First commit for integrating buy_reasons into FT

freqtrade/commands/__init__.py

@@ -6,6 +6,7 @@ Contains all start-commands, subcommands and CLI Interface creation.
 Note: Be careful with file-scoped imports in these subfiles.
     as they are parsed on startup, nothing containing optional modules should be loaded.
 """
+from freqtrade.commands.analyze_commands import start_analysis_entries_exits
 from freqtrade.commands.arguments import Arguments
 from freqtrade.commands.build_config_commands import start_new_config
 from freqtrade.commands.data_commands import (start_convert_data, start_convert_trades,

freqtrade/commands/arguments.py

@@ -101,6 +101,9 @@ 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"]
+
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-data",
                     "hyperopt-list", "hyperopt-show", "backtest-filter",
@@ -182,8 +185,9 @@ class Arguments:
         self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
         self._build_args(optionlist=['version'], parser=self.parser)
 
-        from freqtrade.commands import (start_backtesting, start_backtesting_show,
-                                        start_convert_data, start_convert_db, start_convert_trades,
+        from freqtrade.commands import (start_analysis_entries_exits, start_backtesting,
+                                        start_backtesting_show, start_convert_data,
+                                        start_convert_db, start_convert_trades,
                                         start_create_userdir, start_download_data, start_edge,
                                         start_hyperopt, start_hyperopt_list, start_hyperopt_show,
                                         start_install_ui, start_list_data, start_list_exchanges,
@@ -415,3 +419,9 @@ class Arguments:
                                               parents=[_common_parser])
         webserver_cmd.set_defaults(func=start_webserver)
         self._build_args(optionlist=ARGS_WEBSERVER, parser=webserver_cmd)
+
+        # Add backtesting analysis subcommand
+        analysis_cmd = subparsers.add_parser('analysis', help='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

@@ -614,4 +614,35 @@ AVAILABLE_CLI_OPTIONS = {
               "that do not contain any parameters."),
         action="store_true",
     ),
+    "analysis_groups": Arg(
+        "--analysis_groups",
+        help=("grouping output - ",
+              "0: simple wins/losses by enter tag, ",
+              "1: by enter_tag, ",
+              "2: by enter_tag and exit_tag, ",
+              "3: by pair and enter_tag, ",
+              "4: by pair, enter_ and exit_tag (this can get quite large)"),
+        nargs='?',
+        default="0,1,2",
+    ),
+    "enter_reason_list": Arg(
+        "--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",
+        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",
+        help=("Comma separated list of indicators to analyse. ",
+              "e.g. 'close,rsi,bb_lowerband,profit_abs'"),
+        nargs='?',
+    ),
 }

freqtrade/configuration/configuration.py

@@ -95,6 +95,8 @@ class Configuration:
 
         self._process_data_options(config)
 
+        self._process_analyze_options(config)
+
         # Check if the exchange set by the user is supported
         check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))
 
@@ -433,6 +435,19 @@ class Configuration:
         self._args_to_config(config, argname='candle_types',
                              logstring='Detected --candle-types: {}')
 
+    def _process_analyze_options(self, config: Dict[str, Any]) -> None:
+        self._args_to_config(config, argname='analysis_groups',
+                             logstring='Analysis reason groups: {}')
+
+        self._args_to_config(config, argname='enter_reason_list',
+                             logstring='Analysis enter tag list: {}')
+
+        self._args_to_config(config, argname='exit_reason_list',
+                             logstring='Analysis exit tag list: {}')
+
+        self._args_to_config(config, argname='indicator_list',
+                             logstring='Analysis indicator list: {}')
+
     def _process_runmode(self, config: Dict[str, Any]) -> None:
 
         self._args_to_config(config, argname='dry_run',

freqtrade/data/entryexitanalysis.py

@@ -0,0 +1,258 @@
+import joblib
+import logging
+import os
+
+from pathlib import Path
+from typing import List, Optional
+
+import pandas as pd
+from tabulate import tabulate
+
+from freqtrade.data.btanalysis import (load_backtest_data, get_latest_backtest_filename)
+from freqtrade.exceptions import OperationalException
+
+
+logger = logging.getLogger(__name__)
+
+
+def _load_signal_candles(backtest_dir: Path):
+    scpf = Path(backtest_dir,
+                os.path.splitext(
+                    get_latest_backtest_filename(backtest_dir))[0] + "_signals.pkl"
+                )
+    try:
+        scp = open(scpf, "rb")
+        signal_candles = joblib.load(scp)
+        logger.info(f"Loaded signal candles: {str(scpf)}")
+    except Exception as e:
+        logger.error("Cannot load signal candles from pickled results: ", e)
+
+    return signal_candles
+
+
+def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
+    analysed_trades_dict = {}
+    analysed_trades_dict[strategy_name] = {}
+
+    try:
+        logger.info(f"Processing {strategy_name} : {len(pairlist)} pairs")
+
+        for pair in pairlist:
+            if pair in signal_candles[strategy_name]:
+                analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
+                                                              pair,
+                                                              trades,
+                                                              signal_candles[strategy_name][pair])
+    except Exception:
+        pass
+
+    return analysed_trades_dict
+
+
+def _analyze_candles_and_indicators(pair, trades, signal_candles):
+    buyf = signal_candles
+
+    if len(buyf) > 0:
+        buyf = buyf.set_index('date', drop=False)
+        trades_red = trades.loc[trades['pair'] == pair].copy()
+
+        trades_inds = pd.DataFrame()
+
+        if trades_red.shape[0] > 0 and buyf.shape[0] > 0:
+            for t, v in trades_red.open_date.items():
+                allinds = buyf.loc[(buyf['date'] < v)]
+                if allinds.shape[0] > 0:
+                    tmp_inds = allinds.iloc[[-1]]
+
+                    trades_red.loc[t, 'signal_date'] = tmp_inds['date'].values[0]
+                    trades_red.loc[t, 'enter_reason'] = trades_red.loc[t, 'enter_tag']
+                    tmp_inds.index.rename('signal_date', inplace=True)
+                    trades_inds = pd.concat([trades_inds, tmp_inds])
+
+            if 'signal_date' in trades_red:
+                trades_red['signal_date'] = pd.to_datetime(trades_red['signal_date'], utc=True)
+                trades_red.set_index('signal_date', inplace=True)
+
+                try:
+                    trades_red = pd.merge(trades_red, trades_inds, on='signal_date', how='outer')
+                except Exception as e:
+                    print(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']})
+
+        wins.columns = ['profit_abs_wins']
+        loss = bigdf.loc[bigdf['profit_abs'] < 0] \
+                    .groupby(['enter_reason']) \
+                    .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.merge(new, wins, left_index=True, right_index=True)
+        new = pd.merge(new, loss, left_index=True, right_index=True)
+
+        new['profit_tot'] = new['profit_abs_wins'] - abs(new['profit_abs_loss'])
+
+        new['wl_ratio_pct'] = (new.iloc[:, 1] / new.iloc[:, 0] * 100)
+        new['avg_win'] = (new['profit_abs_wins'] / new.iloc[:, 1])
+        new['avg_loss'] = (new['profit_abs_loss'] / new.iloc[:, 2])
+
+        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']
+
+        _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']
+
+        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'],
+                        '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']
+
+        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 "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']
+
+        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,
+                   enter_reason_list, exit_reason_list,
+                   indicator_list, columns=None):
+
+    if columns is None:
+        columns = ['pair', 'open_date', 'close_date', 'profit_abs', 'enter_reason', 'exit_reason']
+
+    bigdf = pd.DataFrame()
+    for pair, trades in analysed_trades[stratname].items():
+        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(",")
+            _do_group_table_output(bigdf, glist)
+
+        if enter_reason_list is not None and not enter_reason_list == "all":
+            enter_reason_list = enter_reason_list.split(",")
+            bigdf = bigdf.loc[(bigdf['enter_reason'].isin(enter_reason_list))]
+
+        if exit_reason_list is not None and not exit_reason_list == "all":
+            exit_reason_list = exit_reason_list.split(",")
+            bigdf = bigdf.loc[(bigdf['exit_reason'].isin(exit_reason_list))]
+
+        if indicator_list is not None:
+            if indicator_list == "all":
+                print(bigdf)
+            else:
+                available_inds = []
+                for ind in indicator_list.split(","):
+                    if ind in bigdf:
+                        available_inds.append(ind)
+                ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
+                print(tabulate(bigdf[ilist].sort_values(['exit_reason']),
+                      headers='keys', tablefmt='psql', showindex=False))
+        else:
+            print(tabulate(bigdf[columns].sort_values(['pair']),
+                  headers='keys', tablefmt='psql', showindex=False))
+    else:
+        print("\\_ No trades to show")
+
+
+def _print_table(df, sortcols=None, show_index=False):
+    if (sortcols is not None):
+        data = df.sort_values(sortcols)
+    else:
+        data = df
+
+    print(
+        tabulate(
+            data,
+            headers='keys',
+            tablefmt='psql',
+            showindex=show_index
+        )
+    )
+
+
+def process_entry_exit_reasons(backtest_dir: Path,
+                               pairlist: List[str],
+                               strategy_name: str,
+                               analysis_groups: Optional[str] = "0,1,2",
+                               enter_reason_list: Optional[str] = "all",
+                               exit_reason_list: Optional[str] = "all",
+                               indicator_list: Optional[str] = None):
+
+    try:
+        trades = load_backtest_data(backtest_dir, strategy_name)
+    except ValueError as e:
+        raise OperationalException(e) from e
+    if not trades.empty:
+        signal_candles = _load_signal_candles(backtest_dir)
+        analysed_trades_dict = _process_candles_and_indicators(pairlist, strategy_name,
+                                                               trades, signal_candles)
+        _print_results(analysed_trades_dict,
+                       strategy_name,
+                       analysis_groups,
+                       enter_reason_list,
+                       exit_reason_list,
+                       indicator_list)