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Extract signals generation from backtesting class

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This commit is contained in:
Matthias 2023-04-28 16:14:16 +02:00
parent 6e395ad7c9
commit 023c155a25
2 changed files with 48 additions and 45 deletions
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49
freqtrade/optimize/backtesting.py
View File

@ -9,7 +9,6 @@ from copy import deepcopy
from datetime import datetime, timedelta, timezone
from typing import Any, Dict, List, Optional, Tuple
import pandas as pd
from numpy import nan
from pandas import DataFrame
@ -28,7 +27,9 @@ from freqtrade.exchange import (amount_to_contract_precision, price_to_precision
from freqtrade.mixins import LoggingMixin
from freqtrade.optimize.backtest_caching import get_strategy_run_id
from freqtrade.optimize.bt_progress import BTProgress
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_rejected_signals,
generate_trade_signal_candles,
show_backtest_results,
store_backtest_analysis_results,
store_backtest_stats)
from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
@ -1296,53 +1297,13 @@ class Backtesting:
if (self.config.get('export', 'none') == 'signals' and
self.dataprovider.runmode == RunMode.BACKTEST):
self.processed_dfs[strategy_name] = self._generate_trade_signal_candles(
self.processed_dfs[strategy_name] = generate_trade_signal_candles(
preprocessed_tmp, results)
self.rejected_df[strategy_name] = self._generate_rejected_signals(
self.rejected_df[strategy_name] = generate_rejected_signals(
preprocessed_tmp, self.rejected_dict)
return min_date, max_date
def _generate_trade_signal_candles(self, preprocessed_df: Dict[str, pd.DataFrame],
bt_results: Dict[str, Any]) -> pd.DataFrame:
signal_candles_only = {}
for pair in preprocessed_df.keys():
signal_candles_only_df = DataFrame()
pairdf = preprocessed_df[pair]
resdf = bt_results['results']
pairresults = resdf.loc[(resdf["pair"] == pair)]
if pairdf.shape[0] > 0:
for t, v in pairresults.open_date.items():
allinds = pairdf.loc[(pairdf['date'] < v)]
signal_inds = allinds.iloc[[-1]]
signal_candles_only_df = pd.concat([
signal_candles_only_df.infer_objects(),
signal_inds.infer_objects()])
signal_candles_only[pair] = signal_candles_only_df
return signal_candles_only
def _generate_rejected_signals(self, preprocessed_df: Dict[str, DataFrame],
rejected_dict: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
rejected_candles_only = {}
for pair, signals in rejected_dict.items():
rejected_signals_only_df = DataFrame()
pairdf = preprocessed_df[pair]
for t in signals:
data_df_row = pairdf.loc[(pairdf['date'] == t[0])].copy()
data_df_row['pair'] = pair
data_df_row['enter_tag'] = t[1]
rejected_signals_only_df = pd.concat([
rejected_signals_only_df.infer_objects(),
data_df_row.infer_objects()])
rejected_candles_only[pair] = rejected_signals_only_df
return rejected_candles_only
def _get_min_cached_backtest_date(self):
min_backtest_date = None
backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)

44
freqtrade/optimize/optimize_reports.py
View File

@ -4,7 +4,7 @@ from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List, Union
from pandas import DataFrame, to_datetime
from pandas import DataFrame, concat, to_datetime
from tabulate import tabulate
from freqtrade.constants import (BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN,
@ -78,6 +78,48 @@ def store_backtest_analysis_results(
_store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected")
def generate_trade_signal_candles(preprocessed_df: Dict[str, DataFrame],
bt_results: Dict[str, Any]) -> DataFrame:
signal_candles_only = {}
for pair in preprocessed_df.keys():
signal_candles_only_df = DataFrame()
pairdf = preprocessed_df[pair]
resdf = bt_results['results']
pairresults = resdf.loc[(resdf["pair"] == pair)]
if pairdf.shape[0] > 0:
for t, v in pairresults.open_date.items():
allinds = pairdf.loc[(pairdf['date'] < v)]
signal_inds = allinds.iloc[[-1]]
signal_candles_only_df = concat([
signal_candles_only_df.infer_objects(),
signal_inds.infer_objects()])
signal_candles_only[pair] = signal_candles_only_df
return signal_candles_only
def generate_rejected_signals(preprocessed_df: Dict[str, DataFrame],
rejected_dict: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
rejected_candles_only = {}
for pair, signals in rejected_dict.items():
rejected_signals_only_df = DataFrame()
pairdf = preprocessed_df[pair]
for t in signals:
data_df_row = pairdf.loc[(pairdf['date'] == t[0])].copy()
data_df_row['pair'] = pair
data_df_row['enter_tag'] = t[1]
rejected_signals_only_df = concat([
rejected_signals_only_df.infer_objects(),
data_df_row.infer_objects()])
rejected_candles_only[pair] = rejected_signals_only_df
return rejected_candles_only
def _get_line_floatfmt(stake_currency: str) -> List[str]:
"""
Generate floatformat (goes in line with _generate_result_line())