# Copyright 2016-present CERN – European Organization for Nuclear Research
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from datetime import datetime
from typing import List, Tuple
from qf_lib.backtesting.signals.signal import Signal
from qf_lib.backtesting.signals.signals_register import SignalsRegister
from qf_lib.common.tickers.tickers import Ticker
from qf_lib.containers.dataframe.qf_dataframe import QFDataFrame
from qf_lib.containers.series.qf_series import QFSeries
[docs]class BacktestSignalsRegister(SignalsRegister):
""" In memory implementation of Signals Register. """
def __init__(self):
self._signals_data = [] # type: List[Tuple[datetime, str, Signal]]
[docs] def save_signals(self, signals: List[Signal]):
"""
Add the provided signals to the list of all cached signals.
"""
self._signals_data.extend(
((signal.creation_time, self._generate_ticker_name(signal), signal) for signal in signals)
)
[docs] def get_signals(self) -> QFDataFrame:
df = QFDataFrame.from_records(self._signals_data, columns=["Date", "Ticker", "Signal"])
# Modify the dataframe to move all signals for certain tickers to separate columns and set the index to date
df = df.pivot_table(index='Date', columns='Ticker', values='Signal', aggfunc='first')
return QFDataFrame(df)
[docs] def get_signals_for_ticker(self, ticker: Ticker, alpha_model=None) -> QFSeries:
def signal_to_return(signal: Signal):
if alpha_model is None:
return signal.ticker == ticker
else:
return signal.ticker == ticker and str(signal.alpha_model) == str(alpha_model)
signals_data_for_ticker = [(d, s) for (d, _, s) in self._signals_data if signal_to_return(s)]
df = QFDataFrame.from_records(signals_data_for_ticker, columns=["Date", "Signal"])
df = df.set_index("Date").sort_index()
series = df.iloc[:, 0]
return series