eigen vales, covariance, etc. are not calculated until the first
return is passed through, so initialize this values to None, so that
`repr` and its ilk work on a freshly created `RiskMetricsIterative`
object.
The bar timestamps for day start and finish, for NYSE traded stocks,
should be 9:31 AM EST to 4:00 PM EST, for a total of 390 minutes.
Fix starting at 9:30 AM and the creation of 391 bars.
Instead of creating a list of benchmarks in the risk module,
stream benchmarks through the system as events, starting from the
algorithm generator.
Works towards more easily setting arbritrary pricing data as
a a benchmark, as well as working towards live minutely benchmarks.
- Add transaction and order types
- Move TransactionSimulator from trading.py to tradesimulation.py
(only used by other members of the tradesimulation module)
- Make Transaction an independent event, like dividend
- Add Blotter class.
- Flatten the transaction events to be independent of trade bar events
- Make orders into events that reach performance (need to add
handling)
- Issue IDs to orders and tracking each transaction's order id.
- Make volume share slippage fill orders independently, rather than
aggregating them into a single transaction.
- Perf tracker holds orders, serializes them with transactions.
- Order state defined and maintained by order class.
- Minutely emission of orders based on last_modified date.
Also, fix double emission of performance results with the last minute.
Change the perf tracker unit tests so that it doesn't rely on an
'extra' event triggering emission.
Unlike daily, minute emission now emits at the end of the bar in
the PerformanceTracker.transform instead of waiting for the next event.
During minute emissions, it is still helpful to have a final daily
performance result, analogous to what would be the final packet in
a daily emitted backtest, so that all transactions, etc. are contained
in one place.
Prevent an extra performance result with the timestamp of the midnight of
the day from being emitted.
Fix by setting the `saved_dt` value with the dt of the first event,
before entering into the main performance loop, otherwise a performance
result with a midnight timestamp and data from just the first event is
emitted.
The `test_multi_source_as_input_no_start_end` test is designed
to test for a lack of simulation parameters, so make that they are
not passed.
Was unnoticed because sim_params passed as an arg are dropped.
The start and end of the simulation parameters should be 'normalized'
i.e. midnight timestamped.
However, the algorithm tests were using the timestamp of the
first and last trade, which were in market times,
i.e. 9:30 AM and 4:00 PM EST.
Fix passing the sim_params that is used to create the trade_history,
instead of having the sim_params inferred from the source.
(Also may want to consider fixing the logic that infers the date
range from the sources provided.)
Also, add a `num_days` option to `factory.create_simulation_parameters`
so that the a date range that covers the desired number of days is covered.
Since the default sim_params were covering a year, while the test only
supplies 4 values, causing an alignment issue with the record test,
since a years worth of results were returned, but there were only 4 events.
The cost value should always be a float.
The per share calculation shouldn't be changed dramatically by being
a float, (except for potential rounding errors), but change to so
that PerShare and PerTrade are aligned.
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When the cost basis was set to an integer the division in the
calculation would floor down to the nearest integer.
Ensuring that the number is a float during PerTrade's init
will ensure that the calculation doesn't use integer division.
Do the conversion to float in init rather than calculate, so that
calling the builtin `float` is not added to any inner loops.
As currently implemented, progress doesn't currently make sense with
minutely results. Dropping the field from the results so should help
reduce some noise.
Set the default end date to current date, so that trading on
'fresh' data is the default case.
Set the default begin date at 1/1/1990, since that is when the
treasury benchmark data is first available.
The intraday performance results were emitting all transactions
for the entire day up to that point, instead of the desired transaction
list for the current timestamp.
Add a `dt` parameter to the `to_dict` method of PerformancePeriod so
that the transactions are limited to a specific datetime.
When the parameter is `None`, a todays_performance object will
function as previously with returning all transactions for the day.
# Please enter the commit message for your changes. Lines starting
The batch_transform decorator wipes out the doc string of the function
it wraps. Decorate the creator with functools.wraps to preserve function
metadata.
The indexing into performance results during the simulation loop fails
when emitting minutely since 'daily_perf' only exists on daily performance
results, not the minutely results.
Fix by making the key used to index into performance results depend
on the emission rate.
Intent is to both make the inputs to the treasury curve logic
more clear/explicit, as well as reducing the responsibilities
of the RiskMetrics classes.
Functions to move:
- search_day_distance
- get_treasury_rate
- choose_treasury
Also add new function:
- select_treasury_duration, split out of choose_treasury
So that calculations that leverage the range of the treasury_curves,
like `pd.Series.searchsorted` will not overshoot the 'end' of the
range we are calculating risk metrics.
The treasury_duration member in RiskMetrics is never used except
for in unit tests.
Remove the saving of treasury_duration in preparation for the
move of the choose_treasury method out of the RiskMetrics classes.
Down the line, if we do restore the sanving of treasury_duration,
choose_treasury can return a tuple that includes treasury_duration
instead of just returning the rate.
Eddie Hebert