- Adding _some_ of the missing PyObject.Dispose calls. In the cases
where C# is calling the Python side.
- Note that Python calls to C# code is correctly handling the
disposure of resources.
From [pandas-docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html):
> The current default of sorting is deprecated and will change to not-sorting in a future version of pandas.
>
> Explicitly pass sort=True to silence the warning and sort. > Explicitly pass sort=False to silence the warning and not sort.
In the new package:
- C# decimal conversion will use C# double and python float due to the big performance impact of converting C# decimal to python decimal;
- Removing OrderFeeParameters.AccountCurrency. Where required replacing
for constructor parameter defaulting to USD.
- Updating IB fee model to use to correct fee currency
- Removing `AccountCurrency` from `Cash` and `Brokerage` classes.
`ICurrencyConverter` will now provide the `AccountCurrency`
- Adding new static `OrderFee.Zero` which will return a 0 order fee in
`NullCurrency`
- Adding static `Currencies.USD` value, replacing all "USD".
- Addin new static `Currencies.NullCurrency`
- Updating Bitfinex `FeeModel` so it return fees in quote currency.
Adding unit tests
- Refactoring `IFeeModel`. *This is a breaking change* for implementations
inheriting directly from the interface. Deleting old and adding a new method
`OrderFee GetOrderFee(OrderFeeParameters parameters)` that will use a parameter
and a result object.
- Refactoring `CashAmount` so it does not embed a `ICurrencyConverter`
instance.
- Updating unit tests
- The `Security.QuoteCurrency`, a `Cash` instance, will provide access
to the `AccountCurrency` as a property.
- Will maintain backwards compatibility with old python custom
FeeModels, Adding unit test.
> Note that for now, consumers will ignore the currency, as before, and
directly consume the amount
- Modifying `IFillModel` interface removing old methods and adding new
method `Fill Fill(FillModelParameters)`. This is a breaking change.
- Adding new `PythonWrapper` property for the `FillModel` base class.
This is required due to a limitation in PythonNet:
- Given C# class T has `virtual` methods A and B. Where method A
calls method B. And given custom python class L inherits class T.
And overrides method B. When class L calls
base method A (of class T). And when method A internally calls method B.
It will call C# implementation, not the python override. This issue
is solved going back to the `PythonWrapper`. Adding unit tests.
- Adding new `Parameters` property for the `FillModel` base class that will
be set by the call to `Fill()`. The `Parameters` property will be used by
the modified `XxxxFill()` implementations
- Adding new `Fill` result object for the `Fill(FillModelParameters)`
method
- Adding new check before removing a `SubscriptionDataConfig` due to the FillModels consuming the configuration collection when determining which Price to use. WIll now only remove the `SDC` if the symbol was removed from the selecting `universe`, this will avoid the case where the symbol is never deselected and the subscription ends, which happens at the end of all executions.
- Adding unit tests showcasing retro compatibility.
- Enabling C# `CustomModelsAlgorithm` as a regression test. Python
version returns a different result due to random number generation.
- Removing usages of Security.Configuration properties from existing
Volatility Models, without modifying existing interfaces. Adding unit
tests for existing behavior
- Adding new BaseVolatilityModel, which will have a setter for a
ISubscriptionDataConfigProvider which will be called by the system.
Adding unit tests for this behavior.
- Adding new `SubscriptionDataConfigExtensions` static class which will
provide methods used to determine different configuration properties for
a given set of `SubscriptionDataConfigs`. The behavior was extracted
from current `Security` class behavior regarding the `SubscriptionBag`.
Adding unit tests covering exepected behavior here.
In preparation for sweeping changes that will require adding a currency converter
parameter to this method and augmenting the return value to be a CashAmount. This
ensure the future change won't produce a compile-time breaking change. An extension
method was also added as a shim to keep any existing code functional
In preparation for sweeping changes that will require adding a currency converter
parameter to this method and augmenting the return value to be a CashAmount. This
ensure the future change won't produce a compile-time breaking change. An extension
method was also added as a shim to keep any existing code functional
- Adds `MarginCallModelPythonWrapper` to wrap a python class that represents a margin call model.
- Adds `SetMarginCallModel` to enable the setting of custom margin call model.
- Adds `BuyingPowerModelPythonWrapper` to wrap a python class that represents a custom buying power model.
- Adds `SetBuyingPowerModel` and `SetMarginModel` to enable the seeting of custom buying power model.
Since Lean/QuantConnect data from a symbol can be found in Slice.Ticks, Slice.Bars and Slice.QuoteBars, information for all of this members must be used in order present all information in the pandas.DataFrame.
In order to access the custom data classes, the module containing them was added to the ObjectActivator. This was unnecessary if it wasn't a custom data algorithm.
Also, this operation would not be taken into account if the custom data class were defined after the algorithm was created: this is the case for QuantBook.
We refactor how custom data is handled: a new class was added to provide a instance creation factory that creates an instance of each python custom type.
With this constructor, SetConverter method, that was not respecting Lean pattern, is removed. The initialization of _converter field is done once the type is loaded.
Python algorithms with custom data requires an operation that converts a dictionary key into a attribute. In the current implementation the Slice object was converted into a python dictionary. This was not optimal, since we just need to make this conversion when the value of a key in the Slice is accessed.
This implementation proposes a wrapper for the Slice object, PythonSlice, that would just perform the operation described above when needed.
Creates a python wrapper for volatility models created in python algorithms and adds a method to the Security object to set such models.
Adds an algorithm to show how volatility models can be implemented.
In this PR we are disabling the default security seeding (automatically getting the last price for a security when added to the algorithm) for a couple reasons, both when using large universes:
- In live trading, these history requests are sent to a history server, potentially causing timeouts
- In backtesting, depending on the algorithm this could also cause slowdowns up to 30%
- We were excluding Exchange and Suspicious of quote ticks when they can be set with security types other than equity
- Custom data was not expecting non-numerical properties
- Moves PandasData class to its own file
- Refactors PandasData class to deal with list of tick
- Other minor changes requested by peer-review
- Adds unit test for PandasConverter's tick handling
Summary of members in python wrapper classes where pointing to other methods for reference when they should have a meaningful description to be used in documentation
Jared Broad