Merge branch 'freqtrade:develop' into develop

.github/workflows/ci.yml

@@ -57,7 +57,7 @@ jobs:
     - name: Installation - *nix
       if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -163,7 +163,7 @@ jobs:
         rm /usr/local/bin/python3.11-config || true
 
         brew install hdf5 c-blosc
-        python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include
@@ -352,7 +352,7 @@ jobs:
     - name: Installation - *nix
       if: runner.os == 'Linux'
       run: |
-        python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+        python -m pip install --upgrade pip wheel
         export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH
         export TA_LIBRARY_PATH=${HOME}/dependencies/lib
         export TA_INCLUDE_PATH=${HOME}/dependencies/include

.pre-commit-config.yaml

@@ -15,10 +15,10 @@ repos:
         additional_dependencies:
           - types-cachetools==5.3.0.5
           - types-filelock==3.2.7
-          - types-requests==2.28.11.17
+          - types-requests==2.29.0.0
           - types-tabulate==0.9.0.2
           - types-python-dateutil==2.8.19.12
-          - SQLAlchemy==2.0.10
+          - SQLAlchemy==2.0.12
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort
@@ -30,7 +30,7 @@ repos:
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.0.255'
+    rev: 'v0.0.263'
     hooks:
       - id: ruff
 

Dockerfile

@@ -25,7 +25,7 @@ FROM base as python-deps
 RUN  apt-get update \
   && apt-get -y install build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
   && apt-get clean \
-  && pip install --upgrade pip==23.0.1 wheel==0.38.4
+  && pip install --upgrade pip wheel
 
 # Install TA-lib
 COPY build_helpers/* /tmp/

build_helpers/install_windows.ps1

@@ -1,7 +1,7 @@
 # Downloads don't work automatically, since the URL is regenerated via javascript.
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
 
-python -m pip install --upgrade pip==23.0.1 wheel==0.38.4
+python -m pip install --upgrade pip wheel
 
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 

docs/advanced-backtesting.md

@@ -29,7 +29,7 @@ If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl`
 `user_data/backtest_results` folder.
 
 To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
-with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
+with `--analysis-groups` option provided with space-separated arguments:
 
 ``` bash
 freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
@@ -39,6 +39,7 @@ This command will read from the last backtesting results. The `--analysis-groups
 used to specify the various tabular outputs showing the profit fo each group or trade,
 ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
 
+* 0: overall winrate and profit summary by enter_tag
 * 1: profit summaries grouped by enter_tag
 * 2: profit summaries grouped by enter_tag and exit_tag
 * 3: profit summaries grouped by pair and enter_tag
@@ -115,3 +116,38 @@ For example, if your backtest timerange was `20220101-20221231` but you only wan
 ```bash
 freqtrade backtesting-analysis -c <config.json> --timerange 20220101-20220201
 ```
+
+### Printing out rejected signals
+
+Use the `--rejected-signals` option to print out rejected signals.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --rejected-signals
+```
+
+### Writing tables to CSV
+
+Some of the tabular outputs can become large, so printing them out to the terminal is not preferable.
+Use the `--analysis-to-csv` option to disable printing out of tables to standard out and write them to CSV files.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv
+```
+
+By default this will write one file per output table you specified in the `backtesting-analysis` command, e.g.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --rejected-signals --analysis-groups 0 1
+```
+
+This will write to `user_data/backtest_results`:
+
+* rejected_signals.csv
+* group_0.csv
+* group_1.csv
+
+To override where the files will be written, also specify the `--analysis-csv-path` option.
+
+```bash
+freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --analysis-csv-path another/data/path/
+```

docs/installation.md

@@ -30,12 +30,6 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
 !!! Warning "Up-to-date clock"
     The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
 
-!!! Error "Running setup.py install for gym did not run successfully."
-    If you get an error related with gym we suggest you to downgrade setuptools it to version 65.5.0 you can do it with the following command:
-    ```bash
-    pip install setuptools==65.5.0
-    ```
-
 ------
 
 ## Requirements
@@ -242,6 +236,7 @@ source .env/bin/activate
 
 ```bash
 python3 -m pip install --upgrade pip
+python3 -m pip install -r requirements.txt
 python3 -m pip install -e .
 ```
 

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.4.2
-mkdocs-material==9.1.7
+mkdocs-material==9.1.8
 mdx_truly_sane_lists==1.3
 pymdown-extensions==9.11
 jinja2==3.1.2

docs/strategy-advanced.md

@@ -227,8 +227,8 @@ for val in self.buy_ema_short.range:
         f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
     }))
 
-# Append columns to existing dataframe
+# Combine all dataframes, and reassign the original dataframe column
-merged_frame = pd.concat(frames, axis=1)
+dataframe = pd.concat(frames, axis=1)
 ```
 
 Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.

docs/utils.md

@@ -723,6 +723,9 @@ usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
                                       [--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
                                       [--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
                                       [--timerange YYYYMMDD-[YYYYMMDD]]
+                                      [--rejected]
+                                      [--analysis-to-csv]
+                                      [--analysis-csv-path PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -736,19 +739,27 @@ optional arguments:
                         pair and enter_tag, 4: by pair, enter_ and exit_tag
                         (this can get quite large)
   --enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
-                        Comma separated list of entry signals to analyse.
+                        Space separated list of entry signals to analyse.
-                        Default: all. e.g. 'entry_tag_a,entry_tag_b'
+                        Default: all. e.g. 'entry_tag_a entry_tag_b'
   --exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
-                        Comma separated list of exit signals to analyse.
+                        Space separated list of exit signals to analyse.
                         Default: all. e.g.
-                        'exit_tag_a,roi,stop_loss,trailing_stop_loss'
+                        'exit_tag_a roi stop_loss trailing_stop_loss'
   --indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
-                        Comma separated list of indicators to analyse. e.g.
+                        Space separated list of indicators to analyse. e.g.
-                        'close,rsi,bb_lowerband,profit_abs'
+                        'close rsi bb_lowerband profit_abs'
   --timerange YYYYMMDD-[YYYYMMDD]
                         Timerange to filter trades for analysis, 
                         start inclusive, end exclusive. e.g.
                         20220101-20220201
+  --rejected
+                        Print out rejected trades table
+  --analysis-to-csv
+                        Write out tables to individual CSVs, by default to 
+                        'user_data/backtest_results' unless '--analysis-csv-path' is given.
+  --analysis-csv-path [PATH]
+                        Optional path where individual CSVs will be written. If not used,
+                        CSVs will be written to 'user_data/backtest_results'.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).

freqtrade/commands/arguments.py

@@ -106,7 +106,8 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
                       "disableparamexport", "backtest_breakdown"]
 
 ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
-                              "exit_reason_list", "indicator_list", "timerange"]
+                              "exit_reason_list", "indicator_list", "timerange",
+                              "analysis_rejected", "analysis_to_csv", "analysis_csv_path"]
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-freqaimodels",

freqtrade/commands/cli_options.py

@@ -636,30 +636,45 @@ AVAILABLE_CLI_OPTIONS = {
               "4: by pair, enter_ and exit_tag (this can get quite large), "
               "5: by exit_tag"),
         nargs='+',
-        default=['0', '1', '2'],
+        default=[],
         choices=['0', '1', '2', '3', '4', '5'],
     ),
     "enter_reason_list": Arg(
         "--enter-reason-list",
-        help=("Comma separated list of entry signals to analyse. Default: all. "
+        help=("Space separated list of entry signals to analyse. Default: all. "
-              "e.g. 'entry_tag_a,entry_tag_b'"),
+              "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. "
+        help=("Space separated list of exit signals to analyse. Default: all. "
-              "e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
+              "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. "
+        help=("Space separated list of indicators to analyse. "
-              "e.g. 'close,rsi,bb_lowerband,profit_abs'"),
+              "e.g. 'close rsi bb_lowerband profit_abs'"),
         nargs='+',
         default=[],
     ),
+    "analysis_rejected": Arg(
+        '--rejected-signals',
+        help='Analyse rejected signals',
+        action='store_true',
+    ),
+    "analysis_to_csv": Arg(
+        '--analysis-to-csv',
+        help='Save selected analysis tables to individual CSVs',
+        action='store_true',
+    ),
+    "analysis_csv_path": Arg(
+        '--analysis-csv-path',
+        help=("Specify a path to save the analysis CSVs "
+              "if --analysis-to-csv is enabled. Default: user_data/basktesting_results/"),
+    ),
     "freqaimodel": Arg(
         '--freqaimodel',
         help='Specify a custom freqaimodels.',

freqtrade/configuration/configuration.py

@@ -465,6 +465,15 @@ class Configuration:
         self._args_to_config(config, argname='timerange',
                              logstring='Filter trades by timerange: {}')
 
+        self._args_to_config(config, argname='analysis_rejected',
+                             logstring='Analyse rejected signals: {}')
+
+        self._args_to_config(config, argname='analysis_to_csv',
+                             logstring='Store analysis tables to CSV: {}')
+
+        self._args_to_config(config, argname='analysis_csv_path',
+                             logstring='Path to store analysis CSVs: {}')
+
     def _process_runmode(self, config: Config) -> None:
 
         self._args_to_config(config, argname='dry_run',

freqtrade/data/entryexitanalysis.py

@@ -1,5 +1,6 @@
 import logging
 from pathlib import Path
+from typing import List
 
 import joblib
 import pandas as pd
@@ -15,22 +16,31 @@ from freqtrade.exceptions import OperationalException
 logger = logging.getLogger(__name__)
 
 
-def _load_signal_candles(backtest_dir: Path):
+def _load_backtest_analysis_data(backtest_dir: Path, name: str):
     if backtest_dir.is_dir():
         scpf = Path(backtest_dir,
-                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
+                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl"
                     )
     else:
-        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
+        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
 
     try:
         with scpf.open("rb") as scp:
-            signal_candles = joblib.load(scp)
+            loaded_data = joblib.load(scp)
-            logger.info(f"Loaded signal candles: {str(scpf)}")
+            logger.info(f"Loaded {name} candles: {str(scpf)}")
     except Exception as e:
-        logger.error("Cannot load signal candles from pickled results: ", e)
+        logger.error(f"Cannot load {name} data from pickled results: ", e)
+        return None
 
-    return signal_candles
+    return loaded_data
+
+
+def _load_rejected_signals(backtest_dir: Path):
+    return _load_backtest_analysis_data(backtest_dir, "rejected")
+
+
+def _load_signal_candles(backtest_dir: Path):
+    return _load_backtest_analysis_data(backtest_dir, "signals")
 
 
 def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
@@ -43,9 +53,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
         for pair in pairlist:
             if pair in signal_candles[strategy_name]:
                 analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
-                                                              pair,
+                    pair, trades, signal_candles[strategy_name][pair])
-                                                              trades,
-                                                              signal_candles[strategy_name][pair])
     except Exception as e:
         print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
 
@@ -85,7 +93,7 @@ def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles:
         return pd.DataFrame()
 
 
-def _do_group_table_output(bigdf, glist):
+def _do_group_table_output(bigdf, glist, csv_path: Path, to_csv=False, ):
     for g in glist:
         # 0: summary wins/losses grouped by enter tag
         if g == "0":
@@ -116,7 +124,8 @@ def _do_group_table_output(bigdf, glist):
 
             sortcols = ['total_num_buys']
 
-            _print_table(new, sortcols, show_index=True)
+            _print_table(new, sortcols, show_index=True, name="Group 0:",
+                         to_csv=to_csv, csv_path=csv_path)
 
         else:
             agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
@@ -154,11 +163,24 @@ def _do_group_table_output(bigdf, glist):
                 new['mean_profit_pct'] = new['mean_profit_pct'] * 100
                 new['total_profit_pct'] = new['total_profit_pct'] * 100
 
-                _print_table(new, sortcols)
+                _print_table(new, sortcols, name=f"Group {g}:",
+                             to_csv=to_csv, csv_path=csv_path)
             else:
                 logger.warning("Invalid group mask specified.")
 
 
+def _do_rejected_signals_output(rejected_signals_df: pd.DataFrame,
+                                to_csv: bool = False, csv_path=None) -> None:
+    cols = ['pair', 'date', 'enter_tag']
+    sortcols = ['date', 'pair', 'enter_tag']
+    _print_table(rejected_signals_df[cols],
+                 sortcols,
+                 show_index=False,
+                 name="Rejected Signals:",
+                 to_csv=to_csv,
+                 csv_path=csv_path)
+
+
 def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
     if timerange:
         if timerange.starttype == 'date':
@@ -192,38 +214,64 @@ def prepare_results(analysed_trades, stratname,
     return res_df
 
 
-def print_results(res_df, analysis_groups, indicator_list):
+def print_results(res_df: pd.DataFrame, analysis_groups: List[str], indicator_list: List[str],
+                  csv_path: Path, rejected_signals=None, to_csv=False):
     if res_df.shape[0] > 0:
         if analysis_groups:
-            _do_group_table_output(res_df, analysis_groups)
+            _do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)
 
+        if rejected_signals is not None:
+            if rejected_signals.empty:
+                print("There were no rejected signals.")
+            else:
+                _do_rejected_signals_output(rejected_signals, to_csv=to_csv, csv_path=csv_path)
+
+        # NB this can be large for big dataframes!
         if "all" in indicator_list:
-            print(res_df)
+            _print_table(res_df,
-        elif indicator_list is not None:
+                         show_index=False,
+                         name="Indicators:",
+                         to_csv=to_csv,
+                         csv_path=csv_path)
+        elif indicator_list is not None and indicator_list:
             available_inds = []
             for ind in indicator_list:
                 if ind in res_df:
                     available_inds.append(ind)
             ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
-            _print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False)
+            _print_table(res_df[ilist],
+                         sortcols=['exit_reason'],
+                         show_index=False,
+                         name="Indicators:",
+                         to_csv=to_csv,
+                         csv_path=csv_path)
     else:
         print("\\No trades to show")
 
 
-def _print_table(df, sortcols=None, show_index=False):
+def _print_table(df: pd.DataFrame, sortcols=None, *, show_index=False, name=None,
+                 to_csv=False, csv_path: Path):
     if (sortcols is not None):
         data = df.sort_values(sortcols)
     else:
         data = df
 
-    print(
+    if to_csv:
-        tabulate(
+        safe_name = Path(csv_path, name.lower().replace(" ", "_").replace(":", "") + ".csv")
-            data,
+        data.to_csv(safe_name)
-            headers='keys',
+        print(f"Saved {name} to {safe_name}")
-            tablefmt='psql',
+    else:
-            showindex=show_index
+        if name is not None:
+            print(name)
+
+        print(
+            tabulate(
+                data,
+                headers='keys',
+                tablefmt='psql',
+                showindex=show_index
+            )
         )
-    )
 
 
 def process_entry_exit_reasons(config: Config):
@@ -232,6 +280,11 @@ def process_entry_exit_reasons(config: Config):
         enter_reason_list = config.get('enter_reason_list', ["all"])
         exit_reason_list = config.get('exit_reason_list', ["all"])
         indicator_list = config.get('indicator_list', [])
+        do_rejected = config.get('analysis_rejected', False)
+        to_csv = config.get('analysis_to_csv', False)
+        csv_path = Path(config.get('analysis_csv_path', config['exportfilename']))
+        if to_csv and not csv_path.is_dir():
+            raise OperationalException(f"Specified directory {csv_path} does not exist.")
 
         timerange = TimeRange.parse_timerange(None if config.get(
             'timerange') is None else str(config.get('timerange')))
@@ -241,8 +294,16 @@ def process_entry_exit_reasons(config: Config):
         for strategy_name, results in backtest_stats['strategy'].items():
             trades = load_backtest_data(config['exportfilename'], strategy_name)
 
-            if not trades.empty:
+            if trades is not None and not trades.empty:
                 signal_candles = _load_signal_candles(config['exportfilename'])
+
+                rej_df = None
+                if do_rejected:
+                    rejected_signals_dict = _load_rejected_signals(config['exportfilename'])
+                    rej_df = prepare_results(rejected_signals_dict, strategy_name,
+                                             enter_reason_list, exit_reason_list,
+                                             timerange=timerange)
+
                 analysed_trades_dict = _process_candles_and_indicators(
                                         config['exchange']['pair_whitelist'], strategy_name,
                                         trades, signal_candles)
@@ -253,7 +314,10 @@ def process_entry_exit_reasons(config: Config):
 
                 print_results(res_df,
                               analysis_groups,
-                              indicator_list)
+                              indicator_list,
+                              rejected_signals=rej_df,
+                              to_csv=to_csv,
+                              csv_path=csv_path)
 
     except ValueError as e:
         raise OperationalException(e) from e

freqtrade/exchange/exchange.py

@@ -2900,8 +2900,8 @@ class Exchange:
                 if nominal_value >= tier['minNotional']:
                     return (tier['maintenanceMarginRate'], tier['maintAmt'])
 
-            raise OperationalException("nominal value can not be lower than 0")
+            raise ExchangeError("nominal value can not be lower than 0")
             # The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it
             # describes the min amt for a tier, and the lowest tier will always go down to 0
         else:
-            raise OperationalException(f"Cannot get maintenance ratio using {self.name}")
+            raise ExchangeError(f"Cannot get maintenance ratio using {self.name}")

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -94,9 +94,12 @@ class Base3ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
+        # user can play with time if they want
+        truncated = False
+
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -96,9 +96,12 @@ class Base4ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
+        # user can play with time if they want
+        truncated = False
+
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gym import spaces
+from gymnasium import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -101,10 +101,12 @@ class Base5ActionRLEnv(BaseEnvironment):
         )
 
         observation = self._get_observation()
+        # user can play with time if they want
+        truncated = False
 
         self._update_history(info)
 
-        return observation, step_reward, self._done, info
+        return observation, step_reward, self._done, truncated, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/BaseEnvironment.py

@@ -4,11 +4,11 @@ from abc import abstractmethod
 from enum import Enum
 from typing import Optional, Type, Union
 
-import gym
+import gymnasium as gym
 import numpy as np
 import pandas as pd
-from gym import spaces
+from gymnasium import spaces
-from gym.utils import seeding
+from gymnasium.utils import seeding
 from pandas import DataFrame
 
 
@@ -127,6 +127,14 @@ class BaseEnvironment(gym.Env):
         self.history: dict = {}
         self.trade_history: list = []
 
+    def get_attr(self, attr: str):
+        """
+        Returns the attribute of the environment
+        :param attr: attribute to return
+        :return: attribute
+        """
+        return getattr(self, attr)
+
     @abstractmethod
     def set_action_space(self):
         """
@@ -203,7 +211,7 @@ class BaseEnvironment(gym.Env):
         self.close_trade_profit = []
         self._total_unrealized_profit = 1
 
-        return self._get_observation()
+        return self._get_observation(), self.history
 
     @abstractmethod
     def step(self, action: int):

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -6,7 +6,7 @@ from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
 
-import gym
+import gymnasium as gym
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
@@ -16,13 +16,13 @@ from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.monitor import Monitor
 from stable_baselines3.common.utils import set_random_seed
-from stable_baselines3.common.vec_env import SubprocVecEnv
+from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment, Positions
 from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 from freqtrade.persistence import Trade
 
@@ -46,8 +46,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
         th.set_num_threads(self.max_threads)
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
-        self.train_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
+        self.train_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
-        self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
+        self.eval_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
         self.eval_callback: Optional[EvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
@@ -431,9 +431,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             return 0.
 
 
-def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
+def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
              seed: int, train_df: DataFrame, price: DataFrame,
-             monitor: bool = False,
              env_info: Dict[str, Any] = {}) -> Callable:
     """
     Utility function for multiprocessed env.
@@ -450,8 +449,7 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
 
         env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
                       **env_info)
-        if monitor:
+
-            env = Monitor(env)
         return env
     set_random_seed(seed)
     return _init

freqtrade/freqai/RL/TensorboardCallback.py

@@ -3,8 +3,9 @@ from typing import Any, Dict, Type, Union
 
 from stable_baselines3.common.callbacks import BaseCallback
 from stable_baselines3.common.logger import HParam
+from stable_baselines3.common.vec_env import VecEnv
 
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions
 
 
 class TensorboardCallback(BaseCallback):
@@ -12,11 +13,13 @@ class TensorboardCallback(BaseCallback):
     Custom callback for plotting additional values in tensorboard and
     episodic summary reports.
     """
+    # Override training_env type to fix type errors
+    training_env: Union[VecEnv, None] = None
+
     def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
         super().__init__(verbose)
         self.model: Any = None
-        self.logger = None  # type: Any
+        self.logger: Any = None
-        self.training_env: BaseEnvironment = None  # type: ignore
         self.actions: Type[Enum] = actions
 
     def _on_training_start(self) -> None:
@@ -44,6 +47,8 @@ class TensorboardCallback(BaseCallback):
     def _on_step(self) -> bool:
 
         local_info = self.locals["infos"][0]
+        if self.training_env is None:
+            return True
         tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
 
         for metric in local_info:

freqtrade/freqai/base_models/BasePyTorchClassifier.py

@@ -45,6 +45,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
+        :param dk: dk: The datakitchen object
         :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
@@ -78,7 +79,9 @@ class BasePyTorchClassifier(BasePyTorchModel):
         probs = F.softmax(logits, dim=-1)
         predicted_classes = torch.argmax(probs, dim=-1)
         predicted_classes_str = self.decode_class_names(predicted_classes)
-        pred_df_prob = DataFrame(probs.detach().numpy(), columns=class_names)
+        # used .tolist to convert probs into an iterable, in this way Tensors
+        # are automatically moved to the CPU first if necessary.
+        pred_df_prob = DataFrame(probs.detach().tolist(), columns=class_names)
         pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
         pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
         return (pred_df, dk.do_predict)

freqtrade/freqai/base_models/BasePyTorchRegressor.py

@@ -45,6 +45,5 @@ class BasePyTorchRegressor(BasePyTorchModel):
             device=self.device
         )
         y = self.model.model(x)
-        y = y.cpu()
+        pred_df = DataFrame(y.detach().tolist(), columns=[dk.label_list[0]])
-        pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
         return (pred_df, dk.do_predict)

freqtrade/freqai/freqai_interface.py

@@ -242,8 +242,8 @@ class IFreqaiModel(ABC):
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
                 except Exception as msg:
-                    logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
+                    logger.exception(f"Training {pair} raised exception {msg.__class__.__name__}. "
-                                   f"Message: {msg}, skipping.")
+                                     f"Message: {msg}, skipping.")
 
                 self.train_timer('stop', pair)
 

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -1,11 +1,12 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict
+from typing import Any, Dict, Type
 
 import torch as th
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
 
 
@@ -84,7 +85,9 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         return model
 
-    class MyRLEnv(Base5ActionRLEnv):
+    MyRLEnv: Type[BaseEnvironment]
+
+    class MyRLEnv(Base5ActionRLEnv):  # type: ignore[no-redef]
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -3,7 +3,7 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
-from stable_baselines3.common.vec_env import SubprocVecEnv
+from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
@@ -41,22 +41,25 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
 
         env_info = self.pack_env_dict(dk.pair)
 
+        eval_freq = len(train_df) // self.max_threads
+
         env_id = "train_env"
-        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
+        self.train_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
-                                        train_df, prices_train,
+                                                            train_df, prices_train,
-                                        monitor=True,
+                                                            env_info=env_info) for i
-                                        env_info=env_info) for i
+                                                   in range(self.max_threads)]))
-                                        in range(self.max_threads)])
 
         eval_env_id = 'eval_env'
-        self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
+        self.eval_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
-                                                test_df, prices_test,
+                                                           test_df, prices_test,
-                                                monitor=True,
+                                                           env_info=env_info) for i
-                                                env_info=env_info) for i
+                                                  in range(self.max_threads)]))
-                                       in range(self.max_threads)])
+
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
-                                          render=False, eval_freq=len(train_df),
+                                          render=False, eval_freq=eval_freq,
                                           best_model_save_path=str(dk.data_path))
 
+        # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
+        # IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
         actions = self.train_env.env_method("get_actions")[0]
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

freqtrade/freqtradebot.py

@@ -1721,10 +1721,8 @@ class FreqtradeBot(LoggingMixin):
         else:
             trade.exit_order_status = reason
 
-        order = trade.select_order_by_order_id(order_id)
+        order_or_none = trade.select_order_by_order_id(order_id)
-        if not order:
+        order = self.order_obj_or_raise(order_id, order_or_none)
-            raise DependencyException(
-                f"Order_obj not found for {order_id}. This should not have happened.")
 
         profit_rate: float = trade.safe_close_rate
         profit_trade = trade.calc_profit(rate=profit_rate)
@@ -1765,6 +1763,12 @@ class FreqtradeBot(LoggingMixin):
         # Send the message
         self.rpc.send_msg(msg)
 
+    def order_obj_or_raise(self, order_id: str, order_obj: Optional[Order]) -> Order:
+        if not order_obj:
+            raise DependencyException(
+                f"Order_obj not found for {order_id}. This should not have happened.")
+        return order_obj
+
 #
 # Common update trade state methods
 #
@@ -1803,10 +1807,8 @@ class FreqtradeBot(LoggingMixin):
             # Handling of this will happen in check_handle_timedout.
             return True
 
-        order_obj = trade.select_order_by_order_id(order_id)
+        order_obj_or_none = trade.select_order_by_order_id(order_id)
-        if not order_obj:
+        order_obj = self.order_obj_or_raise(order_id, order_obj_or_none)
-            raise DependencyException(
-                f"Order_obj not found for {order_id}. This should not have happened.")
 
         self.handle_order_fee(trade, order_obj, order)
 
@@ -1824,16 +1826,18 @@ class FreqtradeBot(LoggingMixin):
                 # Must also run for partial exits
                 # TODO: Margin will need to use interest_rate as well.
                 # interest_rate = self.exchange.get_interest_rate()
-                trade.set_liquidation_price(self.exchange.get_liquidation_price(
+                try:
-                    pair=trade.pair,
+                    trade.set_liquidation_price(self.exchange.get_liquidation_price(
-                    open_rate=trade.open_rate,
+                        pair=trade.pair,
-                    is_short=trade.is_short,
+                        open_rate=trade.open_rate,
-                    amount=trade.amount,
+                        is_short=trade.is_short,
-                    stake_amount=trade.stake_amount,
+                        amount=trade.amount,
-                    leverage=trade.leverage,
+                        stake_amount=trade.stake_amount,
-                    wallet_balance=trade.stake_amount,
+                        leverage=trade.leverage,
-                ))
+                        wallet_balance=trade.stake_amount,
-
+                    ))
+                except DependencyException:
+                    logger.warning('Unable to calculate liquidation price')
             # Updating wallets when order is closed
             self.wallets.update()
         Trade.commit()

freqtrade/optimize/backtesting.py

@@ -9,7 +9,6 @@ from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
-import pandas as pd
 from numpy import nan
 from pandas import DataFrame
 
@@ -28,8 +27,10 @@ from freqtrade.exchange import (amount_to_contract_precision, price_to_precision
 from freqtrade.mixins import LoggingMixin
 from freqtrade.optimize.backtest_caching import get_strategy_run_id
 from freqtrade.optimize.bt_progress import BTProgress
-from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
+from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_rejected_signals,
-                                                 store_backtest_signal_candles,
+                                                 generate_trade_signal_candles,
+                                                 show_backtest_results,
+                                                 store_backtest_analysis_results,
                                                  store_backtest_stats)
 from freqtrade.persistence import LocalTrade, Order, PairLocks, Trade
 from freqtrade.plugins.pairlistmanager import PairListManager
@@ -84,6 +85,8 @@ class Backtesting:
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
         self.processed_dfs: Dict[str, Dict] = {}
+        self.rejected_dict: Dict[str, List] = {}
+        self.rejected_df: Dict[str, Dict] = {}
 
         self._exchange_name = self.config['exchange']['name']
         self.exchange = ExchangeResolver.load_exchange(
@@ -1056,6 +1059,18 @@ class Backtesting:
             return None
         return row
 
+    def _collate_rejected(self, pair, row):
+        """
+        Temporarily store rejected signal information for downstream use in backtesting_analysis
+        """
+        # It could be fun to enable hyperopt mode to write
+        # a loss function to reduce rejected signals
+        if (self.config.get('export', 'none') == 'signals' and
+                self.dataprovider.runmode == RunMode.BACKTEST):
+            if pair not in self.rejected_dict:
+                self.rejected_dict[pair] = []
+            self.rejected_dict[pair].append([row[DATE_IDX], row[ENTER_TAG_IDX]])
+
     def backtest_loop(
             self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
             open_trade_count_start: int, trade_dir: Optional[LongShort],
@@ -1081,20 +1096,22 @@ class Backtesting:
         if (
             (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
             and is_first
-            and self.trade_slot_available(open_trade_count_start)
             and current_time != end_date
             and trade_dir is not None
             and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
         ):
-            trade = self._enter_trade(pair, row, trade_dir)
+            if (self.trade_slot_available(open_trade_count_start)):
-            if trade:
+                trade = self._enter_trade(pair, row, trade_dir)
-                # TODO: hacky workaround to avoid opening > max_open_trades
+                if trade:
-                # This emulates previous behavior - not sure if this is correct
+                    # TODO: hacky workaround to avoid opening > max_open_trades
-                # Prevents entering if the trade-slot was freed in this candle
+                    # This emulates previous behavior - not sure if this is correct
-                open_trade_count_start += 1
+                    # Prevents entering if the trade-slot was freed in this candle
-                # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
+                    open_trade_count_start += 1
-                LocalTrade.add_bt_trade(trade)
+                    # logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
-                self.wallets.update()
+                    LocalTrade.add_bt_trade(trade)
+                    self.wallets.update()
+            else:
+                self._collate_rejected(pair, row)
 
         for trade in list(LocalTrade.bt_trades_open_pp[pair]):
             # 3. Process entry orders.
@@ -1236,8 +1253,8 @@ class Backtesting:
     def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
                               timerange: TimeRange):
         self.progress.init_step(BacktestState.ANALYZE, 0)
-
+        strategy_name = strat.get_strategy_name()
-        logger.info(f"Running backtesting for Strategy {strat.get_strategy_name()}")
+        logger.info(f"Running backtesting for Strategy {strategy_name}")
         backtest_start_time = datetime.now(timezone.utc)
         self._set_strategy(strat)
 
@@ -1272,37 +1289,21 @@ class Backtesting:
         )
         backtest_end_time = datetime.now(timezone.utc)
         results.update({
-            'run_id': self.run_ids.get(strat.get_strategy_name(), ''),
+            'run_id': self.run_ids.get(strategy_name, ''),
             'backtest_start_time': int(backtest_start_time.timestamp()),
             'backtest_end_time': int(backtest_end_time.timestamp()),
         })
-        self.all_results[self.strategy.get_strategy_name()] = results
+        self.all_results[strategy_name] = results
 
         if (self.config.get('export', 'none') == 'signals' and
                 self.dataprovider.runmode == RunMode.BACKTEST):
-            self._generate_trade_signal_candles(preprocessed_tmp, results)
+            self.processed_dfs[strategy_name] = generate_trade_signal_candles(
+                preprocessed_tmp, results)
+            self.rejected_df[strategy_name] = generate_rejected_signals(
+                preprocessed_tmp, self.rejected_dict)
 
         return min_date, max_date
 
-    def _generate_trade_signal_candles(self, preprocessed_df, bt_results):
-        signal_candles_only = {}
-        for pair in preprocessed_df.keys():
-            signal_candles_only_df = DataFrame()
-
-            pairdf = preprocessed_df[pair]
-            resdf = bt_results['results']
-            pairresults = resdf.loc[(resdf["pair"] == pair)]
-
-            if pairdf.shape[0] > 0:
-                for t, v in pairresults.open_date.items():
-                    allinds = pairdf.loc[(pairdf['date'] < v)]
-                    signal_inds = allinds.iloc[[-1]]
-                    signal_candles_only_df = pd.concat([signal_candles_only_df, signal_inds])
-
-                signal_candles_only[pair] = signal_candles_only_df
-
-        self.processed_dfs[self.strategy.get_strategy_name()] = signal_candles_only
-
     def _get_min_cached_backtest_date(self):
         min_backtest_date = None
         backtest_cache_age = self.config.get('backtest_cache', constants.BACKTEST_CACHE_DEFAULT)
@@ -1365,8 +1366,9 @@ class Backtesting:
 
             if (self.config.get('export', 'none') == 'signals' and
                     self.dataprovider.runmode == RunMode.BACKTEST):
-                store_backtest_signal_candles(
+                store_backtest_analysis_results(
-                    self.config['exportfilename'], self.processed_dfs, dt_appendix)
+                    self.config['exportfilename'], self.processed_dfs, self.rejected_df,
+                    dt_appendix)
 
         # Results may be mixed up now. Sort them so they follow --strategy-list order.
         if 'strategy_list' in self.config and len(self.results) > 0:

freqtrade/optimize/optimize_reports.py

@@ -4,7 +4,7 @@ from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any, Dict, List, Union
 
-from pandas import DataFrame, to_datetime
+from pandas import DataFrame, concat, to_datetime
 from tabulate import tabulate
 
 from freqtrade.constants import (BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN,
@@ -46,29 +46,80 @@ def store_backtest_stats(
     file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
 
 
-def store_backtest_signal_candles(
+def _store_backtest_analysis_data(
-        recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
+        recordfilename: Path, data: Dict[str, Dict],
+        dtappendix: str, name: str) -> Path:
     """
-    Stores backtest trade signal candles
+    Stores backtest trade candles for analysis
     :param recordfilename: Path object, which can either be a filename or a directory.
         Filenames will be appended with a timestamp right before the suffix
-        while for directories, <directory>/backtest-result-<datetime>_signals.pkl will be used
+        while for directories, <directory>/backtest-result-<datetime>_<name>.pkl will be used
         as filename
-    :param stats: Dict containing the backtesting signal candles
+    :param candles: Dict containing the backtesting data for analysis
     :param dtappendix: Datetime to use for the filename
+    :param name: Name to use for the file, e.g. signals, rejected
     """
     if recordfilename.is_dir():
-        filename = (recordfilename / f'backtest-result-{dtappendix}_signals.pkl')
+        filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl')
     else:
         filename = Path.joinpath(
-            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_signals.pkl'
+            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl'
         )
 
-    file_dump_joblib(filename, candles)
+    file_dump_joblib(filename, data)
 
     return filename
 
 
+def store_backtest_analysis_results(
+        recordfilename: Path, candles: Dict[str, Dict], trades: Dict[str, Dict],
+        dtappendix: str) -> None:
+    _store_backtest_analysis_data(recordfilename, candles, dtappendix, "signals")
+    _store_backtest_analysis_data(recordfilename, trades, dtappendix, "rejected")
+
+
+def generate_trade_signal_candles(preprocessed_df: Dict[str, DataFrame],
+                                  bt_results: Dict[str, Any]) -> DataFrame:
+    signal_candles_only = {}
+    for pair in preprocessed_df.keys():
+        signal_candles_only_df = DataFrame()
+
+        pairdf = preprocessed_df[pair]
+        resdf = bt_results['results']
+        pairresults = resdf.loc[(resdf["pair"] == pair)]
+
+        if pairdf.shape[0] > 0:
+            for t, v in pairresults.open_date.items():
+                allinds = pairdf.loc[(pairdf['date'] < v)]
+                signal_inds = allinds.iloc[[-1]]
+                signal_candles_only_df = concat([
+                    signal_candles_only_df.infer_objects(),
+                    signal_inds.infer_objects()])
+
+            signal_candles_only[pair] = signal_candles_only_df
+    return signal_candles_only
+
+
+def generate_rejected_signals(preprocessed_df: Dict[str, DataFrame],
+                              rejected_dict: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
+    rejected_candles_only = {}
+    for pair, signals in rejected_dict.items():
+        rejected_signals_only_df = DataFrame()
+        pairdf = preprocessed_df[pair]
+
+        for t in signals:
+            data_df_row = pairdf.loc[(pairdf['date'] == t[0])].copy()
+            data_df_row['pair'] = pair
+            data_df_row['enter_tag'] = t[1]
+
+            rejected_signals_only_df = concat([
+                rejected_signals_only_df.infer_objects(),
+                data_df_row.infer_objects()])
+
+        rejected_candles_only[pair] = rejected_signals_only_df
+    return rejected_candles_only
+
+
 def _get_line_floatfmt(stake_currency: str) -> List[str]:
     """
     Generate floatformat (goes in line with _generate_result_line())

freqtrade/rpc/api_server/api_v1.py

@@ -247,14 +247,17 @@ def pair_candles(
 
 @router.get('/pair_history', response_model=PairHistory, tags=['candle data'])
 def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
+                 freqaimodel: Optional[str] = None,
                  config=Depends(get_config), exchange=Depends(get_exchange)):
     # The initial call to this endpoint can be slow, as it may need to initialize
     # the exchange class.
     config = deepcopy(config)
     config.update({
         'strategy': strategy,
+        'timerange': timerange,
+        'freqaimodel': freqaimodel if freqaimodel else config.get('freqaimodel'),
     })
-    return RPC._rpc_analysed_history_full(config, pair, timeframe, timerange, exchange)
+    return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange)
 
 
 @router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])

freqtrade/rpc/rpc.py

@@ -420,16 +420,15 @@ class RPC:
             else:
                 return 'draws'
         trades = Trade.get_trades([Trade.is_open.is_(False)], include_orders=False)
-        # Sell reason
+        # Duration
+        dur: Dict[str, List[float]] = {'wins': [], 'draws': [], 'losses': []}
+        # Exit reason
         exit_reasons = {}
         for trade in trades:
             if trade.exit_reason not in exit_reasons:
                 exit_reasons[trade.exit_reason] = {'wins': 0, 'losses': 0, 'draws': 0}
             exit_reasons[trade.exit_reason][trade_win_loss(trade)] += 1
 
-        # Duration
-        dur: Dict[str, List[float]] = {'wins': [], 'draws': [], 'losses': []}
-        for trade in trades:
             if trade.close_date is not None and trade.open_date is not None:
                 trade_dur = (trade.close_date - trade.open_date).total_seconds()
                 dur[trade_win_loss(trade)].append(trade_dur)
@@ -1216,8 +1215,8 @@ class RPC:
 
     @staticmethod
     def _rpc_analysed_history_full(config: Config, pair: str, timeframe: str,
-                                   timerange: str, exchange) -> Dict[str, Any]:
+                                   exchange) -> Dict[str, Any]:
-        timerange_parsed = TimeRange.parse_timerange(timerange)
+        timerange_parsed = TimeRange.parse_timerange(config.get('timerange'))
 
         _data = load_data(
             datadir=config["datadir"],
@@ -1228,7 +1227,8 @@ class RPC:
             candle_type=config.get('candle_type_def', CandleType.SPOT)
         )
         if pair not in _data:
-            raise RPCException(f"No data for {pair}, {timeframe} in {timerange} found.")
+            raise RPCException(
+                f"No data for {pair}, {timeframe} in {config.get('timerange')} found.")
         from freqtrade.data.dataprovider import DataProvider
         from freqtrade.resolvers.strategy_resolver import StrategyResolver
         strategy = StrategyResolver.load_strategy(config)

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools >= 46.4.0", "wheel"]
+requires = ["setuptools >= 64.0.0", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [tool.black]

requirements-dev.txt

@@ -7,7 +7,7 @@
 -r docs/requirements-docs.txt
 
 coveralls==3.3.1
-ruff==0.0.262
+ruff==0.0.263
 mypy==1.2.0
 pre-commit==3.2.2
 pytest==7.3.1
@@ -25,6 +25,6 @@ nbconvert==7.3.1
 # mypy types
 types-cachetools==5.3.0.5
 types-filelock==3.2.7
-types-requests==2.28.11.17
+types-requests==2.29.0.0
 types-tabulate==0.9.0.2
 types-python-dateutil==2.8.19.12

requirements-freqai-rl.txt

@@ -3,10 +3,9 @@
 
 # Required for freqai-rl
 torch==1.13.1; python_version < '3.11'
-stable-baselines3==1.7.0; python_version < '3.11'
+#until these branches will be released we can use this
-sb3-contrib==1.7.0; python_version < '3.11'
+gymnasium==0.28.1
-# Gym is forced to this version by stable-baselines3.
+stable_baselines3==2.0.0a5
-setuptools==65.5.1 # Should be removed when gym is fixed.
+sb3_contrib>=2.0.0a4
-gym==0.21; python_version < '3.11'
 # Progress bar for stable-baselines3 and sb3-contrib
-tqdm==4.65.0; python_version < '3.11'
+tqdm==4.65.0

requirements.txt

@@ -2,16 +2,16 @@ numpy==1.24.3
 pandas==2.0.1
 pandas-ta==0.3.14b
 
-ccxt==3.0.75
+ccxt==3.0.85
 cryptography==40.0.2
 aiohttp==3.8.4
-SQLAlchemy==2.0.10
+SQLAlchemy==2.0.12
 python-telegram-bot==20.2
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.23.3
 arrow==1.2.3
 cachetools==4.2.2
-requests==2.28.2
+requests==2.29.0
 urllib3==1.26.15
 jsonschema==4.17.3
 TA-Lib==0.4.26
@@ -22,7 +22,7 @@ jinja2==3.1.2
 tables==3.8.0
 blosc==1.11.1
 joblib==1.2.0
-rich==13.3.4
+rich==13.3.5
 pyarrow==11.0.0; platform_machine != 'armv7l'
 
 # find first, C search in arrays
@@ -31,7 +31,7 @@ py_find_1st==1.1.5
 # Load ticker files 30% faster
 python-rapidjson==1.10
 # Properly format api responses
-orjson==3.8.10
+orjson==3.8.11
 
 # Notify systemd
 sdnotify==0.3.2
@@ -39,7 +39,7 @@ sdnotify==0.3.2
 # API Server
 fastapi==0.95.1
 pydantic==1.10.7
-uvicorn==0.21.1
+uvicorn==0.22.0
 pyjwt==2.6.0
 aiofiles==23.1.0
 psutil==5.9.5
@@ -60,3 +60,4 @@ websockets==11.0.2
 janus==1.0.0
 
 ast-comments==1.0.1
+packaging==23.1

scripts/rest_client.py

@@ -348,12 +348,13 @@ class FtRestClient():
             params['limit'] = limit
         return self._get("pair_candles", params=params)
 
-    def pair_history(self, pair, timeframe, strategy, timerange=None):
+    def pair_history(self, pair, timeframe, strategy, timerange=None, freqaimodel=None):
         """Return historic, analyzed dataframe
 
         :param pair: Pair to get data for
         :param timeframe: Only pairs with this timeframe available.
         :param strategy: Strategy to analyze and get values for
+        :param freqaimodel: FreqAI model to use for analysis
         :param timerange: Timerange to get data for (same format than --timerange endpoints)
         :return: json object
         """
@@ -361,6 +362,7 @@ class FtRestClient():
             "pair": pair,
             "timeframe": timeframe,
             "strategy": strategy,
+            "freqaimodel": freqaimodel,
             "timerange": timerange if timerange else '',
         })
 

setup.py

@@ -12,16 +12,19 @@ hyperopt = [
 
 freqai = [
     'scikit-learn',
+    'joblib',
     'catboost; platform_machine != "aarch64"',
     'lightgbm',
-    'xgboost'
+    'xgboost',
+    'tensorboard'
 ]
 
 freqai_rl = [
     'torch',
+    'gymnasium',
     'stable-baselines3',
-    'gym==0.21',
+    'sb3-contrib',
-    'sb3-contrib'
+    'tqdm'
 ]
 
 hdf5 = [
@@ -32,11 +35,20 @@ hdf5 = [
 develop = [
     'coveralls',
     'mypy',
+    'ruff',
+    'pre-commit',
     'pytest',
     'pytest-asyncio',
     'pytest-cov',
     'pytest-mock',
     'pytest-random-order',
+    'isort',
+    'time-machine',
+    'types-cachetools',
+    'types-filelock',
+    'types-requests',
+    'types-tabulate',
+    'types-python-dateutil'
 ]
 
 jupyter = [
@@ -91,7 +103,13 @@ setup(
         'aiofiles',
         'schedule',
         'websockets',
-        'janus'
+        'janus',
+        'ast-comments',
+        'aiohttp',
+        'cryptography',
+        'httpx',
+        'python-dateutil',
+        'packaging',
     ],
     extras_require={
         'dev': all_extra,

setup.sh

@@ -49,8 +49,7 @@ function updateenv() {
     source .env/bin/activate
     SYS_ARCH=$(uname -m)
     echo "pip install in-progress. Please wait..."
-    # Setuptools 65.5.0 is the last version that can install gym==0.21.0
+    ${PYTHON} -m pip install --upgrade pip wheel setuptools
-    ${PYTHON} -m pip install --upgrade pip==23.0.1 wheel==0.38.4 setuptools==65.5.1
     REQUIREMENTS_HYPEROPT=""
     REQUIREMENTS_PLOT=""
     REQUIREMENTS_FREQAI=""

tests/data/test_entryexitanalysis.py

@@ -200,8 +200,17 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
     assert 'trailing_stop_loss' in captured.out
 
     # test date filtering
-    args = get_args(base_args + ['--timerange', "20180129-20180130"])
+    args = get_args(base_args +
+                    ['--analysis-groups', "0", "1", "2",
+                     '--timerange', "20180129-20180130"]
+                    )
     start_analysis_entries_exits(args)
     captured = capsys.readouterr()
     assert 'enter_tag_long_a' in captured.out
     assert 'enter_tag_long_b' not in captured.out
+
+    # Due to the backtest mock, there's no rejected signals generated.
+    args = get_args(base_args + ['--rejected-signals'])
+    start_analysis_entries_exits(args)
+    captured = capsys.readouterr()
+    assert 'no rejected signals' in captured.out

tests/exchange/test_exchange.py

@@ -4932,7 +4932,7 @@ def test_get_maintenance_ratio_and_amt_exceptions(mocker, default_conf, leverage
 
     exchange._leverage_tiers = leverage_tiers
     with pytest.raises(
-        OperationalException,
+        DependencyException,
         match='nominal value can not be lower than 0',
     ):
         exchange.get_maintenance_ratio_and_amt('1000SHIB/USDT:USDT', -1)

tests/optimize/test_backtesting.py

@@ -354,7 +354,7 @@ def test_backtesting_start(default_conf, mocker, caplog) -> None:
     mocker.patch('freqtrade.optimize.backtesting.generate_backtest_stats')
     mocker.patch('freqtrade.optimize.backtesting.show_backtest_results')
     sbs = mocker.patch('freqtrade.optimize.backtesting.store_backtest_stats')
-    sbc = mocker.patch('freqtrade.optimize.backtesting.store_backtest_signal_candles')
+    sbc = mocker.patch('freqtrade.optimize.backtesting.store_backtest_analysis_results')
     mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                  PropertyMock(return_value=['UNITTEST/BTC']))
 

tests/optimize/test_optimize_reports.py

@@ -21,7 +21,7 @@ from freqtrade.optimize.optimize_reports import (_get_resample_from_period, gene
                                                  generate_periodic_breakdown_stats,
                                                  generate_strategy_comparison,
                                                  generate_trading_stats, show_sorted_pairlist,
-                                                 store_backtest_signal_candles,
+                                                 store_backtest_analysis_results,
                                                  store_backtest_stats, text_table_bt_results,
                                                  text_table_exit_reason, text_table_strategy)
 from freqtrade.resolvers.strategy_resolver import StrategyResolver
@@ -232,17 +232,17 @@ def test_store_backtest_candles(testdatadir, mocker):
     candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
 
     # mock directory exporting
-    store_backtest_signal_candles(testdatadir, candle_dict, '2022_01_01_15_05_13')
+    store_backtest_analysis_results(testdatadir, candle_dict, {}, '2022_01_01_15_05_13')
 
-    assert dump_mock.call_count == 1
+    assert dump_mock.call_count == 2
     assert isinstance(dump_mock.call_args_list[0][0][0], Path)
     assert str(dump_mock.call_args_list[0][0][0]).endswith('_signals.pkl')
 
     dump_mock.reset_mock()
     # mock file exporting
     filename = Path(testdatadir / 'testresult')
-    store_backtest_signal_candles(filename, candle_dict, '2022_01_01_15_05_13')
+    store_backtest_analysis_results(filename, candle_dict, {}, '2022_01_01_15_05_13')
-    assert dump_mock.call_count == 1
+    assert dump_mock.call_count == 2
     assert isinstance(dump_mock.call_args_list[0][0][0], Path)
     # result will be testdatadir / testresult-<timestamp>_signals.pkl
     assert str(dump_mock.call_args_list[0][0][0]).endswith('_signals.pkl')
@@ -254,10 +254,11 @@ def test_write_read_backtest_candles(tmpdir):
     candle_dict = {'DefStrat': {'UNITTEST/BTC': pd.DataFrame()}}
 
     # test directory exporting
-    stored_file = store_backtest_signal_candles(Path(tmpdir), candle_dict, '2022_01_01_15_05_13')
+    sample_date = '2022_01_01_15_05_13'
-    scp = stored_file.open("rb")
+    store_backtest_analysis_results(Path(tmpdir), candle_dict, {}, sample_date)
-    pickled_signal_candles = joblib.load(scp)
+    stored_file = Path(tmpdir / f'backtest-result-{sample_date}_signals.pkl')
-    scp.close()
+    with stored_file.open("rb") as scp:
+        pickled_signal_candles = joblib.load(scp)
 
     assert pickled_signal_candles.keys() == candle_dict.keys()
     assert pickled_signal_candles['DefStrat'].keys() == pickled_signal_candles['DefStrat'].keys()
@@ -268,10 +269,10 @@ def test_write_read_backtest_candles(tmpdir):
 
     # test file exporting
     filename = Path(tmpdir / 'testresult')
-    stored_file = store_backtest_signal_candles(filename, candle_dict, '2022_01_01_15_05_13')
+    store_backtest_analysis_results(filename, candle_dict, {}, sample_date)
-    scp = stored_file.open("rb")
+    stored_file = Path(tmpdir / f'testresult-{sample_date}_signals.pkl')
-    pickled_signal_candles = joblib.load(scp)
+    with stored_file.open("rb") as scp:
-    scp.close()
+        pickled_signal_candles = joblib.load(scp)
 
     assert pickled_signal_candles.keys() == candle_dict.keys()
     assert pickled_signal_candles['DefStrat'].keys() == pickled_signal_candles['DefStrat'].keys()

tests/rpc/test_rpc_telegram.py

@@ -825,6 +825,9 @@ async def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_shor
     assert 'Exit Reason' in msg_mock.call_args_list[-1][0][0]
     assert 'ROI' in msg_mock.call_args_list[-1][0][0]
     assert 'Avg. Duration' in msg_mock.call_args_list[-1][0][0]
+    # Duration is not only N/A
+    assert '0:19:00' in msg_mock.call_args_list[-1][0][0]
+    assert 'N/A' in msg_mock.call_args_list[-1][0][0]
     msg_mock.reset_mock()