Source code for

# Copyright 2017 Quantopian, Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import abstractmethod
import math
import numpy as np
from pandas import isnull
from toolz import merge

from zipline.assets import Equity, Future
from zipline.errors import HistoryWindowStartsBeforeData
from import AllowedAssetMarker, FinancialModelMeta
from import create_transaction
from zipline.utils.cache import ExpiringCache
from zipline.utils.dummy import DummyMapping
from zipline.utils.input_validation import (

SELL = 1 << 0
BUY = 1 << 1
STOP = 1 << 2
LIMIT = 1 << 3

SQRT_252 = math.sqrt(252)


class LiquidityExceeded(Exception):

def fill_price_worse_than_limit_price(fill_price, order):
    """Checks whether the fill price is worse than the order's limit price.

    fill_price: float
        The price to check.

        The order whose limit price to check.

    bool: Whether the fill price is above the limit price (for a buy) or below
    the limit price (for a sell).
    if order.limit:
        # this is tricky! if an order with a limit price has reached
        # the limit price, we will try to fill the order. do not fill
        # these shares if the impacted price is worse than the limit
        # price. return early to avoid creating the transaction.

        # buy order is worse if the impacted price is greater than
        # the limit price. sell order is worse if the impacted price
        # is less than the limit price
        if (order.direction > 0 and fill_price > order.limit) or (
            order.direction < 0 and fill_price < order.limit
            return True

    return False

[docs]class SlippageModel(metaclass=FinancialModelMeta): """Abstract base class for slippage models. Slippage models are responsible for the rates and prices at which orders fill during a simulation. To implement a new slippage model, create a subclass of :class:`` and implement :meth:`process_order`. Methods ------- process_order(data, order) Attributes ---------- volume_for_bar : int Number of shares that have already been filled for the currently-filling asset in the current minute. This attribute is maintained automatically by the base class. It can be used by subclasses to keep track of the total amount filled if there are multiple open orders for a single asset. Notes ----- Subclasses that define their own constructors should call ``super(<subclass name>, self).__init__()`` before performing other initialization. """ # Asset types that are compatible with the given model. allowed_asset_types = (Equity, Future) def __init__(self): self._volume_for_bar = 0 @property def volume_for_bar(self): return self._volume_for_bar
[docs] @abstractmethod def process_order(self, data, order): """Compute the number of shares and price to fill for ``order`` in the current minute. Parameters ---------- data : zipline.protocol.BarData The data for the given bar. order : The order to simulate. Returns ------- execution_price : float The price of the fill. execution_volume : int The number of shares that should be filled. Must be between ``0`` and ``order.amount - order.filled``. If the amount filled is less than the amount remaining, ``order`` will remain open and will be passed again to this method in the next minute. Raises ------ May be raised if no more orders should be processed for the current asset during the current bar. Notes ----- Before this method is called, :attr:`volume_for_bar` will be set to the number of shares that have already been filled for ``order.asset`` in the current minute. :meth:`process_order` is not called by the base class on bars for which there was no historical volume. """ raise NotImplementedError("process_order")
def simulate(self, data, asset, orders_for_asset): self._volume_for_bar = 0 volume = data.current(asset, "volume") if volume == 0: return # can use the close price, since we verified there's volume in this # bar. price = data.current(asset, "close") # BEGIN # # Remove this block after fixing data to ensure volume always has # corresponding price. if isnull(price): return # END 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: continue txn = None try: execution_price, execution_volume = self.process_order(data, order) if execution_price is not None: txn = create_transaction( order, data.current_dt, execution_price, execution_volume, ) except LiquidityExceeded: break if txn: self._volume_for_bar += abs(txn.amount) yield order, txn def asdict(self): return self.__dict__
class NoSlippage(SlippageModel): """A slippage model where all orders fill immediately and completely at the current close price. Notes ----- This is primarily used for testing. """ @staticmethod def process_order(data, order): return ( data.current(order.asset, "close"), order.amount, ) class EquitySlippageModel(SlippageModel, metaclass=AllowedAssetMarker): """Base class for slippage models which only support equities.""" allowed_asset_types = (Equity,) class FutureSlippageModel(SlippageModel, metaclass=AllowedAssetMarker): """Base class for slippage models which only support futures.""" allowed_asset_types = (Future,)
[docs]class VolumeShareSlippage(SlippageModel): """Model slippage as a quadratic function of percentage of historical volume. Orders to buy will be filled at:: price * (1 + price_impact * (volume_share ** 2)) Orders to sell will be filled at:: price * (1 - price_impact * (volume_share ** 2)) where ``price`` is the close price for the bar, and ``volume_share`` is the percentage of minutely volume filled, up to a max of ``volume_limit``. Parameters ---------- volume_limit : float, optional Maximum percent of historical volume that can fill in each bar. 0.5 means 50% of historical volume. 1.0 means 100%. Default is 0.025 (i.e., 2.5%). price_impact : float, optional Scaling coefficient for price impact. Larger values will result in more simulated price impact. Smaller values will result in less simulated price impact. Default is 0.1. """ def __init__( self, volume_limit=DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT, price_impact=0.1, ): super(VolumeShareSlippage, self).__init__() self.volume_limit = volume_limit self.price_impact = price_impact def __repr__(self): return """ {class_name}( volume_limit={volume_limit}, price_impact={price_impact}) """.strip().format( class_name=self.__class__.__name__, volume_limit=self.volume_limit, price_impact=self.price_impact, ) def process_order(self, data, order): volume = data.current(order.asset, "volume") max_volume = self.volume_limit * volume # price impact accounts for the total volume of transactions # created against the current minute bar remaining_volume = max_volume - self.volume_for_bar if remaining_volume < 1: # we can't fill any more transactions raise LiquidityExceeded() # the current order amount will be the min of the # volume available in the bar or the open amount. cur_volume = int(min(remaining_volume, abs(order.open_amount))) if cur_volume < 1: return None, None # tally the current amount into our total amount ordered. # total amount will be used to calculate price impact total_volume = self.volume_for_bar + cur_volume volume_share = min(total_volume / volume, self.volume_limit) price = data.current(order.asset, "close") # BEGIN # # Remove this block after fixing data to ensure volume always has # corresponding price. if isnull(price): return # END simulated_impact = ( volume_share**2 * math.copysign(self.price_impact, order.direction) * price ) impacted_price = price + simulated_impact if fill_price_worse_than_limit_price(impacted_price, order): return None, None return (impacted_price, math.copysign(cur_volume, order.direction))
[docs]class FixedSlippage(SlippageModel): """Simple model assuming a fixed-size spread for all assets. Parameters ---------- spread : float, optional Size of the assumed spread for all assets. Orders to buy will be filled at ``close + (spread / 2)``. Orders to sell will be filled at ``close - (spread / 2)``. Notes ----- This model does not impose limits on the size of fills. An order for an asset will always be filled as soon as any trading activity occurs in the order's asset, even if the size of the order is greater than the historical volume. """ def __init__(self, spread=0.0): super(FixedSlippage, self).__init__() self.spread = spread def __repr__(self): return "{class_name}(spread={spread})".format( class_name=self.__class__.__name__, spread=self.spread, ) def process_order(self, data, order): price = data.current(order.asset, "close") return (price + (self.spread / 2.0 * order.direction), order.amount)
class MarketImpactBase(SlippageModel): """Base class for slippage models which compute a simulated price impact according to a history lookback. """ NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 10.0 / 10000 def __init__(self): super(MarketImpactBase, self).__init__() self._window_data_cache = ExpiringCache() @abstractmethod def get_txn_volume(self, data, order): """Return the number of shares we would like to order in this minute. Parameters ---------- data : BarData order : Order Return ------ int : the number of shares """ raise NotImplementedError("get_txn_volume") @abstractmethod def get_simulated_impact( self, order, current_price, current_volume, txn_volume, mean_volume, volatility, ): """Calculate simulated price impact. Parameters ---------- order : The order being processed. current_price : Current price of the asset being ordered. current_volume : Volume of the asset being ordered for the current bar. txn_volume : Number of shares/contracts being ordered. mean_volume : Trailing ADV of the asset. volatility : Annualized daily volatility of returns. Return ------ int : impact on the current price. """ raise NotImplementedError("get_simulated_impact") def process_order(self, data, order): if order.open_amount == 0: return None, None minute_data = data.current(order.asset, ["volume", "high", "low"]) mean_volume, volatility = self._get_window_data(data, order.asset, 20) # Price to use is the average of the minute bar's open and close. price = np.mean([minute_data["high"], minute_data["low"]]) volume = minute_data["volume"] if not volume: return None, None txn_volume = int(min(self.get_txn_volume(data, order), abs(order.open_amount))) # If the computed transaction volume is zero or a decimal value, 'int' # will round it down to zero. In that case just bail. if txn_volume == 0: return None, None if mean_volume == 0 or np.isnan(volatility): # If this is the first day the contract exists or there is no # volume history, default to a conservative estimate of impact. simulated_impact = price * self.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT else: simulated_impact = self.get_simulated_impact( order=order, current_price=price, current_volume=volume, txn_volume=txn_volume, mean_volume=mean_volume, volatility=volatility, ) impacted_price = price + math.copysign(simulated_impact, order.direction) if fill_price_worse_than_limit_price(impacted_price, order): return None, None return impacted_price, math.copysign(txn_volume, order.direction) def _get_window_data(self, data, asset, window_length): """Internal utility method to return the trailing mean volume over the past 'window_length' days, and volatility of close prices for a specific asset. Parameters ---------- data : The BarData from which to fetch the daily windows. asset : The Asset whose data we are fetching. window_length : Number of days of history used to calculate the mean volume and close price volatility. Returns ------- (mean volume, volatility) """ try: values = self._window_data_cache.get(asset, data.current_session) except KeyError: try: # Add a day because we want 'window_length' complete days, # excluding the current day. volume_history = data.history( asset, "volume", window_length + 1, "1d", ) close_history = data.history( asset, "close", window_length + 1, "1d", ) except HistoryWindowStartsBeforeData: # If there is not enough data to do a full history call, return # values as if there was no data. return 0, np.NaN # Exclude the first value of the percent change array because it is # always just NaN. close_volatility = ( close_history[:-1] .pct_change()[1:] .std( skipna=False, ) ) values = { "volume": volume_history[:-1].mean(), "close": close_volatility * SQRT_252, } self._window_data_cache.set(asset, values, data.current_session) return values["volume"], values["close"] class VolatilityVolumeShare(MarketImpactBase): """Model slippage for futures contracts according to the following formula: new_price = price + (price * MI / 10000), where 'MI' is market impact, which is defined as: MI = eta * sigma * sqrt(psi) - ``eta`` is a constant which varies by root symbol. - ``sigma`` is 20-day annualized volatility. - ``psi`` is the volume traded in the given bar divided by 20-day ADV. Parameters ---------- volume_limit : float Maximum percentage (as a decimal) of a bar's total volume that can be traded. eta : float or dict Constant used in the market impact formula. If given a float, the eta for all futures contracts is the same. If given a dictionary, it must map root symbols to the eta for contracts of that symbol. """ NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 7.5 / 10000 allowed_asset_types = (Future,) def __init__(self, volume_limit, eta=ROOT_SYMBOL_TO_ETA): super(VolatilityVolumeShare, self).__init__() self.volume_limit = volume_limit # If 'eta' is a constant, use a dummy mapping to treat it as a # dictionary that always returns the same value. # NOTE: This dictionary does not handle unknown root symbols, so it may # be worth revisiting this behavior. if isinstance(eta, (int, float)): self._eta = DummyMapping(float(eta)) else: # Eta is a dictionary. If the user's dictionary does not provide a # value for a certain contract, fall back on the pre-defined eta # values per root symbol. self._eta = merge(ROOT_SYMBOL_TO_ETA, eta) def __repr__(self): if isinstance(self._eta, DummyMapping): # Eta is a constant, so extract it. eta = self._eta["dummy key"] else: eta = "<varies>" return "{class_name}(volume_limit={volume_limit}, eta={eta})".format( class_name=self.__class__.__name__, volume_limit=self.volume_limit, eta=eta, ) def get_simulated_impact( self, order, current_price, current_volume, txn_volume, mean_volume, volatility, ): try: eta = self._eta[order.asset.root_symbol] except Exception: eta = DEFAULT_ETA psi = txn_volume / mean_volume market_impact = eta * volatility * math.sqrt(psi) # We divide by 10,000 because this model computes to basis points. # To convert from bps to % we need to divide by 100, then again to # convert from % to fraction. return (current_price * market_impact) / 10000 def get_txn_volume(self, data, order): volume = data.current(order.asset, "volume") return volume * self.volume_limit class FixedBasisPointsSlippage(SlippageModel): """ Model slippage as a fixed percentage difference from historical minutely close price, limiting the size of fills to a fixed percentage of historical minutely volume. Orders to buy are filled at:: historical_price * (1 + (basis_points * 0.0001)) Orders to sell are filled at:: historical_price * (1 - (basis_points * 0.0001)) Fill sizes are capped at:: historical_volume * volume_limit Parameters ---------- basis_points : float, optional Number of basis points of slippage to apply for each fill. Default is 5 basis points. volume_limit : float, optional Fraction of trading volume that can be filled each minute. Default is 10% of trading volume. Notes ----- - A basis point is one one-hundredth of a percent. - This class, default-constructed, is zipline's default slippage model for equities. """ @expect_bounded( basis_points=(0, None), __funcname="FixedBasisPointsSlippage", ) @expect_strictly_bounded( volume_limit=(0, None), __funcname="FixedBasisPointsSlippage", ) def __init__(self, basis_points=5.0, volume_limit=0.1): super(FixedBasisPointsSlippage, self).__init__() self.basis_points = basis_points self.percentage = self.basis_points / 10000.0 self.volume_limit = volume_limit def __repr__(self): return """ {class_name}( basis_points={basis_points}, volume_limit={volume_limit}, ) """.strip().format( class_name=self.__class__.__name__, basis_points=self.basis_points, volume_limit=self.volume_limit, ) def process_order(self, data, order): volume = data.current(order.asset, "volume") max_volume = int(self.volume_limit * volume) price = data.current(order.asset, "close") shares_to_fill = min(abs(order.open_amount), max_volume - self.volume_for_bar) if shares_to_fill == 0: raise LiquidityExceeded() return ( price + price * (self.percentage * order.direction), shares_to_fill * order.direction, ) if __name__ == "__main__": f = EquitySlippageModel() # print(f.__meta__) print(f.__class__)