Return Asymmetry Commodity
The Return Asymmetry Commodity strategy exploits the difference between positive and negative returns. For each commodity future, the rolling window sums all upward and downward moves separately. A high ratio implies persistent positive drift, while a low ratio points to sustained selling pressure.
At the start of each month, commodities are ranked by this asymmetry measure. The system buys the top N contracts and sells short the weakest N, allocating capital equally. Rebalancing occurs monthly.
Details
- Entry Criteria: Monthly ranking of the asymmetry of daily returns over a lookback window.
- Long/Short: Both directions.
- Exit Criteria: Positions adjusted on monthly rebalance.
- Stops: No explicit stop; position size capped by
MinTradeUsd. - Default Values:
WindowDays = 120TopN = 5MinTradeUsd = 200CandleType = TimeSpan.FromMinutes(5).TimeFrame()
- Filters:
- Category: Momentum
- Direction: Both
- Indicators: Price based
- Stops: No
- Complexity: Intermediate
- Timeframe: Medium-term
- Seasonality: Yes
- Neural Networks: No
- Divergence: No
- Risk Level: Medium
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()