* Improve information tracked in regression's {algorithm}.{lang}.details.log
The details.log file aims at providing a diff-able document that quickly and
easily provides actionable information. Since many regression algorithms use
the algorithm's debug/error messaging facilities to log various pieces of algo
state. This document also support a configuration option: regression-high-fidelity-logging'
that logs EVERY piece of data, again, with the aim of providing an easily diff-able
documenbt to quickly highlight actionable information. I may have missed omse key
pieces of information here, but now that the entire QC knows about this regression
tool, if additional information is required then hopefully it's easy enough at this
point to extend the RegressionResultHandler to suit our needs.
The RegressionResultHandler was initially implemented to provide a concise log of
all orders. This was achieved by simply using the Order.ToString method. While
testing/investigating OptionExerciseOrder behavior, it became evident that more
information was required to properly identify the source of potential failures or
differences between previous regression test runs. This change adds logging for
almost every IResultHandler method and additionally attempts to capture the
actual portfolio impact of every OrderEvent. This is accomplished by logging
the portfolio's TotalPortfolioValue, Cash properties and the security's
SecurityHolding.Quantity property.
This change also standardizes the timestamps used to folloow the ISO-8601 format.
When using the RegressionResultHandler, it is highly recommeded to also disable
'forward-console-message' configuration option to ensure algorithm Debug/Error
message logging is done synchronously to ensure correct ordering with respect to
log messages via Log.Debug/Trace/Error.
* Fix typo in options OrderTests test case name
* Update SymbolRepresentation.GenerationOptionTickerOSI to extension method
Far more convenient as an extension method
* Improve R# default code formatting rules
Many of these rule changes focus on improving the readability of code,
with a particular emphasis on multi-line constructs, chained method calls
and multi-line method invocations/declarations.
* Add braces, use string interpolation and limit long lines
* Refactor OptionExerciseOrder.Quantity to indicate change in #contracts
For all other order types, the Order.Quantity indicates the change in the algorithm's
holdings upon order execution for the order's symbol. For OptionExerciseOrder, this
convention was broken. It appears as though only exercise was initially implemented,
in which case only long positions were supported and a code comment indicated that
only positive values of quantity were acceptable, indicating the number of contracts
to exercise. At a later date, assignment simulation was added and utilized a negative
order quantity. This caused some major inconsistencies in how models view exercise
orders compared to all other order types. This change brings OptionExerciseOrder.Quantity
into alignment with the other order types by making it represent the change in holdings
quantity upon order execution.
This change was originally going to be much larger, but in order to minimize risks and to
make for an easier review experience, the additional changes will be committed separately
and pushed in their own PR. Some of the issues identified include:
* Manual Exercise (especially for OTM) is not covered
* Margin Calculations (in particular taking into account opposing contracts held)
* IBrokerage.OptionPositionAssigned is raised for exercise (later filtered by tx handler)
Fixes OptionPortfolioModelTests to use exercise model to properly model exercise of
non-account quote currency option contract.
* Add OrderRight.GetExerciseDirection(isShort) extension
Returns the OrderDirection resulting from exercise/assignment of a particular
option right
See: BUG #4731
* Fix option exercise/assignment order tags and order event messages
The algorithm manager was doing work to determine whether or not the option ended
in exercise or assignment at expiration. This decision should be left for the exercise
model to decide -- from the algorithm manager's perspective, all that matters is that
the option was expired. The DefaultExerciseModel was updated to properly track whether
the option expired with automatic assignment or exercise, dependending on whether or
not we wrote or bought the option (held liability or right, respectively). Updated unit
tests to check for order event counts and order event messages for option exercise cases.
Fixes: #4731
* Fix typo in algorithm documentation
* Update regression tests order hash
Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
* Support List and OptionFilterUniverse for Py filter
* Regression algorithm for testing
* Unit Tests
* Fix for process
* Tighten filters to reduce load on automated testing
* Address review v2
* DataConsolidator Wrapper for Python Consolidators
* Regression Unit Test
* Refactor Regression test
* Bad test fix
* pre review
* self review
* Add RegisterIndicator for Python Consolidator
* Python base class for consolidators
* Modify regression algo to register indicator
* unit test - attach event
* Test fix
* Fix test python imports
* Add license header file and null check
Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
* When a BaseData instance has Nullable fields, the number of data
points per Series is inconsistent, and results in a Series with
a length different from the other Series we produce, resulting
in an error "ValueError: cannot handle a non-unique multi-index!"
when we were constructing the final DataFrame.
Methods: 'diff', 'div', 'divide', 'drop', 'drop_duplicates', 'droplevel', 'dropna', 'dtypes', 'duplicated'
`BackwardsCompatibilityDataFrame_binary_operator` replaces `BackwardsCompatibilityDataFrame_add` to handle all operations (more to be added in future commits)
`Remapper.__getitem__` was not returns a `Remapper` object when the result was `pandas.DataFrame`. It is needed for a sequence of `.loc.` calls.
Refactors `Remapper._self_mapper` to handle tuples where the key can be found in both first and second position.
Update unit tests that should test `Symbol` object as key, but were using `str(Symbol)`.
- AlgorithmPythonWrapper will directly call base OnFrameworkData()
implementation skipping going through python and it's overhead
- Small performance improvement for adding Tick data points into a Ticks
collection
- For python always wrap slice with PythonSlice, so that slice.Get()
works even when no custom data is present, adding test.
- Remove unrequired `GetBuyingPower`
- Making `BuyingPowerModel.GetMaintenanceMarginRequirement` protected
instead of public
- Adding `GetMaximumOrderQuantityForDeltaBuyingPower` to replace
public `GetMaintenanceMarginRequirement` and improve API experience for
consumers like the `DefaultMarginCallModel`
- Adding new unit tests
- Add support for Symbol key access for pandas ix and iloc results
- Wrapp pdf merge, join, concat method results
- Wrapp pandas.concat method result with `Remapper`
- Adding unit tests
- Use `/usr/share/nltk_data` instead of `/root/nltk_data`.
- Adds test for NLTK.
- Tidies the root directory of the docker image
- Adds support to mlfinlab
- Adding `SecurityCacheProvider` this class allows for two different
`Security` to share the same data type cache through different instance
of `SecurityCache`. This is used to directly access custom data types
through their underlying in a peformant maner
- Some small improvements
- Covers another level on inheritance of Market Data by using `Type.IsAssignableFrom`
- Caches the list of `MethodInfo` for custom data types to avoid redefining that list.
When custom data classes inherited from market data classes such as `TradeBar`, it created duplicate entries. Therefore, we need to exclude the common properties in the private field `PandasData._members`.
Adds IRegisteredSecurityDataTypesProvider to track all the data types
registered in the algorithm. Using this data, we can detect if it's
possible that we'll eventually have a property of a certain type name.
For example, consider I wish to use security.Data.TradeBar but we haven't
received any trade bars yet. Before this change a KeyNotFoundException
would be raised, but since we can determine that we expect to have trade
bars, we can detect this and return an empty list when we haven't received
any data yet. This also removes the need to constantly do a HasData<T>()
check before accessing the dynamic members.
Closes#3620
* Deleted regression algorithms because they tested behavior similar to
other existing regression algorithms
* Fixed new bug in regression algorithm due to AddData changes
* Added unit tests for wrapt version and package existence
* Fix issue where data would be set to raw normalization mode
Michael Handschuh