商品收益不对称
商品收益不对称 策略利用正收益和负收益的差异。对于每个商品期货,在滚动窗口中分别累加所有上涨和下跌的幅度。较高的比率表示持续上涨动能,较低的比率表明卖压持续。
每月初根据该不对称指标对期货进行排序。系统等权买入前 N 个合约并做空后 N 个,持仓在每月再平衡时调整。
细节
- 入场条件:按窗口内日收益的不对称度进行每月排序。
- 多空方向:双向。
- 出场条件:在每月再平衡时调整仓位。
- 止损:没有明确止损,通过
MinTradeUsd控制仓位。 - 默认值:
WindowDays = 120TopN = 5MinTradeUsd = 200CandleType = TimeSpan.FromMinutes(5).TimeFrame()
- 筛选:
- 分类:动量
- 方向:双向
- 指标:价格
- 止损:无
- 复杂度:中等
- 时间框架:中期
- 季节性:是
- 神经网络:否
- 背离:否
- 风险级别:中等
using System;
using System.Collections.Generic;
using Ecng.Common;
using StockSharp.Algo.Indicators;
using StockSharp.Algo.Strategies;
using StockSharp.BusinessEntities;
using StockSharp.Configuration;
using StockSharp.Messages;
namespace StockSharp.Samples.Strategies;
/// <summary>
/// Return asymmetry strategy that trades the primary commodity when its positive-versus-negative return balance diverges from a benchmark commodity.
/// </summary>
public class ReturnAsymmetryCommodityStrategy : Strategy
{
private readonly StrategyParam<string> _security2Id;
private readonly StrategyParam<int> _windowLength;
private readonly StrategyParam<int> _normalizationPeriod;
private readonly StrategyParam<decimal> _entryThreshold;
private readonly StrategyParam<decimal> _exitThreshold;
private readonly StrategyParam<int> _cooldownBars;
private readonly StrategyParam<decimal> _stopLoss;
private readonly StrategyParam<DataType> _candleType;
private Security _benchmark = null!;
private SimpleMovingAverage _spreadAverage = null!;
private StandardDeviation _spreadDeviation = null!;
private readonly Queue<decimal> _primaryReturns = [];
private readonly Queue<decimal> _benchmarkReturns = [];
private decimal? _previousPrimaryClose;
private decimal? _previousBenchmarkClose;
private decimal? _previousZScore;
private decimal _latestPrimaryAsymmetry;
private decimal _latestBenchmarkAsymmetry;
private bool _primaryUpdated;
private bool _benchmarkUpdated;
private int _cooldownRemaining;
/// <summary>
/// Benchmark security identifier.
/// </summary>
public string Security2Id
{
get => _security2Id.Value;
set => _security2Id.Value = value;
}
/// <summary>
/// Lookback period used to compute return asymmetry.
/// </summary>
public int WindowLength
{
get => _windowLength.Value;
set => _windowLength.Value = value;
}
/// <summary>
/// Lookback period used to normalize the asymmetry spread.
/// </summary>
public int NormalizationPeriod
{
get => _normalizationPeriod.Value;
set => _normalizationPeriod.Value = value;
}
/// <summary>
/// Z-score threshold required to open a position.
/// </summary>
public decimal EntryThreshold
{
get => _entryThreshold.Value;
set => _entryThreshold.Value = value;
}
/// <summary>
/// Z-score threshold required to close a position.
/// </summary>
public decimal ExitThreshold
{
get => _exitThreshold.Value;
set => _exitThreshold.Value = value;
}
/// <summary>
/// Closed candles to wait before another position change.
/// </summary>
public int CooldownBars
{
get => _cooldownBars.Value;
set => _cooldownBars.Value = value;
}
/// <summary>
/// Stop loss percentage.
/// </summary>
public decimal StopLoss
{
get => _stopLoss.Value;
set => _stopLoss.Value = value;
}
/// <summary>
/// Candle type used for calculations.
/// </summary>
public DataType CandleType
{
get => _candleType.Value;
set => _candleType.Value = value;
}
public ReturnAsymmetryCommodityStrategy()
{
_security2Id = Param(nameof(Security2Id), Paths.HistoryDefaultSecurity2)
.SetDisplay("Benchmark Security Id", "Identifier of the benchmark commodity", "General");
_windowLength = Param(nameof(WindowLength), 20)
.SetRange(5, 120)
.SetDisplay("Window Length", "Lookback period used to compute return asymmetry", "Indicators");
_normalizationPeriod = Param(nameof(NormalizationPeriod), 16)
.SetRange(5, 120)
.SetDisplay("Normalization Period", "Lookback period used to normalize the asymmetry spread", "Indicators");
_entryThreshold = Param(nameof(EntryThreshold), 1.1m)
.SetRange(0.2m, 5m)
.SetDisplay("Entry Threshold", "Z-score threshold required to open a position", "Signals");
_exitThreshold = Param(nameof(ExitThreshold), 0.25m)
.SetRange(0m, 2m)
.SetDisplay("Exit Threshold", "Z-score threshold required to close a position", "Signals");
_cooldownBars = Param(nameof(CooldownBars), 8)
.SetRange(0, 120)
.SetDisplay("Cooldown Bars", "Closed candles to wait before another position change", "Risk");
_stopLoss = Param(nameof(StopLoss), 3m)
.SetRange(0.5m, 10m)
.SetDisplay("Stop Loss %", "Stop loss percentage", "Risk");
_candleType = Param(nameof(CandleType), TimeSpan.FromHours(4).TimeFrame())
.SetDisplay("Candle Type", "Time frame for candles", "General");
}
/// <inheritdoc />
public override IEnumerable<(Security sec, DataType dt)> GetWorkingSecurities()
{
if (Security != null)
yield return (Security, CandleType);
if (!Security2Id.IsEmpty())
yield return (new Security { Id = Security2Id }, CandleType);
}
/// <inheritdoc />
protected override void OnReseted()
{
base.OnReseted();
_benchmark = null!;
_spreadAverage = null!;
_spreadDeviation = null!;
_primaryReturns.Clear();
_benchmarkReturns.Clear();
_previousPrimaryClose = null;
_previousBenchmarkClose = null;
_previousZScore = null;
_latestPrimaryAsymmetry = 0m;
_latestBenchmarkAsymmetry = 0m;
_primaryUpdated = false;
_benchmarkUpdated = false;
_cooldownRemaining = 0;
}
/// <inheritdoc />
protected override void OnStarted2(DateTime time)
{
base.OnStarted2(time);
if (Security == null)
throw new InvalidOperationException("Primary security is not specified.");
if (Security2Id.IsEmpty())
throw new InvalidOperationException("Benchmark security identifier is not specified.");
_benchmark = this.LookupById(Security2Id) ?? new Security { Id = Security2Id };
_spreadAverage = new SimpleMovingAverage { Length = NormalizationPeriod };
_spreadDeviation = new StandardDeviation { Length = NormalizationPeriod };
var primarySubscription = SubscribeCandles(CandleType, security: Security);
var benchmarkSubscription = SubscribeCandles(CandleType, security: _benchmark);
primarySubscription
.Bind(ProcessPrimaryCandle)
.Start();
benchmarkSubscription
.Bind(ProcessBenchmarkCandle)
.Start();
var area = CreateChartArea();
if (area != null)
{
DrawCandles(area, primarySubscription);
DrawCandles(area, benchmarkSubscription);
DrawOwnTrades(area);
}
StartProtection(
new Unit(2, UnitTypes.Percent),
new Unit(StopLoss, UnitTypes.Percent));
}
private void ProcessPrimaryCandle(ICandleMessage candle)
{
if (candle.State != CandleStates.Finished)
return;
var ret = UpdateReturns(_primaryReturns, candle.ClosePrice, ref _previousPrimaryClose);
if (ret is null)
return;
_latestPrimaryAsymmetry = CalculateAsymmetry(_primaryReturns);
_primaryUpdated = true;
TryProcessSpread(candle.OpenTime);
}
private void ProcessBenchmarkCandle(ICandleMessage candle)
{
if (candle.State != CandleStates.Finished)
return;
var ret = UpdateReturns(_benchmarkReturns, candle.ClosePrice, ref _previousBenchmarkClose);
if (ret is null)
return;
_latestBenchmarkAsymmetry = CalculateAsymmetry(_benchmarkReturns);
_benchmarkUpdated = true;
TryProcessSpread(candle.OpenTime);
}
private decimal? UpdateReturns(Queue<decimal> queue, decimal closePrice, ref decimal? previousClose)
{
if (previousClose is not decimal previous || previous <= 0m)
{
previousClose = closePrice;
return null;
}
var ret = (closePrice - previous) / previous;
previousClose = closePrice;
if (queue.Count == WindowLength)
queue.Dequeue();
queue.Enqueue(ret);
return ret;
}
private static decimal CalculateAsymmetry(IEnumerable<decimal> returns)
{
decimal positive = 0m;
decimal negative = 0m;
foreach (var ret in returns)
{
if (ret > 0m)
positive += ret;
else
negative += Math.Abs(ret);
}
return positive / Math.Max(negative, 0.0001m);
}
private void TryProcessSpread(DateTime time)
{
if (!_primaryUpdated || !_benchmarkUpdated || _primaryReturns.Count < WindowLength || _benchmarkReturns.Count < WindowLength)
return;
_primaryUpdated = false;
_benchmarkUpdated = false;
var spread = _latestPrimaryAsymmetry - _latestBenchmarkAsymmetry;
var mean = _spreadAverage.Process(spread, time, true).ToDecimal();
var deviation = _spreadDeviation.Process(spread, time, true).ToDecimal();
if (!_spreadAverage.IsFormed || !_spreadDeviation.IsFormed || deviation <= 0m)
return;
if (!IsFormedAndOnlineAndAllowTrading())
return;
if (_cooldownRemaining > 0)
_cooldownRemaining--;
var zScore = (spread - mean) / deviation;
var bullishEntry = _previousZScore is decimal previousBullish && previousBullish < EntryThreshold && zScore >= EntryThreshold;
var bearishEntry = _previousZScore is decimal previousBearish && previousBearish > -EntryThreshold && zScore <= -EntryThreshold;
if (_cooldownRemaining == 0 && Position == 0)
{
if (bullishEntry)
{
BuyMarket();
_cooldownRemaining = CooldownBars;
}
else if (bearishEntry)
{
SellMarket();
_cooldownRemaining = CooldownBars;
}
}
else if (Position > 0 && zScore <= ExitThreshold)
{
SellMarket(Position);
_cooldownRemaining = CooldownBars;
}
else if (Position < 0 && zScore >= -ExitThreshold)
{
BuyMarket(Math.Abs(Position));
_cooldownRemaining = CooldownBars;
}
_previousZScore = zScore;
}
}
import clr
import collections
clr.AddReference("StockSharp.Messages")
clr.AddReference("StockSharp.Algo")
clr.AddReference("StockSharp.BusinessEntities")
clr.AddReference("StockSharp.Algo.Indicators")
clr.AddReference("StockSharp.Algo.Strategies")
from System import TimeSpan, Math
from StockSharp.Messages import DataType, CandleStates, Unit, UnitTypes
from StockSharp.Algo.Indicators import SimpleMovingAverage, StandardDeviation
from StockSharp.Algo.Strategies import Strategy
from StockSharp.BusinessEntities import Security
from indicator_extensions import *
class return_asymmetry_commodity_strategy(Strategy):
"""Return asymmetry strategy that trades the primary commodity when its positive-versus-negative return balance diverges from a benchmark commodity."""
def __init__(self):
super(return_asymmetry_commodity_strategy, self).__init__()
self._security2_id = self.Param("Security2Id", "TONUSDT@BNBFT") \
.SetDisplay("Benchmark Security Id", "Identifier of the benchmark commodity", "General")
self._window_length = self.Param("WindowLength", 20) \
.SetRange(5, 120) \
.SetDisplay("Window Length", "Lookback period used to compute return asymmetry", "Indicators")
self._normalization_period = self.Param("NormalizationPeriod", 16) \
.SetRange(5, 120) \
.SetDisplay("Normalization Period", "Lookback period used to normalize the asymmetry spread", "Indicators")
self._entry_threshold = self.Param("EntryThreshold", 1.1) \
.SetRange(0.2, 5.0) \
.SetDisplay("Entry Threshold", "Z-score threshold required to open a position", "Signals")
self._exit_threshold = self.Param("ExitThreshold", 0.25) \
.SetRange(0.0, 2.0) \
.SetDisplay("Exit Threshold", "Z-score threshold required to close a position", "Signals")
self._cooldown_bars = self.Param("CooldownBars", 8) \
.SetRange(0, 120) \
.SetDisplay("Cooldown Bars", "Closed candles to wait before another position change", "Risk")
self._stop_loss = self.Param("StopLoss", 3.0) \
.SetRange(0.5, 10.0) \
.SetDisplay("Stop Loss %", "Stop loss percentage", "Risk")
self._candle_type = self.Param("CandleType", DataType.TimeFrame(TimeSpan.FromHours(4))) \
.SetDisplay("Candle Type", "Time frame for candles", "General")
self._benchmark = None
self._spread_average = None
self._spread_deviation = None
self._primary_returns = collections.deque()
self._benchmark_returns = collections.deque()
self._previous_primary_close = None
self._previous_benchmark_close = None
self._previous_z_score = None
self._latest_primary_asymmetry = 0.0
self._latest_benchmark_asymmetry = 0.0
self._primary_updated = False
self._benchmark_updated = False
self._cooldown_remaining = 0
@property
def candle_type(self):
return self._candle_type.Value
def GetWorkingSecurities(self):
result = []
if self.Security is not None:
result.append((self.Security, self.candle_type))
sec2_id = str(self._security2_id.Value)
if sec2_id:
s = Security()
s.Id = sec2_id
result.append((s, self.candle_type))
return result
def OnReseted(self):
super(return_asymmetry_commodity_strategy, self).OnReseted()
self._benchmark = None
self._spread_average = None
self._spread_deviation = None
self._primary_returns = collections.deque()
self._benchmark_returns = collections.deque()
self._previous_primary_close = None
self._previous_benchmark_close = None
self._previous_z_score = None
self._latest_primary_asymmetry = 0.0
self._latest_benchmark_asymmetry = 0.0
self._primary_updated = False
self._benchmark_updated = False
self._cooldown_remaining = 0
def OnStarted2(self, time):
super(return_asymmetry_commodity_strategy, self).OnStarted2(time)
sec2_id = str(self._security2_id.Value)
if not sec2_id:
raise Exception("Benchmark security identifier is not specified.")
s = Security()
s.Id = sec2_id
self._benchmark = s
norm_period = int(self._normalization_period.Value)
self._spread_average = SimpleMovingAverage()
self._spread_average.Length = norm_period
self._spread_deviation = StandardDeviation()
self._spread_deviation.Length = norm_period
primary_subscription = self.SubscribeCandles(self.candle_type, True, self.Security)
benchmark_subscription = self.SubscribeCandles(self.candle_type, True, self._benchmark)
primary_subscription.Bind(self.ProcessPrimaryCandle).Start()
benchmark_subscription.Bind(self.ProcessBenchmarkCandle).Start()
area = self.CreateChartArea()
if area is not None:
self.DrawCandles(area, primary_subscription)
self.DrawCandles(area, benchmark_subscription)
self.DrawOwnTrades(area)
self.StartProtection(
Unit(2, UnitTypes.Percent),
Unit(float(self._stop_loss.Value), UnitTypes.Percent)
)
def ProcessPrimaryCandle(self, candle):
if candle.State != CandleStates.Finished:
return
ret = self._update_returns(self._primary_returns, float(candle.ClosePrice), "primary")
if ret is None:
return
self._latest_primary_asymmetry = self._calculate_asymmetry(self._primary_returns)
self._primary_updated = True
self.TryProcessSpread(candle.OpenTime)
def ProcessBenchmarkCandle(self, candle):
if candle.State != CandleStates.Finished:
return
ret = self._update_returns(self._benchmark_returns, float(candle.ClosePrice), "benchmark")
if ret is None:
return
self._latest_benchmark_asymmetry = self._calculate_asymmetry(self._benchmark_returns)
self._benchmark_updated = True
self.TryProcessSpread(candle.OpenTime)
def _update_returns(self, queue, close_price, which):
if which == "primary":
prev = self._previous_primary_close
else:
prev = self._previous_benchmark_close
if prev is None or prev <= 0:
if which == "primary":
self._previous_primary_close = close_price
else:
self._previous_benchmark_close = close_price
return None
ret = (close_price - prev) / prev
if which == "primary":
self._previous_primary_close = close_price
else:
self._previous_benchmark_close = close_price
window_len = int(self._window_length.Value)
if len(queue) == window_len:
queue.popleft()
queue.append(ret)
return ret
def _calculate_asymmetry(self, returns):
positive = 0.0
negative = 0.0
for ret in returns:
if ret > 0:
positive += ret
else:
negative += abs(ret)
return positive / max(negative, 0.0001)
def TryProcessSpread(self, time):
window_len = int(self._window_length.Value)
if not self._primary_updated or not self._benchmark_updated or len(self._primary_returns) < window_len or len(self._benchmark_returns) < window_len:
return
self._primary_updated = False
self._benchmark_updated = False
spread = self._latest_primary_asymmetry - self._latest_benchmark_asymmetry
mean = float(process_float(self._spread_average, spread, time, True))
deviation = float(process_float(self._spread_deviation, spread, time, True))
if not self._spread_average.IsFormed or not self._spread_deviation.IsFormed or deviation <= 0:
return
if not self.IsFormedAndOnlineAndAllowTrading():
return
if self._cooldown_remaining > 0:
self._cooldown_remaining -= 1
z_score = (spread - mean) / deviation
entry_thresh = float(self._entry_threshold.Value)
exit_thresh = float(self._exit_threshold.Value)
cooldown = int(self._cooldown_bars.Value)
bullish_entry = self._previous_z_score is not None and self._previous_z_score < entry_thresh and z_score >= entry_thresh
bearish_entry = self._previous_z_score is not None and self._previous_z_score > -entry_thresh and z_score <= -entry_thresh
if self._cooldown_remaining == 0 and self.Position == 0:
if bullish_entry:
self.BuyMarket()
self._cooldown_remaining = cooldown
elif bearish_entry:
self.SellMarket()
self._cooldown_remaining = cooldown
elif self.Position > 0 and z_score <= exit_thresh:
self.SellMarket(self.Position)
self._cooldown_remaining = cooldown
elif self.Position < 0 and z_score >= -exit_thresh:
self.BuyMarket(Math.Abs(self.Position))
self._cooldown_remaining = cooldown
self._previous_z_score = z_score
def CreateClone(self):
return return_asymmetry_commodity_strategy()