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507 lines
16 KiB
Python
507 lines
16 KiB
Python
#
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# Copyright 2017 Quantopian, Inc.
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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 __future__ import division
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from abc import abstractmethod
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import math
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import numpy as np
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from pandas import isnull
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from six import with_metaclass
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from toolz import merge
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from catalyst.assets import Equity, Future
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from catalyst.errors import HistoryWindowStartsBeforeData
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from catalyst.finance.constants import ROOT_SYMBOL_TO_ETA
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from catalyst.finance.shared import AllowedAssetMarker, FinancialModelMeta
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from catalyst.finance.transaction import create_transaction
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from catalyst.utils.cache import ExpiringCache
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from catalyst.utils.dummy import DummyMapping
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SELL = 1 << 0
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BUY = 1 << 1
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STOP = 1 << 2
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LIMIT = 1 << 3
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SQRT_252 = math.sqrt(252)
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DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT = 0.025
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DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT = 0.05
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class LiquidityExceeded(Exception):
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pass
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def fill_price_worse_than_limit_price(fill_price, order):
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"""
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Checks whether the fill price is worse than the order's limit price.
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Parameters
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----------
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fill_price: float
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The price to check.
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order: catalyst.finance.order.Order
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The order whose limit price to check.
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Returns
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-------
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bool: Whether the fill price is above the limit price (for a buy) or below
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the limit price (for a sell).
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"""
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if order.limit:
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# this is tricky! if an order with a limit price has reached
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# the limit price, we will try to fill the order. do not fill
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# these shares if the impacted price is worse than the limit
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# price. return early to avoid creating the transaction.
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# buy order is worse if the impacted price is greater than
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# the limit price. sell order is worse if the impacted price
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# is less than the limit price
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if (order.direction > 0 and fill_price > order.limit) or \
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(order.direction < 0 and fill_price < order.limit):
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return True
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return False
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class SlippageModel(with_metaclass(FinancialModelMeta)):
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"""Abstract interface for defining a slippage model.
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"""
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# Asset types that are compatible with the given model.
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allowed_asset_types = (Equity, Future)
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def __init__(self):
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self._volume_for_bar = 0
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@property
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def volume_for_bar(self):
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return self._volume_for_bar
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@abstractmethod
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def process_order(self, data, order):
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"""Process how orders get filled.
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Parameters
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----------
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data : BarData
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The data for the given bar.
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order : Order
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The order to simulate.
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Returns
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-------
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execution_price : float
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The price to execute the trade at.
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execution_volume : int
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The number of shares that could be filled. This may not be all
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the shares ordered in which case the order will be filled over
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multiple bars.
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"""
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pass
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def simulate(self, data, asset, orders_for_asset):
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self._volume_for_bar = 0
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volume = data.current(asset, "volume")
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if volume == 0:
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return
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# can use the close price, since we verified there's volume in this
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# bar.
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price = data.current(asset, "close")
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# BEGIN
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#
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# Remove this block after fixing data to ensure volume always has
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# corresponding price.
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if isnull(price):
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return
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# END
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dt = data.current_dt
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for order in orders_for_asset:
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if order.open_amount == 0:
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continue
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order.check_triggers(price, dt)
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if not order.triggered:
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continue
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txn = None
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try:
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execution_price, execution_volume = \
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self.process_order(data, order)
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if execution_price is not None:
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txn = create_transaction(
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order,
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data.current_dt,
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execution_price,
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execution_volume
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)
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except LiquidityExceeded:
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break
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if txn:
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self._volume_for_bar += abs(txn.amount)
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yield order, txn
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def asdict(self):
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return self.__dict__
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class EquitySlippageModel(with_metaclass(AllowedAssetMarker, SlippageModel)):
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"""
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Base class for slippage models which only support equities.
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"""
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allowed_asset_types = (Equity,)
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class FutureSlippageModel(with_metaclass(AllowedAssetMarker, SlippageModel)):
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"""
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Base class for slippage models which only support futures.
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"""
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allowed_asset_types = (Future,)
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class VolumeShareSlippage(SlippageModel):
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"""
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Model slippage as a function of the volume of contracts traded.
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"""
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def __init__(self, volume_limit=DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT,
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price_impact=0.1):
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super(VolumeShareSlippage, self).__init__()
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self.volume_limit = volume_limit
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self.price_impact = price_impact
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def __repr__(self):
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return """
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{class_name}(
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volume_limit={volume_limit},
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price_impact={price_impact})
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""".strip().format(class_name=self.__class__.__name__,
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volume_limit=self.volume_limit,
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price_impact=self.price_impact)
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def process_order(self, data, order):
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volume = data.current(order.asset, "volume")
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min_trade_size = order.asset.min_trade_size
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max_volume = self.volume_limit * volume
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# price impact accounts for the total volume of transactions
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# created against the current minute bar
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remaining_volume = max_volume - self.volume_for_bar
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if remaining_volume < min_trade_size:
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# we can't fill any more transactions
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raise LiquidityExceeded()
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# the current order amount will be the min of the
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# volume available in the bar or the open amount.
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cur_volume = min(remaining_volume, abs(order.open_amount))
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if cur_volume < min_trade_size:
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return None, None
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# tally the current amount into our total amount ordered.
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# total amount will be used to calculate price impact
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total_volume = self.volume_for_bar + cur_volume
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volume_share = min(total_volume / volume,
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self.volume_limit)
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price = data.current(order.asset, "close")
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# BEGIN
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#
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# Remove this block after fixing data to ensure volume always has
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# corresponding price.
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if isnull(price):
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return
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# END
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simulated_impact = volume_share ** 2 \
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* math.copysign(self.price_impact, order.direction) \
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* price
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impacted_price = price + simulated_impact
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if fill_price_worse_than_limit_price(impacted_price, order):
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return None, None
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return (
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impacted_price,
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math.copysign(cur_volume, order.direction)
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)
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class FixedSlippage(SlippageModel):
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"""
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Model slippage as a fixed spread.
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Parameters
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----------
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spread : float, optional
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spread / 2 will be added to buys and subtracted from sells.
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"""
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def __init__(self, spread=0.0):
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super(FixedSlippage, self).__init__()
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self.spread = spread
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def __repr__(self):
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return '{class_name}(spread={spread})'.format(
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class_name=self.__class__.__name__, spread=self.spread,
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)
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def process_order(self, data, order):
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price = data.current(order.asset, "close")
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return (
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price + (self.spread / 2.0 * order.direction),
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order.amount
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)
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class MarketImpactBase(SlippageModel):
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"""
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Base class for slippage models which compute a simulated price impact
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according to a history lookback.
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"""
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NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 10.0 / 10000
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def __init__(self):
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super(MarketImpactBase, self).__init__()
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self._window_data_cache = ExpiringCache()
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@abstractmethod
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def get_txn_volume(self, data, order):
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"""
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Return the number of shares we would like to order in this minute.
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Parameters
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----------
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data : BarData
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order : Order
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Return
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------
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int : the number of shares
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"""
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raise NotImplementedError('get_txn_volume')
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@abstractmethod
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def get_simulated_impact(self,
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order,
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current_price,
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current_volume,
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txn_volume,
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mean_volume,
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volatility):
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"""
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Calculate simulated price impact.
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Parameters
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----------
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order : The order being processed.
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current_price : Current price of the asset being ordered.
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current_volume : Volume of the asset being ordered for the current bar.
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txn_volume : Number of shares/contracts being ordered.
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mean_volume : Trailing ADV of the asset.
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volatility : Annualized daily volatility of volume.
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Return
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------
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int : impact on the current price.
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"""
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raise NotImplementedError('get_simulated_impact')
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def process_order(self, data, order):
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if order.open_amount == 0:
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return None, None
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minute_data = data.current(order.asset, ['volume', 'high', 'low'])
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mean_volume, volatility = self._get_window_data(data, order.asset, 20)
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# Price to use is the average of the minute bar's open and close.
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price = np.mean([minute_data['high'], minute_data['low']])
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volume = minute_data['volume']
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if not volume:
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return None, None
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txn_volume = int(
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min(self.get_txn_volume(data, order), abs(order.open_amount))
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)
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# If the computed transaction volume is zero or a decimal value, 'int'
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# will round it down to zero. In that case just bail.
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if txn_volume == 0:
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return None, None
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if mean_volume == 0 or np.isnan(volatility):
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# If this is the first day the contract exists or there is no
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# volume history, default to a conservative estimate of impact.
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simulated_impact = price * self.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT
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else:
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simulated_impact = self.get_simulated_impact(
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order=order,
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current_price=price,
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current_volume=volume,
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txn_volume=txn_volume,
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mean_volume=mean_volume,
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volatility=volatility,
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)
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impacted_price = \
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price + math.copysign(simulated_impact, order.direction)
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if fill_price_worse_than_limit_price(impacted_price, order):
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return None, None
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return impacted_price, math.copysign(txn_volume, order.direction)
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def _get_window_data(self, data, asset, window_length):
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"""
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Internal utility method to return the trailing mean volume over the
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past 'window_length' days, and volatility of close prices for a
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specific asset.
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Parameters
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----------
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data : The BarData from which to fetch the daily windows.
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asset : The Asset whose data we are fetching.
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window_length : Number of days of history used to calculate the mean
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volume and close price volatility.
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Returns
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-------
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(mean volume, volatility)
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"""
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try:
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values = self._window_data_cache.get(asset, data.current_session)
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except KeyError:
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try:
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# Add a day because we want 'window_length' complete days,
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# excluding the current day.
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volume_history = data.history(
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asset, 'volume', window_length + 1, '1d',
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)
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close_history = data.history(
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asset, 'close', window_length + 1, '1d',
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)
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except HistoryWindowStartsBeforeData:
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# If there is not enough data to do a full history call, return
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# values as if there was no data.
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return 0, np.NaN
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# Exclude the first value of the percent change array because it is
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# always just NaN.
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close_volatility = close_history[:-1].pct_change()[1:].std(
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skipna=False,
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)
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values = {
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'volume': volume_history[:-1].mean(),
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'close': close_volatility * SQRT_252,
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}
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self._window_data_cache.set(asset, values, data.current_session)
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return values['volume'], values['close']
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class VolatilityVolumeShare(MarketImpactBase):
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"""
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Model slippage for futures contracts according to the following formula:
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new_price = price + (price * MI / 10000),
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where 'MI' is market impact, which is defined as:
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MI = eta * sigma * sqrt(psi)
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Eta is a constant which varies by root symbol.
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Sigma is 20-day annualized volatility.
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Psi is the volume traded in the given bar divided by 20-day ADV.
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Parameters
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----------
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volume_limit : float
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Maximum percentage (as a decimal) of a bar's total volume that can be
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traded.
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eta : float or dict
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Constant used in the market impact formula. If given a float, the eta
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for all futures contracts is the same. If given a dictionary, it must
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map root symbols to the eta for contracts of that symbol.
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"""
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NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 7.5 / 10000
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allowed_asset_types = (Future,)
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def __init__(self, volume_limit, eta=ROOT_SYMBOL_TO_ETA):
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super(VolatilityVolumeShare, self).__init__()
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self.volume_limit = volume_limit
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# If 'eta' is a constant, use a dummy mapping to treat it as a
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# dictionary that always returns the same value.
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# NOTE: This dictionary does not handle unknown root symbols, so it may
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# be worth revisiting this behavior.
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if isinstance(eta, (int, float)):
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self._eta = DummyMapping(float(eta))
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else:
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# Eta is a dictionary. If the user's dictionary does not provide a
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# value for a certain contract, fall back on the pre-defined eta
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# values per root symbol.
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self._eta = merge(ROOT_SYMBOL_TO_ETA, eta)
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def __repr__(self):
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if isinstance(self._eta, DummyMapping):
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# Eta is a constant, so extract it.
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eta = self._eta['dummy key']
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else:
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eta = '<varies>'
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return '{class_name}(volume_limit={volume_limit}, eta={eta})'.format(
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class_name=self.__class__.__name__,
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volume_limit=self.volume_limit,
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eta=eta,
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)
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def get_simulated_impact(self,
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order,
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current_price,
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current_volume,
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txn_volume,
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mean_volume,
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volatility):
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eta = self._eta[order.asset.root_symbol]
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psi = txn_volume / mean_volume
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market_impact = eta * volatility * math.sqrt(psi)
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# We divide by 10,000 because this model computes to basis points.
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# To convert from bps to % we need to divide by 100, then again to
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# convert from % to fraction.
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return (current_price * market_impact) / 10000
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def get_txn_volume(self, data, order):
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volume = data.current(order.asset, 'volume')
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return volume * self.volume_limit
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