精调均线策略 (Fine Tuning MA)
策略关注简单移动平均线的斜率。连续两个柱同向运行后,均线反转即触发交易。下行后的上拐做多,上行后的下拐做空,反向信号平掉当前仓位。
该系统由 MQL 专家顾问 "Exp_FineTuningMA" 转换而来,使用标准简单移动平均线代替原始自定义指标。
详情
- 入场条件: 均线在连续两柱后改变方向。
- 多空方向: 双向。
- 出场条件: 反向信号或止损止盈。
- 止损: 是,百分比。
- 默认值:
MaLength= 10TakeProfitPercent= 1StopLossPercent= 1CandleType= TimeSpan.FromHours(4)
- 过滤器:
- 类别: 趋势跟随
- 方向: 双向
- 指标: SMA
- 止损: 是
- 复杂度: 中等
- 时间框架: 波段 / 4 小时
- 季节性: 否
- 神经网络: 否
- 背离: 否
- 风险等级: 中
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>
/// Fine Tuning MA strategy. Opens positions when the moving average changes direction.
/// </summary>
public class FineTuningMaStrategy : Strategy
{
private readonly StrategyParam<int> _maLength;
private readonly StrategyParam<decimal> _takeProfitPercent;
private readonly StrategyParam<decimal> _stopLossPercent;
private readonly StrategyParam<DataType> _candleType;
private decimal _prev1;
private decimal _prev2;
private int _candleCount;
public int MaLength { get => _maLength.Value; set => _maLength.Value = value; }
public decimal TakeProfitPercent { get => _takeProfitPercent.Value; set => _takeProfitPercent.Value = value; }
public decimal StopLossPercent { get => _stopLossPercent.Value; set => _stopLossPercent.Value = value; }
public DataType CandleType { get => _candleType.Value; set => _candleType.Value = value; }
public FineTuningMaStrategy()
{
_maLength = Param(nameof(MaLength), 20)
.SetGreaterThanZero()
.SetDisplay("MA Length", "Length of the moving average", "Parameters");
_takeProfitPercent = Param(nameof(TakeProfitPercent), 1m)
.SetDisplay("Take Profit, %", "Take profit level in percent", "Protection");
_stopLossPercent = Param(nameof(StopLossPercent), 1m)
.SetDisplay("Stop Loss, %", "Stop loss level in percent", "Protection");
_candleType = Param(nameof(CandleType), TimeSpan.FromHours(4).TimeFrame())
.SetDisplay("Candle Type", "Type of candles for calculations", "Parameters");
}
/// <inheritdoc />
public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
{
return [(Security, CandleType)];
}
/// <inheritdoc />
protected override void OnReseted()
{
base.OnReseted();
_prev1 = default;
_prev2 = default;
_candleCount = default;
}
/// <inheritdoc />
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
var ma = new ExponentialMovingAverage { Length = MaLength };
var subscription = SubscribeCandles(CandleType);
subscription.Bind(ma, ProcessCandle).Start();
StartProtection(
new Unit(StopLossPercent, UnitTypes.Percent),
new Unit(TakeProfitPercent, UnitTypes.Percent));
}
private void ProcessCandle(ICandleMessage candle, decimal ma)
{
if (candle.State != CandleStates.Finished)
return;
if (!IsFormedAndOnlineAndAllowTrading())
return;
_candleCount++;
if (_candleCount <= 2)
{
_prev2 = _prev1;
_prev1 = ma;
return;
}
var wasRising = _prev1 > _prev2;
var wasFalling = _prev1 < _prev2;
if (wasRising && ma > _prev1 && Position <= 0)
{
if (Position < 0)
BuyMarket();
BuyMarket();
}
else if (wasFalling && ma < _prev1 && Position >= 0)
{
if (Position > 0)
SellMarket();
SellMarket();
}
_prev2 = _prev1;
_prev1 = ma;
}
}
import clr
clr.AddReference("StockSharp.Messages")
clr.AddReference("StockSharp.Algo")
clr.AddReference("StockSharp.Algo.Indicators")
clr.AddReference("StockSharp.Algo.Strategies")
from System import TimeSpan
from StockSharp.Messages import DataType, CandleStates, Unit, UnitTypes
from StockSharp.Algo.Indicators import ExponentialMovingAverage
from StockSharp.Algo.Strategies import Strategy
class fine_tuning_ma_strategy(Strategy):
def __init__(self):
super(fine_tuning_ma_strategy, self).__init__()
self._ma_length = self.Param("MaLength", 20) \
.SetDisplay("MA Length", "Length of the moving average", "Parameters")
self._take_profit_percent = self.Param("TakeProfitPercent", 1.0) \
.SetDisplay("Take Profit, %", "Take profit level in percent", "Protection")
self._stop_loss_percent = self.Param("StopLossPercent", 1.0) \
.SetDisplay("Stop Loss, %", "Stop loss level in percent", "Protection")
self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromHours(4))) \
.SetDisplay("Candle Type", "Type of candles for calculations", "Parameters")
self._prev1 = 0.0
self._prev2 = 0.0
self._candle_count = 0
@property
def MaLength(self):
return self._ma_length.Value
@MaLength.setter
def MaLength(self, value):
self._ma_length.Value = value
@property
def TakeProfitPercent(self):
return self._take_profit_percent.Value
@TakeProfitPercent.setter
def TakeProfitPercent(self, value):
self._take_profit_percent.Value = value
@property
def StopLossPercent(self):
return self._stop_loss_percent.Value
@StopLossPercent.setter
def StopLossPercent(self, value):
self._stop_loss_percent.Value = value
@property
def CandleType(self):
return self._candle_type.Value
@CandleType.setter
def CandleType(self, value):
self._candle_type.Value = value
def OnStarted2(self, time):
super(fine_tuning_ma_strategy, self).OnStarted2(time)
ma = ExponentialMovingAverage()
ma.Length = self.MaLength
self.SubscribeCandles(self.CandleType) \
.Bind(ma, self.ProcessCandle) \
.Start()
self.StartProtection(
stopLoss=Unit(self.StopLossPercent, UnitTypes.Percent),
takeProfit=Unit(self.TakeProfitPercent, UnitTypes.Percent)
)
def ProcessCandle(self, candle, ma_value):
if candle.State != CandleStates.Finished:
return
if not self.IsFormedAndOnlineAndAllowTrading():
return
val = float(ma_value)
self._candle_count += 1
if self._candle_count <= 2:
self._prev2 = self._prev1
self._prev1 = val
return
was_rising = self._prev1 > self._prev2
was_falling = self._prev1 < self._prev2
if was_rising and val > self._prev1 and self.Position <= 0:
if self.Position < 0:
self.BuyMarket()
self.BuyMarket()
elif was_falling and val < self._prev1 and self.Position >= 0:
if self.Position > 0:
self.SellMarket()
self.SellMarket()
self._prev2 = self._prev1
self._prev1 = val
def OnReseted(self):
super(fine_tuning_ma_strategy, self).OnReseted()
self._prev1 = 0.0
self._prev2 = 0.0
self._candle_count = 0
def CreateClone(self):
return fine_tuning_ma_strategy()