Source code for qf_lib.backtesting.signals.backtest_signals_register

#     Copyright 2016-present CERN – European Organization for Nuclear Research
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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 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