First pass changes for cleaning up

freqtrade/commands/arguments.py

@@ -101,8 +101,8 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
                       "print_json", "hyperoptexportfilename", "hyperopt_show_no_header",
                       "disableparamexport", "backtest_breakdown"]
 
-ARGS_ANALYZE_ENTRIES_EXITS = ["analysis_groups", "enter_reason_list",
-                              "exit_reason_list", "indicator_list"]
+ARGS_ANALYZE_ENTRIES_EXITS = ["analysis-groups", "enter-reason-list",
+                              "exit-reason-list", "indicator-list"]
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-data",
@@ -421,7 +421,7 @@ class Arguments:
         self._build_args(optionlist=ARGS_WEBSERVER, parser=webserver_cmd)
 
         # Add backtesting analysis subcommand
-        analysis_cmd = subparsers.add_parser('analysis', help='Analysis module.',
+        analysis_cmd = subparsers.add_parser('analysis', help='Backtest Analysis module.',
                                              parents=[_common_parser, _strategy_parser])
         analysis_cmd.set_defaults(func=start_analysis_entries_exits)
         self._build_args(optionlist=ARGS_ANALYZE_ENTRIES_EXITS, parser=analysis_cmd)

freqtrade/commands/cli_options.py

@@ -615,7 +615,7 @@ AVAILABLE_CLI_OPTIONS = {
         action="store_true",
     ),
     "analysis_groups": Arg(
-        "--analysis_groups",
+        "--analysis-groups",
         help=("grouping output - ",
               "0: simple wins/losses by enter tag, ",
               "1: by enter_tag, ",
@@ -626,21 +626,21 @@ AVAILABLE_CLI_OPTIONS = {
         default="0,1,2",
     ),
     "enter_reason_list": Arg(
-        "--enter_reason_list",
+        "--enter-reason-list",
         help=("Comma separated list of entry signals to analyse. Default: all. ",
               "e.g. 'entry_tag_a,entry_tag_b'"),
         nargs='?',
         default='all',
     ),
     "exit_reason_list": Arg(
-        "--exit_reason_list",
+        "--exit-reason-list",
         help=("Comma separated list of exit signals to analyse. Default: all. ",
               "e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
         nargs='?',
         default='all',
     ),
     "indicator_list": Arg(
-        "--indicator_list",
+        "--indicator-list",
         help=("Comma separated list of indicators to analyse. ",
               "e.g. 'close,rsi,bb_lowerband,profit_abs'"),
         nargs='?',

freqtrade/data/entryexitanalysis.py

@@ -7,7 +7,8 @@ import joblib
 import pandas as pd
 from tabulate import tabulate
 
-from freqtrade.data.btanalysis import get_latest_backtest_filename, load_backtest_data
+from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data,
+                                       load_backtest_stats)
 from freqtrade.exceptions import OperationalException
 
 
@@ -49,8 +50,8 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
                                                               pair,
                                                               trades,
                                                               signal_candles[strategy_name][pair])
-    except Exception:
-        pass
+    except Exception as e:
+        print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
 
     return analysed_trades_dict
 
@@ -82,104 +83,79 @@ def _analyze_candles_and_indicators(pair, trades, signal_candles):
                 try:
                     trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
                 except Exception as e:
-                    print(e)
+                    raise e
         return trades_red
     else:
         return pd.DataFrame()
 
 
 def _do_group_table_output(bigdf, glist):
-    if "0" in glist:
-        wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
-                    .groupby(['enter_reason']) \
-                    .agg({'profit_abs': ['sum']})
+    for g in glist:
+        # 0: summary wins/losses grouped by enter tag
+        if g == "0":
+            group_mask = ['enter_reason']
+            wins = bigdf.loc[bigdf['profit_abs'] >= 0] \
+                        .groupby(group_mask) \
+                        .agg({'profit_abs': ['sum']})
 
-        wins.columns = ['profit_abs_wins']
-        loss = bigdf.loc[bigdf['profit_abs'] < 0] \
-                    .groupby(['enter_reason']) \
-                    .agg({'profit_abs': ['sum']})
-        loss.columns = ['profit_abs_loss']
+            wins.columns = ['profit_abs_wins']
+            loss = bigdf.loc[bigdf['profit_abs'] < 0] \
+                        .groupby(group_mask) \
+                        .agg({'profit_abs': ['sum']})
+            loss.columns = ['profit_abs_loss']
 
-        new = bigdf.groupby(['enter_reason']).agg({'profit_abs': [
-                                                   'count',
-                                                   lambda x: sum(x > 0),
-                                                   lambda x: sum(x <= 0)]})
-        new = pd.concat([new, wins, loss], axis=1).fillna(0)
+            new = bigdf.groupby(group_mask).agg({'profit_abs': [
+                                                    'count',
+                                                    lambda x: sum(x > 0),
+                                                    lambda x: sum(x <= 0)]})
+            new = pd.concat([new, wins, loss], axis=1).fillna(0)
 
-        new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
-        new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
-        new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
-        new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
+            new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
+            new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100).fillna(0)
+            new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1]).fillna(0)
+            new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2]).fillna(0)
 
-        new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
-                       'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
+            new.columns = ['total_num_buys', 'wins', 'losses', 'profit_abs_wins', 'profit_abs_loss',
+                           'profit_tot', 'wl_ratio_pct', 'avg_win', 'avg_loss']
 
-        sortcols = ['total_num_buys']
+            sortcols = ['total_num_buys']
 
-        _print_table(new, sortcols, show_index=True)
-    if "1" in glist:
-        new = bigdf.groupby(['enter_reason']) \
-                   .agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
-                         'profit_ratio': ['sum', 'median', 'mean']}
-                        ).reset_index()
-        new.columns = ['enter_reason', 'num_buys', 'profit_abs_sum', 'profit_abs_median',
-                       'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
-                       'total_profit_pct']
-        sortcols = ['profit_abs_sum', 'enter_reason']
+            _print_table(new, sortcols, show_index=True)
 
-        new['median_profit_pct'] = new['median_profit_pct'] * 100
-        new['mean_profit_pct'] = new['mean_profit_pct'] * 100
-        new['total_profit_pct'] = new['total_profit_pct'] * 100
-
-        _print_table(new, sortcols)
-    if "2" in glist:
-        new = bigdf.groupby(['enter_reason', 'exit_reason']) \
-                   .agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
-                         'profit_ratio': ['sum', 'median', 'mean']}
-                        ).reset_index()
-        new.columns = ['enter_reason', 'exit_reason', 'num_buys', 'profit_abs_sum',
-                       'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
-                       'mean_profit_pct', 'total_profit_pct']
-        sortcols = ['profit_abs_sum', 'enter_reason']
-
-        new['median_profit_pct'] = new['median_profit_pct'] * 100
-        new['mean_profit_pct'] = new['mean_profit_pct'] * 100
-        new['total_profit_pct'] = new['total_profit_pct'] * 100
-
-        _print_table(new, sortcols)
-    if "3" in glist:
-        new = bigdf.groupby(['pair', 'enter_reason']) \
-                   .agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
+        else:
+            agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
                         'profit_ratio': ['sum', 'median', 'mean']}
-                        ).reset_index()
-        new.columns = ['pair', 'enter_reason', 'num_buys', 'profit_abs_sum',
-                       'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
-                       'mean_profit_pct', 'total_profit_pct']
-        sortcols = ['profit_abs_sum', 'enter_reason']
+            agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
+                        'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
+                        'total_profit_pct']
+            sortcols = ['profit_abs_sum', 'enter_reason']
 
-        new['median_profit_pct'] = new['median_profit_pct'] * 100
-        new['mean_profit_pct'] = new['mean_profit_pct'] * 100
-        new['total_profit_pct'] = new['total_profit_pct'] * 100
+            # 1: profit summaries grouped by enter_tag
+            if g == "1":
+                group_mask = ['enter_reason']
 
-        _print_table(new, sortcols)
-    if "4" in glist:
-        new = bigdf.groupby(['pair', 'enter_reason', 'exit_reason']) \
-                   .agg({'profit_abs': ['count', 'sum', 'median', 'mean'],
-                         'profit_ratio': ['sum', 'median', 'mean']}
-                        ).reset_index()
-        new.columns = ['pair', 'enter_reason', 'exit_reason', 'num_buys', 'profit_abs_sum',
-                       'profit_abs_median', 'profit_abs_mean', 'median_profit_pct',
-                       'mean_profit_pct', 'total_profit_pct']
-        sortcols = ['profit_abs_sum', 'enter_reason']
+            # 2: profit summaries grouped by enter_tag and exit_tag
+            if g == "2":
+                group_mask = ['enter_reason', 'exit_reason']
 
-        new['median_profit_pct'] = new['median_profit_pct'] * 100
-        new['mean_profit_pct'] = new['mean_profit_pct'] * 100
-        new['total_profit_pct'] = new['total_profit_pct'] * 100
+            # 3: profit summaries grouped by pair and enter_tag
+            if g == "3":
+                group_mask = ['pair', 'enter_reason']
 
-        _print_table(new, sortcols)
+            # 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
+            if g == "4":
+                group_mask = ['pair', 'enter_reason', 'exit_reason']
+
+            new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
+            new.columns = group_mask + agg_cols
+            new['median_profit_pct'] = new['median_profit_pct'] * 100
+            new['mean_profit_pct'] = new['mean_profit_pct'] * 100
+            new['total_profit_pct'] = new['total_profit_pct'] * 100
+
+            _print_table(new, sortcols)
 
 
-def _print_results(analysed_trades, stratname, group,
+def _print_results(analysed_trades, stratname, analysis_groups,
                    enter_reason_list, exit_reason_list,
                    indicator_list, columns=None):
 
@@ -191,8 +167,8 @@ def _print_results(analysed_trades, stratname, group,
         bigdf = pd.concat([bigdf, trades], ignore_index=True)
 
     if bigdf.shape[0] > 0 and ('enter_reason' in bigdf.columns):
-        if group is not None:
-            glist = group.split(",")
+        if analysis_groups is not None:
+            glist = analysis_groups.split(",")
             _do_group_table_output(bigdf, glist)
 
         if enter_reason_list is not None and not enter_reason_list == "all":
@@ -244,6 +220,9 @@ def process_entry_exit_reasons(backtest_dir: Path,
                                indicator_list: Optional[str] = None):
 
     try:
+        bt_stats = load_backtest_stats(backtest_dir)
+        logger.info(bt_stats)
+        # strategy_name = bt_stats['something']
         trades = load_backtest_data(backtest_dir, strategy_name)
     except ValueError as e:
         raise OperationalException(e) from e

tests/data/test_entryexitanalysis.py

@@ -24,10 +24,10 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
         "exit_profit_only": False,
         "exit_profit_offset": 0.0,
         "ignore_roi_if_entry_signal": False,
-        'analysis_groups': "0",
-        'enter_reason_list': "all",
-        'exit_reason_list': "all",
-        'indicator_list': "rsi"
+        'analysis-groups': "0",
+        'enter-reason-list': "all",
+        'exit-reason-list': "all",
+        'indicator-list': "rsi"
     })
     patch_exchange(mocker)
     result1 = pd.DataFrame({'pair': ['ETH/BTC', 'LTC/BTC'],
@@ -94,8 +94,8 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
         '--config', 'config.json',
         '--datadir', str(testdatadir),
         '--user-data-dir', str(tmpdir),
-        '--analysis_groups', '0',
-        '--indicator_list', 'rsi',
+        '--analysis-groups', '0',
+        '--indicator-list', 'rsi',
         '--strategy',
         'StrategyTestV3Analysis',
     ]