def get_exchange(exchange_name):
exchange_auth = get_exchange_auth(exchange_name)
if exchange_name == 'bitfinex':
return Bitfinex(
key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=None, # TODO: make optional at the exchange
portfolio=None
)
elif exchange_name == 'bittrex':
return Bittrex(
key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=None,
portfolio=None
)
elif exchange_name == 'poloniex':
return Poloniex(
key=exchange_auth['key'],
secret=exchange_auth['secret'],
base_currency=None,
portfolio=None
)
else:
raise ExchangeNotFoundError(exchange_name=exchange_name)
def setup(self):
print ('creating poloniex object')
auth = get_exchange_auth('poloniex')
self.exchange = Poloniex(
key=auth['key'],
secret=auth['secret'],
base_currency='btc'
)
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 load_crypto_market_data(trading_day=None, trading_days=None,
bm_symbol=None, bundle=None, bundle_data=None,
environ=None, exchange=None, start_dt=None,
end_dt=None):
if trading_day is None:
trading_day = get_calendar('OPEN').trading_day
# TODO: consider making configurable
bm_symbol = 'btc_usdt'
# if trading_days is None:
# trading_days = get_calendar('OPEN').schedule
# if start_dt is None:
start_dt = get_calendar('OPEN').first_trading_session
if end_dt is None:
end_dt = pd.Timestamp.utcnow()
# We expect to have benchmark and treasury data that's current up until
# **two** full trading days prior to the most recently completed trading
# day.
# Example:
# On Thu Oct 22 2015, the previous completed trading day is Wed Oct 21.
# However, data for Oct 21 doesn't become available until the early morning
# hours of Oct 22. This means that there are times on the 22nd at which we
# cannot reasonably expect to have data for the 21st available. To be
# conservative, we instead expect that at any time on the 22nd, we can
# download data for Tuesday the 20th, which is two full trading days prior
# to the date on which we're running a test.
# We'll attempt to download new data if the latest entry in our cache is
# before this date.
'''
if(bundle_data):
# If we are using the bundle to retrieve the cryptobenchmark, find the last
# date for which there is trading data in the bundle
asset = bundle_data.asset_finder.lookup_symbol(symbol=bm_symbol,as_of_date=None)
ix = bundle_data.daily_bar_reader._last_rows[asset.sid]
last_date = pd.to_datetime(bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
else:
last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
'''
last_date = trading_days[trading_days.get_loc(end_dt, method='ffill') - 1]
if exchange is None:
# This is exceptional, since placing the import at the module scope
# breaks things and it's only needed here
from catalyst.exchange.poloniex.poloniex import Poloniex
exchange = Poloniex('', '', '')
benchmark_asset = exchange.get_asset(bm_symbol)
# exchange.get_history_window() already ensures that we have the right data
# for the right dates
br = exchange.get_history_window_with_bundle(
assets=[benchmark_asset],
end_dt=last_date,
bar_count=pd.Timedelta(last_date - start_dt).days,
frequency='1d',
field='close',
data_frequency='daily',
force_auto_ingest=True)
br.columns = ['close']
br = br.pct_change(1).iloc[1:]
br.loc[start_dt] = 0
br = br.sort_index()
# Override first_date for treasury data since we have it for many more years
# and is independent of crypto data
first_date_treasury = pd.Timestamp('1990-01-02', tz='UTC')
tc = ensure_treasury_data(
bm_symbol,
first_date_treasury,
last_date,
end_dt,
environ,
)
benchmark_returns = br[br.index.slice_indexer(start_dt, last_date)]
treasury_curves = tc[
tc.index.slice_indexer(first_date_treasury, last_date)]
return benchmark_returns, treasury_curves