- moved Order and Blotter to zipline.finance.blotter
- moved order method from AlgoSimulator to Blotter
- eliminated the set_order method in algorithm
- moved blotter to the algorithm
Refactor PerformanceTracker, Blotter, and AlgorithmSimulator to
work with handling the end of a bar at the AlgorithmSimulator level
instead of within PerformanceTracker.
- PerforamnceTracker and Blotter are longer generators,
both provide functions to process events instead.
- AlgorithmSimulator calls each from within the loop running
over the data generator.
- Change test_perf_tracker utility to be compatible with change
away from PerformanceTracker as a generator.
Has the effect of:
- Fixing the timing of order emission.
- Allow minutely emission of benchmarks, which was prevented
by the extra grouping previously caused by Blotter.
Minutely emission also depends on work for streaming benchmarks
through performance and risk at a minute granularity.
- Create different benchmark containers in performance
depending on emission rate.
- Add a minute close method which updates algorithm and
benchmark returns, and calculates the risk metrics
depending on those methods.
- Provide fake 0.0 values for annualized metrics like
sharpe, sortino, and information, until we figure out
how they should be treated in the context of minutely
calculation.
*NOTE* This does not fully work without the changes to the
simulation loop by @fawce
Eventually should to either return None or remove
progress completely, but in the meantime, return a
constant of 1.0 for progress of minute emissions.
Also, factor out the daily calculation into a property
instead of calculating during process.
Instead of creating a set of perf messages for each event during minute
emission mode, only include the messages on the last event in the bar.
Should cut down on calculations/serialization as well, as work towards
doing more 'end of bar' logic for minute benchmarks.
To fix the grouping of events so that (dt, events) ordering
is preserved, the tracking of order states needs to change
in the following way.
Change how order keeps track of dates:
- Change order's dt field to reflect modified date.
- Add a created field.
Change how performance keeps track of orders by:
- Map dt to transactions
- Map dt to orders
- Map order ids to keep track of updated orders.
The emission of order updates from the blotter were incorrect,
and subsequently, performance.
Previously, only the first action of the order was emitted,
fix so that all status updates are emitted.
The use of np.allclose introduced a severe performance penalty,
caused by the creation of two `np.array`s for each check.
Instead create and use a similar check which maintains tolerance
to floating point rounding, but operates only on scalars.
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 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.
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.
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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.
To make the risk metrics being calculated more clear, change the
naming convention that ratios have a '_risk' suffix.
Also, fixes typo in beta docstring.
Move the risk metric definitions to functions at the module level
with defined parameters.
Both risk implementations call these functions, where the difference
between risk implementations is with which internal data they
send to the various risk metrics.
Metrics moved:
- Sharpe Ratio
- Sortino Ratio
- Information Ration
- Alpha
Following the lead of the RiskMetricsBatch conversion to use
more pandas and numpy.
Bringing the iterative and batch versions closer together as we
work towards folding them into one.
So that the environments' exchange time is used without having to
specify it independently.
Also, moves uses of Delorean.shift for the exchange conversion inside
of environment to use the exchange_dt_to_utc method.
Wires up performance tracker so that when `emission_rate` is set
to `minute`, the performance packets are sent out every minute,
instead of once per day.
Please note, the performance packets that are generated are not
ready for prime time consumption, this patch is merely a step towards
hooking up the ability to inspect minute data.
Known issues:
- The packets do not currently include risk information.
Since we need to consider how this affects the denominators
of the risk calculations.
fawce