mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-01 16:52:26 +08:00
Got trading algorithm and related pieces
This commit is contained in:
Frederic Fortier
@@ -0,0 +1,143 @@
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from datetime import time
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import logbook
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import catalyst.protocol as zp
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from catalyst.algorithm import TradingAlgorithm
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from catalyst.exchange.exchange_clock import ExchangeClock
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from catalyst.gens.tradesimulation import AlgorithmSimulator
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from catalyst.errors import OrderInBeforeTradingStart
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from catalyst.utils.input_validation import error_keywords
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from catalyst.utils.api_support import (
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api_method,
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disallowed_in_before_trading_start)
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from catalyst.utils.calendars.trading_calendar import days_at_time
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log = logbook.Logger("ExchangeTradingAlgorithm")
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class ExchangeAlgorithmExecutor(AlgorithmSimulator):
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def __init__(self, *args, **kwargs):
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super(self.__class__, self).__init__(*args, **kwargs)
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class ExchangeTradingAlgorithm(TradingAlgorithm):
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def __init__(self, *args, **kwargs):
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self.exchange = kwargs.pop('exchange', None)
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self.orders = {}
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super(self.__class__, self).__init__(*args, **kwargs)
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log.info("initialization done")
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def _create_clock(self):
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# This method is taken from TradingAlgorithm.
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# The clock has been replaced to use RealtimeClock
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trading_o_and_c = self.trading_calendar.schedule.ix[
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self.sim_params.sessions]
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market_closes = trading_o_and_c['market_close']
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minutely_emission = False
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if self.sim_params.data_frequency == 'minute':
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market_opens = trading_o_and_c['market_open']
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minutely_emission = self.sim_params.emission_rate == "minute"
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else:
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# in daily mode, we want to have one bar per session, timestamped
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# as the last minute of the session.
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market_opens = market_closes
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# The calendar's execution times are the minutes over which we actually
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# want to run the clock. Typically the execution times simply adhere to
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# the market open and close times. In the case of the futures calendar,
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# for example, we only want to simulate over a subset of the full 24
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# hour calendar, so the execution times dictate a market open time of
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# 6:31am US/Eastern and a close of 5:00pm US/Eastern.
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execution_opens = \
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self.trading_calendar.execution_time_from_open(market_opens)
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execution_closes = \
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self.trading_calendar.execution_time_from_close(market_closes)
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# FIXME generalize these values
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before_trading_start_minutes = days_at_time(
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self.sim_params.sessions,
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time(8, 45),
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"US/Eastern"
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)
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return ExchangeClock(
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self.sim_params.sessions,
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execution_opens,
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execution_closes,
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before_trading_start_minutes,
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minute_emission=minutely_emission,
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time_skew=self.exchange.time_skew
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)
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def _create_generator(self, sim_params):
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# Call the simulation trading algorithm for side-effects:
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# it creates the perf tracker
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TradingAlgorithm._create_generator(self, sim_params)
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self.trading_client = ExchangeAlgorithmExecutor(
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self,
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sim_params,
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self.data_portal,
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self._create_clock(),
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self._create_benchmark_source(),
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self.restrictions,
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universe_func=self._calculate_universe
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)
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return self.trading_client.transform()
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def updated_portfolio(self):
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return self.exchange.portfolio
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def updated_account(self):
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return self.exchange.account
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@api_method
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@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
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def order(self,
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asset,
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amount,
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limit_price=None,
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stop_price=None,
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style=None):
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amount, style = self._calculate_order(asset, amount,
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limit_price, stop_price, style)
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return self.exchange.order(asset, amount, limit_price, stop_price, style)
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@api_method
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def batch_market_order(self, share_counts):
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raise NotImplementedError()
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@error_keywords(sid='Keyword argument `sid` is no longer supported for '
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'get_open_orders. Use `asset` instead.')
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@api_method
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def get_open_orders(self, asset=None):
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return self.exchange.get_open_orders(asset)
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@api_method
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def get_order(self, order_id):
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return self.exchange.get_order(order_id)
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@api_method
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def cancel_order(self, order_param):
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order_id = order_param
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if isinstance(order_param, zp.Order):
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order_id = order_param.id
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self.exchange.cancel_order(order_id)
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@@ -6,6 +6,7 @@ import json
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import time
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import requests
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import pandas as pd
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import collections
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from catalyst.protocol import Portfolio, Account
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# from websocket import create_connection
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from catalyst.exchange.exchange import Exchange
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@@ -15,6 +16,7 @@ from catalyst.finance.execution import (MarketOrder,
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LimitOrder,
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StopOrder,
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StopLimitOrder)
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from catalyst.data.data_portal import BASE_FIELDS
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BITFINEX_URL = 'https://api.bitfinex.com'
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BITFINEX_KEY = 'hjZ7DZzwbBZsIZPWeSSQtrWCPNwyhxw96r3LnY7jtOH'
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@@ -84,6 +86,11 @@ class Bitfinex(Exchange):
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return request
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def _get_v2_symbol(self, asset):
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pair = asset.symbol.split('_')
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symbol = 't' + pair[0].upper() + pair[1].upper()
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return symbol
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def _get_v2_symbols(self, assets):
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"""
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Workaround to support Bitfinex v2
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@@ -95,9 +102,7 @@ class Bitfinex(Exchange):
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v2_symbols = []
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for asset in assets:
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pair = asset.symbol.split('_')
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symbol = 't' + pair[0].upper() + pair[1].upper()
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v2_symbols.append(symbol)
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v2_symbols.append(self._get_v2_symbol(asset))
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return v2_symbols
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@@ -214,13 +219,99 @@ class Bitfinex(Exchange):
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@property
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def time_skew(self):
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# TODO: research the time skew conditions
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return None
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return pd.Timedelta('0s')
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def subscribe_to_market_data(self, symbol):
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pass
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def get_spot_value(self, assets, field, dt, data_frequency):
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raise NotImplementedError()
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def get_spot_value(self, assets, field, dt=None, data_frequency='minute'):
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"""
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Public API method that returns a scalar value representing the value
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of the desired asset's field at either the given dt.
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Parameters
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----------
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assets : Asset, ContinuousFuture, or iterable of same.
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The asset or assets whose data is desired.
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field : {'open', 'high', 'low', 'close', 'volume',
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'price', 'last_traded'}
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The desired field of the asset.
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dt : pd.Timestamp
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The timestamp for the desired value.
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data_frequency : str
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The frequency of the data to query; i.e. whether the data is
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'daily' or 'minute' bars
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Returns
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-------
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value : float, int, or pd.Timestamp
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The spot value of ``field`` for ``asset`` The return type is based
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on the ``field`` requested. If the field is one of 'open', 'high',
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'low', 'close', or 'price', the value will be a float. If the
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``field`` is 'volume' the value will be a int. If the ``field`` is
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'last_traded' the value will be a Timestamp.
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Bitfinex timeframes
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-------------------
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Available values: '1m', '5m', '15m', '30m', '1h', '3h', '6h', '12h',
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'1D', '7D', '14D', '1M'
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"""
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if field not in BASE_FIELDS:
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raise KeyError('Invalid column: ' + str(field))
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if isinstance(assets, collections.Iterable):
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values = list()
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for asset in assets:
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value = self.get_single_spot_value(
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asset, field, data_frequency)
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values.append(value)
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return values
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else:
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return self.get_single_spot_value(
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assets, field, data_frequency)
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def get_single_spot_value(self, asset, field, data_frequency):
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symbol = self._get_v2_symbol(asset)
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log.debug('fetching spot value for symbol {}'.format(symbol))
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if data_frequency == 'minute':
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frequency = '1m'
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elif data_frequency == 'daily':
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frequency = '1d'
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else:
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raise NotImplementedError(
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'Unsupported frequency %s' % data_frequency
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)
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request = requests.get(
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'{url}/v2/candles/trade:{frequency}:{symbol}/last'.format(
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url=self.url,
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frequency=frequency,
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symbol=symbol
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)
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)
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candles = request.json()
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if 'message' in candles:
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raise ValueError(
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'Unable to retrieve candles: %s' % candles['message']
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)
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ohlc = dict(
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open=candles[1],
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high=candles[3],
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low=candles[4],
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close=candles[2],
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volume=candles[5],
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price=candles[2],
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last_traded=pd.Timestamp.utcfromtimestamp(candles[0] / 1000.0),
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)
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if not field in ohlc:
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raise KeyError('Invalid column: ' + str(field))
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return ohlc[field]
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def order(self, asset, amount, limit_price, stop_price, style):
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"""Place an order.
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@@ -0,0 +1,58 @@
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pandas as pd
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from catalyst.data.data_portal import DataPortal
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from logbook import Logger
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log = Logger('DataPortalExchange')
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class DataPortalExchange(DataPortal):
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def __init__(self, exchange, *args, **kwargs):
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self.exchange = exchange
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super(DataPortalExchange, self).__init__(*args, **kwargs)
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def get_history_window(self,
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assets,
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end_dt,
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bar_count,
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frequency,
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field,
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data_frequency,
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ffill=True):
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history_window = super(self.__class__, self).get_history_window(
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assets,
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end_dt,
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bar_count,
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frequency,
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field,
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data_frequency,
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ffill)
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# The returned dataframe contains today's value as a NaN because
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# end_dt points to the current wall clock. We drop today's
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# value to be in sync with the simulation's behavior.
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today = pd.to_datetime('now').date()
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return history_window[history_window.index.date != today]
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def get_spot_value(self, assets, field, dt, data_frequency):
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return self.exchange.get_spot_value(assets, field, dt, data_frequency)
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def get_adjusted_value(self, asset, field, dt,
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perspective_dt,
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data_frequency,
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spot_value=None):
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raise NotImplementedError("get_adjusted_value is not implemented yet!")
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@@ -184,6 +184,32 @@ class Exchange:
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@abstractmethod
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def get_spot_value(self, assets, field, dt, data_frequency):
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"""
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Public API method that returns a scalar value representing the value
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of the desired asset's field at either the given dt.
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Parameters
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----------
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assets : Asset, ContinuousFuture, or iterable of same.
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The asset or assets whose data is desired.
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field : {'open', 'high', 'low', 'close', 'volume',
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'price', 'last_traded'}
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The desired field of the asset.
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dt : pd.Timestamp
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The timestamp for the desired value.
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data_frequency : str
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The frequency of the data to query; i.e. whether the data is
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'daily' or 'minute' bars
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Returns
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-------
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value : float, int, or pd.Timestamp
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The spot value of ``field`` for ``asset`` The return type is based
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on the ``field`` requested. If the field is one of 'open', 'high',
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'low', 'close', or 'price', the value will be a float. If the
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``field`` is 'volume' the value will be a int. If the ``field`` is
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'last_traded' the value will be a Timestamp.
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"""
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pass
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@abc.abstractmethod
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@@ -0,0 +1,81 @@
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
|
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#
|
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# http://www.apache.org/licenses/LICENSE-2.0
|
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#
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# Unless required by applicable law or agreed to in writing, software
|
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
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# limitations under the License.
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from time import sleep
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from logbook import Logger
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import pandas as pd
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from catalyst.gens.sim_engine import (
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BAR,
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SESSION_START,
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SESSION_END,
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MINUTE_END,
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BEFORE_TRADING_START_BAR
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)
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log = Logger('ExchangeClock')
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class ExchangeClock(object):
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"""Realtime clock for live trading.
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This class is a drop-in replacement for
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:class:`zipline.gens.sim_engine.MinuteSimulationClock`.
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This is a stripped down version because crypto exchanges run around the clock.
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The :param:`time_skew` parameter represents the time difference between
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the Broker and the live trading machine's clock.
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"""
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def __init__(self,
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sessions,
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execution_opens,
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execution_closes,
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before_trading_start_minutes,
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minute_emission,
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time_skew=pd.Timedelta("0s")):
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self.sessions = sessions
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self.execution_opens = execution_opens
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self.execution_closes = execution_closes
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self.before_trading_start_minutes = before_trading_start_minutes
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self.minute_emission = minute_emission
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self.time_skew = time_skew
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self._last_emit = None
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self._before_trading_start_bar_yielded = False
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# It is expected to have this clock created once a day (ideally prior
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# to BEFORE_TRADING_START_BAR event). Multiple days (sessions) are
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# not supported.
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assert len(self.sessions) == 1
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def __iter__(self):
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yield self.sessions[0], SESSION_START
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while True:
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current_time = pd.Timestamp.utcnow()
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server_time = (current_time + self.time_skew).floor('1 min')
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if (self._last_emit is None or
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server_time - self._last_emit >=
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pd.Timedelta('1 minute')):
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self._last_emit = server_time
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yield server_time, BAR
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if self.minute_emission:
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yield server_time, MINUTE_END
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else:
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sleep(1)
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@@ -38,7 +38,7 @@ class BaseExchangeTestCase():
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pass
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@abstractmethod
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def test_spot_value(self):
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def test_get_spot_value(self):
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pass
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@abstractmethod
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@@ -64,8 +64,20 @@ class BitfinexTestCase(BaseExchangeTestCase):
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log.info('canceled order: {}'.format(response))
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pass
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def test_spot_value(self):
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log.info('spot valud not implemented')
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def test_get_spot_value(self):
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log.info('spot value not implemented')
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bitfinex = Bitfinex()
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assets = [
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bitfinex.get_asset('eth_usd'),
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bitfinex.get_asset('etc_usd'),
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bitfinex.get_asset('eos_usd'),
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]
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# assets = bitfinex.get_asset('eth_usd')
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value = bitfinex.get_spot_value(
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assets=assets,
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field='close',
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data_frequency='minute'
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)
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pass
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def test_tickers(self):
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Reference in New Issue
Block a user