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quantconnect--lean/Algorithm/QCAlgorithm.Python.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.Data.Consolidators;
using QuantConnect.Data.Market;
using QuantConnect.Indicators;
using System;
using QuantConnect.Securities;
using NodaTime;
using System.Collections.Generic;
using QuantConnect.Python;
using Python.Runtime;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Data.Fundamental;
using System.Linq;
using Newtonsoft.Json;
using QuantConnect.Brokerages;
using QuantConnect.Scheduling;
using QuantConnect.Util;
using QuantConnect.Interfaces;
using QuantConnect.Orders;
using QuantConnect.Commands;
using QuantConnect.Api;
namespace QuantConnect.Algorithm
{
public partial class QCAlgorithm
{
private readonly Dictionary<IntPtr, PythonIndicator> _pythonIndicators = new Dictionary<IntPtr, PythonIndicator>();
/// <summary>
/// PandasConverter for this Algorithm
/// </summary>
public virtual PandasConverter PandasConverter { get; private set; }
/// <summary>
/// Sets pandas converter
/// </summary>
public void SetPandasConverter()
{
PandasConverter = new PandasConverter();
}
/// <summary>
/// AddData a new user defined data source, requiring only the minimum config options.
/// The data is added with a default time zone of NewYork (Eastern Daylight Savings Time).
/// This method is meant for custom data types that require a ticker, but have no underlying Symbol.
/// Examples of data sources that meet this criteria are U.S. Treasury Yield Curve Rates and Trading Economics data
/// </summary>
/// <param name="type">Data source type</param>
/// <param name="ticker">Key/Ticker for data</param>
/// <param name="resolution">Resolution of the data</param>
/// <returns>The new <see cref="Security"/></returns>
[DocumentationAttribute(AddingData)]
public Security AddData(PyObject type, string ticker, Resolution? resolution = null)
{
return AddData(type, ticker, resolution, null, false, 1m);
}
/// <summary>
/// AddData a new user defined data source, requiring only the minimum config options.
/// The data is added with a default time zone of NewYork (Eastern Daylight Savings Time).
/// This adds a Symbol to the `Underlying` property in the custom data Symbol object.
/// Use this method when adding custom data with a ticker from the past, such as "AOL"
/// before it became "TWX", or if you need to filter using custom data and place trades on the
/// Symbol associated with the custom data.
/// </summary>
/// <param name="type">Data source type</param>
/// <param name="underlying">The underlying symbol for the custom data</param>
/// <param name="resolution">Resolution of the data</param>
/// <returns>The new <see cref="Security"/></returns>
/// <remarks>
/// We include three optional unused object parameters so that pythonnet chooses the intended method
/// correctly. Previously, calling the overloaded method that accepts a string would instead call this method.
/// Adding the three unused parameters makes it choose the correct method when using a string or Symbol. This is
/// due to pythonnet's method precedence, as viewable here: https://github.com/QuantConnect/pythonnet/blob/9e29755c54e6008cb016e3dd9d75fbd8cd19fcf7/src/runtime/methodbinder.cs#L215
/// </remarks>
[DocumentationAttribute(AddingData)]
public Security AddData(PyObject type, Symbol underlying, Resolution? resolution = null)
{
return AddData(type, underlying, resolution, null, false, 1m);
}
/// <summary>
/// AddData a new user defined data source, requiring only the minimum config options.
/// This method is meant for custom data types that require a ticker, but have no underlying Symbol.
/// Examples of data sources that meet this criteria are U.S. Treasury Yield Curve Rates and Trading Economics data
/// </summary>
/// <param name="type">Data source type</param>
/// <param name="ticker">Key/Ticker for data</param>
/// <param name="resolution">Resolution of the Data Required</param>
/// <param name="timeZone">Specifies the time zone of the raw data</param>
/// <param name="fillForward">When no data available on a tradebar, return the last data that was generated</param>
/// <param name="leverage">Custom leverage per security</param>
/// <returns>The new <see cref="Security"/></returns>
[DocumentationAttribute(AddingData)]
public Security AddData(PyObject type, string ticker, Resolution? resolution, DateTimeZone timeZone, bool fillForward = false, decimal leverage = 1.0m)
{
return AddData(GetCustomDataType(type), ticker, resolution, timeZone, fillForward, leverage);
}
/// <summary>
/// AddData a new user defined data source, requiring only the minimum config options.
/// This adds a Symbol to the `Underlying` property in the custom data Symbol object.
/// Use this method when adding custom data with a ticker from the past, such as "AOL"
/// before it became "TWX", or if you need to filter using custom data and place trades on the
/// Symbol associated with the custom data.
/// </summary>
/// <param name="type">Data source type</param>
/// <param name="underlying">The underlying symbol for the custom data</param>
/// <param name="resolution">Resolution of the Data Required</param>
/// <param name="timeZone">Specifies the time zone of the raw data</param>
/// <param name="fillForward">When no data available on a tradebar, return the last data that was generated</param>
/// <param name="leverage">Custom leverage per security</param>
/// <returns>The new <see cref="Security"/></returns>
/// <remarks>
/// We include three optional unused object parameters so that pythonnet chooses the intended method
/// correctly. Previously, calling the overloaded method that accepts a string would instead call this method.
/// Adding the three unused parameters makes it choose the correct method when using a string or Symbol. This is
/// due to pythonnet's method precedence, as viewable here: https://github.com/QuantConnect/pythonnet/blob/9e29755c54e6008cb016e3dd9d75fbd8cd19fcf7/src/runtime/methodbinder.cs#L215
/// </remarks>
[DocumentationAttribute(AddingData)]
public Security AddData(PyObject type, Symbol underlying, Resolution? resolution, DateTimeZone timeZone, bool fillForward = false, decimal leverage = 1.0m)
{
return AddData(GetCustomDataType(type), underlying, resolution, timeZone, fillForward, leverage);
}
/// <summary>
/// AddData a new user defined data source, requiring only the minimum config options.
/// This method is meant for custom data types that require a ticker, but have no underlying Symbol.
/// Examples of data sources that meet this criteria are U.S. Treasury Yield Curve Rates and Trading Economics data
/// </summary>
/// <param name="dataType">Data source type</param>
/// <param name="ticker">Key/Ticker for data</param>
/// <param name="resolution">Resolution of the Data Required</param>
/// <param name="timeZone">Specifies the time zone of the raw data</param>
/// <param name="fillForward">When no data available on a tradebar, return the last data that was generated</param>
/// <param name="leverage">Custom leverage per security</param>
/// <returns>The new <see cref="Security"/></returns>
[DocumentationAttribute(AddingData)]
public Security AddData(Type dataType, string ticker, Resolution? resolution, DateTimeZone timeZone, bool fillForward = false, decimal leverage = 1.0m)
{
// NOTE: Invoking methods on BaseData w/out setting the symbol may provide unexpected behavior
var baseInstance = dataType.GetBaseDataInstance();
if (!baseInstance.RequiresMapping())
{
var symbol = new Symbol(
SecurityIdentifier.GenerateBase(dataType, ticker, Market.USA, baseInstance.RequiresMapping()),
ticker);
return AddDataImpl(dataType, symbol, resolution, timeZone, fillForward, leverage);
}
// If we need a mappable ticker and we can't find one in the SymbolCache, throw
Symbol underlying;
if (!SymbolCache.TryGetSymbol(ticker, out underlying))
{
throw new InvalidOperationException($"The custom data type {dataType.Name} requires mapping, but the provided ticker is not in the cache. " +
$"Please add this custom data type using a Symbol or perform this call after " +
$"a Security has been added using AddEquity, AddForex, AddCfd, AddCrypto, AddFuture, AddOption or AddSecurity. " +
$"An example use case can be found in CustomDataAddDataRegressionAlgorithm");
}
return AddData(dataType, underlying, resolution, timeZone, fillForward, leverage);
}
/// <summary>
/// AddData a new user defined data source, requiring only the minimum config options.
/// This adds a Symbol to the `Underlying` property in the custom data Symbol object.
/// Use this method when adding custom data with a ticker from the past, such as "AOL"
/// before it became "TWX", or if you need to filter using custom data and place trades on the
/// Symbol associated with the custom data.
/// </summary>
/// <param name="dataType">Data source type</param>
/// <param name="underlying"></param>
/// <param name="resolution">Resolution of the Data Required</param>
/// <param name="timeZone">Specifies the time zone of the raw data</param>
/// <param name="fillForward">When no data available on a tradebar, return the last data that was generated</param>
/// <param name="leverage">Custom leverage per security</param>
/// <returns>The new <see cref="Security"/></returns>
/// <remarks>
/// We include three optional unused object parameters so that pythonnet chooses the intended method
/// correctly. Previously, calling the overloaded method that accepts a string would instead call this method.
/// Adding the three unused parameters makes it choose the correct method when using a string or Symbol. This is
/// due to pythonnet's method precedence, as viewable here: https://github.com/QuantConnect/pythonnet/blob/9e29755c54e6008cb016e3dd9d75fbd8cd19fcf7/src/runtime/methodbinder.cs#L215
/// </remarks>
[DocumentationAttribute(AddingData)]
public Security AddData(Type dataType, Symbol underlying, Resolution? resolution = null, DateTimeZone timeZone = null, bool fillForward = false, decimal leverage = 1.0m)
{
var symbol = QuantConnect.Symbol.CreateBase(dataType, underlying, underlying.ID.Market);
return AddDataImpl(dataType, symbol, resolution, timeZone, fillForward, leverage);
}
/// <summary>
/// AddData a new user defined data source including symbol properties and exchange hours,
/// all other vars are not required and will use defaults.
/// This overload reflects the C# equivalent for custom properties and market hours
/// </summary>
/// <param name="type">Data source type</param>
/// <param name="ticker">Key/Ticker for data</param>
/// <param name="properties">The properties of this new custom data</param>
/// <param name="exchangeHours">The Exchange hours of this symbol</param>
/// <param name="resolution">Resolution of the Data Required</param>
/// <param name="fillForward">When no data available on a tradebar, return the last data that was generated</param>
/// <param name="leverage">Custom leverage per security</param>
/// <returns>The new <see cref="Security"/></returns>
[DocumentationAttribute(AddingData)]
public Security AddData(PyObject type, string ticker, SymbolProperties properties, SecurityExchangeHours exchangeHours, Resolution? resolution = null, bool fillForward = false, decimal leverage = 1.0m)
{
// Get the right key for storage of base type symbols
var dataType = GetCustomDataType(type);
var key = SecurityIdentifier.GenerateBaseSymbol(dataType, ticker);
// Add entries to our Symbol Properties DB and MarketHours DB
SetDatabaseEntries(key, properties, exchangeHours);
// Then add the data
return AddData(dataType, ticker, resolution, null, fillForward, leverage);
}
/// <summary>
/// Creates and adds a new Future Option contract to the algorithm.
/// </summary>
/// <param name="futureSymbol">The Future canonical symbol (i.e. Symbol returned from <see cref="AddFuture"/>)</param>
/// <param name="optionFilter">Filter to apply to option contracts loaded as part of the universe</param>
/// <returns>The new Option security, containing a Future as its underlying.</returns>
/// <exception cref="ArgumentException">The symbol provided is not canonical.</exception>
[DocumentationAttribute(AddingData)]
public void AddFutureOption(Symbol futureSymbol, PyObject optionFilter)
{
Func<OptionFilterUniverse, OptionFilterUniverse> optionFilterUniverse;
if (!optionFilter.TrySafeAs(out optionFilterUniverse))
{
throw new ArgumentException("Option contract universe filter provided is not a function");
}
AddFutureOption(futureSymbol, optionFilterUniverse);
}
/// <summary>
/// Adds the provided final Symbol with/without underlying set to the algorithm.
/// This method is meant for custom data types that require a ticker, but have no underlying Symbol.
/// Examples of data sources that meet this criteria are U.S. Treasury Yield Curve Rates and Trading Economics data
/// </summary>
/// <param name="dataType">Data source type</param>
/// <param name="symbol">Final symbol that includes underlying (if any)</param>
/// <param name="resolution">Resolution of the Data required</param>
/// <param name="timeZone">Specifies the time zone of the raw data</param>
/// <param name="fillForward">When no data available on a tradebar, return the last data that was generated</param>
/// <param name="leverage">Custom leverage per security</param>
/// <returns>The new <see cref="Security"/></returns>
private Security AddDataImpl(Type dataType, Symbol symbol, Resolution? resolution, DateTimeZone timeZone, bool fillForward, decimal leverage)
{
var alias = symbol.ID.Symbol;
SymbolCache.Set(alias, symbol);
if (timeZone != null)
{
// user set time zone
MarketHoursDatabase.SetEntryAlwaysOpen(symbol.ID.Market, alias, SecurityType.Base, timeZone);
}
//Add this new generic data as a tradeable security:
var config = SubscriptionManager.SubscriptionDataConfigService.Add(
dataType,
symbol,
resolution,
fillForward,
isCustomData: true,
extendedMarketHours: true);
var security = Securities.CreateSecurity(symbol, config, leverage, addToSymbolCache: false);
return AddToUserDefinedUniverse(security, new List<SubscriptionDataConfig> { config });
}
/// <summary>
/// Resolves the custom data <see cref="Type"/> from the PyObject argument of <see cref="AddData(PyObject, string, Resolution?)"/> and overloads,
/// throwing a clear exception if the caller passed something other than a custom data class (e.g. a string ticker).
/// </summary>
private static Type GetCustomDataType(PyObject type)
{
if (type.TryCreateType(out var dataType))
{
return dataType;
}
using var _ = Py.GIL();
throw new ArgumentException(Messages.QCAlgorithm.AddDataInvalidPyObjectType(type.Repr()));
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This is for coarse fundamental US Equity data and
/// will be executed on day changes in the NewYork time zone (<see cref="TimeZones.NewYork"/>)
/// </summary>
/// <param name="pyObject">Defines an initial coarse selection</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject pyObject)
{
Func<IEnumerable<Fundamental>, object> fundamentalSelector;
Universe universe;
if (pyObject.TryCreateType(out var type))
{
return AddUniverse(pyObject, null, null);
}
// TODO: to be removed when https://github.com/QuantConnect/pythonnet/issues/62 is solved
else if (pyObject.TryConvert(out universe))
{
return AddUniverse(universe);
}
else if (pyObject.TryConvert(out universe, allowPythonDerivative: true))
{
return AddUniverse(new UniversePythonWrapper(pyObject));
}
else if (pyObject.TrySafeAs(out fundamentalSelector))
{
return AddUniverse(FundamentalUniverse.USA(fundamentalSelector));
}
else
{
using (Py.GIL())
{
throw new ArgumentException($"QCAlgorithm.AddUniverse: {pyObject.Repr()} is not a valid argument.");
}
}
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This is for coarse and fine fundamental US Equity data and
/// will be executed on day changes in the NewYork time zone (<see cref="TimeZones.NewYork"/>)
/// </summary>
/// <param name="pyObject">Defines an initial coarse selection or a universe</param>
/// <param name="pyfine">Defines a more detailed selection with access to more data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject pyObject, PyObject pyfine)
{
Func<IEnumerable<CoarseFundamental>, object> coarseFunc;
Func<IEnumerable<FineFundamental>, object> fineFunc;
try
{
// this is due to a pythonNet limitation even if defining 'AddUniverse(IDateRule, PyObject)'
// it will chose this method instead
IDateRule dateRule;
using (Py.GIL())
{
dateRule = pyObject.As<IDateRule>();
}
if (pyfine.TrySafeAs(out coarseFunc))
{
return AddUniverse(dateRule, coarseFunc.ConvertToUniverseSelectionSymbolDelegate());
}
}
catch (InvalidCastException)
{
// pass
}
if (pyObject.TryCreateType(out var type))
{
return AddUniverse(pyObject, null, pyfine);
}
else if (pyObject.TryConvert(out Universe universe) && pyfine.TrySafeAs(out fineFunc))
{
return AddUniverse(universe, fineFunc.ConvertToUniverseSelectionSymbolDelegate());
}
else if (pyObject.TrySafeAs(out coarseFunc) && pyfine.TrySafeAs(out fineFunc))
{
return AddUniverse(coarseFunc.ConvertToUniverseSelectionSymbolDelegate(),
fineFunc.ConvertToUniverseSelectionSymbolDelegate());
}
else
{
using (Py.GIL())
{
throw new ArgumentException($"QCAlgorithm.AddUniverse: {pyObject.Repr()} or {pyfine.Repr()} is not a valid argument.");
}
}
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This can be used to return a list of string
/// symbols retrieved from anywhere and will loads those symbols under the US Equity market.
/// </summary>
/// <param name="name">A unique name for this universe</param>
/// <param name="resolution">The resolution this universe should be triggered on</param>
/// <param name="pySelector">Function delegate that accepts a DateTime and returns a collection of string symbols</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(string name, Resolution resolution, PyObject pySelector)
{
var selector = pySelector.SafeAs<Func<DateTime, object>>();
return AddUniverse(name, resolution, selector.ConvertToUniverseSelectionStringDelegate());
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This can be used to return a list of string
/// symbols retrieved from anywhere and will loads those symbols under the US Equity market.
/// </summary>
/// <param name="name">A unique name for this universe</param>
/// <param name="pySelector">Function delegate that accepts a DateTime and returns a collection of string symbols</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(string name, PyObject pySelector)
{
var selector = pySelector.SafeAs<Func<DateTime, object>>();
return AddUniverse(name, selector.ConvertToUniverseSelectionStringDelegate());
}
/// <summary>
/// Creates a new user defined universe that will fire on the requested resolution during market hours.
/// </summary>
/// <param name="securityType">The security type of the universe</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="resolution">The resolution this universe should be triggered on</param>
/// <param name="market">The market of the universe</param>
/// <param name="universeSettings">The subscription settings used for securities added from this universe</param>
/// <param name="pySelector">Function delegate that accepts a DateTime and returns a collection of string symbols</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(SecurityType securityType, string name, Resolution resolution, string market, UniverseSettings universeSettings, PyObject pySelector)
{
var selector = pySelector.SafeAs<Func<DateTime, object>>();
return AddUniverse(securityType, name, resolution, market, universeSettings, selector.ConvertToUniverseSelectionStringDelegate());
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This will use the default universe settings
/// specified via the <see cref="UniverseSettings"/> property. This universe will use the defaults
/// of SecurityType.Equity, Resolution.Daily, Market.USA, and UniverseSettings
/// </summary>
/// <param name="T">The data type</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="selector">Function delegate that performs selection on the universe data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject T, string name, PyObject selector)
{
return AddUniverse(T.CreateType(), null, name, null, null, null, selector);
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This will use the default universe settings
/// specified via the <see cref="UniverseSettings"/> property. This universe will use the defaults
/// of SecurityType.Equity, Market.USA and UniverseSettings
/// </summary>
/// <param name="T">The data type</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="resolution">The expected resolution of the universe data</param>
/// <param name="selector">Function delegate that performs selection on the universe data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject T, string name, Resolution resolution, PyObject selector)
{
return AddUniverse(T.CreateType(), null, name, resolution, null, null, selector);
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This will use the default universe settings
/// specified via the <see cref="UniverseSettings"/> property. This universe will use the defaults
/// of SecurityType.Equity, and Market.USA
/// </summary>
/// <param name="T">The data type</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="resolution">The expected resolution of the universe data</param>
/// <param name="universeSettings">The settings used for securities added by this universe</param>
/// <param name="selector">Function delegate that performs selection on the universe data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject T, string name, Resolution resolution, UniverseSettings universeSettings, PyObject selector)
{
return AddUniverse(T.CreateType(), null, name, resolution, null, universeSettings, selector);
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This will use the default universe settings
/// specified via the <see cref="UniverseSettings"/> property. This universe will use the defaults
/// of SecurityType.Equity, Resolution.Daily, and Market.USA
/// </summary>
/// <param name="T">The data type</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="universeSettings">The settings used for securities added by this universe</param>
/// <param name="selector">Function delegate that performs selection on the universe data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject T, string name, UniverseSettings universeSettings, PyObject selector)
{
return AddUniverse(T.CreateType(), null, name, null, null, universeSettings, selector);
}
/// <summary>
/// Creates a new universe and adds it to the algorithm. This will use the default universe settings
/// specified via the <see cref="UniverseSettings"/> property.
/// </summary>
/// <param name="T">The data type</param>
/// <param name="securityType">The security type the universe produces</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="resolution">The expected resolution of the universe data</param>
/// <param name="market">The market for selected symbols</param>
/// <param name="selector">Function delegate that performs selection on the universe data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject T, SecurityType securityType, string name, Resolution resolution, string market, PyObject selector)
{
return AddUniverse(T.CreateType(), securityType, name, resolution, market, null, selector);
}
/// <summary>
/// Creates a new universe and adds it to the algorithm
/// </summary>
/// <param name="T">The data type</param>
/// <param name="securityType">The security type the universe produces</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="resolution">The expected resolution of the universe data</param>
/// <param name="market">The market for selected symbols</param>
/// <param name="universeSettings">The subscription settings to use for newly created subscriptions</param>
/// <param name="selector">Function delegate that performs selection on the universe data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(PyObject T, SecurityType securityType, string name, Resolution resolution, string market, UniverseSettings universeSettings, PyObject selector)
{
return AddUniverse(T.CreateType(), securityType, name, resolution, market, universeSettings, selector);
}
/// <summary>
/// Creates a new universe and adds it to the algorithm
/// </summary>
/// <param name="dataType">The data type</param>
/// <param name="securityType">The security type the universe produces</param>
/// <param name="name">A unique name for this universe</param>
/// <param name="resolution">The expected resolution of the universe data</param>
/// <param name="market">The market for selected symbols</param>
/// <param name="universeSettings">The subscription settings to use for newly created subscriptions</param>
/// <param name="pySelector">Function delegate that performs selection on the universe data</param>
[DocumentationAttribute(Universes)]
public Universe AddUniverse(Type dataType, SecurityType? securityType = null, string name = null, Resolution? resolution = null, string market = null, UniverseSettings universeSettings = null, PyObject pySelector = null)
{
if (market.IsNullOrEmpty())
{
market = Market.USA;
}
securityType ??= SecurityType.Equity;
Func<IEnumerable<BaseData>, IEnumerable<Symbol>> wrappedSelector = null;
if (pySelector != null)
{
var selector = pySelector.SafeAs<Func<IEnumerable<IBaseData>, object>>();
wrappedSelector = baseDatas =>
{
var result = selector(baseDatas);
if (ReferenceEquals(result, Universe.Unchanged))
{
return Universe.Unchanged;
}
return ((object[])result).Select(x => x is Symbol symbol ? symbol : QuantConnect.Symbol.Create((string)x, securityType.Value, market, baseDataType: dataType));
};
}
return AddUniverseSymbolSelector(dataType, name, resolution, market, universeSettings, wrappedSelector);
}
/// <summary>
/// Creates a new universe selection model and adds it to the algorithm. This universe selection model will chain to the security
/// changes of a given <see cref="Universe"/> selection output and create a new <see cref="OptionChainUniverse"/> for each of them
/// </summary>
/// <param name="universe">The universe we want to chain an option universe selection model too</param>
/// <param name="optionFilter">The option filter universe to use</param>
[DocumentationAttribute(Universes)]
public void AddUniverseOptions(PyObject universe, PyObject optionFilter)
{
Func<OptionFilterUniverse, OptionFilterUniverse> convertedOptionChain;
Universe universeToChain;
if (universe.TryConvert(out universeToChain) && optionFilter.TrySafeAs(out convertedOptionChain))
{
AddUniverseOptions(universeToChain, convertedOptionChain);
}
else
{
using (Py.GIL())
{
throw new ArgumentException($"QCAlgorithm.AddChainedEquityOptionUniverseSelectionModel: {universe.Repr()} or {optionFilter.Repr()} is not a valid argument.");
}
}
}
/// <summary>
/// Registers the consolidator to receive automatic updates as well as configures the indicator to receive updates
/// from the consolidator.
/// </summary>
/// <param name="symbol">The symbol to register against</param>
/// <param name="indicator">The indicator to receive data from the consolidator</param>
/// <param name="resolution">The resolution at which to send data to the indicator, null to use the same resolution as the subscription</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(ConsolidatingData)]
public void RegisterIndicator(Symbol symbol, PyObject indicator, Resolution? resolution = null, PyObject selector = null)
{
RegisterIndicator(symbol, indicator, ResolveConsolidator(symbol, resolution), selector);
}
/// <summary>
/// Registers the consolidator to receive automatic updates as well as configures the indicator to receive updates
/// from the consolidator.
/// </summary>
/// <param name="symbol">The symbol to register against</param>
/// <param name="indicator">The indicator to receive data from the consolidator</param>
/// <param name="resolution">The resolution at which to send data to the indicator, null to use the same resolution as the subscription</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(ConsolidatingData)]
public void RegisterIndicator(Symbol symbol, PyObject indicator, TimeSpan? resolution = null, PyObject selector = null)
{
RegisterIndicator(symbol, indicator, ResolveConsolidator(symbol, resolution), selector);
}
/// <summary>
/// Registers the consolidator to receive automatic updates as well as configures the indicator to receive updates
/// from the consolidator.
/// </summary>
/// <param name="symbol">The symbol to register against</param>
/// <param name="indicator">The indicator to receive data from the consolidator</param>
/// <param name="pyObject">The python object that it is trying to register with, could be consolidator or a timespan</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(ConsolidatingData)]
public void RegisterIndicator(Symbol symbol, PyObject indicator, PyObject pyObject, PyObject selector = null)
{
// First check if this is just a regular IDataConsolidator
IDataConsolidator dataConsolidator;
if (pyObject.TryConvert(out dataConsolidator))
{
RegisterIndicator(symbol, indicator, dataConsolidator, selector);
return;
}
try
{
dataConsolidator = new DataConsolidatorPythonWrapper(pyObject);
}
catch
{
// Finally, since above didn't work, just try it as a timespan
// Issue #4668 Fix
using (Py.GIL())
{
try
{
// tryConvert does not work for timespan
TimeSpan? timeSpan = pyObject.SafeAs<TimeSpan>();
if (timeSpan != default(TimeSpan))
{
RegisterIndicator(symbol, indicator, timeSpan, selector);
return;
}
}
catch (Exception e)
{
throw new ArgumentException("Invalid third argument, should be either a valid consolidator or timedelta object. The following exception was thrown: ", e);
}
}
}
RegisterIndicator(symbol, indicator, dataConsolidator, selector);
}
/// <summary>
/// Registers the consolidator to receive automatic updates as well as configures the indicator to receive updates
/// from the consolidator.
/// </summary>
/// <param name="symbol">The symbol to register against</param>
/// <param name="indicator">The indicator to receive data from the consolidator</param>
/// <param name="consolidator">The consolidator to receive raw subscription data</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(ConsolidatingData)]
public void RegisterIndicator(Symbol symbol, PyObject indicator, IDataConsolidator consolidator, PyObject selector = null)
{
// TODO: to be removed when https://github.com/QuantConnect/pythonnet/issues/62 is solved
var convertedIndicator = ConvertPythonIndicator(indicator);
switch (convertedIndicator)
{
case PythonIndicator pythonIndicator:
RegisterIndicator(symbol, pythonIndicator, consolidator,
selector?.SafeAs<Func<IBaseData, IBaseData>>());
break;
case IndicatorBase<IndicatorDataPoint> dataPointIndicator:
RegisterIndicator(symbol, dataPointIndicator, consolidator,
selector?.SafeAs<Func<IBaseData, decimal>>());
break;
case IndicatorBase<IBaseDataBar> baseDataBarIndicator:
RegisterIndicator(symbol, baseDataBarIndicator, consolidator,
selector?.SafeAs<Func<IBaseData, IBaseDataBar>>());
break;
case IndicatorBase<TradeBar> tradeBarIndicator:
RegisterIndicator(symbol, tradeBarIndicator, consolidator,
selector?.SafeAs<Func<IBaseData, TradeBar>>());
break;
case IndicatorBase<IBaseData> baseDataIndicator:
RegisterIndicator(symbol, baseDataIndicator, consolidator,
selector?.SafeAs<Func<IBaseData, IBaseData>>());
break;
case IndicatorBase<BaseData> baseDataIndicator:
RegisterIndicator(symbol, baseDataIndicator, consolidator,
selector?.SafeAs<Func<IBaseData, BaseData>>());
break;
default:
// Shouldn't happen, ConvertPythonIndicator will wrap the PyObject in a PythonIndicator instance if it can't convert it
throw new ArgumentException($"Indicator type {indicator.GetPythonType().Name} is not supported.");
}
}
/// <summary>
/// Warms up a given indicator with historical data
/// </summary>
/// <param name="symbol">The symbol whose indicator we want</param>
/// <param name="indicator">The indicator we want to warm up</param>
/// <param name="resolution">The resolution</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(HistoricalData)]
public void WarmUpIndicator(Symbol symbol, PyObject indicator, Resolution? resolution = null, PyObject selector = null)
{
// TODO: to be removed when https://github.com/QuantConnect/pythonnet/issues/62 is solved
WarmUpIndicator([symbol], indicator, resolution, selector);
}
/// <summary>
/// Warms up a given indicator with historical data
/// </summary>
/// <param name="symbol">The symbol or symbols to retrieve historical data for</param>
/// <param name="indicator">The indicator we want to warm up</param>
/// <param name="resolution">The resolution</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(HistoricalData)]
public void WarmUpIndicator(PyObject symbol, PyObject indicator, Resolution? resolution = null, PyObject selector = null)
{
// TODO: to be removed when https://github.com/QuantConnect/pythonnet/issues/62 is solved
var symbols = symbol.ConvertToSymbolEnumerable();
WarmUpIndicator(symbols, indicator, resolution, selector);
}
/// <summary>
/// Warms up a given indicator with historical data
/// </summary>
/// <param name="symbols">The symbols to retrieve historical data for</param>
/// <param name="indicator">The indicator we want to warm up</param>
/// <param name="resolution">The resolution</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
private void WarmUpIndicator(IEnumerable<Symbol> symbols, PyObject indicator, Resolution? resolution = null, PyObject selector = null)
{
// TODO: to be removed when https://github.com/QuantConnect/pythonnet/issues/62 is solved
var convertedIndicator = ConvertPythonIndicator(indicator);
switch (convertedIndicator)
{
case PythonIndicator pythonIndicator:
WarmUpIndicator(symbols, pythonIndicator, resolution, selector?.SafeAs<Func<IBaseData, IBaseData>>());
break;
case IndicatorBase<IndicatorDataPoint> dataPointIndicator:
WarmUpIndicator(symbols, dataPointIndicator, resolution, selector?.SafeAs<Func<IBaseData, decimal>>());
break;
case IndicatorBase<IBaseDataBar> baseDataBarIndicator:
WarmUpIndicator(symbols, baseDataBarIndicator, resolution, selector?.SafeAs<Func<IBaseData, IBaseDataBar>>());
break;
case IndicatorBase<TradeBar> tradeBarIndicator:
WarmUpIndicator(symbols, tradeBarIndicator, resolution, selector?.SafeAs<Func<IBaseData, TradeBar>>());
break;
case IndicatorBase<IBaseData> baseDataIndicator:
WarmUpIndicator(symbols, baseDataIndicator, resolution, selector?.SafeAs<Func<IBaseData, IBaseData>>());
break;
case IndicatorBase<BaseData> baseDataIndicator:
WarmUpIndicator(symbols, baseDataIndicator, resolution, selector?.SafeAs<Func<IBaseData, BaseData>>());
break;
default:
// Shouldn't happen, ConvertPythonIndicator will wrap the PyObject in a PythonIndicator instance if it can't convert it
throw new ArgumentException($"Indicator type {indicator.GetPythonType().Name} is not supported.");
}
}
/// <summary>
/// Warms up a given indicator with historical data
/// </summary>
/// <param name="symbol">The symbol whose indicator we want</param>
/// <param name="indicator">The indicator we want to warm up</param>
/// <param name="period">The necessary period to warm up the indicator</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(HistoricalData)]
public void WarmUpIndicator(Symbol symbol, PyObject indicator, TimeSpan period, PyObject selector = null)
{
WarmUpIndicator([symbol], indicator, period, selector);
}
/// <summary>
/// Warms up a given indicator with historical data
/// </summary>
/// <param name="symbol">The symbol or symbols to retrieve historical data for</param>
/// <param name="indicator">The indicator we want to warm up</param>
/// <param name="period">The necessary period to warm up the indicator</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
[DocumentationAttribute(Indicators)]
[DocumentationAttribute(HistoricalData)]
public void WarmUpIndicator(PyObject symbol, PyObject indicator, TimeSpan period, PyObject selector = null)
{
var symbols = symbol.ConvertToSymbolEnumerable();
WarmUpIndicator(symbols, indicator, period, selector);
}
/// <summary>
/// Warms up a given indicator with historical data
/// </summary>
/// <param name="symbols">The symbols to retrieve historical data for</param>
/// <param name="indicator">The indicator we want to warm up</param>
/// <param name="period">The necessary period to warm up the indicator</param>
/// <param name="selector">Selects a value from the BaseData send into the indicator, if null defaults to a cast (x => (T)x)</param>
private void WarmUpIndicator(IEnumerable<Symbol> symbols, PyObject indicator, TimeSpan period, PyObject selector = null)
{
var convertedIndicator = ConvertPythonIndicator(indicator);
switch (convertedIndicator)
{
case PythonIndicator pythonIndicator:
WarmUpIndicator(symbols, pythonIndicator, period, selector?.SafeAs<Func<IBaseData, IBaseData>>());
break;
case IndicatorBase<IndicatorDataPoint> dataPointIndicator:
WarmUpIndicator(symbols, dataPointIndicator, period, selector?.SafeAs<Func<IBaseData, decimal>>());
break;
case IndicatorBase<IBaseDataBar> baseDataBarIndicator:
WarmUpIndicator(symbols, baseDataBarIndicator, period, selector?.SafeAs<Func<IBaseData, IBaseDataBar>>());
break;
case IndicatorBase<TradeBar> tradeBarIndicator:
WarmUpIndicator(symbols, tradeBarIndicator, period, selector?.SafeAs<Func<IBaseData, TradeBar>>());
break;
case IndicatorBase<IBaseData> baseDataIndicator:
WarmUpIndicator(symbols, baseDataIndicator, period, selector?.SafeAs<Func<IBaseData, IBaseData>>());
break;
case IndicatorBase<BaseData> baseDataIndicator:
WarmUpIndicator(symbols, baseDataIndicator, period, selector?.SafeAs<Func<IBaseData, BaseData>>());
break;
default:
// Shouldn't happen, ConvertPythonIndicator will wrap the PyObject in a PythonIndicator instance if it can't convert it
throw new ArgumentException($"Indicator type {indicator.GetPythonType().Name} is not supported.");
}
}
/// <summary>
/// Plot a chart using string series name, with value.
/// </summary>
/// <param name="series">Name of the plot series</param>
/// <param name="pyObject">PyObject with the value to plot</param>
/// <seealso cref="Plot(string,decimal)"/>
[DocumentationAttribute(Charting)]
public void Plot(string series, PyObject pyObject)
{
using (Py.GIL())
{
if (pyObject.TryConvert(out IndicatorBase indicator, true))
{
Plot(series, indicator);
}
else
{
try
{
var value = (((dynamic)pyObject).Value as PyObject).GetAndDispose<decimal>();
Plot(series, value);
}
catch
{
var pythonType = pyObject.GetPythonType().Repr();
throw new ArgumentException($"QCAlgorithm.Plot(): The last argument should be a QuantConnect Indicator object, {pythonType} was provided.");
}
}
}
}
/// <summary>
/// Plots the value of each indicator on the chart
/// </summary>
/// <param name="chart">The chart's name</param>
/// <param name="first">The first indicator to plot</param>
/// <param name="second">The second indicator to plot</param>
/// <param name="third">The third indicator to plot</param>
/// <param name="fourth">The fourth indicator to plot</param>
/// <seealso cref="Plot(string,string,decimal)"/>
[DocumentationAttribute(Charting)]
public void Plot(string chart, Indicator first, Indicator second = null, Indicator third = null, Indicator fourth = null)
{
Plot(chart, new[] { first, second, third, fourth }.Where(x => x != null).ToArray());
}
/// <summary>
/// Plots the value of each indicator on the chart
/// </summary>
/// <param name="chart">The chart's name</param>
/// <param name="first">The first indicator to plot</param>
/// <param name="second">The second indicator to plot</param>
/// <param name="third">The third indicator to plot</param>
/// <param name="fourth">The fourth indicator to plot</param>
/// <seealso cref="Plot(string,string,decimal)"/>
[DocumentationAttribute(Charting)]
public void Plot(string chart, BarIndicator first, BarIndicator second = null, BarIndicator third = null, BarIndicator fourth = null)
{
Plot(chart, new[] { first, second, third, fourth }.Where(x => x != null).ToArray());
}
/// <summary>
/// Plots the value of each indicator on the chart
/// </summary>
/// <param name="chart">The chart's name</param>
/// <param name="first">The first indicator to plot</param>
/// <param name="second">The second indicator to plot</param>
/// <param name="third">The third indicator to plot</param>
/// <param name="fourth">The fourth indicator to plot</param>
/// <seealso cref="Plot(string,string,decimal)"/>
[DocumentationAttribute(Charting)]
public void Plot(string chart, TradeBarIndicator first, TradeBarIndicator second = null, TradeBarIndicator third = null, TradeBarIndicator fourth = null)
{
Plot(chart, new[] { first, second, third, fourth }.Where(x => x != null).ToArray());
}
/// <summary>
/// Automatically plots each indicator when a new value is available
/// </summary>
[DocumentationAttribute(Charting)]
[DocumentationAttribute(Indicators)]
public void PlotIndicator(string chart, PyObject first, PyObject second = null, PyObject third = null, PyObject fourth = null)
{
var array = GetIndicatorArray(first, second, third, fourth);
PlotIndicator(chart, array[0], array[1], array[2], array[3]);
}
/// <summary>
/// Automatically plots each indicator when a new value is available
/// </summary>
[DocumentationAttribute(Charting)]
[DocumentationAttribute(Indicators)]
public void PlotIndicator(string chart, bool waitForReady, PyObject first, PyObject second = null, PyObject third = null, PyObject fourth = null)
{
var array = GetIndicatorArray(first, second, third, fourth);
PlotIndicator(chart, waitForReady, array[0], array[1], array[2], array[3]);
}
/// <summary>
/// Creates a new FilteredIdentity indicator for the symbol The indicator will be automatically
/// updated on the symbol's subscription resolution
/// </summary>
/// <param name="symbol">The symbol whose values we want as an indicator</param>
/// <param name="selector">Selects a value from the BaseData, if null defaults to the .Value property (x => x.Value)</param>
/// <param name="filter">Filters the IBaseData send into the indicator, if null defaults to true (x => true) which means no filter</param>
/// <param name="fieldName">The name of the field being selected</param>
/// <returns>A new FilteredIdentity indicator for the specified symbol and selector</returns>
[DocumentationAttribute(Indicators)]
public FilteredIdentity FilteredIdentity(Symbol symbol, PyObject selector = null, PyObject filter = null, string fieldName = null)
{
var resolution = GetSubscription(symbol).Resolution;
return FilteredIdentity(symbol, resolution, selector, filter, fieldName);
}
/// <summary>
/// Creates a new FilteredIdentity indicator for the symbol The indicator will be automatically
/// updated on the symbol's subscription resolution
/// </summary>
/// <param name="symbol">The symbol whose values we want as an indicator</param>
/// <param name="resolution">The desired resolution of the data</param>
/// <param name="selector">Selects a value from the BaseData, if null defaults to the .Value property (x => x.Value)</param>
/// <param name="filter">Filters the IBaseData send into the indicator, if null defaults to true (x => true) which means no filter</param>
/// <param name="fieldName">The name of the field being selected</param>
/// <returns>A new FilteredIdentity indicator for the specified symbol and selector</returns>
[DocumentationAttribute(Indicators)]
public FilteredIdentity FilteredIdentity(Symbol symbol, Resolution resolution, PyObject selector = null, PyObject filter = null, string fieldName = null)
{
var name = CreateIndicatorName(symbol, fieldName ?? "close", resolution);
var pyselector = PythonUtil.ToFunc<IBaseData, IBaseDataBar>(selector);
var filteredIdentity = new FilteredIdentity(name, filter);
RegisterIndicator(symbol, filteredIdentity, resolution, pyselector);
return filteredIdentity;
}
/// <summary>
/// Creates a new FilteredIdentity indicator for the symbol The indicator will be automatically
/// updated on the symbol's subscription resolution
/// </summary>
/// <param name="symbol">The symbol whose values we want as an indicator</param>
/// <param name="resolution">The desired resolution of the data</param>
/// <param name="selector">Selects a value from the BaseData, if null defaults to the .Value property (x => x.Value)</param>
/// <param name="filter">Filters the IBaseData send into the indicator, if null defaults to true (x => true) which means no filter</param>
/// <param name="fieldName">The name of the field being selected</param>
/// <returns>A new FilteredIdentity indicator for the specified symbol and selector</returns>
[DocumentationAttribute(Indicators)]
public FilteredIdentity FilteredIdentity(Symbol symbol, TimeSpan resolution, PyObject selector = null, PyObject filter = null, string fieldName = null)
{
var name = $"{symbol}({fieldName ?? "close"}_{resolution.ToStringInvariant(null)})";
var pyselector = PythonUtil.ToFunc<IBaseData, IBaseDataBar>(selector);
var filteredIdentity = new FilteredIdentity(name, filter);
RegisterIndicator(symbol, filteredIdentity, ResolveConsolidator(symbol, resolution), pyselector);
return filteredIdentity;
}
/// <summary>
/// Gets the historical data for the specified symbol. The exact number of bars will be returned.
/// The symbol must exist in the Securities collection.
/// </summary>
/// <param name="tickers">The symbols to retrieve historical data for</param>
/// <param name="periods">The number of bars to request</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>A python dictionary with pandas DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject tickers, int periods, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
if (tickers.TryConvert<Universe>(out var universe))
{
resolution ??= universe.Configuration.Resolution;
var requests = CreateBarCountHistoryRequests(new[] { universe.Symbol }, universe.DataType, periods, resolution, fillForward, extendedMarketHours,
dataMappingMode, dataNormalizationMode, contractDepthOffset);
// we pass in 'BaseDataCollection' type so we clean up the data frame if we can
return GetDataFrame(History(requests.Where(x => x != null)), flatten, typeof(BaseDataCollection));
}
if (tickers.TryCreateType(out var type))
{
var requests = CreateBarCountHistoryRequests(Securities.Keys, type, periods, resolution, fillForward, extendedMarketHours,
dataMappingMode, dataNormalizationMode, contractDepthOffset);
return GetDataFrame(History(requests.Where(x => x != null)), flatten, type);
}
var symbols = tickers.ConvertToSymbolEnumerable().ToArray();
var dataType = Extensions.GetCustomDataTypeFromSymbols(symbols);
return GetDataFrame(
History(symbols, periods, resolution, fillForward, extendedMarketHours, dataMappingMode, dataNormalizationMode, contractDepthOffset),
flatten,
dataType);
}
/// <summary>
/// Gets the historical data for the specified symbols over the requested span.
/// The symbols must exist in the Securities collection.
/// </summary>
/// <param name="tickers">The symbols to retrieve historical data for</param>
/// <param name="span">The span over which to retrieve recent historical data</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>A python dictionary with pandas DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject tickers, TimeSpan span, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
return History(tickers, Time - span, Time, resolution, fillForward, extendedMarketHours, dataMappingMode, dataNormalizationMode,
contractDepthOffset, flatten);
}
/// <summary>
/// Gets the historical data for the specified symbols between the specified dates. The symbols must exist in the Securities collection.
/// </summary>
/// <param name="tickers">The symbols to retrieve historical data for</param>
/// <param name="start">The start time in the algorithm's time zone</param>
/// <param name="end">The end time in the algorithm's time zone</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>A python dictionary with a pandas DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject tickers, DateTime start, DateTime end, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
if (tickers.TryConvert<Universe>(out var universe))
{
resolution ??= universe.Configuration.Resolution;
var requests = CreateDateRangeHistoryRequests(new[] { universe.Symbol }, universe.DataType, start, end, resolution, fillForward, extendedMarketHours,
dataMappingMode, dataNormalizationMode, contractDepthOffset);
// we pass in 'BaseDataCollection' type so we clean up the data frame if we can
return GetDataFrame(History(requests.Where(x => x != null)), flatten, typeof(BaseDataCollection));
}
if (tickers.TryCreateType(out var type))
{
var requests = CreateDateRangeHistoryRequests(Securities.Keys, type, start, end, resolution, fillForward, extendedMarketHours,
dataMappingMode, dataNormalizationMode, contractDepthOffset);
return GetDataFrame(History(requests.Where(x => x != null)), flatten, type);
}
var symbols = tickers.ConvertToSymbolEnumerable().ToArray();
var dataType = Extensions.GetCustomDataTypeFromSymbols(symbols);
return GetDataFrame(
History(symbols, start, end, resolution, fillForward, extendedMarketHours, dataMappingMode, dataNormalizationMode, contractDepthOffset),
flatten,
dataType);
}
/// <summary>
/// Gets the historical data for the specified symbols between the specified dates. The symbols must exist in the Securities collection.
/// </summary>
/// <param name="type">The data type of the symbols</param>
/// <param name="tickers">The symbols to retrieve historical data for</param>
/// <param name="start">The start time in the algorithm's time zone</param>
/// <param name="end">The end time in the algorithm's time zone</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>pandas.DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject type, PyObject tickers, DateTime start, DateTime end, Resolution? resolution = null,
bool? fillForward = null, bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null,
DataNormalizationMode? dataNormalizationMode = null, int? contractDepthOffset = null, bool flatten = false)
{
var symbols = tickers.ConvertToSymbolEnumerable().ToArray();
var requestedType = type.CreateType();
var requests = CreateDateRangeHistoryRequests(symbols, requestedType, start, end, resolution, fillForward, extendedMarketHours,
dataMappingMode, dataNormalizationMode, contractDepthOffset);
return GetDataFrame(History(requests.Where(x => x != null)), flatten, requestedType);
}
/// <summary>
/// Gets the historical data for the specified symbols. The exact number of bars will be returned for
/// each symbol. This may result in some data start earlier/later than others due to when various
/// exchanges are open. The symbols must exist in the Securities collection.
/// </summary>
/// <param name="type">The data type of the symbols</param>
/// <param name="tickers">The symbols to retrieve historical data for</param>
/// <param name="periods">The number of bars to request</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>pandas.DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject type, PyObject tickers, int periods, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
var symbols = tickers.ConvertToSymbolEnumerable().ToArray();
var requestedType = type.CreateType();
CheckPeriodBasedHistoryRequestResolution(symbols, resolution, requestedType);
var requests = CreateBarCountHistoryRequests(symbols, requestedType, periods, resolution, fillForward, extendedMarketHours,
dataMappingMode, dataNormalizationMode, contractDepthOffset);
return GetDataFrame(History(requests.Where(x => x != null)), flatten, requestedType);
}
/// <summary>
/// Gets the historical data for the specified symbols over the requested span.
/// The symbols must exist in the Securities collection.
/// </summary>
/// <param name="type">The data type of the symbols</param>
/// <param name="tickers">The symbols to retrieve historical data for</param>
/// <param name="span">The span over which to retrieve recent historical data</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>pandas.DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject type, PyObject tickers, TimeSpan span, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
return History(type, tickers, Time - span, Time, resolution, fillForward, extendedMarketHours, dataMappingMode, dataNormalizationMode,
contractDepthOffset, flatten);
}
/// <summary>
/// Gets the historical data for the specified symbols between the specified dates. The symbols must exist in the Securities collection.
/// </summary>
/// <param name="type">The data type of the symbols</param>
/// <param name="symbol">The symbol to retrieve historical data for</param>
/// <param name="start">The start time in the algorithm's time zone</param>
/// <param name="end">The end time in the algorithm's time zone</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>pandas.DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject type, Symbol symbol, DateTime start, DateTime end, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
return History(type.CreateType(), symbol, start, end, resolution, fillForward, extendedMarketHours, dataMappingMode,
dataNormalizationMode, contractDepthOffset, flatten);
}
/// <summary>
/// Gets the historical data for the specified symbols between the specified dates. The symbols must exist in the Securities collection.
/// </summary>
/// <param name="type">The data type of the symbols</param>
/// <param name="symbol">The symbol to retrieve historical data for</param>
/// <param name="start">The start time in the algorithm's time zone</param>
/// <param name="end">The end time in the algorithm's time zone</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>pandas.DataFrame containing the requested historical data</returns>
private PyObject History(Type type, Symbol symbol, DateTime start, DateTime end, Resolution? resolution, bool? fillForward,
bool? extendedMarketHours, DataMappingMode? dataMappingMode, DataNormalizationMode? dataNormalizationMode,
int? contractDepthOffset, bool flatten)
{
var requests = CreateDateRangeHistoryRequests(new[] { symbol }, type, start, end, resolution, fillForward,
extendedMarketHours, dataMappingMode, dataNormalizationMode, contractDepthOffset);
if (requests.IsNullOrEmpty())
{
throw new ArgumentException($"No history data could be fetched. " +
$"This could be due to the specified security not being of the requested type. Symbol: {symbol} Requested Type: {type.Name}");
}
return GetDataFrame(History(requests), flatten, type);
}
/// <summary>
/// Gets the historical data for the specified symbols. The exact number of bars will be returned for
/// each symbol. This may result in some data start earlier/later than others due to when various
/// exchanges are open. The symbols must exist in the Securities collection.
/// </summary>
/// <param name="type">The data type of the symbols</param>
/// <param name="symbol">The symbol to retrieve historical data for</param>
/// <param name="periods">The number of bars to request</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>pandas.DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject type, Symbol symbol, int periods, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
var managedType = type.CreateType();
resolution = GetResolution(symbol, resolution, managedType);
CheckPeriodBasedHistoryRequestResolution(new[] { symbol }, resolution, managedType);
var marketHours = GetMarketHours(symbol, managedType);
var start = _historyRequestFactory.GetStartTimeAlgoTz(symbol, periods, resolution.Value, marketHours.ExchangeHours,
marketHours.DataTimeZone, managedType, extendedMarketHours);
return History(managedType, symbol, start, Time, resolution, fillForward, extendedMarketHours, dataMappingMode, dataNormalizationMode,
contractDepthOffset, flatten);
}
/// <summary>
/// Gets the historical data for the specified symbols over the requested span.
/// The symbols must exist in the Securities collection.
/// </summary>
/// <param name="type">The data type of the symbols</param>
/// <param name="symbol">The symbol to retrieve historical data for</param>
/// <param name="span">The span over which to retrieve recent historical data</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="fillForward">True to fill forward missing data, false otherwise</param>
/// <param name="extendedMarketHours">True to include extended market hours data, false otherwise</param>
/// <param name="dataMappingMode">The contract mapping mode to use for the security history request</param>
/// <param name="dataNormalizationMode">The price scaling mode to use for the securities history</param>
/// <param name="contractDepthOffset">The continuous contract desired offset from the current front month.
/// For example, 0 will use the front month, 1 will use the back month contract</param>
/// <param name="flatten">Whether to flatten the resulting data frame.
/// e.g. for universe requests, the each row represents a day of data, and the data is stored in a list in a cell of the data frame.
/// If flatten is true, the resulting data frame will contain one row per universe constituent,
/// and each property of the constituent will be a column in the data frame.</param>
/// <returns>pandas.DataFrame containing the requested historical data</returns>
[DocumentationAttribute(HistoricalData)]
public PyObject History(PyObject type, Symbol symbol, TimeSpan span, Resolution? resolution = null, bool? fillForward = null,
bool? extendedMarketHours = null, DataMappingMode? dataMappingMode = null, DataNormalizationMode? dataNormalizationMode = null,
int? contractDepthOffset = null, bool flatten = false)
{
return History(type, symbol, Time - span, Time, resolution, fillForward, extendedMarketHours, dataMappingMode, dataNormalizationMode,
contractDepthOffset, flatten);
}
/// <summary>
/// Sets the specified function as the benchmark, this function provides the value of
/// the benchmark at each date/time requested
/// </summary>
/// <param name="benchmark">The benchmark producing function</param>
[DocumentationAttribute(TradingAndOrders)]
[DocumentationAttribute(SecuritiesAndPortfolio)]
[DocumentationAttribute(Indicators)]
public void SetBenchmark(PyObject benchmark)
{
using (Py.GIL())
{
var pyBenchmark = PythonUtil.ToFunc<DateTime, decimal>(benchmark);
if (pyBenchmark != null)
{
SetBenchmark(pyBenchmark);
return;
}
SetBenchmark((Symbol)benchmark.AsManagedObject(typeof(Symbol)));
}
}
/// <summary>
/// Sets the brokerage to emulate in backtesting or paper trading.
/// This can be used to set a custom brokerage model.
/// </summary>
/// <param name="model">The brokerage model to use</param>
[DocumentationAttribute(Modeling)]
public void SetBrokerageModel(PyObject model)
{
var brokerageModel = PythonUtil.CreateInstanceOrWrapper<IBrokerageModel>(
model,
py => new BrokerageModelPythonWrapper(py)
);
SetBrokerageModel(brokerageModel);
}
/// <summary>
/// Sets the implementation used to handle messages from the brokerage.
/// The default implementation will forward messages to debug or error
/// and when a <see cref="BrokerageMessageType.Error"/> occurs, the algorithm
/// is stopped.
/// </summary>
/// <param name="handler">The message handler to use</param>
[DocumentationAttribute(Modeling)]
[DocumentationAttribute(Logging)]
public void SetBrokerageMessageHandler(PyObject handler)
{
var brokerageMessageHandler = PythonUtil.CreateInstanceOrWrapper<IBrokerageMessageHandler>(
handler,
py => new BrokerageMessageHandlerPythonWrapper(py)
);
SetBrokerageMessageHandler(brokerageMessageHandler);
}
/// <summary>
/// Sets the risk free interest rate model to be used in the algorithm
/// </summary>
/// <param name="model">The risk free interest rate model to use</param>
[DocumentationAttribute(Modeling)]
public void SetRiskFreeInterestRateModel(PyObject model)
{
var riskFreeInterestRateModel = PythonUtil.CreateInstanceOrWrapper<IRiskFreeInterestRateModel>(
model,
py => new RiskFreeInterestRateModelPythonWrapper(py)
);
SetRiskFreeInterestRateModel(riskFreeInterestRateModel);
}
/// <summary>
/// Sets the security initializer function, used to initialize/configure securities after creation
/// </summary>
/// <param name="securityInitializer">The security initializer function or class</param>
[DocumentationAttribute(AddingData)]
[DocumentationAttribute(Modeling)]
public void SetSecurityInitializer(PyObject securityInitializer)
{
var securityInitializer1 = PythonUtil.ToAction<Security>(securityInitializer);
if (securityInitializer1 != null)
{
SetSecurityInitializer(securityInitializer1);
return;
}
SetSecurityInitializer(new SecurityInitializerPythonWrapper(securityInitializer));
}
/// <summary>
/// Adds a security initializer, used to initialize/configure securities after creation.
/// The initializer will appended to the default initializer and others that might have been
/// added using this method, and will be applied to all universes and manually added securities.
/// </summary>
/// <param name="securityInitializer">The security initializer function or class</param>
[DocumentationAttribute(AddingData)]
[DocumentationAttribute(Modeling)]
public void AddSecurityInitializer(PyObject securityInitializer)
{
var securityInitializer1 = PythonUtil.ToAction<Security>(securityInitializer);
if (securityInitializer1 != null)
{
AddSecurityInitializer(securityInitializer1);
return;
}
AddSecurityInitializer(new SecurityInitializerPythonWrapper(securityInitializer));
}
/// <summary>
/// Downloads the requested resource as a <see cref="string"/>.
/// The resource to download is specified as a <see cref="string"/> containing the URI.
/// </summary>
/// <param name="address">A string containing the URI to download</param>
/// <param name="headers">Defines header values to add to the request</param>
/// <returns>The requested resource as a <see cref="string"/></returns>
[DocumentationAttribute(AddingData)]
[DocumentationAttribute(MachineLearning)]
public string Download(string address, PyObject headers) => Download(address, headers, null, null);
/// <summary>
/// Downloads the requested resource as a <see cref="string"/>.
/// The resource to download is specified as a <see cref="string"/> containing the URI.
/// </summary>
/// <param name="address">A string containing the URI to download</param>
/// <param name="headers">Defines header values to add to the request</param>
/// <param name="userName">The user name associated with the credentials</param>
/// <param name="password">The password for the user name associated with the credentials</param>
/// <returns>The requested resource as a <see cref="string"/></returns>
[DocumentationAttribute(AddingData)]
[DocumentationAttribute(MachineLearning)]
public string Download(string address, PyObject headers, string userName, string password)
{
var dict = new Dictionary<string, string>();
if (headers != null)
{
using (Py.GIL())
{
// In python algorithms, headers must be a python dictionary
// In order to convert it into a C# Dictionary
if (PyDict.IsDictType(headers))
{
using var iterator = headers.GetIterator();
foreach (PyObject pyKey in iterator)
{
var key = (string)pyKey.AsManagedObject(typeof(string));
var value = (string)headers.GetItem(pyKey).AsManagedObject(typeof(string));
dict.Add(key, value);
}
}
else
{
throw new ArgumentException($"QCAlgorithm.Fetch(): Invalid argument. {headers.Repr()} is not a dict");
}
}
}
return Download(address, dict, userName, password);
}
/// <summary>
/// Send a debug message to the web console:
/// </summary>
/// <param name="message">Message to send to debug console</param>
/// <seealso cref="Log(PyObject)"/>
/// <seealso cref="Error(PyObject)"/>
[DocumentationAttribute(Logging)]
public void Debug(PyObject message)
{
Debug(message.ToSafeString());
}
/// <summary>
/// Send a string error message to the Console.
/// </summary>
/// <param name="message">Message to display in errors grid</param>
/// <seealso cref="Debug(PyObject)"/>
/// <seealso cref="Log(PyObject)"/>
[DocumentationAttribute(Logging)]
public void Error(PyObject message)
{
Error(message.ToSafeString());
}
/// <summary>
/// Added another method for logging if user guessed.
/// </summary>
/// <param name="message">String message to log.</param>
/// <seealso cref="Debug(PyObject)"/>
/// <seealso cref="Error(PyObject)"/>
[DocumentationAttribute(Logging)]
public void Log(PyObject message)
{
Log(message.ToSafeString());
}
/// <summary>
/// Terminate the algorithm after processing the current event handler.
/// </summary>
/// <param name="message">Exit message to display on quitting</param>
[DocumentationAttribute(Logging)]
public void Quit(PyObject message)
{
Quit(message.ToSafeString());
}
/// <summary>
/// Creates and registers a consolidator for the following bar types: RenkoBar, VolumeRenkoBar, or RangeBar
/// for the specified symbol and threshold. The specified handler will be invoked with each new consolidated bar.
/// </summary>
/// <param name="type">The Python type of the bar (RenkoBar, VolumeRenkoBar, or RangeBar)</param>
/// <param name="symbol">The symbol whose data is to be consolidated</param>
/// <param name="size">The size value for the consolidator (e.g., brick size, range size or maxCount)</param>
/// <param name="tickType">The tick type to consolidate. If null, the first matching subscription is used.</param>
/// <param name="handler">The callback to invoke with each new consolidated bar</param>
/// <returns>The created and registered <see cref="IDataConsolidator"/> instance</returns>
[DocumentationAttribute(ConsolidatingData)]
public IDataConsolidator Consolidate(PyObject type, Symbol symbol, decimal size, TickType? tickType, PyObject handler)
{
var convertedType = type.CreateType();
if (convertedType == typeof(RenkoBar))
{
// size will be used as barSize
return Consolidate(symbol, size, tickType, handler.SafeAs<Action<RenkoBar>>());
}
else if (convertedType == typeof(VolumeRenkoBar))
{
// size will be used as barSize
return Consolidate(symbol, size, tickType, handler.SafeAs<Action<VolumeRenkoBar>>());
}
else if (convertedType == typeof(RangeBar))
{
// size will be used as rangeSize
return Consolidate(symbol, (int)size, tickType, handler.SafeAs<Action<RangeBar>>());
}
else if (convertedType == typeof(TradeBar))
{
// size will be used as maxCount
return Consolidate(symbol, (int)size, tickType, handler.SafeAs<Action<TradeBar>>());
}
else if (convertedType == typeof(QuoteBar))
{
// size will be used as maxCount
return Consolidate(symbol, (int)size, tickType, handler.SafeAs<Action<QuoteBar>>());
}
else
{
// size will be used as maxCount
return Consolidate(symbol, (int)size, tickType, handler.SafeAs<Action<BaseData>>());
}
}
/// <summary>
/// Registers the <paramref name="handler"/> to receive consolidated data for the specified symbol
/// </summary>
/// <param name="symbol">The symbol who's data is to be consolidated</param>
/// <param name="period">The consolidation period</param>
/// <param name="handler">Data handler receives new consolidated data when generated</param>
/// <returns>A new consolidator matching the requested parameters with the handler already registered</returns>
[DocumentationAttribute(ConsolidatingData)]
public IDataConsolidator Consolidate(Symbol symbol, Resolution period, PyObject handler)
{
return Consolidate(symbol, period, null, handler);
}
/// <summary>
/// Registers the <paramref name="handler"/> to receive consolidated data for the specified symbol
/// </summary>
/// <param name="symbol">The symbol who's data is to be consolidated</param>
/// <param name="period">The consolidation period</param>
/// <param name="tickType">The tick type of subscription used as data source for consolidator. Specify null to use first subscription found.</param>
/// <param name="handler">Data handler receives new consolidated data when generated</param>
/// <returns>A new consolidator matching the requested parameters with the handler already registered</returns>
[DocumentationAttribute(ConsolidatingData)]
public IDataConsolidator Consolidate(Symbol symbol, Resolution period, TickType? tickType, PyObject handler)
{
// resolve consolidator input subscription
var type = GetSubscription(symbol, tickType).Type;
if (type == typeof(TradeBar))
{
return Consolidate(symbol, period, tickType, handler.SafeAs<Action<TradeBar>>());
}
if (type == typeof(QuoteBar))
{
return Consolidate(symbol, period, tickType, handler.SafeAs<Action<QuoteBar>>());
}
return Consolidate(symbol, period, tickType, handler.SafeAs<Action<BaseData>>());
}
/// <summary>
/// Registers the <paramref name="handler"/> to receive consolidated data for the specified symbol
/// </summary>
/// <param name="symbol">The symbol who's data is to be consolidated</param>
/// <param name="period">The consolidation period</param>
/// <param name="handler">Data handler receives new consolidated data when generated</param>
/// <returns>A new consolidator matching the requested parameters with the handler already registered</returns>
[DocumentationAttribute(ConsolidatingData)]
public IDataConsolidator Consolidate(Symbol symbol, TimeSpan period, PyObject handler)
{
return Consolidate(symbol, period, null, handler);
}
/// <summary>
/// Registers the <paramref name="handler"/> to receive consolidated data for the specified symbol
/// </summary>
/// <param name="symbol">The symbol who's data is to be consolidated</param>
/// <param name="period">The consolidation period</param>
/// <param name="tickType">The tick type of subscription used as data source for consolidator. Specify null to use first subscription found.</param>
/// <param name="handler">Data handler receives new consolidated data when generated</param>
/// <returns>A new consolidator matching the requested parameters with the handler already registered</returns>
[DocumentationAttribute(ConsolidatingData)]
public IDataConsolidator Consolidate(Symbol symbol, TimeSpan period, TickType? tickType, PyObject handler)
{
// resolve consolidator input subscription
var type = GetSubscription(symbol, tickType).Type;
if (type == typeof(TradeBar))
{
return Consolidate(symbol, period, tickType, handler.SafeAs<Action<TradeBar>>());
}
if (type == typeof(QuoteBar))
{
return Consolidate(symbol, period, tickType, handler.SafeAs<Action<QuoteBar>>());
}
return Consolidate(symbol, period, tickType, handler.SafeAs<Action<BaseData>>());
}
/// <summary>
/// Registers the <paramref name="handler"/> to receive consolidated data for the specified symbol
/// </summary>
/// <param name="symbol">The symbol who's data is to be consolidated</param>
/// <param name="calendar">The consolidation calendar</param>
/// <param name="handler">Data handler receives new consolidated data when generated</param>
/// <returns>A new consolidator matching the requested parameters with the handler already registered</returns>
[DocumentationAttribute(ConsolidatingData)]
public IDataConsolidator Consolidate(Symbol symbol, Func<DateTime, CalendarInfo> calendar, PyObject handler)
{
return Consolidate(symbol, calendar, null, handler);
}
/// <summary>
/// Schedules the provided training code to execute immediately
/// </summary>
/// <param name="trainingCode">The training code to be invoked</param>
[DocumentationAttribute(MachineLearning)]
[DocumentationAttribute(ScheduledEvents)]
public ScheduledEvent Train(PyObject trainingCode)
{
return Schedule.TrainingNow(trainingCode);
}
/// <summary>
/// Schedules the training code to run using the specified date and time rules
/// </summary>
/// <param name="dateRule">Specifies what dates the event should run</param>
/// <param name="timeRule">Specifies the times on those dates the event should run</param>
/// <param name="trainingCode">The training code to be invoked</param>
[DocumentationAttribute(MachineLearning)]
[DocumentationAttribute(ScheduledEvents)]
public ScheduledEvent Train(IDateRule dateRule, ITimeRule timeRule, PyObject trainingCode)
{
return Schedule.Training(dateRule, timeRule, trainingCode);
}
/// <summary>
/// Registers the <paramref name="handler"/> to receive consolidated data for the specified symbol
/// </summary>
/// <param name="symbol">The symbol who's data is to be consolidated</param>
/// <param name="calendar">The consolidation calendar</param>
/// <param name="tickType">The tick type of subscription used as data source for consolidator. Specify null to use first subscription found.</param>
/// <param name="handler">Data handler receives new consolidated data when generated</param>
/// <returns>A new consolidator matching the requested parameters with the handler already registered</returns>
[DocumentationAttribute(ConsolidatingData)]
public IDataConsolidator Consolidate(Symbol symbol, Func<DateTime, CalendarInfo> calendar, TickType? tickType, PyObject handler)
{
// resolve consolidator input subscription
var type = GetSubscription(symbol, tickType).Type;
if (type == typeof(TradeBar))
{
return Consolidate(symbol, calendar, tickType, handler.SafeAs<Action<TradeBar>>());
}
if (type == typeof(QuoteBar))
{
return Consolidate(symbol, calendar, tickType, handler.SafeAs<Action<QuoteBar>>());
}
return Consolidate(symbol, calendar, tickType, handler.SafeAs<Action<BaseData>>());
}
/// <summary>
/// Gets the historical data of an indicator for the specified symbol. The exact number of bars will be returned.
/// The symbol must exist in the Securities collection.
/// </summary>
/// <param name="indicator">The target indicator</param>
/// <param name="symbol">The symbol or symbols to retrieve historical data for</param>
/// <param name="period">The number of bars to request</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="selector">Selects a value from the BaseData to send into the indicator, if null defaults to the Value property of BaseData (x => x.Value)</param>
/// <returns>pandas.DataFrame of historical data of an indicator</returns>
public IndicatorHistory IndicatorHistory(PyObject indicator, PyObject symbol, int period, Resolution? resolution = null, PyObject selector = null)
{
var symbols = symbol.ConvertToSymbolEnumerable();
var convertedIndicator = ConvertPythonIndicator(indicator);
switch (convertedIndicator)
{
case PythonIndicator pythonIndicator:
return IndicatorHistory(pythonIndicator, symbols, period, resolution, selector?.SafeAs<Func<IBaseData, IBaseData>>());
case IndicatorBase<IndicatorDataPoint> dataPointIndicator:
return IndicatorHistory(dataPointIndicator, symbols, period, resolution, selector?.SafeAs<Func<IBaseData, decimal>>());
case IndicatorBase<IBaseDataBar> baseDataBarIndicator:
return IndicatorHistory(baseDataBarIndicator, symbols, period, resolution, selector?.SafeAs<Func<IBaseData, IBaseDataBar>>());
case IndicatorBase<TradeBar> tradeBarIndicator:
return IndicatorHistory(tradeBarIndicator, symbols, period, resolution, selector?.SafeAs<Func<IBaseData, TradeBar>>());
case IndicatorBase<IBaseData> baseDataIndicator:
return IndicatorHistory(baseDataIndicator, symbols, period, resolution, selector?.SafeAs<Func<IBaseData, IBaseData>>());
case IndicatorBase<BaseData> baseDataIndicator:
return IndicatorHistory(baseDataIndicator, symbols, period, resolution, selector?.SafeAs<Func<IBaseData, BaseData>>());
default:
// Shouldn't happen, ConvertPythonIndicator will wrap the PyObject in a PythonIndicator instance if it can't convert it
throw new ArgumentException($"Indicator type {indicator.GetPythonType().Name} is not supported.");
}
}
/// <summary>
/// Gets the historical data of an indicator for the specified symbol. The exact number of bars will be returned.
/// The symbol must exist in the Securities collection.
/// </summary>
/// <param name="indicator">The target indicator</param>
/// <param name="symbol">The symbol or symbols to retrieve historical data for</param>
/// <param name="span">The span over which to retrieve recent historical data</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="selector">Selects a value from the BaseData to send into the indicator, if null defaults to the Value property of BaseData (x => x.Value)</param>
/// <returns>pandas.DataFrame of historical data of an indicator</returns>
public IndicatorHistory IndicatorHistory(PyObject indicator, PyObject symbol, TimeSpan span, Resolution? resolution = null, PyObject selector = null)
{
return IndicatorHistory(indicator, symbol, Time - span, Time, resolution, selector);
}
/// <summary>
/// Gets the historical data of an indicator for the specified symbol. The exact number of bars will be returned.
/// The symbol must exist in the Securities collection.
/// </summary>
/// <param name="indicator">The target indicator</param>
/// <param name="symbol">The symbol or symbols to retrieve historical data for</param>
/// <param name="start">The start time in the algorithm's time zone</param>
/// <param name="end">The end time in the algorithm's time zone</param>
/// <param name="resolution">The resolution to request</param>
/// <param name="selector">Selects a value from the BaseData to send into the indicator, if null defaults to the Value property of BaseData (x => x.Value)</param>
/// <returns>pandas.DataFrame of historical data of an indicator</returns>
public IndicatorHistory IndicatorHistory(PyObject indicator, PyObject symbol, DateTime start, DateTime end, Resolution? resolution = null, PyObject selector = null)
{
var symbols = symbol.ConvertToSymbolEnumerable();
var convertedIndicator = ConvertPythonIndicator(indicator);
switch (convertedIndicator)
{
case PythonIndicator pythonIndicator:
return IndicatorHistory(pythonIndicator, symbols, start, end, resolution, selector?.SafeAs<Func<IBaseData, IBaseData>>());
case IndicatorBase<IndicatorDataPoint> dataPointIndicator:
return IndicatorHistory(dataPointIndicator, symbols, start, end, resolution, selector?.SafeAs<Func<IBaseData, decimal>>());
case IndicatorBase<IBaseDataBar> baseDataBarIndicator:
return IndicatorHistory(baseDataBarIndicator, symbols, start, end, resolution, selector?.SafeAs<Func<IBaseData, IBaseDataBar>>());
case IndicatorBase<TradeBar> tradeBarIndicator:
return IndicatorHistory(tradeBarIndicator, symbols, start, end, resolution, selector?.SafeAs<Func<IBaseData, TradeBar>>());
case IndicatorBase<IBaseData> baseDataIndicator:
return IndicatorHistory(baseDataIndicator, symbols, start, end, resolution, selector?.SafeAs<Func<IBaseData, IBaseData>>());
case IndicatorBase<BaseData> baseDataIndicator:
return IndicatorHistory(baseDataIndicator, symbols, start, end, resolution, selector?.SafeAs<Func<IBaseData, BaseData>>());
default:
// Shouldn't happen, ConvertPythonIndicator will wrap the PyObject in a PythonIndicator instance if it can't convert it
throw new ArgumentException($"Indicator type {indicator.GetPythonType().Name} is not supported.");
}
}
/// <summary>
/// Gets the historical data of an indicator and convert it into pandas.DataFrame
/// </summary>
/// <param name="indicator">The target indicator</param>
/// <param name="history">Historical data used to calculate the indicator</param>
/// <param name="selector">Selects a value from the BaseData to send into the indicator, if null defaults to the Value property of BaseData (x => x.Value)</param>
/// <returns>pandas.DataFrame containing the historical data of <paramref name="indicator"/></returns>
public IndicatorHistory IndicatorHistory(PyObject indicator, IEnumerable<Slice> history, PyObject selector = null)
{
var convertedIndicator = ConvertPythonIndicator(indicator);
switch (convertedIndicator)
{
case PythonIndicator pythonIndicator:
return IndicatorHistory(pythonIndicator, history, selector?.SafeAs<Func<IBaseData, IBaseData>>());
case IndicatorBase<IndicatorDataPoint> dataPointIndicator:
return IndicatorHistory(dataPointIndicator, history, selector?.SafeAs<Func<IBaseData, decimal>>());
case IndicatorBase<IBaseDataBar> baseDataBarIndicator:
return IndicatorHistory(baseDataBarIndicator, history, selector?.SafeAs<Func<IBaseData, IBaseDataBar>>());
case IndicatorBase<TradeBar> tradeBarIndicator:
return IndicatorHistory(tradeBarIndicator, history, selector?.SafeAs<Func<IBaseData, TradeBar>>());
case IndicatorBase<IBaseData> baseDataIndicator:
return IndicatorHistory(baseDataIndicator, history, selector?.SafeAs<Func<IBaseData, IBaseData>>());
case IndicatorBase<BaseData> baseDataIndicator:
return IndicatorHistory(baseDataIndicator, history, selector?.SafeAs<Func<IBaseData, BaseData>>());
default:
// Shouldn't happen, ConvertPythonIndicator will wrap the PyObject in a PythonIndicator instance if it can't convert it
throw new ArgumentException($"Indicator type {indicator.GetPythonType().Name} is not supported.");
}
}
/// <summary>
/// Liquidate your portfolio holdings
/// </summary>
/// <param name="symbols">List of symbols to liquidate in Python</param>
/// <param name="asynchronous">Flag to indicate if the symbols should be liquidated asynchronously</param>
/// <param name="tag">Custom tag to know who is calling this</param>
/// <param name="orderProperties">Order properties to use</param>
[DocumentationAttribute(TradingAndOrders)]
public List<OrderTicket> Liquidate(PyObject symbols, bool asynchronous = false, string tag = "Liquidated", IOrderProperties orderProperties = null)
{
return Liquidate(symbols.ConvertToSymbolEnumerable(), asynchronous, tag, orderProperties);
}
/// <summary>
/// Register a command type to be used
/// </summary>
/// <param name="type">The command type</param>
public void AddCommand(PyObject type)
{
// create a test instance to validate interface is implemented accurate
var testInstance = new CommandPythonWrapper(type);
var wrappedType = Extensions.CreateType(type);
_registeredCommands[wrappedType.Name] = (CallbackCommand command) =>
{
var commandWrapper = new CommandPythonWrapper(type, command.Payload);
return commandWrapper.Run(this);
};
}
/// <summary>
/// Get the option chains for the specified symbols at the current time (<see cref="Time"/>)
/// </summary>
/// <param name="symbols">
/// The symbols for which the option chain is asked for.
/// It can be either the canonical options or the underlying symbols.
/// </param>
/// <param name="flatten">
/// Whether to flatten the resulting data frame.
/// See <see cref="History(PyObject, int, Resolution?, bool?, bool?, DataMappingMode?, DataNormalizationMode?, int?, bool)"/>
/// </param>
/// <returns>The option chains</returns>
[DocumentationAttribute(AddingData)]
public OptionChains OptionChains(PyObject symbols, bool flatten = false)
{
return OptionChains(symbols.ConvertToSymbolEnumerable(), flatten);
}
/// <summary>
/// Get an authenticated link to execute the given command instance
/// </summary>
/// <param name="command">The target command</param>
/// <returns>The authenticated link</returns>
public string Link(PyObject command)
{
var payload = ConvertCommandToPayload(command, out var typeName);
return CommandLink(typeName, payload);
}
/// <summary>
/// Broadcast a live command
/// </summary>
/// <param name="command">The target command</param>
/// <returns><see cref="RestResponse"/></returns>
public RestResponse BroadcastCommand(PyObject command)
{
var payload = ConvertCommandToPayload(command, out var typeName);
return SendBroadcast(typeName, payload);
}
/// <summary>
/// Convert the command to a dictionary payload
/// </summary>
/// <param name="command">The target command</param>
/// <param name="typeName">The type of the command</param>
/// <returns>The dictionary payload</returns>
private Dictionary<string, object> ConvertCommandToPayload(PyObject command, out string typeName)
{
using var _ = Py.GIL();
var strResult = CommandPythonWrapper.Serialize(command);
using var pyType = command.GetPythonType();
typeName = Extensions.CreateType(pyType).Name;
return JsonConvert.DeserializeObject<Dictionary<string, object>>(strResult);
}
/// <summary>
/// Gets indicator base type
/// </summary>
/// <param name="type">Indicator type</param>
/// <returns>Indicator base type</returns>
private Type GetIndicatorBaseType(Type type)
{
if (type.BaseType == typeof(object))
{
return type;
}
return GetIndicatorBaseType(type.BaseType);
}
/// <summary>
/// Converts the sequence of PyObject objects into an array of dynamic objects that represent indicators of the same type
/// </summary>
/// <returns>Array of dynamic objects with indicator</returns>
private dynamic[] GetIndicatorArray(PyObject first, PyObject second = null, PyObject third = null, PyObject fourth = null)
{
using (Py.GIL())
{
var array = new[] { first, second, third, fourth }
.Select(
x =>
{
if (x == null) return null;
Type type;
return x.GetPythonType().TryConvert(out type)
? x.AsManagedObject(type)
: WrapPythonIndicator(x);
}
).ToArray();
var types = array.Where(x => x != null).Select(x => GetIndicatorBaseType(x.GetType())).Distinct();
if (types.Count() > 1)
{
throw new Exception("QCAlgorithm.GetIndicatorArray(). All indicators must be of the same type: data point, bar or tradebar.");
}
return array;
}
}
/// <summary>
/// Converts the given PyObject into an indicator
/// </summary>
private IndicatorBase ConvertPythonIndicator(PyObject pyIndicator)
{
IndicatorBase convertedIndicator;
if (pyIndicator.TryConvert(out PythonIndicator pythonIndicator))
{
convertedIndicator = WrapPythonIndicator(pyIndicator, pythonIndicator);
}
else if (!pyIndicator.TryConvert(out convertedIndicator))
{
convertedIndicator = WrapPythonIndicator(pyIndicator);
}
return convertedIndicator;
}
/// <summary>
/// Wraps a custom python indicator and save its reference to _pythonIndicators dictionary
/// </summary>
/// <param name="pyObject">The python implementation of <see cref="IndicatorBase{IBaseDataBar}"/></param>
/// <param name="convertedPythonIndicator">The C# converted <paramref name="pyObject"/> to avoid re-conversion</param>
/// <returns><see cref="PythonIndicator"/> that wraps the python implementation</returns>
private PythonIndicator WrapPythonIndicator(PyObject pyObject, PythonIndicator convertedPythonIndicator = null)
{
PythonIndicator pythonIndicator;
if (!_pythonIndicators.TryGetValue(pyObject.Handle, out pythonIndicator))
{
if (convertedPythonIndicator == null)
{
pyObject.TryConvert(out pythonIndicator);
}
else
{
pythonIndicator = convertedPythonIndicator;
}
if (pythonIndicator == null)
{
pythonIndicator = new PythonIndicator(pyObject);
}
else
{
pythonIndicator.SetIndicator(pyObject);
}
// Save to prevent future additions
_pythonIndicators.Add(pyObject.Handle, pythonIndicator);
}
return pythonIndicator;
}
/// <summary>
/// Converts an enumerable of Slice into a Python Pandas data frame
/// </summary>
protected PyObject GetDataFrame(IEnumerable<Slice> data, bool flatten, Type dataType = null)
{
var history = PandasConverter.GetDataFrame(RemoveMemoizing(data), flatten, dataType);
return flatten ? history : TryCleanupCollectionDataFrame(dataType, history);
}
/// <summary>
/// Converts an enumerable of BaseData into a Python Pandas data frame
/// </summary>
protected PyObject GetDataFrame<T>(IEnumerable<T> data, bool flatten)
where T : IBaseData
{
var history = PandasConverter.GetDataFrame(RemoveMemoizing(data), flatten: flatten);
return flatten ? history : TryCleanupCollectionDataFrame(typeof(T), history);
}
private IEnumerable<T> RemoveMemoizing<T>(IEnumerable<T> data)
{
var memoizingEnumerable = data as MemoizingEnumerable<T>;
if (memoizingEnumerable != null)
{
// we don't need the internal buffer which will just generate garbage, so we disable it
// the user will only have access to the final pandas data frame object
memoizingEnumerable.Enabled = false;
}
return data;
}
private PyObject TryCleanupCollectionDataFrame(Type dataType, PyObject history)
{
if (dataType != null && dataType.IsAssignableTo(typeof(BaseDataCollection)))
{
// clear out the first symbol level since it doesn't make sense, it's the universe generic symbol
// let's directly return the data property which is where all the data points are in a BaseDataCollection, save the user some pain
dynamic dynamic = history;
using (Py.GIL())
{
if (!dynamic.empty)
{
using var columns = new PySequence(dynamic.columns);
using var dataKey = "data".ToPython();
if (columns.Contains(dataKey))
{
history = dynamic["data"];
}
else
{
dynamic.index = dynamic.index.droplevel("symbol");
history = dynamic;
}
}
}
}
return history;
}
}
}
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