Matthias
4 years ago
committed by
GitHub
commit
ec40e79362
@ -0,0 +1,69 @@
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import logging
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from pathlib import Path
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from typing import Any, Dict
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from freqtrade.configuration import setup_utils_configuration
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from freqtrade.enums import RunMode
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from freqtrade.exceptions import OperationalException
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logger = logging.getLogger(__name__)
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def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str, Any]:
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"""
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Prepare the configuration for the entry/exit reason analysis module
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:param args: Cli args from Arguments()
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:param method: Bot running mode
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:return: Configuration
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"""
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config = setup_utils_configuration(args, method)
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no_unlimited_runmodes = {
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RunMode.BACKTEST: 'backtesting',
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}
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if method in no_unlimited_runmodes.keys():
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from freqtrade.data.btanalysis import get_latest_backtest_filename
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if 'exportfilename' in config:
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if config['exportfilename'].is_dir():
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btfile = Path(get_latest_backtest_filename(config['exportfilename']))
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signals_file = f"{config['exportfilename']}/{btfile.stem}_signals.pkl"
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else:
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if config['exportfilename'].exists():
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btfile = Path(config['exportfilename'])
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signals_file = f"{btfile.parent}/{btfile.stem}_signals.pkl"
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else:
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raise OperationalException(f"{config['exportfilename']} does not exist.")
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else:
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raise OperationalException('exportfilename not in config.')
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if (not Path(signals_file).exists()):
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raise OperationalException(
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(f"Cannot find latest backtest signals file: {signals_file}."
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"Run backtesting with `--export signals`.")
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)
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return config
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def start_analysis_entries_exits(args: Dict[str, Any]) -> None:
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"""
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Start analysis script
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:param args: Cli args from Arguments()
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:return: None
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"""
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from freqtrade.data.entryexitanalysis import process_entry_exit_reasons
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# Initialize configuration
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config = setup_analyze_configuration(args, RunMode.BACKTEST)
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logger.info('Starting freqtrade in analysis mode')
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process_entry_exit_reasons(config['exportfilename'],
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config['exchange']['pair_whitelist'],
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config['analysis_groups'],
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config['enter_reason_list'],
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config['exit_reason_list'],
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config['indicator_list']
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)
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@ -0,0 +1,227 @@
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import logging
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from pathlib import Path
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from typing import List, Optional
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import joblib
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import pandas as pd
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from tabulate import tabulate
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from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
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load_backtest_stats)
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from freqtrade.exceptions import OperationalException
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logger = logging.getLogger(__name__)
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def _load_signal_candles(backtest_dir: Path):
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if backtest_dir.is_dir():
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scpf = Path(backtest_dir,
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Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
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)
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else:
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scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
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try:
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scp = open(scpf, "rb")
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signal_candles = joblib.load(scp)
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logger.info(f"Loaded signal candles: {str(scpf)}")
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except Exception as e:
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logger.error("Cannot load signal candles from pickled results: ", e)
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return signal_candles
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def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
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analysed_trades_dict = {}
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analysed_trades_dict[strategy_name] = {}
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try:
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logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
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for pair in pairlist:
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if pair in signal_candles[strategy_name]:
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analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
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pair,
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trades,
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signal_candles[strategy_name][pair])
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except Exception as e:
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print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
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return analysed_trades_dict
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def _analyze_candles_and_indicators(pair, trades, signal_candles):
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buyf = signal_candles
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if len(buyf) > 0:
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buyf = buyf.set_index('date', drop=False)
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trades_red = trades.loc[trades['pair'] == pair].copy()
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trades_inds = pd.DataFrame()
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if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
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for t, v in trades_red.open_date.items():
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allinds = buyf.loc[(buyf['date'] < v)]
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if allinds.shape[0] > 0:
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tmp_inds = allinds.iloc[[-1]]
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trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0]
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trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
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tmp_inds.index.rename('signal_date', inplace=True)
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trades_inds = pd.concat([trades_inds, tmp_inds])
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if 'signal_date' in trades_red:
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trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
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trades_red.set_index('signal_date', inplace=True)
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try:
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trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
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except Exception as e:
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raise e
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return trades_red
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else:
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return pd.DataFrame()
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def _do_group_table_output(bigdf, glist):
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for g in glist:
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# 0: summary wins/losses grouped by enter tag
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if g == "0":
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group_mask = ['enter_reason']
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wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
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.groupby(group_mask) \
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.agg({'profit_abs': ['sum']})
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wins.columns = ['profit_abs_wins']
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loss = bigdf.loc[bigdf['profit_abs'] < 0] \
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.groupby(group_mask) \
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.agg({'profit_abs': ['sum']})
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loss.columns = ['profit_abs_loss']
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new = bigdf.groupby(group_mask).agg({'profit_abs': [
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'count',
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lambda x: sum(x > 0),
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lambda x: sum(x <= 0)]})
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new = pd.concat([new, wins, loss], axis=1).fillna(0)
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new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
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new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
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new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
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new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
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new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
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'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
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sortcols = ['total_num_buys']
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_print_table(new, sortcols, show_index=True)
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else:
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agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
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'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
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'total_profit_pct']
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sortcols = ['profit_abs_sum', 'enter_reason']
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# 1: profit summaries grouped by enter_tag
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if g == "1":
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group_mask = ['enter_reason']
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# 2: profit summaries grouped by enter_tag and exit_tag
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if g == "2":
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group_mask = ['enter_reason', 'exit_reason']
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# 3: profit summaries grouped by pair and enter_tag
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if g == "3":
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group_mask = ['pair', 'enter_reason']
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# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
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if g == "4":
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group_mask = ['pair', 'enter_reason', 'exit_reason']
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if group_mask:
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new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
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new.columns = group_mask + agg_cols
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new['median_profit_pct'] = new['median_profit_pct'] * 100
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new['mean_profit_pct'] = new['mean_profit_pct'] * 100
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new['total_profit_pct'] = new['total_profit_pct'] * 100
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_print_table(new, sortcols)
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else:
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logger.warning("Invalid group mask specified.")
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def _print_results(analysed_trades, stratname, analysis_groups,
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enter_reason_list, exit_reason_list,
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indicator_list, columns=None):
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if columns is None:
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columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
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bigdf = pd.DataFrame()
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for pair, trades in analysed_trades[stratname].items():
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bigdf = pd.concat([bigdf, trades], ignore_index=True)
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if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
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if analysis_groups:
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_do_group_table_output(bigdf, analysis_groups)
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if enter_reason_list and "all" not in enter_reason_list:
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bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
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if exit_reason_list and "all" not in exit_reason_list:
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bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
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if "all" in indicator_list:
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print(bigdf)
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elif indicator_list is not None:
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available_inds = []
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for ind in indicator_list:
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if ind in bigdf:
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available_inds.append(ind)
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ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
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_print_table(bigdf[ilist], sortcols=['exit_reason'], show_index=False)
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else:
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print("\\_ No trades to show")
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def _print_table(df, sortcols=None, show_index=False):
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if (sortcols is not None):
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data = df.sort_values(sortcols)
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else:
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data = df
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print(
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tabulate(
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data,
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headers='keys',
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tablefmt='psql',
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showindex=show_index
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)
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)
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def process_entry_exit_reasons(backtest_dir: Path,
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pairlist: List[str],
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analysis_groups: Optional[List[str]] = ["0", "1", "2"],
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enter_reason_list: Optional[List[str]] = ["all"],
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exit_reason_list: Optional[List[str]] = ["all"],
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indicator_list: Optional[List[str]] = []):
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try:
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backtest_stats = load_backtest_stats(backtest_dir)
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for strategy_name, results in backtest_stats['strategy'].items():
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trades = load_backtest_data(backtest_dir, strategy_name)
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if not trades.empty:
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signal_candles = _load_signal_candles(backtest_dir)
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analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
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trades, signal_candles)
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_print_results(analysed_trades_dict,
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strategy_name,
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analysis_groups,
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enter_reason_list,
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exit_reason_list,
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indicator_list)
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except ValueError as e:
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raise OperationalException(e) from e
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@ -0,0 +1,191 @@
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import logging
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from unittest.mock import MagicMock, PropertyMock
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import pandas as pd
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import pytest
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from freqtrade.commands.analyze_commands import start_analysis_entries_exits
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from freqtrade.commands.optimize_commands import start_backtesting
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from freqtrade.enums import ExitType
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from freqtrade.optimize.backtesting import Backtesting
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from tests.conftest import get_args, patch_exchange, patched_configuration_load_config_file
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@pytest.fixture(autouse=True)
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def entryexitanalysis_cleanup() -> None:
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yield None
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Backtesting.cleanup()
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def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmpdir, capsys):
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caplog.set_level(logging.INFO)
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default_conf.update({
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"use_exit_signal": True,
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"exit_profit_only": False,
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"exit_profit_offset": 0.0,
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"ignore_roi_if_entry_signal": False,
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})
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patch_exchange(mocker)
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result1 = pd.DataFrame({'pair': ['ETH/BTC', 'LTC/BTC', 'ETH/BTC', 'LTC/BTC'],
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'profit_ratio': [0.025, 0.05, -0.1, -0.05],
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'profit_abs': [0.5, 2.0, -4.0, -2.0],
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'open_date': pd.to_datetime(['2018-01-29 18:40:00',
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'2018-01-30 03:30:00',
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'2018-01-30 08:10:00',
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'2018-01-31 13:30:00', ], utc=True
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),
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'close_date': pd.to_datetime(['2018-01-29 20:45:00',
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'2018-01-30 05:35:00',
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'2018-01-30 09:10:00',
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'2018-01-31 15:00:00', ], utc=True),
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'trade_duration': [235, 40, 60, 90],
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'is_open': [False, False, False, False],
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'stake_amount': [0.01, 0.01, 0.01, 0.01],
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'open_rate': [0.104445, 0.10302485, 0.10302485, 0.10302485],
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'close_rate': [0.104969, 0.103541, 0.102041, 0.102541],
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"is_short": [False, False, False, False],
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'enter_tag': ["enter_tag_long_a",
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"enter_tag_long_b",
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"enter_tag_long_a",
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"enter_tag_long_b"],
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'exit_reason': [ExitType.ROI,
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ExitType.EXIT_SIGNAL,
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ExitType.STOP_LOSS,
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ExitType.TRAILING_STOP_LOSS]
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})
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backtestmock = MagicMock(side_effect=[
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{
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'results': result1,
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'config': default_conf,
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'locks': [],
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'rejected_signals': 20,
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'timedout_entry_orders': 0,
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'timedout_exit_orders': 0,
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'canceled_trade_entries': 0,
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'canceled_entry_orders': 0,
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'replaced_entry_orders': 0,
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'final_balance': 1000,
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}
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])
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mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
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PropertyMock(return_value=['ETH/BTC', 'LTC/BTC', 'DASH/BTC']))
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mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
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patched_configuration_load_config_file(mocker, default_conf)
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args = [
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'backtesting',
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'--config', 'config.json',
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'--datadir', str(testdatadir),
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'--user-data-dir', str(tmpdir),
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'--timeframe', '5m',
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'--timerange', '1515560100-1517287800',
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'--export', 'signals',
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'--cache', 'none',
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]
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args = get_args(args)
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start_backtesting(args)
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captured = capsys.readouterr()
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assert 'BACKTESTING REPORT' in captured.out
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assert 'EXIT REASON STATS' in captured.out
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assert 'LEFT OPEN TRADES REPORT' in captured.out
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base_args = [
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'backtesting-analysis',
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'--config', 'config.json',
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'--datadir', str(testdatadir),
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'--user-data-dir', str(tmpdir),
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]
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# test group 0 and indicator list
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args = get_args(base_args +
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['--analysis-groups', "0",
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'--indicator-list', "close", "rsi", "profit_abs"]
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)
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start_analysis_entries_exits(args)
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captured = capsys.readouterr()
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assert 'LTC/BTC' in captured.out
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assert 'ETH/BTC' in captured.out
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assert 'enter_tag_long_a' in captured.out
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assert 'enter_tag_long_b' in captured.out
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assert 'exit_signal' in captured.out
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assert 'roi' in captured.out
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assert 'stop_loss' in captured.out
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assert 'trailing_stop_loss' in captured.out
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assert '0.5' in captured.out
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assert '-4' in captured.out
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assert '-2' in captured.out
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assert '-3.5' in captured.out
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assert '50' in captured.out
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assert '0' in captured.out
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assert '0.01616' in captured.out
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assert '34.049' in captured.out
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assert '0.104104' in captured.out
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assert '47.0996' in captured.out
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# test group 1
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args = get_args(base_args + ['--analysis-groups', "1"])
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start_analysis_entries_exits(args)
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captured = capsys.readouterr()
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assert 'enter_tag_long_a' in captured.out
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assert 'enter_tag_long_b' in captured.out
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assert 'total_profit_pct' in captured.out
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assert '-3.5' in captured.out
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assert '-1.75' in captured.out
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assert '-7.5' in captured.out
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assert '-3.75' in captured.out
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assert '0' in captured.out
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# test group 2
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args = get_args(base_args + ['--analysis-groups', "2"])
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start_analysis_entries_exits(args)
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captured = capsys.readouterr()
|
||||
assert 'enter_tag_long_a' in captured.out
|
||||
assert 'enter_tag_long_b' in captured.out
|
||||
assert 'exit_signal' in captured.out
|
||||
assert 'roi' in captured.out
|
||||
assert 'stop_loss' in captured.out
|
||||
assert 'trailing_stop_loss' in captured.out
|
||||
assert 'total_profit_pct' in captured.out
|
||||
assert '-10' in captured.out
|
||||
assert '-5' in captured.out
|
||||
assert '2.5' in captured.out
|
||||
|
||||
# test group 3
|
||||
args = get_args(base_args + ['--analysis-groups', "3"])
|
||||
start_analysis_entries_exits(args)
|
||||
captured = capsys.readouterr()
|
||||
assert 'LTC/BTC' in captured.out
|
||||
assert 'ETH/BTC' in captured.out
|
||||
assert 'enter_tag_long_a' in captured.out
|
||||
assert 'enter_tag_long_b' in captured.out
|
||||
assert 'total_profit_pct' in captured.out
|
||||
assert '-7.5' in captured.out
|
||||
assert '-3.75' in captured.out
|
||||
assert '-1.75' in captured.out
|
||||
assert '0' in captured.out
|
||||
assert '2' in captured.out
|
||||
|
||||
# test group 4
|
||||
args = get_args(base_args + ['--analysis-groups', "4"])
|
||||
start_analysis_entries_exits(args)
|
||||
captured = capsys.readouterr()
|
||||
assert 'LTC/BTC' in captured.out
|
||||
assert 'ETH/BTC' in captured.out
|
||||
assert 'enter_tag_long_a' in captured.out
|
||||
assert 'enter_tag_long_b' in captured.out
|
||||
assert 'exit_signal' in captured.out
|
||||
assert 'roi' in captured.out
|
||||
assert 'stop_loss' in captured.out
|
||||
assert 'trailing_stop_loss' in captured.out
|
||||
assert 'total_profit_pct' in captured.out
|
||||
assert '-10' in captured.out
|
||||
assert '-5' in captured.out
|
||||
assert '-4' in captured.out
|
||||
assert '0.5' in captured.out
|
||||
assert '1' in captured.out
|
||||
assert '2.5' in captured.out
|
||||
@ -0,0 +1,175 @@
|
||||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
|
||||
RealParameter)
|
||||
|
||||
|
||||
class StrategyTestV3Analysis(IStrategy):
|
||||
"""
|
||||
Strategy used by tests freqtrade bot.
|
||||
Please do not modify this strategy, it's intended for internal use only.
|
||||
Please look at the SampleStrategy in the user_data/strategy directory
|
||||
or strategy repository https://github.com/freqtrade/freqtrade-strategies
|
||||
for samples and inspiration.
|
||||
"""
|
||||
INTERFACE_VERSION = 3
|
||||
|
||||
# Minimal ROI designed for the strategy
|
||||
minimal_roi = {
|
||||
"40": 0.0,
|
||||
"30": 0.01,
|
||||
"20": 0.02,
|
||||
"0": 0.04
|
||||
}
|
||||
|
||||
# Optimal stoploss designed for the strategy
|
||||
stoploss = -0.10
|
||||
|
||||
# Optimal timeframe for the strategy
|
||||
timeframe = '5m'
|
||||
|
||||
# Optional order type mapping
|
||||
order_types = {
|
||||
'entry': 'limit',
|
||||
'exit': 'limit',
|
||||
'stoploss': 'limit',
|
||||
'stoploss_on_exchange': False
|
||||
}
|
||||
|
||||
# Number of candles the strategy requires before producing valid signals
|
||||
startup_candle_count: int = 20
|
||||
|
||||
# Optional time in force for orders
|
||||
order_time_in_force = {
|
||||
'entry': 'gtc',
|
||||
'exit': 'gtc',
|
||||
}
|
||||
|
||||
buy_params = {
|
||||
'buy_rsi': 35,
|
||||
# Intentionally not specified, so "default" is tested
|
||||
# 'buy_plusdi': 0.4
|
||||
}
|
||||
|
||||
sell_params = {
|
||||
'sell_rsi': 74,
|
||||
'sell_minusdi': 0.4
|
||||
}
|
||||
|
||||
buy_rsi = IntParameter([0, 50], default=30, space='buy')
|
||||
buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
|
||||
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
|
||||
sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
|
||||
load=False)
|
||||
protection_enabled = BooleanParameter(default=True)
|
||||
protection_cooldown_lookback = IntParameter([0, 50], default=30)
|
||||
|
||||
# TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
|
||||
# @property
|
||||
# def protections(self):
|
||||
# prot = []
|
||||
# if self.protection_enabled.value:
|
||||
# prot.append({
|
||||
# "method": "CooldownPeriod",
|
||||
# "stop_duration_candles": self.protection_cooldown_lookback.value
|
||||
# })
|
||||
# return prot
|
||||
|
||||
bot_started = False
|
||||
|
||||
def bot_start(self):
|
||||
self.bot_started = True
|
||||
|
||||
def informative_pairs(self):
|
||||
|
||||
return []
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
# Momentum Indicator
|
||||
# ------------------------------------
|
||||
|
||||
# ADX
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
|
||||
# MACD
|
||||
macd = ta.MACD(dataframe)
|
||||
dataframe['macd'] = macd['macd']
|
||||
dataframe['macdsignal'] = macd['macdsignal']
|
||||
dataframe['macdhist'] = macd['macdhist']
|
||||
|
||||
# Minus Directional Indicator / Movement
|
||||
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
|
||||
# Plus Directional Indicator / Movement
|
||||
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
||||
|
||||
# RSI
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
|
||||
# Stoch fast
|
||||
stoch_fast = ta.STOCHF(dataframe)
|
||||
dataframe['fastd'] = stoch_fast['fastd']
|
||||
dataframe['fastk'] = stoch_fast['fastk']
|
||||
|
||||
# Bollinger bands
|
||||
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
||||
dataframe['bb_lowerband'] = bollinger['lower']
|
||||
dataframe['bb_middleband'] = bollinger['mid']
|
||||
dataframe['bb_upperband'] = bollinger['upper']
|
||||
|
||||
# EMA - Exponential Moving Average
|
||||
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe['rsi'] < self.buy_rsi.value) &
|
||||
(dataframe['fastd'] < 35) &
|
||||
(dataframe['adx'] > 30) &
|
||||
(dataframe['plus_di'] > self.buy_plusdi.value)
|
||||
) |
|
||||
(
|
||||
(dataframe['adx'] > 65) &
|
||||
(dataframe['plus_di'] > self.buy_plusdi.value)
|
||||
),
|
||||
['enter_long', 'enter_tag']] = 1, 'enter_tag_long'
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
|
||||
),
|
||||
['enter_short', 'enter_tag']] = 1, 'enter_tag_short'
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
dataframe.loc[
|
||||
(
|
||||
(
|
||||
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
|
||||
(qtpylib.crossed_above(dataframe['fastd'], 70))
|
||||
) &
|
||||
(dataframe['adx'] > 10) &
|
||||
(dataframe['minus_di'] > 0)
|
||||
) |
|
||||
(
|
||||
(dataframe['adx'] > 70) &
|
||||
(dataframe['minus_di'] > self.sell_minusdi.value)
|
||||
),
|
||||
['exit_long', 'exit_tag']] = 1, 'exit_tag_long'
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
|
||||
),
|
||||
['exit_long', 'exit_tag']] = 1, 'exit_tag_short'
|
||||
|
||||
return dataframe
|
||||
Loading…
Reference in new issue