def _bundle_trading_environment(bundle_data, environ):
prefix, connstr = re.split(r'sqlite:///',
str(bundle_data.asset_finder.engine.url),
maxsplit=1)
if prefix:
raise ValueError("invalid url %r, must begin with 'sqlite:///'" %
str(bundle_data.asset_finder.engine.url))
return TradingEnvironment(asset_db_path=connstr, environ=environ)
def get_trading_env_and_data(bundles):
env = data = None
b = 'poloniex'
if len(bundles) == 0:
return env, data
elif len(bundles) == 1:
b = bundles[0]
bundle_data = load(
b,
environ,
bundle_timestamp,
)
prefix, connstr = re.split(
r'sqlite:///',
str(bundle_data.asset_finder.engine.url),
maxsplit=1,
)
if prefix:
raise ValueError(
"invalid url %r, must begin with 'sqlite:///'" %
str(bundle_data.asset_finder.engine.url), )
open_calendar = get_calendar('OPEN')
env = TradingEnvironment(
load=partial(load_crypto_market_data,
bundle=b,
bundle_data=bundle_data,
environ=environ),
bm_symbol='USDT_BTC',
trading_calendar=open_calendar,
asset_db_path=connstr,
environ=environ,
)
first_trading_day = bundle_data.minute_bar_reader.first_trading_day
data = DataPortal(
env.asset_finder,
open_calendar,
first_trading_day=first_trading_day,
minute_reader=bundle_data.minute_bar_reader,
five_minute_reader=bundle_data.five_minute_bar_reader,
daily_reader=bundle_data.daily_bar_reader,
adjustment_reader=bundle_data.adjustment_reader,
)
return env, data
def _run(handle_data, initialize, before_trading_start, analyze, algofile,
algotext, defines, data_frequency, capital_base, data, bundle,
bundle_timestamp, start, end, output, print_algo, local_namespace,
environ, live, exchange, algo_namespace, base_currency, live_graph):
"""Run a backtest for the given algorithm.
This is shared between the cli and :func:`catalyst.run_algo`.
"""
if algotext is not None:
if local_namespace:
ip = get_ipython() # noqa
namespace = ip.user_ns
else:
namespace = {}
for assign in defines:
try:
name, value = assign.split('=', 2)
except ValueError:
raise ValueError(
'invalid define %r, should be of the form name=value' %
assign, )
try:
# evaluate in the same namespace so names may refer to
# eachother
namespace[name] = eval(value, namespace)
except Exception as e:
raise ValueError(
'failed to execute definition for name %r: %s' %
(name, e), )
elif defines:
raise _RunAlgoError(
'cannot pass define without `algotext`',
"cannot pass '-D' / '--define' without '-t' / '--algotext'",
)
else:
namespace = {}
if algofile is not None:
algotext = algofile.read()
if print_algo:
if PYGMENTS:
highlight(
algotext,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout,
)
else:
click.echo(algotext)
mode = 'live' if live else 'backtest'
log.info('running algo in {mode} mode'.format(mode=mode))
if live and exchange is not None:
exchange_name = exchange
start = pd.Timestamp.utcnow()
end = start + timedelta(minutes=1439)
portfolio = get_algo_object(algo_name=algo_namespace,
key='portfolio_{}'.format(exchange_name),
environ=environ)
if portfolio is None:
portfolio = ExchangePortfolio(start_date=pd.Timestamp.utcnow())
exchange_auth = get_exchange_auth(exchange_name)
if exchange_name == 'bitfinex':
exchange = Bitfinex(key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=base_currency,
portfolio=portfolio)
elif exchange_name == 'bittrex':
exchange = Bittrex(key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=base_currency,
portfolio=portfolio)
else:
raise NotImplementedError('exchange not supported: %s' %
exchange_name)
open_calendar = get_calendar('OPEN')
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=capital_base,
data_frequency=data_frequency,
emission_rate=data_frequency,
)
if live and exchange is not None:
env = TradingEnvironment(environ=environ,
exchange_tz='UTC',
asset_db_path=None)
env.asset_finder = AssetFinderExchange(exchange)
data = DataPortalExchange(exchange=exchange,
asset_finder=env.asset_finder,
trading_calendar=open_calendar,
first_trading_day=pd.to_datetime('today',
utc=True))
choose_loader = None
def fetch_capital_base(attempt_index=0):
"""
Fetch the base currency amount required to bootstrap
the algorithm against the exchange.
The algorithm cannot continue without this value.
:param attempt_index:
:return capital_base: the amount of base currency available for
trading
"""
try:
log.debug('retrieving capital base in {} to bootstrap '
'exchange {}'.format(base_currency, exchange_name))
balances = exchange.get_balances()
except ExchangeRequestError as e:
if attempt_index < 20:
sleep(5)
return fetch_capital_base(attempt_index + 1)
else:
raise ExchangeRequestErrorTooManyAttempts(
attempts=attempt_index, error=e)
if base_currency in balances:
return balances[base_currency]
else:
raise BaseCurrencyNotFoundError(base_currency=base_currency,
exchange=exchange_name)
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=fetch_capital_base(),
emission_rate='minute',
data_frequency='minute')
elif bundle is not None:
bundles = bundle.split(',')
def get_trading_env_and_data(bundles):
env = data = None
b = 'poloniex'
if len(bundles) == 0:
return env, data
elif len(bundles) == 1:
b = bundles[0]
bundle_data = load(
b,
environ,
bundle_timestamp,
)
prefix, connstr = re.split(
r'sqlite:///',
str(bundle_data.asset_finder.engine.url),
maxsplit=1,
)
if prefix:
raise ValueError(
"invalid url %r, must begin with 'sqlite:///'" %
str(bundle_data.asset_finder.engine.url), )
env = TradingEnvironment(
load=partial(load_crypto_market_data,
bundle=b,
bundle_data=bundle_data,
environ=environ),
bm_symbol='USDT_BTC',
trading_calendar=open_calendar,
asset_db_path=connstr,
environ=environ,
)
first_trading_day = bundle_data.minute_bar_reader.first_trading_day
data = DataPortal(
env.asset_finder,
open_calendar,
first_trading_day=first_trading_day,
minute_reader=bundle_data.minute_bar_reader,
five_minute_reader=bundle_data.five_minute_bar_reader,
daily_reader=bundle_data.daily_bar_reader,
adjustment_reader=bundle_data.adjustment_reader,
)
return env, data
def get_loader_for_bundle(b):
bundle_data = load(
b,
environ,
bundle_timestamp,
)
if b == 'poloniex':
return CryptoPricingLoader(
bundle_data,
data_frequency,
CryptoPricing,
)
elif b == 'quandl':
return USEquityPricingLoader(
bundle_data,
data_frequency,
USEquityPricing,
)
raise ValueError("No PipelineLoader registered for bundle %s." % b)
loaders = [get_loader_for_bundle(b) for b in bundles]
env, data = get_trading_env_and_data(bundles)
def choose_loader(column):
for loader in loaders:
if column in loader.columns:
return loader
raise ValueError("No PipelineLoader registered for column %s." %
column)
else:
env = TradingEnvironment(environ=environ)
choose_loader = None
TradingAlgorithmClass = (partial(ExchangeTradingAlgorithm,
exchange=exchange,
algo_namespace=algo_namespace,
live_graph=live_graph)
if live and exchange else TradingAlgorithm)
perf = TradingAlgorithmClass(
namespace=namespace,
env=env,
get_pipeline_loader=choose_loader,
sim_params=sim_params,
**{
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze,
} if algotext is None else {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext,
}).run(
data,
overwrite_sim_params=False,
)
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the catalyst magic not write any data
perf.to_pickle(output)
return perf
def _run(handle_data,
initialize,
before_trading_start,
analyze,
algofile,
algotext,
defines,
data_frequency,
capital_base,
data,
bundle,
bundle_timestamp,
start,
end,
output,
print_algo,
local_namespace,
environ,
live,
exchange,
algo_namespace,
quote_currency,
live_graph,
analyze_live,
simulate_orders,
auth_aliases,
stats_output):
"""Run a backtest for the given algorithm.
This is shared between the cli and :func:`catalyst.run_algo`.
"""
# TODO: refactor for more granularity
if algotext is not None:
if local_namespace:
ip = get_ipython() # noqa
namespace = ip.user_ns
else:
namespace = {}
for assign in defines:
try:
name, value = assign.split('=', 2)
except ValueError:
raise ValueError(
'invalid define %r, should be of the form name=value' %
assign,
)
try:
# evaluate in the same namespace so names may refer to
# eachother
namespace[name] = eval(value, namespace)
except Exception as e:
raise ValueError(
'failed to execute definition for name %r: %s' % (name, e),
)
elif defines:
raise _RunAlgoError(
'cannot pass define without `algotext`',
"cannot pass '-D' / '--define' without '-t' / '--algotext'",
)
else:
namespace = {}
if algofile is not None:
algotext = algofile.read()
if print_algo:
if PYGMENTS:
highlight(
algotext,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout,
)
else:
click.echo(algotext)
log.info('Catalyst version {}'.format(catalyst.__version__))
if not DISABLE_ALPHA_WARNING:
log.warn(ALPHA_WARNING_MESSAGE)
# sleep(3)
if live:
if simulate_orders:
mode = 'paper-trading'
else:
mode = 'live-trading'
else:
mode = 'backtest'
log.info('running algo in {mode} mode'.format(mode=mode))
exchange_name = exchange
if exchange_name is None:
raise ValueError('Please specify at least one exchange.')
if isinstance(auth_aliases, string_types):
aliases = auth_aliases.split(',')
if len(aliases) < 2 or len(aliases) % 2 != 0:
raise ValueError(
'the `auth_aliases` parameter must contain an even list '
'of comma-delimited values. For example, '
'"binance,auth2" or "binance,auth2,bittrex,auth2".'
)
auth_aliases = dict(zip(aliases[::2], aliases[1::2]))
exchange_list = [x.strip().lower() for x in exchange.split(',')]
exchanges = dict()
for name in exchange_list:
if auth_aliases is not None and name in auth_aliases:
auth_alias = auth_aliases[name]
else:
auth_alias = None
exchanges[name] = get_exchange(
exchange_name=name,
quote_currency=quote_currency,
must_authenticate=(live and not simulate_orders),
skip_init=True,
auth_alias=auth_alias,
)
open_calendar = get_calendar('OPEN')
env = TradingEnvironment(
load=partial(
load_crypto_market_data,
environ=environ,
start_dt=start,
end_dt=end
),
environ=environ,
exchange_tz='UTC',
asset_db_path=None # We don't need an asset db, we have exchanges
)
env.asset_finder = ExchangeAssetFinder(exchanges=exchanges)
def choose_loader(column):
bound_cols = TradingPairPricing.columns
if column in bound_cols:
return ExchangePricingLoader(data_frequency)
raise ValueError(
"No PipelineLoader registered for column %s." % column
)
if live:
# TODO: fix the start data.
# is_start checks if a start date was specified by user
# needed for live clock
is_start = True
if start is None:
start = pd.Timestamp.utcnow()
is_start = False
elif start:
assert pd.Timestamp.utcnow() <= start, \
"specified start date is in the past."
elif start and end:
assert start < end, "start date is later than end date."
# TODO: fix the end data.
# is_end checks if an end date was specified by user
# needed for live clock
is_end = True
if end is None:
end = start + timedelta(hours=8760)
is_end = False
data = DataPortalExchangeLive(
exchanges=exchanges,
asset_finder=env.asset_finder,
trading_calendar=open_calendar,
first_trading_day=pd.to_datetime('today', utc=True)
)
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=capital_base,
emission_rate='minute',
data_frequency='minute'
)
# TODO: use the constructor instead
sim_params._arena = 'live'
algorithm_class = partial(
ExchangeTradingAlgorithmLive,
exchanges=exchanges,
algo_namespace=algo_namespace,
live_graph=live_graph,
simulate_orders=simulate_orders,
stats_output=stats_output,
analyze_live=analyze_live,
start=start,
is_start=is_start,
end=end,
is_end=is_end,
)
elif exchanges:
# Removed the existing Poloniex fork to keep things simple
# We can add back the complexity if required.
# I don't think that we should have arbitrary price data bundles
# Instead, we should center this data around exchanges.
# We still need to support bundles for other misc data, but we
# can handle this later.
if (start and start != pd.tslib.normalize_date(start)) or \
(end and end != pd.tslib.normalize_date(end)):
# todo: add to Sim_Params the option to
# start & end at specific times
log.warn(
"Catalyst currently starts and ends on the start and "
"end of the dates specified, respectively. We hope to "
"Modify this and support specific times in a future release."
)
data = DataPortalExchangeBacktest(
exchange_names=[ex_name for ex_name in exchanges],
asset_finder=None,
trading_calendar=open_calendar,
first_trading_day=start,
last_available_session=end
)
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=capital_base,
data_frequency=data_frequency,
emission_rate=data_frequency,
)
algorithm_class = partial(
ExchangeTradingAlgorithmBacktest,
exchanges=exchanges
)
elif bundle is not None:
bundle_data = load(
bundle,
environ,
bundle_timestamp,
)
prefix, connstr = re.split(
r'sqlite:///',
str(bundle_data.asset_finder.engine.url),
maxsplit=1,
)
if prefix:
raise ValueError(
"invalid url %r, must begin with 'sqlite:///'" %
str(bundle_data.asset_finder.engine.url),
)
env = TradingEnvironment(asset_db_path=connstr, environ=environ)
first_trading_day = \
bundle_data.equity_minute_bar_reader.first_trading_day
data = DataPortal(
env.asset_finder, open_calendar,
first_trading_day=first_trading_day,
equity_minute_reader=bundle_data.equity_minute_bar_reader,
equity_daily_reader=bundle_data.equity_daily_bar_reader,
adjustment_reader=bundle_data.adjustment_reader,
)
perf = algorithm_class(
namespace=namespace,
env=env,
get_pipeline_loader=choose_loader,
sim_params=sim_params,
**{
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze,
} if algotext is None else {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext,
}
).run(
data,
overwrite_sim_params=False,
)
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the catalyst magic not write any data
perf.to_pickle(output)
return perf
def _run(handle_data, initialize, before_trading_start, analyze, algofile,
algotext, defines, data_frequency, capital_base, data, bundle,
bundle_timestamp, start, end, output, print_algo, local_namespace,
environ, live, exchange, algo_namespace, base_currency, live_graph):
"""Run a backtest for the given algorithm.
This is shared between the cli and :func:`catalyst.run_algo`.
"""
if algotext is not None:
if local_namespace:
ip = get_ipython() # noqa
namespace = ip.user_ns
else:
namespace = {}
for assign in defines:
try:
name, value = assign.split('=', 2)
except ValueError:
raise ValueError(
'invalid define %r, should be of the form name=value' %
assign, )
try:
# evaluate in the same namespace so names may refer to
# eachother
namespace[name] = eval(value, namespace)
except Exception as e:
raise ValueError(
'failed to execute definition for name %r: %s' %
(name, e), )
elif defines:
raise _RunAlgoError(
'cannot pass define without `algotext`',
"cannot pass '-D' / '--define' without '-t' / '--algotext'",
)
else:
namespace = {}
if algofile is not None:
algotext = algofile.read()
if print_algo:
if PYGMENTS:
highlight(
algotext,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout,
)
else:
click.echo(algotext)
mode = 'live' if live else 'backtest'
log.info('running algo in {mode} mode'.format(mode=mode))
exchange_name = exchange
if exchange_name is None:
raise ValueError('Please specify at least one exchange.')
exchange_list = [x.strip().lower() for x in exchange.split(',')]
exchanges = dict()
for exchange_name in exchange_list:
# Looking for the portfolio from the cache first
portfolio = get_algo_object(algo_name=algo_namespace,
key='portfolio_{}'.format(exchange_name),
environ=environ)
if portfolio is None:
portfolio = ExchangePortfolio(start_date=pd.Timestamp.utcnow())
# This corresponds to the json file containing api token info
exchange_auth = get_exchange_auth(exchange_name)
if live and (exchange_auth['key'] == ''
or exchange_auth['secret'] == ''):
raise ExchangeAuthEmpty(exchange=exchange_name.title(),
filename=os.path.join(
get_exchange_folder(
exchange_name, environ),
'auth.json'))
if exchange_name == 'bitfinex':
exchanges[exchange_name] = Bitfinex(key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=base_currency,
portfolio=portfolio)
elif exchange_name == 'bittrex':
exchanges[exchange_name] = Bittrex(key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=base_currency,
portfolio=portfolio)
elif exchange_name == 'poloniex':
exchanges[exchange_name] = Poloniex(key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=base_currency,
portfolio=portfolio)
else:
raise ExchangeNotFoundError(exchange_name=exchange_name)
open_calendar = get_calendar('OPEN')
env = TradingEnvironment(
load=partial(load_crypto_market_data,
environ=environ,
start_dt=start,
end_dt=end),
environ=environ,
exchange_tz='UTC',
asset_db_path=None # We don't need an asset db, we have exchanges
)
env.asset_finder = AssetFinderExchange()
choose_loader = None # TODO: use the DataPortal for in the algorithm class for this
if live:
start = pd.Timestamp.utcnow()
# TODO: fix the end data.
end = start + timedelta(hours=8760)
data = DataPortalExchangeLive(exchanges=exchanges,
asset_finder=env.asset_finder,
trading_calendar=open_calendar,
first_trading_day=pd.to_datetime(
'today', utc=True))
def fetch_capital_base(exchange, attempt_index=0):
"""
Fetch the base currency amount required to bootstrap
the algorithm against the exchange.
The algorithm cannot continue without this value.
:param exchange: the targeted exchange
:param attempt_index:
:return capital_base: the amount of base currency available for
trading
"""
try:
log.debug('retrieving capital base in {} to bootstrap '
'exchange {}'.format(base_currency, exchange_name))
balances = exchange.get_balances()
except ExchangeRequestError as e:
if attempt_index < 20:
log.warn('could not retrieve balances on {}: {}'.format(
exchange.name, e))
sleep(5)
return fetch_capital_base(exchange, attempt_index + 1)
else:
raise ExchangeRequestErrorTooManyAttempts(
attempts=attempt_index, error=e)
if base_currency in balances:
return balances[base_currency]
else:
raise BaseCurrencyNotFoundError(base_currency=base_currency,
exchange=exchange_name)
capital_base = 0
for exchange_name in exchanges:
exchange = exchanges[exchange_name]
capital_base += fetch_capital_base(exchange)
sim_params = create_simulation_parameters(start=start,
end=end,
capital_base=capital_base,
emission_rate='minute',
data_frequency='minute')
# TODO: use the constructor instead
sim_params._arena = 'live'
algorithm_class = partial(ExchangeTradingAlgorithmLive,
exchanges=exchanges,
algo_namespace=algo_namespace,
live_graph=live_graph)
else:
# Removed the existing Poloniex fork to keep things simple
# We can add back the complexity if required.
# I don't think that we should have arbitrary price data bundles
# Instead, we should center this data around exchanges.
# We still need to support bundles for other misc data, but we
# can handle this later.
data = DataPortalExchangeBacktest(exchanges=exchanges,
asset_finder=None,
trading_calendar=open_calendar,
first_trading_day=start,
last_available_session=end)
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=capital_base,
data_frequency=data_frequency,
emission_rate=data_frequency,
)
algorithm_class = partial(ExchangeTradingAlgorithmBacktest,
exchanges=exchanges)
perf = algorithm_class(
namespace=namespace,
env=env,
get_pipeline_loader=choose_loader,
sim_params=sim_params,
**{
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze,
} if algotext is None else {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext,
}).run(
data,
overwrite_sim_params=False,
)
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the catalyst magic not write any data
perf.to_pickle(output)
return perf
def __enter__(self):
return TradingEnvironment(
load=self._load,
asset_db_path=super(tmp_trading_env, self).__enter__().engine,
)
def _run(handle_data, initialize, before_trading_start, analyze, algofile,
algotext, defines, data_frequency, capital_base, data, bundle,
bundle_timestamp, start, end, output, print_algo, local_namespace,
environ):
"""Run a backtest for the given algorithm.
This is shared between the cli and :func:`catalyst.run_algo`.
"""
if algotext is not None:
if local_namespace:
ip = get_ipython() # noqa
namespace = ip.user_ns
else:
namespace = {}
for assign in defines:
try:
name, value = assign.split('=', 2)
except ValueError:
raise ValueError(
'invalid define %r, should be of the form name=value' %
assign, )
try:
# evaluate in the same namespace so names may refer to
# eachother
namespace[name] = eval(value, namespace)
except Exception as e:
raise ValueError(
'failed to execute definition for name %r: %s' %
(name, e), )
elif defines:
raise _RunAlgoError(
'cannot pass define without `algotext`',
"cannot pass '-D' / '--define' without '-t' / '--algotext'",
)
else:
namespace = {}
if algofile is not None:
algotext = algofile.read()
if print_algo:
if PYGMENTS:
highlight(
algotext,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout,
)
else:
click.echo(algotext)
if bundle is not None:
bundles = bundle.split(',')
def get_trading_env_and_data(bundles):
env = data = None
b = 'poloniex'
if len(bundles) == 0:
return env, data
elif len(bundles) == 1:
b = bundles[0]
bundle_data = load(
b,
environ,
bundle_timestamp,
)
prefix, connstr = re.split(
r'sqlite:///',
str(bundle_data.asset_finder.engine.url),
maxsplit=1,
)
if prefix:
raise ValueError(
"invalid url %r, must begin with 'sqlite:///'" %
str(bundle_data.asset_finder.engine.url), )
open_calendar = get_calendar('OPEN')
env = TradingEnvironment(
load=partial(load_crypto_market_data, environ=environ),
bm_symbol='USDT_BTC',
trading_calendar=open_calendar,
asset_db_path=connstr,
environ=environ,
)
first_trading_day = bundle_data.minute_bar_reader.first_trading_day
data = DataPortal(
env.asset_finder,
open_calendar,
first_trading_day=first_trading_day,
minute_reader=bundle_data.minute_bar_reader,
five_minute_reader=bundle_data.five_minute_bar_reader,
daily_reader=bundle_data.daily_bar_reader,
adjustment_reader=bundle_data.adjustment_reader,
)
return env, data
def get_loader_for_bundle(b):
bundle_data = load(
b,
environ,
bundle_timestamp,
)
if b == 'poloniex':
return CryptoPricingLoader(
bundle_data,
data_frequency,
CryptoPricing,
)
elif b == 'quandl':
return USEquityPricingLoader(
bundle_data,
data_frequency,
USEquityPricing,
)
raise ValueError("No PipelineLoader registered for bundle %s." % b)
loaders = [get_loader_for_bundle(b) for b in bundles]
env, data = get_trading_env_and_data(bundles)
def choose_loader(column):
for loader in loaders:
if column in loader.columns:
return loader
raise ValueError("No PipelineLoader registered for column %s." %
column)
else:
env = TradingEnvironment(environ=environ)
choose_loader = None
perf = TradingAlgorithm(
namespace=namespace,
env=env,
get_pipeline_loader=choose_loader,
sim_params=create_simulation_parameters(
start=start,
end=end,
capital_base=capital_base,
data_frequency=data_frequency,
emission_rate=data_frequency,
),
**{
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze,
} if algotext is None else {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext,
}).run(
data,
overwrite_sim_params=False,
)
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the catalyst magic not write any data
perf.to_pickle(output)
return perf
def _build_algo_and_data(handle_data, initialize, before_trading_start,
analyze, algofile, algotext, defines, data_frequency,
capital_base, data, bundle, bundle_timestamp, start,
end, output, print_algo, local_namespace, environ,
live, exchange, algo_namespace, base_currency,
live_graph, analyze_live, simulate_orders,
stats_output):
namespace = _build_namespace(algotext, local_namespace, defines)
if algotext is not None:
algotext = algofile.read()
if print_algo:
_pretty_print_code(algotext)
mode = _mode(simulate_orders, live)
log.info('running algo in {mode} mode'.format(mode=mode))
exchanges = _build_exchanges_dict(exchange, live, simulate_orders,
base_currency)
open_calendar = get_calendar('OPEN')
env = TradingEnvironment(
load=partial(load_crypto_market_data,
environ=environ,
start_dt=start,
end_dt=end),
environ=environ,
exchange_tz='UTC',
asset_db_path=None) # We don't need an asset db, we have exchanges
env.asset_finder = ExchangeAssetFinder(exchanges=exchanges)
choose_loader = partial(_choose_loader, data_frequency)
if live:
start, end = _get_live_time_range()
data_frequency = 'minute' # TODO double check if this is the desired behavior
sim_params = create_simulation_parameters(start=start,
end=end,
capital_base=capital_base,
emission_rate=data_frequency,
data_frequency=data_frequency)
if algotext is None:
algorithm_class_kwargs = {
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze
}
else:
algorithm_class_kwargs = {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext
}
if live:
return _build_live_algo_and_data(
sim_params, exchanges, env, open_calendar, simulate_orders,
algo_namespace, capital_base, live_graph, stats_output,
analyze_live, base_currency, namespace, choose_loader,
algorithm_class_kwargs)
else:
return _build_backtest_algo_and_data(exchanges, bundle, env, environ,
bundle_timestamp, open_calendar,
start, end, namespace,
choose_loader, sim_params,
algorithm_class_kwargs)
def _run(handle_data, initialize, before_trading_start, analyze, algofile,
algotext, defines, data_frequency, capital_base, data, bundle,
bundle_timestamp, start, end, output, print_algo, local_namespace,
environ, live, exchange, algo_namespace, base_currency, live_graph,
analyze_live, simulate_orders, stats_output):
"""Run a backtest for the given algorithm.
This is shared between the cli and :func:`catalyst.run_algo`.
"""
if algotext is not None:
if local_namespace:
ip = get_ipython() # noqa
namespace = ip.user_ns
else:
namespace = {}
for assign in defines:
try:
name, value = assign.split('=', 2)
except ValueError:
raise ValueError(
'invalid define %r, should be of the form name=value' %
assign, )
try:
# evaluate in the same namespace so names may refer to
# eachother
namespace[name] = eval(value, namespace)
except Exception as e:
raise ValueError(
'failed to execute definition for name %r: %s' %
(name, e), )
elif defines:
raise _RunAlgoError(
'cannot pass define without `algotext`',
"cannot pass '-D' / '--define' without '-t' / '--algotext'",
)
else:
namespace = {}
if algofile is not None:
algotext = algofile.read()
if print_algo:
if PYGMENTS:
highlight(
algotext,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout,
)
else:
click.echo(algotext)
mode = 'paper-trading' if simulate_orders else 'live-trading' \
if live else 'backtest'
log.info('running algo in {mode} mode'.format(mode=mode))
exchange_name = exchange
if exchange_name is None:
raise ValueError('Please specify at least one exchange.')
exchange_list = [x.strip().lower() for x in exchange.split(',')]
exchanges = dict()
for exchange_name in exchange_list:
exchanges[exchange_name] = get_exchange(
exchange_name=exchange_name,
base_currency=base_currency,
must_authenticate=(live and not simulate_orders),
skip_init=True,
)
open_calendar = get_calendar('OPEN')
env = TradingEnvironment(
load=partial(load_crypto_market_data,
environ=environ,
start_dt=start,
end_dt=end),
environ=environ,
exchange_tz='UTC',
asset_db_path=None # We don't need an asset db, we have exchanges
)
env.asset_finder = ExchangeAssetFinder(exchanges=exchanges)
def choose_loader(column):
bound_cols = TradingPairPricing.columns
if column in bound_cols:
return ExchangePricingLoader(data_frequency)
raise ValueError("No PipelineLoader registered for column %s." %
column)
if live:
start = pd.Timestamp.utcnow()
# TODO: fix the end data.
end = start + timedelta(hours=8760)
data = DataPortalExchangeLive(exchanges=exchanges,
asset_finder=env.asset_finder,
trading_calendar=open_calendar,
first_trading_day=pd.to_datetime(
'today', utc=True))
def fetch_capital_base(exchange, attempt_index=0):
"""
Fetch the base currency amount required to bootstrap
the algorithm against the exchange.
The algorithm cannot continue without this value.
:param exchange: the targeted exchange
:param attempt_index:
:return capital_base: the amount of base currency available for
trading
"""
try:
log.debug('retrieving capital base in {} to bootstrap '
'exchange {}'.format(base_currency, exchange_name))
balances = exchange.get_balances()
except ExchangeRequestError as e:
if attempt_index < 20:
log.warn('could not retrieve balances on {}: {}'.format(
exchange.name, e))
sleep(5)
return fetch_capital_base(exchange, attempt_index + 1)
else:
raise ExchangeRequestErrorTooManyAttempts(
attempts=attempt_index, error=e)
if base_currency in balances:
base_currency_available = balances[base_currency]['free']
log.info(
'base currency available in the account: {} {}'.format(
base_currency_available, base_currency))
return base_currency_available
else:
raise BaseCurrencyNotFoundError(base_currency=base_currency,
exchange=exchange_name)
if not simulate_orders:
for exchange_name in exchanges:
exchange = exchanges[exchange_name]
balance = fetch_capital_base(exchange)
if balance < capital_base:
raise NotEnoughCapitalError(
exchange=exchange_name,
base_currency=base_currency,
balance=balance,
capital_base=capital_base,
)
sim_params = create_simulation_parameters(start=start,
end=end,
capital_base=capital_base,
emission_rate='minute',
data_frequency='minute')
# TODO: use the constructor instead
sim_params._arena = 'live'
algorithm_class = partial(
ExchangeTradingAlgorithmLive,
exchanges=exchanges,
algo_namespace=algo_namespace,
live_graph=live_graph,
simulate_orders=simulate_orders,
stats_output=stats_output,
analyze_live=analyze_live,
)
elif exchanges:
# Removed the existing Poloniex fork to keep things simple
# We can add back the complexity if required.
# I don't think that we should have arbitrary price data bundles
# Instead, we should center this data around exchanges.
# We still need to support bundles for other misc data, but we
# can handle this later.
data = DataPortalExchangeBacktest(
exchange_names=[exchange_name for exchange_name in exchanges],
asset_finder=None,
trading_calendar=open_calendar,
first_trading_day=start,
last_available_session=end)
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=capital_base,
data_frequency=data_frequency,
emission_rate=data_frequency,
)
algorithm_class = partial(ExchangeTradingAlgorithmBacktest,
exchanges=exchanges)
elif bundle is not None:
bundle_data = load(
bundle,
environ,
bundle_timestamp,
)
prefix, connstr = re.split(
r'sqlite:///',
str(bundle_data.asset_finder.engine.url),
maxsplit=1,
)
if prefix:
raise ValueError(
"invalid url %r, must begin with 'sqlite:///'" %
str(bundle_data.asset_finder.engine.url), )
env = TradingEnvironment(asset_db_path=connstr, environ=environ)
first_trading_day = \
bundle_data.equity_minute_bar_reader.first_trading_day
data = DataPortal(
env.asset_finder,
open_calendar,
first_trading_day=first_trading_day,
equity_minute_reader=bundle_data.equity_minute_bar_reader,
equity_daily_reader=bundle_data.equity_daily_bar_reader,
adjustment_reader=bundle_data.adjustment_reader,
)
perf = algorithm_class(
namespace=namespace,
env=env,
get_pipeline_loader=choose_loader,
sim_params=sim_params,
**{
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze,
} if algotext is None else {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext,
}).run(
data,
overwrite_sim_params=False,
)
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the catalyst magic not write any data
perf.to_pickle(output)
return perf
def generate_minute_test_data(first_day,
last_day,
starting_open,
starting_volume,
multipliers_list,
path):
"""
Utility method to generate fake minute-level CSV data.
:param first_day: first trading day
:param last_day: last trading day
:param starting_open: first open value, raw value.
:param starting_volume: first volume value, raw value.
:param multipliers_list: ordered list of pd.Timestamp -> float, one per day
in the range
:param path: path to save the CSV
:return: None
"""
full_minutes = BcolzMinuteBarWriter.full_minutes_for_days(
first_day, last_day)
minutes_count = len(full_minutes)
minutes = TradingEnvironment.instance().minutes_for_days_in_range(
first_day, last_day)
o = np.zeros(minutes_count, dtype=np.uint32)
h = np.zeros(minutes_count, dtype=np.uint32)
l = np.zeros(minutes_count, dtype=np.uint32)
c = np.zeros(minutes_count, dtype=np.uint32)
v = np.zeros(minutes_count, dtype=np.uint32)
last_open = starting_open * 1000
last_volume = starting_volume
for minute in minutes:
# ugly, but works
idx = full_minutes.searchsorted(minute)
new_open = last_open + round((random.random() * 5), 2)
o[idx] = new_open
h[idx] = new_open + round((random.random() * 10000), 2)
l[idx] = new_open - round((random.random() * 10000), 2)
c[idx] = (h[idx] + l[idx]) / 2
v[idx] = int(last_volume + (random.randrange(-10, 10) * 1e4))
last_open = o[idx]
last_volume = v[idx]
# now deal with multipliers
if len(multipliers_list) > 0:
for idx, multiplier_info in enumerate(multipliers_list):
start_idx = idx * 390
end_idx = start_idx + 390
# dividing by the multipler because we're going backwards
# and generating the original data that will then be adjusted.
o[start_idx:end_idx] /= multiplier_info[1]
h[start_idx:end_idx] /= multiplier_info[1]
l[start_idx:end_idx] /= multiplier_info[1]
c[start_idx:end_idx] /= multiplier_info[1]
v[start_idx:end_idx] *= multiplier_info[1]
df = pd.DataFrame({
"open": o,
"high": h,
"low": l,
"close": c,
"volume": v
}, columns=[
"open",
"high",
"low",
"close",
"volume"
], index=minutes)
df.to_csv(path, index_label="minute")
def generate_daily_test_data(first_day,
last_day,
starting_open,
starting_volume,
multipliers_list,
path):
days = TradingEnvironment.instance().days_in_range(first_day, last_day)
days_count = len(days)
o = np.zeros(days_count, dtype=np.uint32)
h = np.zeros(days_count, dtype=np.uint32)
l = np.zeros(days_count, dtype=np.uint32)
c = np.zeros(days_count, dtype=np.uint32)
v = np.zeros(days_count, dtype=np.uint32)
last_open = starting_open * 1000
last_volume = starting_volume
for idx in range(days_count):
new_open = last_open + round((random.random() * 5), 2)
o[idx] = new_open
h[idx] = new_open + round((random.random() * 10000), 2)
l[idx] = new_open - round((random.random() * 10000), 2)
c[idx] = (h[idx] + l[idx]) / 2
v[idx] = int(last_volume + (random.randrange(-10, 10) * 1e4))
last_open = o[idx]
last_volume = v[idx]
# now deal with multipliers
if len(multipliers_list) > 0:
range_start = 0
for multiplier_info in multipliers_list:
range_end = days.searchsorted(multiplier_info[0])
# dividing by the multiplier because we're going backwards
# and generating the original data that will then be adjusted.
o[range_start:range_end] /= multiplier_info[1]
h[range_start:range_end] /= multiplier_info[1]
l[range_start:range_end] /= multiplier_info[1]
c[range_start:range_end] /= multiplier_info[1]
v[range_start:range_end] *= multiplier_info[1]
range_start = range_end
df = pd.DataFrame({
"open": o,
"high": h,
"low": l,
"close": c,
"volume": v
}, columns=[
"open",
"high",
"low",
"close",
"volume"
], index=days)
df.to_csv(path, index_label="day")
