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Three Soldiers Stochastic 策略
该策略复刻 MetaTrader 专家 Expert_ABC_WS_Stoch.mq5,将经典的三根蜡烛反转形态与随机指标结合使用。当出现多头的“三个白兵”形态并且随机指标信号线位于超卖区时开多;当出现空头的“三只乌鸦”形态并且信号线位于超买区时开空。平仓逻辑基于信号线穿越可配置的阈值带。
交易逻辑
- 形态识别
- 监控最近三根已完成的蜡烛。
- Three White Soldiers:三根蜡烛全部收阳,且每根收盘价高于上一根。
- Three Black Crows:三根蜡烛全部收阴,且每根收盘价低于上一根。
- 随机指标确认
- 计算随机指标,参数
%K = 47、%D = 9、Slowing = 13(默认值与原始脚本一致)。
- 使用信号线(
%D)作为确认条件:
- 当上一根信号线低于超卖阈值(默认
30)时确认买入。
- 当上一根信号线高于超买阈值(默认
70)时确认卖出。
- 离场条件
- 当信号线从下向上穿越下限或上限阈值(默认
20、80)时平掉多单。
- 当信号线从上向下穿越这些阈值时平掉空单。
- 通过比较上一根与上上一根信号线数值来判断是否真正穿越。
参数
| 参数 |
默认值 |
说明 |
CandleType |
1 小时 |
订阅蜡烛的时间周期。 |
StochKPeriod |
47 |
%K 的回溯周期。 |
StochDPeriod |
9 |
信号线的平滑周期。 |
StochSlowing |
13 |
%K 的额外平滑系数。 |
OversoldLevel |
30 |
确认多头的超卖阈值。 |
OverboughtLevel |
70 |
确认空头的超买阈值。 |
ExitLowerLevel |
20 |
平多使用的下限阈值。 |
ExitUpperLevel |
80 |
平空使用的上限阈值。 |
所有数值参数都提供优化范围,可在 Strategy Designer 中直接调优。
订单管理
- 出现反向信号时通过增加当前仓位的绝对值来实现反向开仓。
- 调用
StartProtection() 激活平台的风险控制,默认不设置固定止损或止盈。
可视化
在 Strategy Designer 中运行时将绘制:
- 指定品种及时间框架的蜡烛图;
- 配置好的随机指标;
- 入场与离场的成交标记。
使用建议
- 确保行情历史充足,以便随机指标有足够的数据进行计算。
- 实盘部署时可叠加波动率、交易时段等额外过滤条件。
- 阈值参数全部外部化,便于快速试验不同的确认区间。
namespace StockSharp.Samples.Strategies;
using System;
using Ecng.Common;
using StockSharp.Algo.Indicators;
using StockSharp.Algo.Strategies;
using StockSharp.Messages;
/// <summary>
/// Three Soldiers Stochastic strategy: detects three consecutive bullish/bearish candles
/// confirmed by RSI levels.
/// </summary>
public class ThreeSoldiersStochasticStrategy : Strategy
{
private readonly StrategyParam<DataType> _candleType;
private readonly StrategyParam<int> _rsiPeriod;
private readonly StrategyParam<int> _signalCooldownCandles;
private int _bullCount;
private int _bearCount;
private int _candlesSinceTrade;
public DataType CandleType { get => _candleType.Value; set => _candleType.Value = value; }
public int RsiPeriod { get => _rsiPeriod.Value; set => _rsiPeriod.Value = value; }
public int SignalCooldownCandles { get => _signalCooldownCandles.Value; set => _signalCooldownCandles.Value = value; }
public ThreeSoldiersStochasticStrategy()
{
_candleType = Param(nameof(CandleType), TimeSpan.FromMinutes(60).TimeFrame())
.SetDisplay("Candle Type", "Candle timeframe", "General");
_rsiPeriod = Param(nameof(RsiPeriod), 14)
.SetGreaterThanZero()
.SetDisplay("RSI Period", "RSI period for confirmation", "Indicators");
_signalCooldownCandles = Param(nameof(SignalCooldownCandles), 6)
.SetGreaterThanZero()
.SetDisplay("Signal Cooldown", "Bars to wait between trades", "Trading");
}
/// <inheritdoc />
protected override void OnReseted()
{
base.OnReseted();
_bullCount = 0;
_bearCount = 0;
_candlesSinceTrade = SignalCooldownCandles;
}
/// <inheritdoc />
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
_bullCount = 0;
_bearCount = 0;
_candlesSinceTrade = SignalCooldownCandles;
var rsi = new RelativeStrengthIndex { Length = RsiPeriod };
var subscription = SubscribeCandles(CandleType);
subscription.Bind(rsi, ProcessCandle).Start();
}
private void ProcessCandle(ICandleMessage candle, decimal rsi)
{
if (candle.State != CandleStates.Finished) return;
if (_candlesSinceTrade < SignalCooldownCandles)
_candlesSinceTrade++;
if (candle.ClosePrice > candle.OpenPrice)
{
_bullCount++;
_bearCount = 0;
}
else if (candle.ClosePrice < candle.OpenPrice)
{
_bearCount++;
_bullCount = 0;
}
else
{
_bullCount = 0;
_bearCount = 0;
}
if (_bullCount >= 3 && rsi < 65 && Position <= 0 && _candlesSinceTrade >= SignalCooldownCandles)
{
BuyMarket();
_bullCount = 0;
_candlesSinceTrade = 0;
}
else if (_bearCount >= 3 && rsi > 35 && Position >= 0 && _candlesSinceTrade >= SignalCooldownCandles)
{
SellMarket();
_bearCount = 0;
_candlesSinceTrade = 0;
}
}
}
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 RelativeStrengthIndex
from StockSharp.Algo.Strategies import Strategy
class three_soldiers_stochastic_strategy(Strategy):
def __init__(self):
super(three_soldiers_stochastic_strategy, self).__init__()
self._rsi_period = self.Param("RsiPeriod", 14) \
.SetDisplay("RSI Period", "RSI period for confirmation", "Indicators")
self._signal_cooldown = self.Param("SignalCooldownCandles", 6) \
.SetDisplay("Signal Cooldown", "Bars to wait between trades", "Trading")
self._rsi = None
self._bull_count = 0
self._bear_count = 0
self._candles_since_trade = 0
@property
def rsi_period(self):
return self._rsi_period.Value
@property
def signal_cooldown(self):
return self._signal_cooldown.Value
def OnReseted(self):
super(three_soldiers_stochastic_strategy, self).OnReseted()
self._rsi = None
self._bull_count = 0
self._bear_count = 0
self._candles_since_trade = self.signal_cooldown
def OnStarted2(self, time):
super(three_soldiers_stochastic_strategy, self).OnStarted2(time)
self._rsi = RelativeStrengthIndex()
self._rsi.Length = self.rsi_period
self._bull_count = 0
self._bear_count = 0
self._candles_since_trade = self.signal_cooldown
subscription = self.SubscribeCandles(DataType.TimeFrame(TimeSpan.FromMinutes(60)))
subscription.Bind(self._rsi, self._process_candle)
subscription.Start()
def _process_candle(self, candle, rsi_value):
if candle.State != CandleStates.Finished:
return
if not self._rsi.IsFormed:
return
rsi_val = float(rsi_value)
if self._candles_since_trade < self.signal_cooldown:
self._candles_since_trade += 1
close = float(candle.ClosePrice)
open_p = float(candle.OpenPrice)
if close > open_p:
self._bull_count += 1
self._bear_count = 0
elif close < open_p:
self._bear_count += 1
self._bull_count = 0
else:
self._bull_count = 0
self._bear_count = 0
if self._bull_count >= 3 and rsi_val < 65.0 and self.Position <= 0 and self._candles_since_trade >= self.signal_cooldown:
self.BuyMarket()
self._bull_count = 0
self._candles_since_trade = 0
elif self._bear_count >= 3 and rsi_val > 35.0 and self.Position >= 0 and self._candles_since_trade >= self.signal_cooldown:
self.SellMarket()
self._bear_count = 0
self._candles_since_trade = 0
def CreateClone(self):
return three_soldiers_stochastic_strategy()