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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from qf_lib.common.enums.price_field import PriceField
from qf_lib.containers.dataframe.prices_dataframe import PricesDataFrame
from qf_lib.containers.series.cast_series import cast_series
from qf_lib.containers.series.prices_series import PricesSeries
from qf_lib.containers.series.simple_returns_series import SimpleReturnsSeries
[docs]def close_open_gap(prices_df: PricesDataFrame, initial_price: int = 1, transaction_cost_percentage: float = 0,
transaction_cost_value: float = 0) -> PricesSeries:
"""Calculates price changes during the night gap (opening price compared to closing price from the
previous day). May be interpreted as performance of strategy based on buying at close and selling at next open.
Parameters
----------
prices_df
PricesDataFrame of at least 2 series: PriceField.Open and PriceField.Close
initial_price
initial price of the timeseries. If no price will be specified, then it will be assumed to be 1.
transaction_cost_percentage
cost of a single transaction [%]; percentage of the transaction value
by default set to 0
can't have a non-zero value if transaction_cost_value is set!
transaction_cost_value
cost of a single transaction [currency of examined asset]
by default set to 0
can't have a non-zero value if transaction_cost_percentage is set!
Returns
-------
PricesSeries
price changes
"""
assert prices_df.num_of_rows > 1
assert transaction_cost_percentage >= 0 and transaction_cost_value >= 0
assert not (transaction_cost_percentage > 0 and transaction_cost_value > 0) # only one type may be used
o1 = prices_df[PriceField.Open]
c0 = prices_df[PriceField.Close].shift(1)
sell_price = o1
buy_price = c0
if transaction_cost_value > 0:
sell_price = o1 - transaction_cost_value
buy_price = c0 + transaction_cost_value
elif transaction_cost_percentage > 0:
sell_price = o1 * (1 - transaction_cost_percentage / 100)
buy_price = c0 * (1 + transaction_cost_percentage / 100)
ret_tms = (sell_price / buy_price) - 1
ret_tms = cast_series(ret_tms.iloc[1:], SimpleReturnsSeries)
prices_tms = ret_tms.to_prices(initial_price)
return prices_tms