From 9865ce9815809faeb29132b16dddf9535f525ca5 Mon Sep 17 00:00:00 2001
From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com>
Date: Tue, 10 Dec 2024 02:37:35 +0800
Subject: [PATCH] Add example on volume share slippage model (#8437)
* add csharp example
* add python example
* regression
* add pythom implementation as example model
* peer review
* dependencies
* fix regression test
---
.../VolumeShareSlippageModelAlgorithm.cs | 135 ++++++++++++++++++
.../VolumeShareSlippageModelAlgorithm.py | 53 +++++++
.../Slippage/VolumeShareSlippageModel.py | 61 ++++++++
Common/QuantConnect.csproj | 3 +
4 files changed, 252 insertions(+)
create mode 100644 Algorithm.CSharp/VolumeShareSlippageModelAlgorithm.cs
create mode 100644 Algorithm.Python/VolumeShareSlippageModelAlgorithm.py
create mode 100644 Common/Orders/Slippage/VolumeShareSlippageModel.py
diff --git a/Algorithm.CSharp/VolumeShareSlippageModelAlgorithm.cs b/Algorithm.CSharp/VolumeShareSlippageModelAlgorithm.cs
new file mode 100644
index 000000000000..5f6861c6b935
--- /dev/null
+++ b/Algorithm.CSharp/VolumeShareSlippageModelAlgorithm.cs
@@ -0,0 +1,135 @@
+/*
+ * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+ * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+*/
+
+using System.Collections.Generic;
+using System.Linq;
+using QuantConnect.Algorithm.Framework.Portfolio;
+using QuantConnect.Data;
+using QuantConnect.Data.UniverseSelection;
+using QuantConnect.Interfaces;
+using QuantConnect.Orders.Slippage;
+using QuantConnect.Securities;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+ ///
+ /// Example algorithm implementing VolumeShareSlippageModel.
+ ///
+ public class VolumeShareSlippageModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+ {
+ private List _longs = new();
+ private List _shorts = new();
+
+ public override void Initialize()
+ {
+ SetStartDate(2020, 11, 29);
+ SetEndDate(2020, 12, 2);
+ // To set the slippage model to limit to fill only 30% volume of the historical volume, with 5% slippage impact.
+ SetSecurityInitializer((security) => security.SetSlippageModel(new VolumeShareSlippageModel(0.3m, 0.05m)));
+
+ // Create SPY symbol to explore its constituents.
+ var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+
+ UniverseSettings.Resolution = Resolution.Daily;
+ // Add universe to trade on the most and least weighted stocks among SPY constituents.
+ AddUniverse(Universe.ETF(spy, universeFilterFunc: Selection));
+ }
+
+ private IEnumerable Selection(IEnumerable constituents)
+ {
+ var sortedByDollarVolume = constituents.OrderBy(x => x.Weight).ToList();
+ // Add the 10 most weighted stocks to the universe to long later.
+ _longs = sortedByDollarVolume.TakeLast(10)
+ .Select(x => x.Symbol)
+ .ToList();
+ // Add the 10 least weighted stocks to the universe to short later.
+ _shorts = sortedByDollarVolume.Take(10)
+ .Select(x => x.Symbol)
+ .ToList();
+
+ return _longs.Union(_shorts);
+ }
+
+ public override void OnData(Slice slice)
+ {
+ // Equally invest into the selected stocks to evenly dissipate capital risk.
+ // Dollar neutral of long and short stocks to eliminate systematic risk, only capitalize the popularity gap.
+ var targets = _longs.Select(symbol => new PortfolioTarget(symbol, 0.05m)).ToList();
+ targets.AddRange(_shorts.Select(symbol => new PortfolioTarget(symbol, -0.05m)).ToList());
+
+ // Liquidate the ones not being the most and least popularity stocks to release fund for higher expected return trades.
+ SetHoldings(targets, liquidateExistingHoldings: true);
+ }
+
+ ///
+ /// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
+ ///
+ public bool CanRunLocally { get; } = true;
+
+ ///
+ /// This is used by the regression test system to indicate which languages this algorithm is written in.
+ ///
+ public List Languages { get; } = new() { Language.CSharp, Language.Python };
+
+ ///
+ /// Data Points count of all timeslices of algorithm
+ ///
+ public long DataPoints => 1035;
+
+ ///
+ /// Data Points count of the algorithm history
+ ///
+ public int AlgorithmHistoryDataPoints => 0;
+
+ ///
+ /// Final status of the algorithm
+ ///
+ public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
+
+ ///
+ /// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
+ ///
+ public Dictionary ExpectedStatistics => new Dictionary
+ {
+ {"Total Orders", "4"},
+ {"Average Win", "0%"},
+ {"Average Loss", "0%"},
+ {"Compounding Annual Return", "20.900%"},
+ {"Drawdown", "0%"},
+ {"Expectancy", "0"},
+ {"Start Equity", "100000"},
+ {"End Equity", "100190.84"},
+ {"Net Profit", "0.191%"},
+ {"Sharpe Ratio", "9.794"},
+ {"Sortino Ratio", "0"},
+ {"Probabilistic Sharpe Ratio", "0%"},
+ {"Loss Rate", "0%"},
+ {"Win Rate", "0%"},
+ {"Profit-Loss Ratio", "0"},
+ {"Alpha", "0.297"},
+ {"Beta", "-0.064"},
+ {"Annual Standard Deviation", "0.017"},
+ {"Annual Variance", "0"},
+ {"Information Ratio", "-18.213"},
+ {"Tracking Error", "0.099"},
+ {"Treynor Ratio", "-2.695"},
+ {"Total Fees", "$4.00"},
+ {"Estimated Strategy Capacity", "$4400000000.00"},
+ {"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
+ {"Portfolio Turnover", "4.22%"},
+ {"OrderListHash", "9d2bd0df7c094c393e77f72b7739bfa0"}
+ };
+ }
+}
diff --git a/Algorithm.Python/VolumeShareSlippageModelAlgorithm.py b/Algorithm.Python/VolumeShareSlippageModelAlgorithm.py
new file mode 100644
index 000000000000..e585ec109c3e
--- /dev/null
+++ b/Algorithm.Python/VolumeShareSlippageModelAlgorithm.py
@@ -0,0 +1,53 @@
+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from AlgorithmImports import *
+from Orders.Slippage.VolumeShareSlippageModel import VolumeShareSlippageModel
+
+###
+### Example algorithm implementing VolumeShareSlippageModel.
+###
+class VolumeShareSlippageModelAlgorithm(QCAlgorithm):
+ longs = []
+ shorts = []
+
+ def initialize(self) -> None:
+ self.set_start_date(2020, 11, 29)
+ self.set_end_date(2020, 12, 2)
+ # To set the slippage model to limit to fill only 30% volume of the historical volume, with 5% slippage impact.
+ self.set_security_initializer(lambda security: security.set_slippage_model(VolumeShareSlippageModel(0.3, 0.05)))
+
+ # Create SPY symbol to explore its constituents.
+ spy = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
+
+ self.universe_settings.resolution = Resolution.DAILY
+ # Add universe to trade on the most and least weighted stocks among SPY constituents.
+ self.add_universe(self.universe.etf(spy, universe_filter_func=self.selection))
+
+ def selection(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
+ sorted_by_weight = sorted(constituents, key=lambda c: c.weight)
+ # Add the 10 most weighted stocks to the universe to long later.
+ self.longs = [c.symbol for c in sorted_by_weight[-10:]]
+ # Add the 10 least weighted stocks to the universe to short later.
+ self.shorts = [c.symbol for c in sorted_by_weight[:10]]
+
+ return self.longs + self.shorts
+
+ def on_data(self, slice: Slice) -> None:
+ # Equally invest into the selected stocks to evenly dissipate capital risk.
+ # Dollar neutral of long and short stocks to eliminate systematic risk, only capitalize the popularity gap.
+ targets = [PortfolioTarget(symbol, 0.05) for symbol in self.longs]
+ targets += [PortfolioTarget(symbol, -0.05) for symbol in self.shorts]
+
+ # Liquidate the ones not being the most and least popularity stocks to release fund for higher expected return trades.
+ self.set_holdings(targets, liquidate_existing_holdings=True)
diff --git a/Common/Orders/Slippage/VolumeShareSlippageModel.py b/Common/Orders/Slippage/VolumeShareSlippageModel.py
new file mode 100644
index 000000000000..3921c414a741
--- /dev/null
+++ b/Common/Orders/Slippage/VolumeShareSlippageModel.py
@@ -0,0 +1,61 @@
+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from AlgorithmImports import *
+
+class VolumeShareSlippageModel:
+ '''Represents a slippage model that is calculated by multiplying the price impact constant by the square of the ratio of the order to the total volume.'''
+
+ def __init__(self, volume_limit: float = 0.025, price_impact: float = 0.1) -> None:
+ '''Initializes a new instance of the "VolumeShareSlippageModel" class
+ Args:
+ volume_limit:
+ price_impact: Defines how large of an impact the order will have on the price calculation'''
+ self.volume_limit = volume_limit
+ self.price_impact = price_impact
+
+ def get_slippage_approximation(self, asset: Security, order: Order) -> float:
+ '''Slippage Model. Return a decimal cash slippage approximation on the order.
+ Args:
+ asset: The Security instance of the security of the order.
+ order: The Order instance being filled.'''
+ last_data = asset.get_last_data()
+ if not last_data:
+ return 0
+
+ bar_volume = 0
+ slippage_percent = self.volume_limit * self.volume_limit * self.price_impact
+
+ if last_data.data_type == MarketDataType.TRADE_BAR:
+ bar_volume = last_data.volume
+ elif last_data.data_type == MarketDataType.QUOTE_BAR:
+ bar_volume = last_data.last_bid_size if order.direction == OrderDirection.BUY else last_data.last_ask_size
+ else:
+ raise InvalidOperationException(Messages.VolumeShareSlippageModel.invalid_market_data_type(last_data))
+
+ # If volume is zero or negative, we use the maximum slippage percentage since the impact of any quantity is infinite
+ # In FX/CFD case, we issue a warning and return zero slippage
+ if bar_volume <= 0:
+ security_type = asset.symbol.id.security_type
+ if security_type == SecurityType.CFD or security_type == SecurityType.FOREX or security_type == SecurityType.CRYPTO:
+ Log.error(Messages.VolumeShareSlippageModel.volume_not_reported_for_market_data_type(security_type))
+ return 0
+
+ Log.error(Messages.VolumeShareSlippageModel.negative_or_zero_bar_volume(bar_volume, slippage_percent))
+ else:
+ # Ratio of the order to the total volume
+ volume_share = min(order.absolute_quantity / bar_volume, self.volume_limit)
+
+ slippage_percent = volume_share * volume_share * self.price_impact
+
+ return slippage_percent * last_data.Value;
diff --git a/Common/QuantConnect.csproj b/Common/QuantConnect.csproj
index e9fac89bc4a2..088dfd992381 100644
--- a/Common/QuantConnect.csproj
+++ b/Common/QuantConnect.csproj
@@ -63,5 +63,8 @@
PreserveNewest
true
+
+ PreserveNewest
+