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356 lines (301 loc) · 20.7 KB
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# -*- coding: utf-8 -*-
from __future__ import print_function, absolute_import, division, unicode_literals
import json
from contextlib import closing
import websocket
from quantopian_tools import schema, session
from quantopian_tools.exceptions import RequestError, ResponseValidationError, QuantopianException
from quantopian_tools.helpers import build_url
def log_payload_schema():
return {
'count': schema.integer(required=True, nullable=False, min=0, rename='num_lines')
}
def position_schema():
return {
'a': schema.number(required=True, nullable=False, rename='amount'),
'cb': schema.number(required=True, nullable=False, rename='cost_basis'),
's': schema.integer(required=True, nullable=False, min=0, rename='sid'),
'ls': schema.number(required=True, nullable=False, rename='last_sale_price')
}
def performance_schema():
return {
'be': schema.number(required=True, nullable=True, default=None, rename='beta'),
'al': schema.number(required=True, nullable=True, default=None, rename='alpha'),
'vo': schema.number(required=True, nullable=True, default=None, rename='volatility'),
'bv': schema.number(required=True, nullable=True, default=None), # , rename='bv'), # TODO: Find actual name
'pnl': schema.number(required=True, nullable=True, default=None),
'br': schema.number(required=True, nullable=True, default=None, rename='benchmark_returns'),
'in': schema.number(required=True, nullable=True, default=None, rename='information_ratio'),
'cu': schema.number(required=True, nullable=True, default=None, rename='cushion'),
'md': schema.number(required=True, nullable=True, default=None, rename='max_drawdown'),
'ml': schema.number(required=True, nullable=True, default=None, rename='max_leverage'),
'tr': schema.number(required=True, nullable=True, default=None, rename='total_returns'),
'sh': schema.number(required=True, nullable=True, default=None, rename='sharpe'),
'so': schema.number(required=True, nullable=True, default=None, rename='sorentino')
}
def daily_result_schema():
return {
'o': schema.list_(required=True, nullable=True, default=None), # , rename='o'), # TODO: Find actual name
'rv': schema.dictionary(required=True, nullable=True, default=None, rename='custom_data'),
'd': schema.date_(required=True, nullable=True, min=0, default=None, rename='date',
coerce=('millis_timestamp', 'datetime_to_date')),
'c': schema.dictionary(required=True, nullable=True, default=None, rename='performance',
schema=performance_schema()),
'l': schema.number(required=True, nullable=True, default=None, rename='leverage'),
'ec': schema.number(required=True, nullable=True, default=None, rename='equity_with_loan'),
'p': schema.list_(required=True, nullable=True, default=None, rename='positions',
schema=schema.dictionary(schema=position_schema())),
'pnl': schema.number(required=True, nullable=True, default=None),
't': schema.list_(required=True, nullable=True, default=None), # , rename='t'), # TODO: Find actual name
'cb': schema.number(required=True, nullable=True, default=None), # , rename='t'), # TODO: Find actual name
'bm': schema.number(required=True, nullable=True, default=None) # , rename='t') # TODO: Find actual name
}
def performance_payload_schema():
return {
'cursor': schema.integer(required=True, nullable=False),
'pc': schema.number(required=True, nullable=False, rename='percent_complete'),
'sa': schema.datetime_(required=True, nullable=False, min=0, rename='timestamp', coerce='millis_timestamp'),
'daily': schema.dictionary(required=True, nullable=True, default=None, rename='daily_performance',
coerce='first_item', schema=daily_result_schema())
}
def risk_report_payload_schema():
return {
'metrics': schema.dictionary(
required=True,
nullable=False,
schema={
'information': schema.dictionary(required=True, nullable=False),
'algorithm_period_return': schema.dictionary(required=True, nullable=False),
'max_drawdown': schema.dictionary(required=True, nullable=False),
'sortino': schema.dictionary(required=True, nullable=False),
'algo_volatility': schema.dictionary(required=True, nullable=False),
'trading_days': schema.dictionary(required=True, nullable=False),
'max_leverage': schema.dictionary(required=True, nullable=False),
'benchmark_volatility': schema.dictionary(required=True, nullable=False),
'beta': schema.dictionary(required=True, nullable=False),
'excess_return': schema.dictionary(required=True, nullable=False),
'treasury_period_return': schema.dictionary(required=True, nullable=False),
'sharpe': schema.dictionary(required=True, nullable=False),
'alpha': schema.dictionary(required=True, nullable=False),
'benchmark_period_return': schema.dictionary(required=True, nullable=False)
}
)
}
def stack_schema():
return {
'lineno': schema.integer(required=True, nullable=True, min=0),
'line': schema.string(required=True, nullable=True, empty=True),
'method': schema.string(required=True, nullable=True, empty=True),
'filename': schema.string(required=True, nullable=True, empty=True)
}
def done_payload_schema():
return {
'completed_at': schema.datetime_(required=True, nullable=False, coerce='millis_timestamp'),
'reason': schema.string(required=True, nullable=False)
}
def exception_payload_schema():
return {
'date': schema.datetime_(required=True, nullable=False, rename='timestamp', coerce='millis_timestamp'),
'message': schema.string(required=True, nullable=False),
'name': schema.string(required=True, nullable=False),
'stack': schema.list_(required=True, nullable=False, schema=stack_schema())
}
def _log_level(value):
if value is None:
return None
return {
1: 'DEBUG',
2: 'INFO',
4: 'WARNING',
5: 'ERROR',
10: 'DEBUG',
11: 'INFO',
13: 'WARNING',
14: 'ERROR'
}.get(value) or 'UNKNOWN'
def start_backtest(algorithm, start_date, end_date, capital_base, data_frequency='minute'):
url = build_url('backtests', 'start_ide_backtest')
headers = {
'x-csrf-token': session.browser.get_csrf_token(build_url('algorithms', algorithm['id'])),
'x-requested-with': 'XMLHttpRequest'
}
data = {
'algo_id': algorithm['id'],
'code': algorithm['code'],
'backtest_start_date_year': start_date.year,
'backtest_start_date_month': start_date.month,
'backtest_start_date_day': start_date.day,
'backtest_end_date_year': end_date.year,
'backtest_end_date_month': end_date.month,
'backtest_end_date_day': end_date.day,
'backtest_capital_base': capital_base,
'backtest_data_frequency_value': data_frequency
}
response = session.browser.post(url, data=data, headers=headers)
if not response.ok:
raise RequestError('failed to start backtest', response)
valid, data_or_errors = schema.validate(response.json(), {
'data': schema.dictionary(required=True, schema={
'id': schema.string(required=True, nullable=False, empty=False),
'debug_open_msg': schema.string(required=True, nullable=False, empty=False),
'debug_ws_url': schema.string(required=True, nullable=False, empty=False),
'ws_open_msg': schema.string(required=True, nullable=False, empty=False),
'ws_url': schema.string(required=True, nullable=False, empty=False)
})
}, allow_unknown=True)
if not valid:
raise ResponseValidationError('POST', url, algorithm, data_or_errors)
return data_or_errors['data']
def debug_backtest(backtest):
"""
Open:
sent: {
'e': 'start',
'p': 'U2FsdGVkX1/F+fCWU4bOM'
'/RGz9PO6UpGXdQ3eBWa0leZcjfacvfLk23ihYnLi9gmco1EmXQnS1idNnIWatxFNMf8r61FUahwc2iSYkZDYTbkDcjgZLERViD'
'+TycXjuYIG+32aBCqD3ZAe5UmM3BYpAvI3z9Op/+D+L2s6ltAozZ+3WhXliJJq4WnUPUyx2E0QSS3mDBmAqBsJX92i7kGqA=='
}
sent: {'e': 'set_watch', 'p': []}
sent: {'e': 'set_watch', 'p': ['get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__']}
# Hits first BP
recv: {'p': [], 'e': 'watchlist'}
recv: {'p': {'index': 0, 'stack': [{'line': 36, 'code': ' schedule_function(rebalance, date_rules.week_start(), time_rules.market_open(hours=1))', 'file': '<algorithm>', 'func': 'initialize'}]}, 'e': 'stack'}
recv: {'p': [], 'e': 'watchlist'}
recv: {'p': [{'expr': 'get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'str\', \'nodes\': [], \'data\': \'2016-01-04 00:00:00\', \'id\': \'15dc03a3d27d4ed2800e64708c08eca1\'}'}], 'e': 'watchlist'}
Set breakpoint
sent: {'e':'set_break', 'p':{'line': 112, 'cond': null}}
recv:
Delete breakpoint
sent: {'e': 'clear_break', 'p': {'line': 111}}
recv:
Set breakpoint with condition
sent: {'e': 'set_break', 'p': {'line': 112, 'cond': 'ontext.foo == \'bar\''}}
recv:
Step into
sent: {'e':'step'}
recv: {'p': [{'expr': 'get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'str\', \'nodes\': [], \'data\': \'2016-01-04 15:30:00\', \'id\': \'2f0ef3930317424f8b4e73a9732242a4\'}'}], 'e': 'watchlist'}
recv: {'p': {'index': 1, 'stack': [{'line': 111, 'code': ' security_weights = get_weights(context, data)', 'file': '<algorithm>', 'func': 'rebalance'}, {'line': 159, 'code': ' prices = data.history(context.security_list,', 'file': '<algorithm>', 'func': 'get_weights'}]}, 'e': 'stack'}
Step out
sent: {'e':'return'}
sent: {'e':'step'}
recv: {'p': [{'expr': 'get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'str\', \'nodes\': [], \'data\': \'2016-01-04 15:30:00\', \'id\': \'61725d03a6f44152be8febe02c31a70a\'}'}], 'e': 'watchlist'}
recv: {'p': {'index': 1, 'stack': [{'line': 111, 'code': ' security_weights = get_weights(context, data)', 'file': '<algorithm>', 'func': 'rebalance'}, {'line': 163, 'code': ' security_weights = {}', 'file': '<algorithm>', 'func': 'get_weights'}]}, 'e': 'stack'}
recv: {'p': [{'expr': 'get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'str\', \'nodes\': [], \'data\': \'2016-01-04 15:30:00\', \'id\': \'f047bc5ad9aa4b2a81eae95bab07ee75\'}'}], 'e': 'watchlist'}
recv: {'p': {'index': 1, 'stack': [{'line': 111, 'code': ' security_weights = get_weights(context, data)', 'file': '<algorithm>', 'func': 'rebalance'}, {'line': 164, 'code': ' for security in context.security_list:', 'file': '<algorithm>', 'func': 'get_weights'}]}, 'e': 'stack'}
Step over
sent: {'e':'next'}
recv: {'p': [{'expr': 'get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'str\', \'nodes\': [], \'data\': \'2016-01-04 15:30:00\', \'id\': \'06ede009f52c4ef9af484034104dbac7\'}'}], 'e': 'watchlist'}
recv: {'p': {'index': 1, 'stack': [{'line': 111, 'code': ' security_weights = get_weights(context, data)', 'file': '<algorithm>', 'func': 'rebalance'}, {'line': 165, 'code': ' weights = np.array(weighting_fn(prices, security) for weighting_fn in context.history_weighting_fns)', 'file': '<algorithm>', 'func': 'get_weights'}]}, 'e': 'stack'}
Continue
sent: {'e':'continue'}
recv: {'p': [{'expr': 'get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'str\', \'nodes\': [], \'data\': \'2016-01-04 00:00:00\', \'id\': \'b7d989ae20c04977b48089a1538ee9c7\'}'}], 'e': 'watchlist'}
recv: {'p': {'index': 0, 'stack': [{'line': 42, 'code': ' context.pipelines = get_pipelines()', 'file': '<algorithm>', 'func': 'initialize'}]}, 'e': 'stack'}
Finish
sent: {'e':'disable','p':'soft'}
recv: {'p': null, 'e': 'disable'}
Set watch expr
sent: {'e':'set_watch','p':['context']}
recv: {'p': [{'expr': 'get_datetime().strftime(\'%Y-%m-%d %H:%M:%S\')#__QUANTOPIAN__', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'str\', \'nodes\': [], \'data\': \'2016-01-04 00:00:00\', \'id\': \'0646a2fa3ff244439749a886273401b5\'}'}, {'expr': 'context', 'exc': null, 'value': '{\'truncated\': 0, \'context\': null, \'objtype\': \'AlgorithmContext\', \'nodes\': [\''portfolio'\', \''account'\'], \'data\': \'AlgorithmContext({'portfolio': Portfolio({'portfolio_value': 1000000.0, 'positions_exposure': 0.0, 'cash': 1000000.0, 'starting_cash': 1000000.0, 'returns': 0.0, 'capital_used': 0.0, 'pnl': 0.0, 'positions': {}, 'positions_value': 0.0, 'start_date': Timestamp('2016-01-04 00:00:00+0000', tz='UTC', offset='C')}), 'account': Account({'day_trades_remaining': inf, 'leverage': 0.0, 'regt_equity': 1000000.0, 'regt_margin': inf, 'available_funds': 1000000.0, 'maintenance_margin_requirement': 0.0, 'equity_with_loan': 1000000.0, 'buying_power': inf, 'initial_margin_requirement': 0.0, 'excess_liquidity': 1000000.0, 'settled_cash': 1000000.0, 'net_liquidation': 1000000.0, 'cushion': 1.0, 'total_positions_value': 0.0, 'net_leverage': 0.0, 'accrued_interest': 0.0, 'total_positions_exposure': 0.0})})\', \'id\': \'0bdea00673644178a54551fbbb45a5ad\'}'}], 'e': 'watchlist'}
Eval
sent: {'e':'eval','p':'context'}
recv: {'p': {'input': 'context', 'exc': null, 'output': '{\'truncated\': 0, \'context\': null, \'objtype\': \'AlgorithmContext\', \'nodes\': [\''portfolio'\', \''account'\'], \'data\': \'AlgorithmContext({'portfolio': Portfolio({'portfolio_value': 1000000.0, 'positions_exposure': 0.0, 'cash': 1000000.0, 'starting_cash': 1000000.0, 'returns': 0.0, 'capital_used': 0.0, 'pnl': 0.0, 'positions': {}, 'positions_value': 0.0, 'start_date': Timestamp('2016-01-04 00:00:00+0000', tz='UTC', offset='C')}), 'account': Account({'day_trades_remaining': inf, 'leverage': 0.0, 'regt_equity': 1000000.0, 'regt_margin': inf, 'available_funds': 1000000.0, 'maintenance_margin_requirement': 0.0, 'equity_with_loan': 1000000.0, 'buying_power': inf, 'initial_margin_requirement': 0.0, 'excess_liquidity': 1000000.0, 'settled_cash': 1000000.0, 'net_liquidation': 1000000.0, 'cushion': 1.0, 'total_positions_value': 0.0, 'net_leverage': 0.0, 'accrued_interest': 0.0, 'total_positions_exposure': 0.0})})\', \'id\': \'197893af0ef94dfe9c0eea7e4b6a8c53\'}'}, 'e': 'print'}
Expand watch expr tree by node index (Anything with nodes can be expanded)
sent: {'e':'tree_expand','p':{'parent_id':'c7d0349fb76948ce8017a24d78d0ea4c','node':0}}
recv: {'p': '{\'truncated\': 0, \'context\': {\'node\': 0, \'parent_id\': \'c7d0349fb76948ce8017a24d78d0ea4c\'}, \'objtype\': \'Portfolio\', \'nodes\': [\'capital_used\', \'cash\', \'pnl\', \'portfolio_value\', \'positions\', \'positions_exposure\', \'positions_value\', \'returns\', \'start_date\', \'starting_cash\'], \'data\': \'Portfolio({'portfolio_value': 1000000.0, 'positions_exposure': 0.0, 'cash': 1000000.0, 'starting_cash': 1000000.0, 'returns': 0.0, 'capital_used': 0.0, 'pnl': 0.0, 'positions': {}, 'positions_value': 0.0, 'start_date': Timestamp('2016-01-04 00:00:00+0000', tz='UTC', offset='C')})\', \'id\': \'510dd51c5f4f489b8432c29e2affe50f\'}', 'e': 'tree_node'}
"""
pass
def get_backtest_results(backtest):
with closing(websocket.create_connection(backtest['ws_url'])) as ws:
ws.send(json.dumps({
'e': 'open',
'p': {
'a': backtest['ws_open_msg'],
'cursor': 0,
'include_txn': False
}
}))
while True:
msg = json.loads(ws.recv())
if msg['e'] == 'log':
yield 'log', schema.validate(msg['p'], log_payload_schema(), raise_exc=True)
elif msg['e'] == 'performance':
yield 'performance', schema.validate(msg['p'], performance_payload_schema(), raise_exc=True)
elif msg['e'] == 'risk_report':
yield 'risk_report', schema.validate(msg['p'], risk_report_payload_schema(), raise_exc=True)
elif msg['e'] == 'done':
yield 'done', schema.validate(msg['p'], done_payload_schema(), raise_exc=True)
ws.send('ACK')
break
elif msg['e'] == 'exception':
exc = schema.validate(msg['p'], exception_payload_schema(), raise_exc=True)
trace = '\n'.join(" File \"{}\", line {}, in {}\n {}"
"".format(s['filename'], s['lineno'], s['method'], s['line'])
for s in exc['stack'])
raise RuntimeError("Traceback (most recent call last):\n{}\n{}: {}"
"".format(trace, exc['name'], exc['message']))
else:
raise QuantopianException("unknown backtest event '{}'".format(msg['e']))
def get_backtest_logs(backtest, start=0, end=200):
url = build_url('backtests', 'log_entries', backtest_id=backtest['id'], start=start, end=end)
headers = {
'x-csrf-token': session.browser.get_csrf_token(build_url('algorithms')),
'x-requested-with': 'XMLHttpRequest'
}
response = session.browser.get(url, headers=headers)
if not response.ok:
raise RequestError('failed to get backtest logs', response)
valid, data_or_errors = schema.validate(response.json(), {
'status': schema.string(required=True, nullable=False),
'data': schema.dictionary(
required=True,
nullable=True,
schema={
'id': schema.string(required=True, nullable=False, allowed=[backtest['id']]),
'log_count': schema.integer(required=True, nullable=False, min=-1),
'min_avail': schema.integer(required=True, nullable=True, min=-1),
'max_avail': schema.integer(required=True, nullable=True, min=-1),
'logs': schema.list_(
required=True,
nullable=False,
schema=schema.dictionary(
schema={
'a': schema.datetime_(required=True, nullable=False, min=0, rename='trading_timestamp',
coerce='millis_timestamp'),
'c': schema.string(required=True, nullable=True, strip=True, rename='log_class'),
'f': schema.string(required=True, nullable=True, empty=False, rename='method'),
'i': schema.integer(required=True, nullable=True, min=1, rename='line'),
'l': schema.string(required=True, nullable=False, coerce=_log_level, rename='level'),
'm': schema.string(required=True, nullable=False, empty=True, rename='message'),
'o': schema.integer(required=True, nullable=False, min=1, rename='count'),
'r': schema.boolean(required=True, nullable=True, rename='truncated'),
't': schema.datetime_(required=True, nullable=False, min=0, rename='log_timestamp',
coerce='millis_timestamp'),
}
)
)
}
)
}, allow_unknown=True)
if not valid:
raise ResponseValidationError('GET', url, None, data_or_errors)
data_or_errors['data']['logs'].sort(key=lambda log: log['count'])
return data_or_errors['data']
def cancel_backtest(backtest, algorithm):
url = build_url('backtests', backtest['id'], 'cancel_backtest')
headers = {
'x-csrf-token': session.browser.get_csrf_token(build_url('algorithms', algorithm['id'])),
'x-requested-with': 'XMLHttpRequest'
}
data = {
'algo_id': algorithm['id'],
'backtest_id': backtest['id']
}
response = session.browser.post(url, data=data, headers=headers)
if not response.ok:
raise RequestError('failed to cancel backtest', response)
valid, data_or_errors = schema.validate(response.json(), {
'status': schema.string(required=True, nullable=False)
}, allow_unknown=True)
if not valid:
raise ResponseValidationError('POST', url, data, data_or_errors)
return data_or_errors['status'].lower() == 'ok'