Source code for

import numpy as np
import pandas as pd
from logbook import Logger
from redo import retry

from catalyst.assets._assets import TradingPair
from catalyst.constants import LOG_LEVEL
from import ExchangeRequestError
from import Blotter
from import CommissionModel
from import ORDER_STATUS
from import SlippageModel
from import create_transaction
from catalyst.utils.input_validation import expect_types

log = Logger('exchange_blotter', level=LOG_LEVEL)

class TradingPairFeeSchedule(CommissionModel):
    Calculates a commission for a transaction based on a per percentage fee.

    maker : float, optional
        The percentage maker fee.

    taker: float, optional
        The percentage taker fee.

    def __init__(self, maker=None, taker=None):
        self.maker = maker
        self.taker = taker

    def __repr__(self):
        return (
            '{class_name}(maker={maker}, '

    def get_maker_taker(self, asset):
        maker = self.maker if self.maker is not None else asset.maker
        taker = self.taker if self.taker is not None else asset.taker
        return maker, taker

    def calculate(self, order, transaction):
        Calculate the final fee based on the order parameters.

        :param order: Order
        :param transaction: Transaction

        :return float:
            The total commission.
        cost = abs(transaction.amount) * transaction.price

        asset = order.asset
        maker, taker = self.get_maker_taker(asset)

        multiplier = taker
        if order.limit is not None:
            multiplier = maker \
                if ((order.amount > 0 and order.limit < transaction.price)
                    or (order.amount < 0 and
                        order.limit > transaction.price)) \
                and order.limit_reached else taker

        fee = cost * multiplier
        return fee

[docs]class TradingPairFixedSlippage(SlippageModel): """ Model slippage as a fixed value. Parameters ---------- slippage : float, optional fixed slippage will be added to buys and subtracted from sells. """ def __init__(self, slippage=0.0001): super(TradingPairFixedSlippage, self).__init__() self.slippage = slippage def __repr__(self): return '{class_name}(slippage={slippage})'.format( class_name=self.__class__.__name__, slippage=self.slippage, ) def simulate(self, data, asset, orders_for_asset): self._volume_for_bar = 0 price = data.current(asset, 'close') dt = data.current_dt for order in orders_for_asset: if order.open_amount == 0: continue order.check_triggers(price, dt) if not order.triggered: 'order has not reached the trigger at current ' 'price {}'.format(price) ) continue execution_price, execution_volume = self.process_order(data, order) if execution_price is not None: transaction = create_transaction( order, dt, execution_price, execution_volume ) self._volume_for_bar += abs(transaction.amount) yield order, transaction def process_order(self, data, order): price = data.current(order.asset, 'close') if order.amount > 0: # Buy order adj_price = price * (1 + self.slippage) else: # Sell order adj_price = price * (1 - self.slippage) log.debug('added slippage to price: {} => {}'.format(price, adj_price)) return adj_price, order.amount
class ExchangeBlotter(Blotter): def __init__(self, *args, **kwargs): self.simulate_orders = kwargs.pop('simulate_orders', False) self.attempts = kwargs.pop('attempts', False) self.exchanges = kwargs.pop('exchanges', None) if not self.exchanges: raise ValueError( 'ExchangeBlotter must have an `exchanges` attribute.' ) super(ExchangeBlotter, self).__init__(*args, **kwargs) # Using the equity models for now # We may be able to define more sophisticated models based on the fee # structure of each exchange. self.slippage_models = { TradingPair: TradingPairFixedSlippage() } self.commission_models = { TradingPair: TradingPairFeeSchedule() } def exchange_order(self, asset, amount, style=None): exchange = self.exchanges[] return exchange.order( asset, amount, style ) @expect_types(asset=TradingPair) def order(self, asset, amount, style, order_id=None): log.debug('ordering {} {}'.format(amount, asset.symbol)) if amount == 0: log.warn('skipping 0 amount orders') return None if self.simulate_orders: return super(ExchangeBlotter, self).order( asset, amount, style, order_id ) else: order = retry( action=self.exchange_order, attempts=self.attempts['order_attempts'], sleeptime=self.attempts['retry_sleeptime'], retry_exceptions=(ExchangeRequestError,), cleanup=lambda: log.warn('Ordering again.'), args=(asset, amount, style), ) self.open_orders[order.asset].append(order) self.orders[] = order self.new_orders.append(order) return def check_open_orders(self): """ Loop through the list of open orders in the Portfolio object. For each executed order found, create a transaction and apply to the Portfolio. Returns ------- list[Transaction] """ for asset in self.open_orders: exchange = self.exchanges[] for order in self.open_orders[asset]: log.debug('found open order: {}'.format( transactions = exchange.process_order(order) # This is a temporary measure, we should really update all # trades, not just when the order gets filled. I just think # that this is safer until we have a robust way to track # the trades already processed by the algo. We can't loose # them if the algo shuts down. if transactions and order.status == ORDER_STATUS.FILLED: avg_price = np.average( a=[t.price for t in transactions], weights=[t.amount for t in transactions], ) ostatus = 'filled' if order.open_amount == 0 else 'partial' '{} order {} / {}: {}, avg price: {}'.format( ostatus,, asset.symbol, order.filled, avg_price, ) ) for transaction in transactions: yield order, transaction elif order.status == ORDER_STATUS.CANCELLED: yield order, None else: delta = pd.Timestamp.utcnow() - order.dt '{exchange} order {order_id} for {symbol} still open ' 'after {delta}'.format(,, delta=delta, symbol=order.asset.symbol, ) ) def get_exchange_transactions(self): closed_orders = [] transactions = [] commissions = [] for order, txn in self.check_open_orders(): order.dt = txn.dt transactions.append(txn) if not closed_orders.append(order) return transactions, commissions, closed_orders def get_transactions(self, bar_data): if self.simulate_orders: return super(ExchangeBlotter, self).get_transactions(bar_data) else: return retry( action=self.get_exchange_transactions, attempts=self.attempts['get_transactions_attempts'], sleeptime=self.attempts['retry_sleeptime'], retry_exceptions=(ExchangeRequestError,), cleanup=lambda: log.warn( 'Fetching exchange transactions again.' ) )