在 GitHub 上查看
Artificial Intelligence Right 策略
概览
该策略来源于 MetaTrader 4 的专家顾问 ArtificialIntelligence_Right.mq4。它通过对加速/减速振荡指标
(Acceleration/Deceleration, AC) 的四个延迟样本执行单层感知机计算,用感知机的符号来决定开仓方向以及是否需要反向。
移植到 StockSharp 之后,所有动作都在已完成的 K 线收盘价上执行,使得回测与参数优化的结果更加可重复。
指标与计算方法
- 加速/减速振荡器 由 Awesome Oscillator 重建:先使用 HL2((High+Low)/2)计算 5 周期与 34 周期 SMA 的差值得到 AO,
再对 AO 应用 5 周期 SMA 并做差,得到最终 AC 数值。
- 策略维护一个长度为 22 的数组来存放最新的 AC 数据,正好可以访问索引 0、7、14、21,与原始 MQL 脚本完全一致。
- 感知机的每个权重都先减去 100 (
w = x - 100),然后与对应的 AC 数据点做点乘,复现原始信号公式。
交易逻辑
- 入场
- 感知机大于零且当前无持仓时,按市价买入。
- 感知机小于零且当前无持仓时,按市价卖出。
- 止损管理
- 每次开仓后都会记录一个虚拟止损价位,其距离为
StopLossPoints * PriceStep,与 MetaTrader 中 Point
的概念保持一致。
- 收盘价穿越止损水平时,立即通过市价单平仓,模拟真实的止损执行。
- 移动止损与反向
- 当浮动盈利超过
(2 * StopLossPoints + SpreadPoints) 点时,原脚本会启动移动止损,或者在感知机翻转时直接反向。
- 移植版本沿用同样的触发条件:若感知机转向,则以当前仓位的两倍成交量下单,实现与
OrderCloseBy
类似的反向操作;否则把虚拟止损价位跟随价格移动,保持原有的距离。
所有反向操作都采用“双倍成交量”的方式,以保证结果仓位的方向相反而手数不变。
参数说明
| 名称 |
说明 |
X1 … X4 |
感知机权重,默认值(135、127、16、93)与 MQL 源码一致。 |
StopLoss |
止损距离(点数),会乘以标的物的 PriceStep 转换为真实价格。 |
Spread |
移动止损触发条件中的额外点差缓冲,默认为 3 点。 |
Candle Type |
计算所用的 K 线类型,默认 1 分钟。 |
Volume 属性固定为 1 手,对应原脚本的 lots 参数。
实现细节
- 每次重置策略时都会清空指标缓冲区,避免旧数据造成误触发。
- 如果标的物没有提供
PriceStep,策略会退化为使用点值 1,确保在各种回测环境下都能运行。
- 策略内部没有真实挂出止损单,而是在 K 线处理函数中通过市价单模拟,这种方式能够跨平台保持一致的执行效果。
using System;
using System.Collections.Generic;
using StockSharp.Algo.Indicators;
using StockSharp.Algo.Strategies;
using StockSharp.BusinessEntities;
using StockSharp.Messages;
namespace StockSharp.Samples.Strategies;
/// <summary>
/// Artificial Intelligence Right strategy - perceptron-like logic.
/// Uses fast/slow SMA difference (Awesome Oscillator approximation).
/// Buys when AO crosses above 0, sells when below.
/// </summary>
public class ArtificialIntelligenceRightStrategy : Strategy
{
private readonly StrategyParam<int> _fastPeriod;
private readonly StrategyParam<int> _slowPeriod;
private readonly StrategyParam<DataType> _candleType;
private decimal _prevDiff;
private bool _hasPrev;
public int FastPeriod { get => _fastPeriod.Value; set => _fastPeriod.Value = value; }
public int SlowPeriod { get => _slowPeriod.Value; set => _slowPeriod.Value = value; }
public DataType CandleType { get => _candleType.Value; set => _candleType.Value = value; }
public ArtificialIntelligenceRightStrategy()
{
_fastPeriod = Param(nameof(FastPeriod), 5)
.SetDisplay("Fast SMA", "Fast SMA period", "Indicators");
_slowPeriod = Param(nameof(SlowPeriod), 34)
.SetDisplay("Slow SMA", "Slow SMA period", "Indicators");
_candleType = Param(nameof(CandleType), TimeSpan.FromHours(4).TimeFrame())
.SetDisplay("Candle Type", "Candle timeframe", "General");
}
/// <inheritdoc />
public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
{
return [(Security, CandleType)];
}
/// <inheritdoc />
protected override void OnReseted()
{
base.OnReseted();
_prevDiff = 0m;
_hasPrev = false;
}
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
_hasPrev = false;
var fast = new SimpleMovingAverage { Length = FastPeriod };
var slow = new SimpleMovingAverage { Length = SlowPeriod };
var subscription = SubscribeCandles(CandleType);
subscription
.Bind(fast, slow, ProcessCandle)
.Start();
}
private void ProcessCandle(ICandleMessage candle, decimal fast, decimal slow)
{
if (candle.State != CandleStates.Finished)
return;
var diff = fast - slow;
if (!_hasPrev)
{
_prevDiff = diff;
_hasPrev = true;
return;
}
// AO crosses above 0
if (_prevDiff <= 0 && diff > 0 && Position <= 0)
{
if (Position < 0)
BuyMarket();
BuyMarket();
}
// AO crosses below 0
else if (_prevDiff >= 0 && diff < 0 && Position >= 0)
{
if (Position > 0)
SellMarket();
SellMarket();
}
_prevDiff = diff;
}
}
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 SimpleMovingAverage
from StockSharp.Algo.Strategies import Strategy
class artificial_intelligence_right_strategy(Strategy):
def __init__(self):
super(artificial_intelligence_right_strategy, self).__init__()
self._fast_period = self.Param("FastPeriod", 5) \
.SetDisplay("Fast SMA", "Fast SMA period", "Indicators")
self._slow_period = self.Param("SlowPeriod", 34) \
.SetDisplay("Slow SMA", "Slow SMA period", "Indicators")
self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromHours(4))) \
.SetDisplay("Candle Type", "Candle timeframe", "General")
self._prev_diff = 0.0
self._has_prev = False
@property
def fast_period(self):
return self._fast_period.Value
@property
def slow_period(self):
return self._slow_period.Value
@property
def candle_type(self):
return self._candle_type.Value
def OnReseted(self):
super(artificial_intelligence_right_strategy, self).OnReseted()
self._prev_diff = 0.0
self._has_prev = False
def OnStarted2(self, time):
super(artificial_intelligence_right_strategy, self).OnStarted2(time)
self._has_prev = False
fast = SimpleMovingAverage()
fast.Length = self.fast_period
slow = SimpleMovingAverage()
slow.Length = self.slow_period
subscription = self.SubscribeCandles(self.candle_type)
subscription.Bind(fast, slow, self.process_candle).Start()
def process_candle(self, candle, fast, slow):
if candle.State != CandleStates.Finished:
return
fast_val = float(fast)
slow_val = float(slow)
diff = fast_val - slow_val
if not self._has_prev:
self._prev_diff = diff
self._has_prev = True
return
if self._prev_diff <= 0 and diff > 0 and self.Position <= 0:
if self.Position < 0:
self.BuyMarket()
self.BuyMarket()
elif self._prev_diff >= 0 and diff < 0 and self.Position >= 0:
if self.Position > 0:
self.SellMarket()
self.SellMarket()
self._prev_diff = diff
def CreateClone(self):
return artificial_intelligence_right_strategy()