First pass changes for cleaning up
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froggleston
parent
145faf9817
commit
e7c5818d16
@ -101,8 +101,8 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
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"print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
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"disableparamexport", "backtest_breakdown"]
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ARGS_ANALYZE_ENTRIES_EXITS = ["analysis_groups", "enter_reason_list",
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"exit_reason_list", "indicator_list"]
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ARGS_ANALYZE_ENTRIES_EXITS = ["analysis-groups", "enter-reason-list",
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"exit-reason-list", "indicator-list"]
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NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
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"list-markets", "list-pairs", "list-strategies", "list-data",
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@ -421,7 +421,7 @@ class Arguments:
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self._build_args(optionlist=ARGS_WEBSERVER, parser=webserver_cmd)
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# Add backtesting analysis subcommand
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analysis_cmd = subparsers.add_parser('analysis', help='Analysis module.',
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analysis_cmd = subparsers.add_parser('analysis', help='Backtest Analysis module.',
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parents=[_common_parser, _strategy_parser])
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analysis_cmd.set_defaults(func=start_analysis_entries_exits)
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self._build_args(optionlist=ARGS_ANALYZE_ENTRIES_EXITS, parser=analysis_cmd)
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@ -615,7 +615,7 @@ AVAILABLE_CLI_OPTIONS = {
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action="store_true",
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),
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"analysis_groups": Arg(
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"--analysis_groups",
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"--analysis-groups",
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help=("grouping output - ",
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"0: simple wins/losses by enter tag, ",
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"1: by enter_tag, ",
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@ -626,21 +626,21 @@ AVAILABLE_CLI_OPTIONS = {
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default="0,1,2",
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),
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"enter_reason_list": Arg(
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"--enter_reason_list",
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"--enter-reason-list",
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help=("Comma separated list of entry signals to analyse. Default: all. ",
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"e.g. 'entry_tag_a,entry_tag_b'"),
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nargs='?',
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default='all',
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),
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"exit_reason_list": Arg(
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"--exit_reason_list",
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"--exit-reason-list",
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help=("Comma separated list of exit signals to analyse. Default: all. ",
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"e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
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nargs='?',
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default='all',
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),
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"indicator_list": Arg(
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"--indicator_list",
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"--indicator-list",
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help=("Comma separated list of indicators to analyse. ",
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"e.g. 'close,rsi,bb_lowerband,profit_abs'"),
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nargs='?',
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@ -7,7 +7,8 @@ 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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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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@ -49,8 +50,8 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
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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:
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pass
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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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@ -82,104 +83,79 @@ def _analyze_candles_and_indicators(pair, trades, signal_candles):
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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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print(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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if "0" in glist:
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wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
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.groupby(['enter_reason']) \
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.agg({'profit_abs': ['sum']})
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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(['enter_reason']) \
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.agg({'profit_abs': ['sum']})
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loss.columns = ['profit_abs_loss']
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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(['enter_reason']).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 = 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['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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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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sortcols = ['total_num_buys']
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_print_table(new, sortcols, show_index=True)
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if "1" in glist:
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new = bigdf.groupby(['enter_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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).reset_index()
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new.columns = ['enter_reason', '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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_print_table(new, sortcols, show_index=True)
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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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if "2" in glist:
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new = bigdf.groupby(['enter_reason', 'exit_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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).reset_index()
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new.columns = ['enter_reason', 'exit_reason', 'num_buys', 'profit_abs_sum',
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'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
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'mean_profit_pct', 'total_profit_pct']
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sortcols = ['profit_abs_sum', 'enter_reason']
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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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if "3" in glist:
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new = bigdf.groupby(['pair', 'enter_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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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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).reset_index()
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new.columns = ['pair', 'enter_reason', 'num_buys', 'profit_abs_sum',
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'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
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'mean_profit_pct', 'total_profit_pct']
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sortcols = ['profit_abs_sum', 'enter_reason']
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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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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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# 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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_print_table(new, sortcols)
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if "4" in glist:
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new = bigdf.groupby(['pair', 'enter_reason', 'exit_reason']) \
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.agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
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'profit_ratio': ['sum', 'median', 'mean']}
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).reset_index()
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new.columns = ['pair', 'enter_reason', 'exit_reason', 'num_buys', 'profit_abs_sum',
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'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
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'mean_profit_pct', 'total_profit_pct']
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sortcols = ['profit_abs_sum', '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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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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# 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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_print_table(new, sortcols)
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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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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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def _print_results(analysed_trades, stratname, group,
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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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@ -191,8 +167,8 @@ def _print_results(analysed_trades, stratname, group,
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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 group is not None:
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glist = group.split(",")
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if analysis_groups is not None:
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glist = analysis_groups.split(",")
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_do_group_table_output(bigdf, glist)
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if enter_reason_list is not None and not enter_reason_list == "all":
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@ -244,6 +220,9 @@ def process_entry_exit_reasons(backtest_dir: Path,
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indicator_list: Optional[str] = None):
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try:
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bt_stats = load_backtest_stats(backtest_dir)
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logger.info(bt_stats)
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# strategy_name = bt_stats['something']
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trades = load_backtest_data(backtest_dir, strategy_name)
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except ValueError as e:
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raise OperationalException(e) from e
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@ -24,10 +24,10 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
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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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'analysis_groups': "0",
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'enter_reason_list': "all",
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'exit_reason_list': "all",
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'indicator_list': "rsi"
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'analysis-groups': "0",
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'enter-reason-list': "all",
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'exit-reason-list': "all",
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'indicator-list': "rsi"
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})
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patch_exchange(mocker)
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result1 = pd.DataFrame({'pair': ['ETH/BTC', 'LTC/BTC'],
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@ -94,8 +94,8 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
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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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'--analysis_groups', '0',
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'--indicator_list', 'rsi',
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'--analysis-groups', '0',
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'--indicator-list', 'rsi',
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'--strategy',
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'StrategyTestV3Analysis',
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]
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