-
Notifications
You must be signed in to change notification settings - Fork 4.6k
/
metric.py
743 lines (638 loc) · 24 KB
/
metric.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
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
#
# Copyright 2018 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.
import datetime
from functools import partial
import operator as op
from dateutil.relativedelta import relativedelta
import empyrical as ep
import numpy as np
import pandas as pd
from six import iteritems
from zipline.utils.exploding_object import NamedExplodingObject
from zipline.finance._finance_ext import minute_annual_volatility
class SimpleLedgerField(object):
"""Emit the current value of a ledger field every bar or every session.
Parameters
----------
ledger_field : str
The ledger field to read.
packet_field : str, optional
The name of the field to populate in the packet. If not provided,
``ledger_field`` will be used.
"""
def __init__(self, ledger_field, packet_field=None):
self._get_ledger_field = op.attrgetter(ledger_field)
if packet_field is None:
self._packet_field = ledger_field.rsplit('.', 1)[-1]
else:
self._packet_field = packet_field
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf'][self._packet_field] = self._get_ledger_field(
ledger,
)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
packet['daily_perf'][self._packet_field] = self._get_ledger_field(
ledger,
)
class DailyLedgerField(object):
"""Like :class:`~zipline.finance.metrics.metric.SimpleLedgerField` but
also puts the current value in the ``cumulative_perf`` section.
Parameters
----------
ledger_field : str
The ledger field to read.
packet_field : str, optional
The name of the field to populate in the packet. If not provided,
``ledger_field`` will be used.
"""
def __init__(self, ledger_field, packet_field=None):
self._get_ledger_field = op.attrgetter(ledger_field)
if packet_field is None:
self._packet_field = ledger_field.rsplit('.', 1)[-1]
else:
self._packet_field = packet_field
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
field = self._packet_field
packet['cumulative_perf'][field] = packet['minute_perf'][field] = (
self._get_ledger_field(ledger)
)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
field = self._packet_field
packet['cumulative_perf'][field] = packet['daily_perf'][field] = (
self._get_ledger_field(ledger)
)
class StartOfPeriodLedgerField(object):
"""Keep track of the value of a ledger field at the start of the period.
Parameters
----------
ledger_field : str
The ledger field to read.
packet_field : str, optional
The name of the field to populate in the packet. If not provided,
``ledger_field`` will be used.
"""
def __init__(self, ledger_field, packet_field=None):
self._get_ledger_field = op.attrgetter(ledger_field)
if packet_field is None:
self._packet_field = ledger_field.rsplit('.', 1)[-1]
else:
self._packet_field = packet_field
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._start_of_simulation = self._get_ledger_field(ledger)
def start_of_session(self, ledger, session, data_portal):
self._previous_day = self._get_ledger_field(ledger)
def _end_of_period(self, sub_field, packet, ledger):
packet_field = self._packet_field
packet['cumulative_perf'][packet_field] = self._start_of_simulation
packet[sub_field][packet_field] = self._previous_day
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._end_of_period('minute_perf', packet, ledger)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
self._end_of_period('daily_perf', packet, ledger)
class Returns(object):
"""Tracks the daily and cumulative returns of the algorithm.
"""
def _end_of_period(field,
packet,
ledger,
dt,
session_ix,
data_portal):
packet[field]['returns'] = ledger.todays_returns
packet['cumulative_perf']['returns'] = ledger.portfolio.returns
packet['cumulative_risk_metrics']['algorithm_period_return'] = (
ledger.portfolio.returns
)
end_of_bar = partial(_end_of_period, 'minute_perf')
end_of_session = partial(_end_of_period, 'daily_perf')
class BenchmarkReturnsAndVolatility(object):
"""Tracks daily and cumulative returns for the benchmark as well as the
volatility of the benchmark returns.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
daily_returns_series = benchmark_source.daily_returns(
sessions[0],
sessions[-1],
)
self._daily_returns = daily_returns_array = daily_returns_series.values
self._daily_cumulative_returns = (
np.cumprod(1 + daily_returns_array) - 1
)
self._daily_annual_volatility = (
daily_returns_series.expanding(2).std(ddof=1) * np.sqrt(252)
).values
if emission_rate == 'daily':
self._minute_cumulative_returns = NamedExplodingObject(
'self._minute_cumulative_returns',
'does not exist in daily emission rate',
)
self._minute_annual_volatility = NamedExplodingObject(
'self._minute_annual_volatility',
'does not exist in daily emission rate',
)
else:
open_ = trading_calendar.session_open(sessions[0])
close = trading_calendar.session_close(sessions[-1])
returns = benchmark_source.get_range(open_, close)
self._minute_cumulative_returns = (
(1 + returns).cumprod() - 1
)
self._minute_annual_volatility = pd.Series(
minute_annual_volatility(
returns.index.normalize().view('int64'),
returns.values,
daily_returns_array,
),
index=returns.index,
)
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
r = self._minute_cumulative_returns[dt]
if np.isnan(r):
r = None
packet['cumulative_risk_metrics']['benchmark_period_return'] = r
v = self._minute_annual_volatility[dt]
if np.isnan(v):
v = None
packet['cumulative_risk_metrics']['benchmark_volatility'] = v
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
r = self._daily_cumulative_returns[session_ix]
if np.isnan(r):
r = None
packet['cumulative_risk_metrics']['benchmark_period_return'] = r
v = self._daily_annual_volatility[session_ix]
if np.isnan(v):
v = None
packet['cumulative_risk_metrics']['benchmark_volatility'] = v
class PNL(object):
"""Tracks daily and cumulative PNL.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._previous_pnl = 0.0
def start_of_session(self, ledger, session, data_portal):
self._previous_pnl = ledger.portfolio.pnl
def _end_of_period(self, field, packet, ledger):
pnl = ledger.portfolio.pnl
packet[field]['pnl'] = pnl - self._previous_pnl
packet['cumulative_perf']['pnl'] = ledger.portfolio.pnl
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._end_of_period('minute_perf', packet, ledger)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
self._end_of_period('daily_perf', packet, ledger)
class CashFlow(object):
"""Tracks daily and cumulative cash flow.
Notes
-----
For historical reasons, this field is named 'capital_used' in the packets.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._previous_cash_flow = 0.0
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
cash_flow = ledger.portfolio.cash_flow
packet['minute_perf']['capital_used'] = (
cash_flow - self._previous_cash_flow
)
packet['cumulative_perf']['capital_used'] = cash_flow
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
cash_flow = ledger.portfolio.cash_flow
packet['daily_perf']['capital_used'] = (
cash_flow - self._previous_cash_flow
)
packet['cumulative_perf']['capital_used'] = cash_flow
self._previous_cash_flow = cash_flow
class Orders(object):
"""Tracks daily orders.
"""
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf']['orders'] = ledger.orders(dt)
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['daily_perf']['orders'] = ledger.orders()
class Transactions(object):
"""Tracks daily transactions.
"""
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf']['transactions'] = ledger.transactions(dt)
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['daily_perf']['transactions'] = ledger.transactions()
class Positions(object):
"""Tracks daily positions.
"""
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf']['positions'] = ledger.positions(dt)
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['daily_perf']['positions'] = ledger.positions()
class ReturnsStatistic(object):
"""A metric that reports an end of simulation scalar or time series
computed from the algorithm returns.
Parameters
----------
function : callable
The function to call on the daily returns.
field_name : str, optional
The name of the field. If not provided, it will be
``function.__name__``.
"""
def __init__(self, function, field_name=None):
if field_name is None:
field_name = function.__name__
self._function = function
self._field_name = field_name
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
res = self._function(ledger.daily_returns_array[:session_ix + 1])
if not np.isfinite(res):
res = None
packet['cumulative_risk_metrics'][self._field_name] = res
end_of_session = end_of_bar
class AlphaBeta(object):
"""End of simulation alpha and beta to the benchmark.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._daily_returns_array = benchmark_source.daily_returns(
sessions[0],
sessions[-1],
).values
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
risk = packet['cumulative_risk_metrics']
alpha, beta = ep.alpha_beta_aligned(
ledger.daily_returns_array[:session_ix + 1],
self._daily_returns_array[:session_ix + 1],
)
if not np.isfinite(alpha):
alpha = None
if np.isnan(beta):
beta = None
risk['alpha'] = alpha
risk['beta'] = beta
end_of_session = end_of_bar
class MaxLeverage(object):
"""Tracks the maximum account leverage.
"""
def start_of_simulation(self, *args):
self._max_leverage = 0.0
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._max_leverage = max(self._max_leverage, ledger.account.leverage)
packet['cumulative_risk_metrics']['max_leverage'] = self._max_leverage
end_of_session = end_of_bar
class NumTradingDays(object):
"""Report the number of trading days.
"""
def start_of_simulation(self, *args):
self._num_trading_days = 0
def start_of_session(self, *args):
self._num_trading_days += 1
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['cumulative_risk_metrics']['trading_days'] = (
self._num_trading_days
)
end_of_session = end_of_bar
class _ConstantCumulativeRiskMetric(object):
"""A metric which does not change, ever.
Notes
-----
This exists to maintain the existing structure of the perf packets. We
should kill this as soon as possible.
"""
def __init__(self, field, value):
self._field = field
self._value = value
def end_of_bar(self, packet, *args):
packet['cumulative_risk_metrics'][self._field] = self._value
def end_of_session(self, packet, *args):
packet['cumulative_risk_metrics'][self._field] = self._value
class PeriodLabel(object):
"""Backwards compat, please kill me.
"""
def start_of_session(self, ledger, session, data_portal):
self._label = session.strftime('%Y-%m')
def end_of_bar(self, packet, *args):
packet['cumulative_risk_metrics']['period_label'] = self._label
end_of_session = end_of_bar
class _ClassicRiskMetrics(object):
"""Produces original risk packet.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._leverages = np.full_like(sessions, np.nan, dtype='float64')
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._leverages[session_ix] = ledger.account.leverage
@classmethod
def risk_metric_period(cls,
start_session,
end_session,
algorithm_returns,
benchmark_returns,
algorithm_leverages):
"""
Creates a dictionary representing the state of the risk report.
Parameters
----------
start_session : pd.Timestamp
Start of period (inclusive) to produce metrics on
end_session : pd.Timestamp
End of period (inclusive) to produce metrics on
algorithm_returns : pd.Series(pd.Timestamp -> float)
Series of algorithm returns as of the end of each session
benchmark_returns : pd.Series(pd.Timestamp -> float)
Series of benchmark returns as of the end of each session
algorithm_leverages : pd.Series(pd.Timestamp -> float)
Series of algorithm leverages as of the end of each session
Returns
-------
risk_metric : dict[str, any]
Dict of metrics that with fields like:
{
'algorithm_period_return': 0.0,
'benchmark_period_return': 0.0,
'treasury_period_return': 0,
'excess_return': 0.0,
'alpha': 0.0,
'beta': 0.0,
'sharpe': 0.0,
'sortino': 0.0,
'period_label': '1970-01',
'trading_days': 0,
'algo_volatility': 0.0,
'benchmark_volatility': 0.0,
'max_drawdown': 0.0,
'max_leverage': 0.0,
}
"""
algorithm_returns = algorithm_returns[
(algorithm_returns.index >= start_session) &
(algorithm_returns.index <= end_session)
]
# Benchmark needs to be masked to the same dates as the algo returns
benchmark_returns = benchmark_returns[
(benchmark_returns.index >= start_session) &
(benchmark_returns.index <= algorithm_returns.index[-1])
]
benchmark_period_returns = ep.cum_returns(benchmark_returns).iloc[-1]
algorithm_period_returns = ep.cum_returns(algorithm_returns).iloc[-1]
alpha, beta = ep.alpha_beta_aligned(
algorithm_returns.values,
benchmark_returns.values,
)
benchmark_volatility = ep.annual_volatility(benchmark_returns)
sharpe = ep.sharpe_ratio(algorithm_returns)
# The consumer currently expects a 0.0 value for sharpe in period,
# this differs from cumulative which was np.nan.
# When factoring out the sharpe_ratio, the different return types
# were collapsed into `np.nan`.
# TODO: Either fix consumer to accept `np.nan` or make the
# `sharpe_ratio` return type configurable.
# In the meantime, convert nan values to 0.0
if pd.isnull(sharpe):
sharpe = 0.0
sortino = ep.sortino_ratio(
algorithm_returns.values,
_downside_risk=ep.downside_risk(algorithm_returns.values),
)
rval = {
'algorithm_period_return': algorithm_period_returns,
'benchmark_period_return': benchmark_period_returns,
'treasury_period_return': 0,
'excess_return': algorithm_period_returns,
'alpha': alpha,
'beta': beta,
'sharpe': sharpe,
'sortino': sortino,
'period_label': end_session.strftime("%Y-%m"),
'trading_days': len(benchmark_returns),
'algo_volatility': ep.annual_volatility(algorithm_returns),
'benchmark_volatility': benchmark_volatility,
'max_drawdown': ep.max_drawdown(algorithm_returns.values),
'max_leverage': algorithm_leverages.max(),
}
# check if a field in rval is nan or inf, and replace it with None
# except period_label which is always a str
return {
k: (
None
if k != 'period_label' and not np.isfinite(v) else
v
)
for k, v in iteritems(rval)
}
@classmethod
def _periods_in_range(cls,
months,
end_session,
end_date,
algorithm_returns,
benchmark_returns,
algorithm_leverages,
months_per):
if months.size < months_per:
return
end_date = end_date.tz_convert(None)
for period_timestamp in months:
period = period_timestamp.to_period(freq='%dM' % months_per)
if period.end_time > end_date:
break
yield cls.risk_metric_period(
start_session=period.start_time,
end_session=min(period.end_time, end_session),
algorithm_returns=algorithm_returns,
benchmark_returns=benchmark_returns,
algorithm_leverages=algorithm_leverages,
)
@classmethod
def risk_report(cls,
algorithm_returns,
benchmark_returns,
algorithm_leverages):
start_session = algorithm_returns.index[0]
end_session = algorithm_returns.index[-1]
end = end_session.replace(day=1) + relativedelta(months=1)
months = pd.date_range(
start=start_session,
# Ensure we have at least one month
end=end - datetime.timedelta(days=1),
freq='M',
tz='utc',
)
periods_in_range = partial(
cls._periods_in_range,
months=months,
end_session=end_session.tz_convert(None),
end_date=end,
algorithm_returns=algorithm_returns,
benchmark_returns=benchmark_returns,
algorithm_leverages=algorithm_leverages,
)
return {
'one_month': list(periods_in_range(months_per=1)),
'three_month': list(periods_in_range(months_per=3)),
'six_month': list(periods_in_range(months_per=6)),
'twelve_month': list(periods_in_range(months_per=12)),
}
def end_of_simulation(self,
packet,
ledger,
trading_calendar,
sessions,
data_portal,
benchmark_source):
packet.update(self.risk_report(
algorithm_returns=ledger.daily_returns_series,
benchmark_returns=benchmark_source.daily_returns(
sessions[0],
sessions[-1],
),
algorithm_leverages=self._leverages,
))