-
Notifications
You must be signed in to change notification settings - Fork 4.7k
/
tracker.py
458 lines (383 loc) · 18.6 KB
/
tracker.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
# Copyright 2013 Quantopian, Inc.
#
# 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.
"""
Performance Tracking
====================
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| period_start | The beginning of the period to be tracked. datetime|
| | in pytz.utc timezone. Will always be 0:00 on the |
| | date in UTC. The fact that the time may be on the |
| | prior day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| period_end | The end of the period to be tracked. datetime |
| | in pytz.utc timezone. Will always be 23:59 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| progress | percentage of test completed |
+-----------------+----------------------------------------------------+
| capital_base | The initial capital assumed for this tracker. |
+-----------------+----------------------------------------------------+
| cumulative_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
+-----------------+----------------------------------------------------+
| todays_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker with datetime stamps between last_open|
| | and last_close. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
| | TODO: adding this because we calculate it. May be |
| | overkill. |
+-----------------+----------------------------------------------------+
| cumulative_risk | A dictionary representing the risk metrics |
| _metrics | calculated based on the positions aggregated |
| | through all the events delivered to this tracker. |
| | For details look at the comments for |
| | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
+-----------------+----------------------------------------------------+
"""
from __future__ import division
import logbook
import numpy as np
import pandas as pd
from pandas.tseries.tools import normalize_date
import zipline.protocol as zp
import zipline.finance.risk as risk
from zipline.finance import trading
from . period import PerformancePeriod
log = logbook.Logger('Performance')
class PerformanceTracker(object):
"""
Tracks the performance of the algorithm.
"""
def __init__(self, sim_params):
self.sim_params = sim_params
self.period_start = self.sim_params.period_start
self.period_end = self.sim_params.period_end
self.last_close = self.sim_params.last_close
first_day = self.sim_params.first_open
self.market_open, self.market_close = \
trading.environment.get_open_and_close(first_day)
self.total_days = self.sim_params.days_in_period
self.capital_base = self.sim_params.capital_base
self.emission_rate = sim_params.emission_rate
all_trading_days = trading.environment.trading_days
mask = ((all_trading_days >= normalize_date(self.period_start)) &
(all_trading_days <= normalize_date(self.period_end)))
self.trading_days = all_trading_days[mask]
self.dividend_frame = pd.DataFrame()
self._dividend_count = 0
self.perf_periods = []
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=self.trading_days)
self.intraday_risk_metrics = None
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
elif self.emission_rate == 'minute':
self.all_benchmark_returns = pd.Series(index=pd.date_range(
self.sim_params.first_open, self.sim_params.last_close,
freq='Min'))
self.intraday_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params,
returns_frequency='daily',
create_first_day_stats=True)
self.minute_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the cumulative period will be calculated over the
# entire test.
self.period_start,
self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumualtive period
serialize_positions=False
)
self.perf_periods.append(self.minute_performance)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the cumulative period will be calculated over the entire test.
self.period_start,
self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumualtive period
serialize_positions=False
)
self.perf_periods.append(self.cumulative_performance)
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
self.capital_base,
# the daily period will be calculated for the market day
self.market_open,
self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True
)
self.perf_periods.append(self.todays_performance)
self.saved_dt = self.period_start
self.returns = pd.Series(index=self.trading_days)
# one indexed so that we reach 100%
self.day_count = 0.0
self.txn_count = 0
self.event_count = 0
def __repr__(self):
return "%s(%r)" % (
self.__class__.__name__,
{'simulation parameters': self.sim_params})
@property
def progress(self):
if self.emission_rate == 'minute':
# Fake a value
return 1.0
elif self.emission_rate == 'daily':
return self.day_count / self.total_days
def set_date(self, date):
if self.emission_rate == 'minute':
self.saved_dt = date
self.todays_performance.period_close = self.saved_dt
def update_dividends(self, new_dividends):
"""
Update our dividend frame with new dividends. @new_dividends should be
a DataFrame with columns containing at least the entries in
zipline.protocol.DIVIDEND_FIELDS.
"""
# Mark each new dividend with a unique integer id. This ensures that
# we can differentiate dividends whose date/sid fields are otherwise
# identical.
new_dividends['id'] = np.arange(
self._dividend_count,
self._dividend_count + len(new_dividends),
)
self._dividend_count += len(new_dividends)
self.dividend_frame = pd.concat(
[self.dividend_frame, new_dividends]
).sort(['pay_date', 'ex_date']).set_index('id', drop=False)
def update_performance(self):
# calculate performance as of last trade
for perf_period in self.perf_periods:
perf_period.calculate_performance()
def get_portfolio(self):
self.update_performance()
return self.cumulative_performance.as_portfolio()
def to_dict(self, emission_type=None):
"""
Creates a dictionary representing the state of this tracker.
Returns a dict object of the form described in header comments.
"""
if not emission_type:
emission_type = self.emission_rate
_dict = {
'period_start': self.period_start,
'period_end': self.period_end,
'capital_base': self.capital_base,
'cumulative_perf': self.cumulative_performance.to_dict(),
'progress': self.progress,
'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict()
}
if emission_type == 'daily':
_dict.update({'daily_perf': self.todays_performance.to_dict()})
elif emission_type == 'minute':
_dict.update({
'intraday_risk_metrics': self.intraday_risk_metrics.to_dict(),
'minute_perf': self.todays_performance.to_dict(self.saved_dt)
})
return _dict
def process_event(self, event):
self.event_count += 1
if event.type == zp.DATASOURCE_TYPE.TRADE:
# update last sale
for perf_period in self.perf_periods:
perf_period.update_last_sale(event)
elif event.type == zp.DATASOURCE_TYPE.TRANSACTION:
# Trade simulation always follows a transaction with the
# TRADE event that was used to simulate it, so we don't
# check for end of day rollover messages here.
self.txn_count += 1
for perf_period in self.perf_periods:
perf_period.execute_transaction(event)
elif event.type == zp.DATASOURCE_TYPE.DIVIDEND:
log.info("Ignoring DIVIDEND event.")
elif event.type == zp.DATASOURCE_TYPE.SPLIT:
for perf_period in self.perf_periods:
perf_period.handle_split(event)
elif event.type == zp.DATASOURCE_TYPE.ORDER:
for perf_period in self.perf_periods:
perf_period.record_order(event)
elif event.type == zp.DATASOURCE_TYPE.COMMISSION:
for perf_period in self.perf_periods:
perf_period.handle_commission(event)
elif event.type == zp.DATASOURCE_TYPE.CUSTOM:
pass
elif event.type == zp.DATASOURCE_TYPE.BENCHMARK:
if (
self.sim_params.data_frequency == 'minute'
and
self.sim_params.emission_rate == 'daily'
):
# Minute data benchmarks should have a timestamp of market
# close, so that calculations are triggered at the right time.
# However, risk module uses midnight as the 'day'
# marker for returns, so adjust back to midgnight.
midnight = pd.tseries.tools.normalize_date(event.dt)
else:
midnight = event.dt
self.all_benchmark_returns[midnight] = event.returns
def check_upcoming_dividends(self, midnight_of_date_that_just_ended):
"""
Check if we currently own any stocks with dividends whose ex_date is
the next trading day. Track how much we should be payed on those
dividends' pay dates.
Then check if we are owed cash/stock for any dividends whose pay date
is the next trading day. Apply all such benefits, then recalculate
performance.
"""
if len(self.dividend_frame) == 0:
# We don't currently know about any dividends for this simulation
# period, so bail.
return
next_trading_day_idx = self.trading_days.get_loc(
midnight_of_date_that_just_ended,
) + 1
if next_trading_day_idx < len(self.trading_days):
next_trading_day = self.trading_days[next_trading_day_idx]
else:
# Bail if the next trading day is outside our trading range, since
# we won't simulate the next day.
return
# Dividends whose ex_date is the next trading day. We need to check if
# we own any of these stocks so we know to pay them out when the pay
# date comes.
ex_date_mask = (self.dividend_frame['ex_date'] == next_trading_day)
dividends_earnable = self.dividend_frame[ex_date_mask]
# Dividends whose pay date is the next trading day. If we held any of
# these stocks on midnight before the ex_date, we need to pay these out
# now.
pay_date_mask = (self.dividend_frame['pay_date'] == next_trading_day)
dividends_payable = self.dividend_frame[pay_date_mask]
for period in self.perf_periods:
# TODO_SS: There's no reason we should have to duplicate this
# computation, but we do it currently because each perf
# period maintains its own separate positiondict. We
# should eventually remove this duplication and give each
# period a (preferably read-only) DataFrame of positions.
if len(dividends_earnable):
period.earn_dividends(dividends_earnable)
if len(dividends_payable):
period.pay_dividends(dividends_payable)
def handle_minute_close(self, dt):
self.update_performance()
todays_date = normalize_date(dt)
minute_returns = self.minute_performance.returns
self.minute_performance.rollover()
# the intraday risk is calculated on top of minute performance
# returns for the bench and the algo
self.intraday_risk_metrics.update(dt,
minute_returns,
self.all_benchmark_returns[dt])
bench_since_open = \
self.intraday_risk_metrics.benchmark_cumulative_returns[dt]
self.cumulative_risk_metrics.update(todays_date,
self.todays_performance.returns,
bench_since_open)
# if this is the close, save the returns objects for cumulative risk
# calculations and update dividends for the next day.
if dt == self.market_close:
self.check_upcoming_dividends(todays_date)
self.returns[todays_date] = self.todays_performance.returns
def handle_intraday_market_close(self, new_mkt_open, new_mkt_close):
"""
Function called at market close only when emitting at minutely
frequency.
TODO_SS: Why dont' we call this if we're emitting at daily frequency
but running with a minutely datasource? Is that just not a
valid combination? If so, why do we draw a distinction between
emission rate and data frequency?
"""
# update_performance should have been called in handle_minute_close
# so it is not repeated here.
self.intraday_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params)
# increment the day counter before we move markers forward.
self.day_count += 1.0
self.market_open = new_mkt_open
self.market_close = new_mkt_close
def handle_market_close_daily(self):
"""
Function called after handle_data when running with daily emission
rate.
"""
self.update_performance()
completed_date = normalize_date(self.market_close)
# add the return results from today to the returns series
self.returns[completed_date] = self.todays_performance.returns
# update risk metrics for cumulative performance
self.cumulative_risk_metrics.update(
completed_date,
self.todays_performance.returns,
self.all_benchmark_returns[completed_date])
# increment the day counter before we move markers forward.
self.day_count += 1.0
# Take a snapshot of our current performance to return to the
# browser.
daily_update = self.to_dict()
# On the last day of the test, don't create tomorrow's performance
# period. We may not be able to find the next trading day if we're at
# the end of our historical data
if self.market_close >= self.last_close:
return daily_update
# move the market day markers forward
self.market_open, self.market_close = \
trading.environment.next_open_and_close(self.market_open)
# Roll over positions to current day.
self.todays_performance.rollover()
self.todays_performance.period_open = self.market_open
self.todays_performance.period_close = self.market_close
self.check_upcoming_dividends(completed_date)
return daily_update
def handle_simulation_end(self):
"""
When the simulation is complete, run the full period risk report
and send it out on the results socket.
"""
log_msg = "Simulated {n} trading days out of {m}."
log.info(log_msg.format(n=int(self.day_count), m=self.total_days))
log.info("first open: {d}".format(
d=self.sim_params.first_open))
log.info("last close: {d}".format(
d=self.sim_params.last_close))
bms = self.cumulative_risk_metrics.benchmark_returns
ars = self.cumulative_risk_metrics.algorithm_returns
self.risk_report = risk.RiskReport(
ars,
self.sim_params,
benchmark_returns=bms)
risk_dict = self.risk_report.to_dict()
return risk_dict