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三个神经网络策略
概述
该策略是 MetaTrader 智能交易系统“三个神经网络”的 StockSharp 高级 API 版本。策略通过订阅三种不同周期(H1、H4、D1)的蜡烛,并使用内置的 SmoothedMovingAverage 指标来重建原版中的三个神经层,全部逻辑都在 StockSharp 的高级框架内完成。
工作流程
- 启动时分别订阅 H1、H4、D1 的蜡烛数据,并绑定基于中价的平滑移动平均值,以复现 MetaTrader 中
iMA(..., MODE_SMMA, PRICE_MEDIAN) 的调用方式。
- 每个周期都会维护一个考虑到偏移量的滚动窗口。当收集到四个偏移后的值时,就会按照原策略的加权差分公式计算三个神经元输出,并把结果四舍五入到四位小数。
- 每当 H1 蜡烛收盘时,策略会组合三个神经元输出:
- 三个输出都为正 → 建立或维持多头仓位;
- H1 输出为正且 H4、D1 输出为负 → 建立或维持空头仓位。
- 持仓量可在固定手数与风险百分比两种模式之间切换。风险模式下会按照投资组合当前价值的
VolumeOrRisk 百分比估算资金,并用当前价格转换为交易量。
- 风险控制逻辑继承自 EA:在仓位方向发生变化后立即根据点值设置止损与止盈,并在每根 H1 蜡烛收盘时,如果价格突破“拖尾距离 + 拖尾步长”,就重新收紧拖尾止损。
- 每根完成的 H1 蜡烛都会先检查当前止损或止盈是否被触发,如满足条件就通过
ClosePosition() 以市价平仓;EnableDetailedLog 参数可输出与原 EA InpPrintLog 类似的详细日志。
参数
| 参数 |
默认值 |
说明 |
StopLossPips |
50 |
止损距离(点)。为 0 时禁用止损。 |
TakeProfitPips |
50 |
止盈距离(点)。为 0 时禁用止盈。 |
TrailingStopPips |
15 |
拖尾止损距离。 |
TrailingStepPips |
5 |
每次调整拖尾止损所需的最小改善幅度。 |
ManagementMode |
RiskPercent |
资金管理模式:FixedLot 表示固定手数,RiskPercent 表示风险百分比。 |
VolumeOrRisk |
1 |
固定手数或风险百分比(取决于资金管理模式)。 |
H1Period, H1Shift |
2, 5 |
H1 平滑均线的周期与偏移。 |
H4Period, H4Shift |
2, 5 |
H4 平滑均线的周期与偏移。 |
D1Period, D1Shift |
2, 5 |
D1 平滑均线的周期与偏移。 |
P1, P2, P3 |
0.1 |
H1 神经元的权重。 |
Q1, Q2, Q3 |
0.1 |
H4 神经元的权重。 |
K1, K2, K3 |
0.1 |
D1 神经元的权重。 |
EnableDetailedLog |
false |
输出模拟原 EA 日志的详细信息。 |
风险管理
- 策略会自动识别 3/5 位报价并转换点值,在仓位方向发生变化后立即根据
StopLossPips 与 TakeProfitPips 计算价格级别。
- 拖尾止损只有在价格突破
TrailingStopPips + TrailingStepPips 后才会启动,并且只有当改进幅度大于 TrailingStepPips 才会再次上移/下移。
- 因为高级 API 没有服务器端止损/止盈订单,所以所有离场都使用
ClosePosition() 市价完成。
说明
- 原始 EA 中对冻结区/最小止损距离的检查在 StockSharp 中不可用,本策略通过点值换算以及
VolumeStep、VolumeMin、VolumeMax 对交易量进行归一化。
- 风险百分比模式根据投资组合价值与当前价格估算交易手数,能够保持与 MetaTrader
CheckOpenLong/Short 类似的行为,但不依赖券商的保证金计算。
EnableDetailedLog 可用于调试,生成与 InpPrintLog 接近的逐步日志。
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;
public class ThreeNeuralNetworksStrategy : Strategy
{
private readonly StrategyParam<int> _fastPeriod;
private readonly StrategyParam<int> _slowPeriod;
private readonly StrategyParam<int> _stopLossPoints;
private readonly StrategyParam<int> _takeProfitPoints;
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
private decimal _prevFast;
private decimal _prevSlow;
private decimal _entryPrice;
private int _cooldown;
public int FastPeriod { get => _fastPeriod.Value; set => _fastPeriod.Value = value; }
public int SlowPeriod { get => _slowPeriod.Value; set => _slowPeriod.Value = value; }
public int StopLossPoints { get => _stopLossPoints.Value; set => _stopLossPoints.Value = value; }
public int TakeProfitPoints { get => _takeProfitPoints.Value; set => _takeProfitPoints.Value = value; }
public ThreeNeuralNetworksStrategy()
{
_fastPeriod = Param(nameof(FastPeriod), 14).SetGreaterThanZero().SetDisplay("Fast Period", "Fast EMA period", "Indicator");
_slowPeriod = Param(nameof(SlowPeriod), 50).SetGreaterThanZero().SetDisplay("Slow Period", "Slow EMA period", "Indicator");
_stopLossPoints = Param(nameof(StopLossPoints), 200).SetNotNegative().SetDisplay("Stop Loss", "Stop-loss in price steps", "Risk");
_takeProfitPoints = Param(nameof(TakeProfitPoints), 400).SetNotNegative().SetDisplay("Take Profit", "Take-profit in price steps", "Risk");
}
public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
{
yield return (Security, TimeSpan.FromMinutes(5).TimeFrame());
}
protected override void OnReseted()
{
base.OnReseted();
_fast = null; _slow = null;
_prevFast = 0; _prevSlow = 0; _entryPrice = 0; _cooldown = 0;
}
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
_fast = new ExponentialMovingAverage { Length = FastPeriod };
_slow = new ExponentialMovingAverage { Length = SlowPeriod };
var subscription = SubscribeCandles(TimeSpan.FromMinutes(5).TimeFrame());
subscription.Bind(_fast, _slow, ProcessCandle);
subscription.Start();
}
private void ProcessCandle(ICandleMessage candle, decimal fastValue, decimal slowValue)
{
if (candle.State != CandleStates.Finished) return;
if (!_fast.IsFormed || !_slow.IsFormed) { _prevFast = fastValue; _prevSlow = slowValue; return; }
if (_cooldown > 0) { _cooldown--; _prevFast = fastValue; _prevSlow = slowValue; return; }
var close = candle.ClosePrice;
var step = Security?.PriceStep ?? 1m;
if (Position > 0 && _entryPrice > 0)
{
if (StopLossPoints > 0 && close <= _entryPrice - StopLossPoints * step) { SellMarket(); _entryPrice = 0; _cooldown = 100; _prevFast = fastValue; _prevSlow = slowValue; return; }
if (TakeProfitPoints > 0 && close >= _entryPrice + TakeProfitPoints * step) { SellMarket(); _entryPrice = 0; _cooldown = 100; _prevFast = fastValue; _prevSlow = slowValue; return; }
}
else if (Position < 0 && _entryPrice > 0)
{
if (StopLossPoints > 0 && close >= _entryPrice + StopLossPoints * step) { BuyMarket(); _entryPrice = 0; _cooldown = 100; _prevFast = fastValue; _prevSlow = slowValue; return; }
if (TakeProfitPoints > 0 && close <= _entryPrice - TakeProfitPoints * step) { BuyMarket(); _entryPrice = 0; _cooldown = 100; _prevFast = fastValue; _prevSlow = slowValue; return; }
}
if (_prevFast <= _prevSlow && fastValue > slowValue && Position <= 0)
{ if (Position < 0) BuyMarket(); BuyMarket(); _entryPrice = close; _cooldown = 100; }
else if (_prevFast >= _prevSlow && fastValue < slowValue && Position >= 0)
{ if (Position > 0) SellMarket(); SellMarket(); _entryPrice = close; _cooldown = 100; }
_prevFast = fastValue; _prevSlow = slowValue;
}
}
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
from StockSharp.Algo.Indicators import ExponentialMovingAverage
from StockSharp.Algo.Strategies import Strategy
class three_neural_networks_strategy(Strategy):
def __init__(self):
super(three_neural_networks_strategy, self).__init__()
self._fast_period = self.Param("FastPeriod", 14) \
.SetDisplay("Fast Period", "Fast MA period", "Indicator")
self._slow_period = self.Param("SlowPeriod", 50) \
.SetDisplay("Slow Period", "Slow MA period", "Indicator")
self._stop_loss_points = self.Param("StopLossPoints", 200) \
.SetDisplay("Stop Loss", "Stop-loss in price steps", "Risk")
self._take_profit_points = self.Param("TakeProfitPoints", 400) \
.SetDisplay("Take Profit", "Take-profit in price steps", "Risk")
self._fast = None
self._slow = None
self._prev_fast = 0.0
self._prev_slow = 0.0
self._entry_price = 0.0
self._cooldown = 0
@property
def fast_period(self):
return self._fast_period.Value
@property
def slow_period(self):
return self._slow_period.Value
@property
def stop_loss_points(self):
return self._stop_loss_points.Value
@property
def take_profit_points(self):
return self._take_profit_points.Value
def OnReseted(self):
super(three_neural_networks_strategy, self).OnReseted()
self._fast = None
self._slow = None
self._prev_fast = 0.0
self._prev_slow = 0.0
self._entry_price = 0.0
self._cooldown = 0
def OnStarted2(self, time):
super(three_neural_networks_strategy, self).OnStarted2(time)
self._fast = ExponentialMovingAverage()
self._fast.Length = self.fast_period
self._slow = ExponentialMovingAverage()
self._slow.Length = self.slow_period
subscription = self.SubscribeCandles(DataType.TimeFrame(TimeSpan.FromMinutes(5)))
subscription.Bind(self._fast, self._slow, self._process_candle)
subscription.Start()
def _process_candle(self, candle, fast_value, slow_value):
if candle.State != CandleStates.Finished:
return
fast_val = float(fast_value)
slow_val = float(slow_value)
if not self._fast.IsFormed or not self._slow.IsFormed:
self._prev_fast = fast_val
self._prev_slow = slow_val
return
if self._cooldown > 0:
self._cooldown -= 1
self._prev_fast = fast_val
self._prev_slow = slow_val
return
close = float(candle.ClosePrice)
step = float(self.Security.PriceStep) if self.Security is not None and self.Security.PriceStep is not None else 1.0
if self.Position > 0 and self._entry_price > 0:
if self.stop_loss_points > 0 and close <= self._entry_price - self.stop_loss_points * step:
self.SellMarket()
self._entry_price = 0.0
self._cooldown = 100
self._prev_fast = fast_val
self._prev_slow = slow_val
return
if self.take_profit_points > 0 and close >= self._entry_price + self.take_profit_points * step:
self.SellMarket()
self._entry_price = 0.0
self._cooldown = 100
self._prev_fast = fast_val
self._prev_slow = slow_val
return
elif self.Position < 0 and self._entry_price > 0:
if self.stop_loss_points > 0 and close >= self._entry_price + self.stop_loss_points * step:
self.BuyMarket()
self._entry_price = 0.0
self._cooldown = 100
self._prev_fast = fast_val
self._prev_slow = slow_val
return
if self.take_profit_points > 0 and close <= self._entry_price - self.take_profit_points * step:
self.BuyMarket()
self._entry_price = 0.0
self._cooldown = 100
self._prev_fast = fast_val
self._prev_slow = slow_val
return
if self._prev_fast <= self._prev_slow and fast_val > slow_val and self.Position <= 0:
if self.Position < 0:
self.BuyMarket()
self.BuyMarket()
self._entry_price = close
self._cooldown = 100
elif self._prev_fast >= self._prev_slow and fast_val < slow_val and self.Position >= 0:
if self.Position > 0:
self.SellMarket()
self.SellMarket()
self._entry_price = close
self._cooldown = 100
self._prev_fast = fast_val
self._prev_slow = slow_val
def CreateClone(self):
return three_neural_networks_strategy()