等级: 中等
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>
/// OBV Mean Reversion strategy.
/// The bands lie Multiplier standard deviations around the average of the last AveragePeriod OBV values, the current one included.
/// OBV below the lower band with the close below the AveragePeriod simple moving average goes long and OBV above the upper band with the close above it goes short,
/// reversing an opposite position. A long closes once OBV is back above its average and a short once it is back below it, and a percent stop limits the loss.
/// </summary>
public class ObvMeanReversionStrategy : Strategy
{
private readonly StrategyParam<int> _averagePeriod;
private readonly StrategyParam<decimal> _multiplier;
private readonly StrategyParam<decimal> _stopLossPercent;
private readonly StrategyParam<DataType> _candleType;
private readonly Queue<decimal> _values = [];
/// <summary>
/// Values of OBV the average and the standard deviation span.
/// </summary>
public int AveragePeriod
{
get => _averagePeriod.Value;
set => _averagePeriod.Value = value;
}
/// <summary>
/// Standard deviations between the average and a band.
/// </summary>
public decimal Multiplier
{
get => _multiplier.Value;
set => _multiplier.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 ObvMeanReversionStrategy()
{
_averagePeriod = Param(nameof(AveragePeriod), 20)
.SetGreaterThanZero()
.SetDisplay("Average Period", "Values of OBV the average and the standard deviation span", "Indicators");
_multiplier = Param(nameof(Multiplier), 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 obv = new OnBalanceVolume();
var sma = new SimpleMovingAverage { Length = AveragePeriod };
var subscription = SubscribeCandles(CandleType);
subscription
.BindEx(obv, sma, 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, sma);
DrawOwnTrades(area);
var oscillators = CreateChartArea();
if (oscillators != null)
{
DrawIndicator(oscillators, obv);
}
}
}
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 obvValue, IIndicatorValue smaValue)
{
if (candle.State != CandleStates.Finished)
return;
if (!smaValue.IsFormed)
return;
if (!obvValue.IsFormed)
return;
var value = obvValue.GetValue<decimal>();
_values.Enqueue(value);
if (_values.Count > AveragePeriod)
_values.Dequeue();
if (_values.Count < AveragePeriod)
return;
if (!IsFormedAndOnlineAndAllowTrading())
return;
var mean = _values.Average();
var deviation = (decimal)Math.Sqrt((double)_values.Average(v => (v - mean) * (v - mean)));
var upper = mean + Multiplier * deviation;
var lower = mean - Multiplier * deviation;
var close = candle.ClosePrice;
var ma = smaValue.GetValue<decimal>();
if (value < lower && close < ma && Position <= 0)
BuyMarket(Volume + Math.Abs(Position));
else if (value > upper && close > ma && 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 OnBalanceVolume, SimpleMovingAverage
from StockSharp.Algo.Strategies import Strategy
class obv_mean_reversion_strategy(Strategy):
"""
OBV Mean Reversion strategy.
The bands lie Multiplier standard deviations around the average of the last AveragePeriod OBV values, the current one included.
OBV below the lower band with the close below the AveragePeriod simple moving average goes long and OBV above the upper band with the close above it goes short,
reversing an opposite position. A long closes once OBV is back above its average and a short once it is back below it, and a percent stop limits the loss.
"""
def __init__(self):
super(obv_mean_reversion_strategy, self).__init__()
self._average_period = self.Param("AveragePeriod", 20).SetGreaterThanZero().SetDisplay("Average Period", "Values of OBV the average and the standard deviation span", "Indicators")
self._multiplier = self.Param("Multiplier", 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(obv_mean_reversion_strategy, self).OnReseted()
self._reset_state()
def OnStarted2(self, time):
super(obv_mean_reversion_strategy, self).OnStarted2(time)
self._reset_state()
obv = OnBalanceVolume()
sma = SimpleMovingAverage()
sma.Length = self._average_period.Value
subscription = self.SubscribeCandles(self.candle_type)
subscription.BindEx(obv, sma, 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, sma)
self.DrawOwnTrades(area)
oscillators = self.CreateChartArea()
if oscillators is not None:
self.DrawIndicator(oscillators, obv)
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, obv_value, sma_value):
if candle.State != CandleStates.Finished:
return
if not sma_value.IsFormed:
return
if not obv_value.IsFormed:
return
value = obv_value.GetValue[Decimal](None)
period = self._average_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._multiplier.Value)
upper = mean + multiplier * deviation
lower = mean - multiplier * deviation
close = candle.ClosePrice
ma = sma_value.GetValue[Decimal](None)
if value < lower and close < ma and self.Position <= 0:
self.BuyMarket(self.Volume + abs(self.Position))
elif value > upper and close > ma 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 obv_mean_reversion_strategy()