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Merge branch 'master' into feature-ib-tier-fee-model
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@LouisSzeto
LouisSzeto authored Dec 10, 2024
2 parents d6b94be + 9865ce9 commit fbd8c90
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166 changes: 166 additions & 0 deletions Algorithm.CSharp/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.cs
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/*
* 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
{
/// <summary>
/// This regression algorithm asserts the consolidated US equity daily bars from the hour bars exactly matches
/// the daily bars returned from the database
/// </summary>
public class ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy;
private RelativeStrengthIndex _rsi;
private RelativeStrengthIndex _rsiTimeDelta;
private Dictionary<DateTime, decimal> _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");
}
}

/// <summary>
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
/// </summary>
public bool CanRunLocally { get; } = true;

/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };

/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 290;

/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 20;

/// <summary>
/// Final status of the algorithm
/// </summary>
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;

/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"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"}
};
}
}
135 changes: 135 additions & 0 deletions Algorithm.CSharp/VolumeShareSlippageModelAlgorithm.cs
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/*
* 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
{
/// <summary>
/// Example algorithm implementing VolumeShareSlippageModel.
/// </summary>
public class VolumeShareSlippageModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private List<Symbol> _longs = new();
private List<Symbol> _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<Symbol> Selection(IEnumerable<ETFConstituentUniverse> 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);
}

/// <summary>
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
/// </summary>
public bool CanRunLocally { get; } = true;

/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };

/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 1035;

/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 0;

/// <summary>
/// Final status of the algorithm
/// </summary>
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;

/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"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"}
};
}
}
69 changes: 69 additions & 0 deletions Algorithm.Python/ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm.py
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# 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 *

### <summary>
### This regression algorithm asserts the consolidated US equity daily bars from the hour bars exactly matches
### the daily bars returned from the database
### </summary>
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")
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