diff --git a/Algorithm.CSharp/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.cs b/Algorithm.CSharp/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.cs
new file mode 100644
index 000000000000..2b6fdea31d1b
--- /dev/null
+++ b/Algorithm.CSharp/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.cs
@@ -0,0 +1,166 @@
+/*
+ * 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 QuantConnect.Data;
+using QuantConnect.Indicators;
+using QuantConnect.Interfaces;
+using System;
+using System.Collections.Generic;
+using QuantConnect.Data.Market;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+ ///
+ /// This regression algorithm asserts the consolidated US equity daily bars from the hour bars exactly matches
+ /// the daily bars returned from the database
+ ///
+ public class ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+ {
+ private Symbol _spy;
+ private RelativeStrengthIndex _rsi;
+ private RelativeStrengthIndex _rsiTimeDelta;
+ private Dictionary _values = new();
+ private int _count;
+ private bool _indicatorsCompared;
+
+ public override void Initialize()
+ {
+ SetStartDate(2020, 5, 1);
+ SetEndDate(2020, 6, 5);
+
+ _spy = AddEquity("SPY", Resolution.Hour).Symbol;
+
+ // We will use these two indicators to compare the daily consolidated bars equals
+ // the ones returned from the database. We use this specific type of indicator as
+ // it depends on its previous values. Thus, if at some point the bars received by
+ // the indicators differ, so will their final values
+ _rsi = new RelativeStrengthIndex("FIRST", 15, MovingAverageType.Wilders);
+ RegisterIndicator(_spy, _rsi, Resolution.Daily, selector: (bar) =>
+ {
+ var tradeBar = (TradeBar)bar;
+ return (tradeBar.Close + tradeBar.Open) / 2;
+ });
+
+ // We won't register this indicator as we will update it manually at the end of the
+ // month, so that we can compare the values of the indicator that received consolidated
+ // bars and the values of this one
+ _rsiTimeDelta = new RelativeStrengthIndex("SECOND" ,15, MovingAverageType.Wilders);
+ }
+
+ public override void OnData(Slice slice)
+ {
+ if (IsWarmingUp) return;
+
+ if (slice.ContainsKey(_spy) && slice[_spy] != null)
+ {
+ if (Time.Month == EndDate.Month)
+ {
+ var history = History(_spy, _count, Resolution.Daily);
+ foreach (var bar in history)
+ {
+ var time = bar.EndTime.Date;
+ var average = (bar.Close + bar.Open) / 2;
+ _rsiTimeDelta.Update(bar.EndTime, average);
+ if (_rsiTimeDelta.Current.Value != _values[time])
+ {
+ throw new RegressionTestException($"Both {_rsi.Name} and {_rsiTimeDelta.Name} should have the same values, but they differ. {_rsi.Name}: {_values[time]} | {_rsiTimeDelta.Name}: {_rsiTimeDelta.Current.Value}");
+ }
+ }
+ _indicatorsCompared = true;
+ Quit();
+ }
+ else
+ {
+ _values[Time.Date] = _rsi.Current.Value;
+
+ // Since the symbol resolution is hour and the symbol is equity, we know the last bar received in a day will
+ // be at the market close, this is 16h. We need to count how many daily bars were consolidated in order to know
+ // how many we need to request from the history
+ if (Time.Hour == 16)
+ {
+ _count++;
+ }
+ }
+ }
+ }
+
+ public override void OnEndOfAlgorithm()
+ {
+ if (!_indicatorsCompared)
+ {
+ throw new RegressionTestException($"Indicators {_rsi.Name} and {_rsiTimeDelta.Name} should have been compared, but they were not. Please make sure the indicators are getting SPY data");
+ }
+ }
+
+ ///
+ /// 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 => 290;
+
+ ///
+ /// Data Points count of the algorithm history
+ ///
+ public int AlgorithmHistoryDataPoints => 20;
+
+ ///
+ /// 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", "0"},
+ {"Average Win", "0%"},
+ {"Average Loss", "0%"},
+ {"Compounding Annual Return", "0%"},
+ {"Drawdown", "0%"},
+ {"Expectancy", "0"},
+ {"Start Equity", "100000"},
+ {"End Equity", "100000"},
+ {"Net Profit", "0%"},
+ {"Sharpe Ratio", "0"},
+ {"Sortino Ratio", "0"},
+ {"Probabilistic Sharpe Ratio", "0%"},
+ {"Loss Rate", "0%"},
+ {"Win Rate", "0%"},
+ {"Profit-Loss Ratio", "0"},
+ {"Alpha", "0"},
+ {"Beta", "0"},
+ {"Annual Standard Deviation", "0"},
+ {"Annual Variance", "0"},
+ {"Information Ratio", "-5.215"},
+ {"Tracking Error", "0.159"},
+ {"Treynor Ratio", "0"},
+ {"Total Fees", "$0.00"},
+ {"Estimated Strategy Capacity", "$0"},
+ {"Lowest Capacity Asset", ""},
+ {"Portfolio Turnover", "0%"},
+ {"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
+ };
+ }
+}
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/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.py b/Algorithm.Python/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.py
new file mode 100644
index 000000000000..f9eedfedb999
--- /dev/null
+++ b/Algorithm.Python/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.py
@@ -0,0 +1,69 @@
+# 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 *
+
+###
+### This regression algorithm asserts the consolidated US equity daily bars from the hour bars exactly matches
+### the daily bars returned from the database
+###
+class ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm(QCAlgorithm):
+ def initialize(self):
+ self.set_start_date(2020, 5, 1)
+ self.set_end_date(2020, 6, 5)
+
+ self.spy = self.add_equity("SPY", Resolution.HOUR).symbol
+
+ # We will use these two indicators to compare the daily consolidated bars equals
+ # the ones returned from the database. We use this specific type of indicator as
+ # it depends on its previous values. Thus, if at some point the bars received by
+ # the indicators differ, so will their final values
+ self._rsi = RelativeStrengthIndex("First", 15, MovingAverageType.WILDERS)
+ self.register_indicator(self.spy, self._rsi, Resolution.DAILY, selector= lambda bar: (bar.close + bar.open) / 2)
+
+ # We won't register this indicator as we will update it manually at the end of the
+ # month, so that we can compare the values of the indicator that received consolidated
+ # bars and the values of this one
+ self._rsi_timedelta = RelativeStrengthIndex("Second", 15, MovingAverageType.WILDERS)
+ self._values = {}
+ self.count = 0;
+ self._indicators_compared = False;
+
+ def on_data(self, data: Slice):
+ if self.is_warming_up:
+ return
+
+ if data.contains_key(self.spy) and data[self.spy] != None:
+ if self.time.month == self.end_date.month:
+ history = self.history[TradeBar](self.spy, self.count, Resolution.DAILY)
+ for bar in history:
+ time = bar.end_time.strftime('%Y-%m-%d')
+ average = (bar.close + bar.open) / 2
+ self._rsi_timedelta.update(bar.end_time, average)
+ if self._rsi_timedelta.current.value != self._values[time]:
+ raise Exception(f"Both {self._rsi.name} and {self._rsi_timedelta.name} should have the same values, but they differ. {self._rsi.name}: {self._values[time]} | {self._rsi_timedelta.name}: {self._rsi_timedelta.current.value}")
+ self._indicators_compared = True
+ self.quit()
+ else:
+ time = self.time.strftime('%Y-%m-%d')
+ self._values[time] = self._rsi.current.value
+
+ # Since the symbol resolution is hour and the symbol is equity, we know the last bar received in a day will
+ # be at the market close, this is 16h. We need to count how many daily bars were consolidated in order to know
+ # how many we need to request from the history
+ if self.time.hour == 16:
+ self.count += 1
+
+ def on_end_of_algorithm(self):
+ if not self._indicators_compared:
+ raise Exception(f"Indicators {self._rsi.name} and {self._rsi_timedelta.name} should have been compared, but they were not. Please make sure the indicators are getting SPY data")
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/Data/Consolidators/MarketHourAwareConsolidator.cs b/Common/Data/Consolidators/MarketHourAwareConsolidator.cs
index 8fa437885378..2b9565f5cb24 100644
--- a/Common/Data/Consolidators/MarketHourAwareConsolidator.cs
+++ b/Common/Data/Consolidators/MarketHourAwareConsolidator.cs
@@ -132,7 +132,12 @@ public virtual void Update(IBaseData data)
{
Initialize(data);
- if (_extendedMarketHours || ExchangeHours.IsOpen(data.Time, false))
+ // US equity hour data from the database starts at 9am but the exchange opens at 9:30am. Thus, we need to handle
+ // this case specifically to avoid skipping the first hourly bar. To avoid this, we assert the period is daily,
+ // the data resolution is hour and the exchange opens at any point in time over the data.Time to data.EndTime interval
+ if (_extendedMarketHours ||
+ ExchangeHours.IsOpen(data.Time, false) ||
+ (Period == Time.OneDay && (data.EndTime - data.Time == Time.OneHour) && ExchangeHours.IsOpen(data.Time, data.EndTime, false)))
{
Consolidator.Update(data);
}
diff --git a/Common/Data/Consolidators/PeriodCountConsolidatorBase.cs b/Common/Data/Consolidators/PeriodCountConsolidatorBase.cs
index 386d6a0ad310..d4f94e4ea3de 100644
--- a/Common/Data/Consolidators/PeriodCountConsolidatorBase.cs
+++ b/Common/Data/Consolidators/PeriodCountConsolidatorBase.cs
@@ -322,10 +322,21 @@ protected DateTime GetRoundedBarTime(DateTime time)
protected DateTime GetRoundedBarTime(IBaseData inputData)
{
var potentialStartTime = GetRoundedBarTime(inputData.Time);
- if(_period.HasValue && potentialStartTime + _period < inputData.EndTime)
+ if (_period.HasValue && potentialStartTime + _period < inputData.EndTime)
{
- // whops! the end time we were giving is beyond our potential end time, so let's use the giving bars star time instead
- potentialStartTime = inputData.Time;
+ // US equity hour bars from the database starts at 9am but the exchange opens at 9:30am. Thus, the method
+ // GetRoundedBarTime(inputData.Time) returns the market open of the previous day, which is not consistent
+ // with the given end time. For that reason we need to handle this case specifically, by calling
+ // GetRoundedBarTime(inputData.EndTime) as it will return our expected start time: 9:30am
+ if (inputData.EndTime - inputData.Time == Time.OneHour && potentialStartTime.Date < inputData.Time.Date)
+ {
+ potentialStartTime = GetRoundedBarTime(inputData.EndTime);
+ }
+ else
+ {
+ // whops! the end time we were giving is beyond our potential end time, so let's use the giving bars star time instead
+ potentialStartTime = inputData.Time;
+ }
}
return potentialStartTime;
diff --git a/Common/Indicators/RollingWindow.cs b/Common/Indicators/RollingWindow.cs
index d2f7ed526813..b1172752499f 100644
--- a/Common/Indicators/RollingWindow.cs
+++ b/Common/Indicators/RollingWindow.cs
@@ -16,6 +16,7 @@
using System;
using System.Collections;
using System.Collections.Generic;
+using System.Runtime.CompilerServices;
using System.Threading;
namespace QuantConnect.Indicators
@@ -174,7 +175,7 @@ public T this [int i]
return default;
}
- return _list[(_list.Count + _tail - i - 1) % _list.Count];
+ return _list[GetListIndex(i, _list.Count, _tail)];
}
finally
{
@@ -206,7 +207,7 @@ public T this [int i]
}
}
- _list[(_list.Count + _tail - i - 1) % _list.Count] = value;
+ _list[GetListIndex(i, _list.Count, _tail)] = value;
}
finally
{
@@ -244,18 +245,20 @@ public bool IsReady
/// 1
public IEnumerator GetEnumerator()
{
- // we make a copy on purpose so the enumerator isn't tied
- // to a mutable object, well it is still mutable but out of scope
- var temp = new List(_list.Count);
try
{
_listLock.EnterReadLock();
- for (int i = 0; i < _list.Count; i++)
+ // we make a copy on purpose so the enumerator isn't tied
+ // to a mutable object, well it is still mutable but out of scope
+ var count = _list.Count;
+ var temp = new T[count];
+ for (int i = 0; i < count; i++)
{
- temp.Add(this[i]);
+ temp[i] = _list[GetListIndex(i, count, _tail)];
}
- return temp.GetEnumerator();
+
+ return ((IEnumerable) temp).GetEnumerator();
}
finally
{
@@ -325,6 +328,12 @@ public void Reset()
}
}
+ [MethodImpl(MethodImplOptions.AggressiveInlining)]
+ private static int GetListIndex(int index, int listCount, int tail)
+ {
+ return (listCount + tail - index - 1) % listCount;
+ }
+
private void Resize(int size)
{
try
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 489d5b15c1bb..c438ad020749 100644
--- a/Common/QuantConnect.csproj
+++ b/Common/QuantConnect.csproj
@@ -67,5 +67,8 @@
PreserveNewest
true
+
+ PreserveNewest
+
diff --git a/Tests/Common/Data/MarketHourAwareConsolidatorTests.cs b/Tests/Common/Data/MarketHourAwareConsolidatorTests.cs
index 14dc97c84367..f025f3dac927 100644
--- a/Tests/Common/Data/MarketHourAwareConsolidatorTests.cs
+++ b/Tests/Common/Data/MarketHourAwareConsolidatorTests.cs
@@ -117,6 +117,71 @@ public void Daily(bool strictEndTime)
Assert.AreEqual(1, latestBar.Low);
}
+ [Test]
+ public void BarIsSkippedWhenDataResolutionIsNotHourAndMarketIsClose()
+ {
+ var symbol = Symbols.SPY;
+ using var consolidator = new MarketHourAwareConsolidator(true, Resolution.Daily, typeof(TradeBar), TickType.Trade, false);
+ var consolidatedBarsCount = 0;
+ TradeBar latestBar = null;
+
+ consolidator.DataConsolidated += (sender, bar) =>
+ {
+ latestBar = (TradeBar)bar;
+ consolidatedBarsCount++;
+ };
+
+ var time = new DateTime(2020, 05, 01, 09, 30, 0);
+ // this bar will be ignored because it's during market closed hours and the bar resolution is not Hour
+ consolidator.Update(new TradeBar() { Time = time.Subtract(Time.OneMinute), Period = Time.OneMinute, Symbol = symbol, Open = 1 });
+ Assert.IsNull(latestBar);
+ Assert.AreEqual(0, consolidatedBarsCount);
+ }
+
+ [Test]
+ public void DailyBarCanBeConsolidatedFromHourData()
+ {
+ var symbol = Symbols.SPY;
+ using var consolidator = new MarketHourAwareConsolidator(true, Resolution.Daily, typeof(TradeBar), TickType.Trade, false);
+ var consolidatedBarsCount = 0;
+ TradeBar latestBar = null;
+
+ consolidator.DataConsolidated += (sender, bar) =>
+ {
+ latestBar = (TradeBar)bar;
+ consolidatedBarsCount++;
+ };
+
+ var time = new DateTime(2020, 05, 01, 09, 0, 0);
+ var hourBars = new List()
+ {
+ new TradeBar() { Time = time, Period = Time.OneHour, Symbol = symbol, Open = 2 },
+ new TradeBar() { Time = time.AddHours(1), Period = Time.OneHour, Symbol = symbol, High = 200 },
+ new TradeBar() { Time = time.AddHours(2), Period = Time.OneHour, Symbol = symbol, Low = 0.02m },
+ new TradeBar() { Time = time.AddHours(3), Period = Time.OneHour, Symbol = symbol, Close = 20 },
+ new TradeBar() { Time = time.AddHours(4), Period = Time.OneHour, Symbol = symbol, Open = 3 },
+ new TradeBar() { Time = time.AddHours(5), Period = Time.OneHour, Symbol = symbol, High = 300 },
+ new TradeBar() { Time = time.AddHours(6), Period = Time.OneHour, Symbol = symbol, Low = 0.03m, Close = 30 },
+ };
+
+ foreach (var bar in hourBars)
+ {
+ consolidator.Update(bar);
+ }
+
+ consolidator.Scan(time.AddHours(7));
+
+ // Assert that the bar emitted
+ Assert.IsNotNull(latestBar);
+ Assert.AreEqual(time.AddHours(7), latestBar.EndTime);
+ Assert.AreEqual(time.AddMinutes(30), latestBar.Time);
+ Assert.AreEqual(1, consolidatedBarsCount);
+ Assert.AreEqual(2, latestBar.Open);
+ Assert.AreEqual(300, latestBar.High);
+ Assert.AreEqual(0.02, latestBar.Low);
+ Assert.AreEqual(30, latestBar.Close);
+ }
+
[TestCase(true)]
[TestCase(false)]
public void DailyExtendedMarketHours(bool strictEndTime)
diff --git a/Tests/Indicators/RollingWindowTests.cs b/Tests/Indicators/RollingWindowTests.cs
index c451e8838b71..5c7001c50c6f 100644
--- a/Tests/Indicators/RollingWindowTests.cs
+++ b/Tests/Indicators/RollingWindowTests.cs
@@ -114,6 +114,12 @@ public void EnumeratesAsExpected()
Assert.AreEqual(2, inOrder[0]);
Assert.AreEqual(1, inOrder[1]);
Assert.AreEqual(0, inOrder[2]);
+
+ window.Add(3);
+ var inOrder2 = window.ToList();
+ Assert.AreEqual(3, inOrder2[0]);
+ Assert.AreEqual(2, inOrder2[1]);
+ Assert.AreEqual(1, inOrder2[2]);
}
[Test]