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Merge branch 'freqtrade:develop' into develop

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hippocritical
2023-05-06 21:59:04 +02:00
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parent ce979b21f9 e365e913c7
commit ac4aa8ed2b
42 changed files with 465 additions and 215 deletions
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6
.github/workflows/ci.yml vendored
View File

@@ -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

6
.pre-commit-config.yaml
View File

@@ -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

2
Dockerfile
View File

@@ -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/

2
build_helpers/install_windows.ps1
View File

@@ -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}')"

38
docs/advanced-backtesting.md
View File

@@ -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/
```

7
docs/installation.md
View File

@@ -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 .
```

2
docs/requirements-docs.txt
View File

@@ -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

4
docs/strategy-advanced.md
View File

@@ -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
merged_frame = pd.concat(frames, axis=1)
# Combine all dataframes, and reassign the original dataframe column
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.

23
docs/utils.md
View File

@@ -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.
Default: all. e.g. 'entry_tag_a,entry_tag_b'
Space separated list of entry signals to analyse.
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.
'close,rsi,bb_lowerband,profit_abs'
Space separated list of indicators to analyse. e.g.
'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).

3
freqtrade/commands/arguments.py
View File

@@ -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",

29
freqtrade/commands/cli_options.py
View File

@@ -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. "
"e.g. 'entry_tag_a,entry_tag_b'"),
help=("Space 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'"),
help=("Space 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'"),
help=("Space separated list of indicators to analyse. "
"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.',

9
freqtrade/configuration/configuration.py
View File

@@ -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',

120
freqtrade/data/entryexitanalysis.py
View File

@@ -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)
logger.info(f"Loaded signal candles: {str(scpf)}")
loaded_data = joblib.load(scp)
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,
trades,
signal_candles[strategy_name][pair])
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)
elif indicator_list is not None:
_print_table(res_df,
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(
tabulate(
data,
headers='keys',
tablefmt='psql',
showindex=show_index
if to_csv:
safe_name = Path(csv_path, name.lower().replace(" ", "_").replace(":", "") + ".csv")
data.to_csv(safe_name)
print(f"Saved {name} to {safe_name}")
else:
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

4
freqtrade/exchange/exchange.py
View File

@@ -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}")

7
freqtrade/freqai/RL/Base3ActionRLEnv.py
View File

@@ -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:
"""

7
freqtrade/freqai/RL/Base4ActionRLEnv.py
View File

@@ -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:
"""

6
freqtrade/freqai/RL/Base5ActionRLEnv.py
View File

@@ -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:
"""

16
freqtrade/freqai/RL/BaseEnvironment.py
View File

@@ -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 gym.utils import seeding
from gymnasium import spaces
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):

16
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@@ -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.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
self.train_env: Union[VecMonitor, SubprocVecEnv, 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

11
freqtrade/freqai/RL/TensorboardCallback.py
View File

@@ -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.training_env: BaseEnvironment = None # type: ignore
self.logger: Any = None
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:

5
freqtrade/freqai/base_models/BasePyTorchClassifier.py
View File

@@ -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)

3
freqtrade/freqai/base_models/BasePyTorchRegressor.py
View File

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

4
freqtrade/freqai/freqai_interface.py
View File

@@ -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__}. "
f"Message: {msg}, skipping.")
logger.exception(f"Training {pair} raised exception {msg.__class__.__name__}. "
f"Message: {msg}, skipping.")
self.train_timer('stop', pair)

7
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@@ -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.

27
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@@ -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,
train_df, prices_train,
monitor=True,
env_info=env_info) for i
in range(self.max_threads)])
self.train_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
train_df, prices_train,
env_info=env_info) for i
in range(self.max_threads)]))
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
test_df, prices_test,
monitor=True,
env_info=env_info) for i
in range(self.max_threads)])
self.eval_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
test_df, prices_test,
env_info=env_info) for i
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)

40
freqtrade/freqtradebot.py
View File

@@ -1721,10 +1721,8 @@ class FreqtradeBot(LoggingMixin):
else:
trade.exit_order_status = reason
order = trade.select_order_by_order_id(order_id)
if not order:
raise DependencyException(
f"Order_obj not found for {order_id}. This should not have happened.")
order_or_none = trade.select_order_by_order_id(order_id)
order = self.order_obj_or_raise(order_id, order_or_none)
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)
if not order_obj:
raise DependencyException(
f"Order_obj not found for {order_id}. This should not have happened.")
order_obj_or_none = trade.select_order_by_order_id(order_id)
order_obj = self.order_obj_or_raise(order_id, order_obj_or_none)
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(
pair=trade.pair,
open_rate=trade.open_rate,
is_short=trade.is_short,
amount=trade.amount,
stake_amount=trade.stake_amount,
leverage=trade.leverage,
wallet_balance=trade.stake_amount,
))
try:
trade.set_liquidation_price(self.exchange.get_liquidation_price(
pair=trade.pair,
open_rate=trade.open_rate,
is_short=trade.is_short,
amount=trade.amount,
stake_amount=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()

80
freqtrade/optimize/backtesting.py
View File

@@ -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,
store_backtest_signal_candles,
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_rejected_signals,
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 trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behavior - not sure if this is correct
# Prevents entering if the trade-slot was freed in this candle
open_trade_count_start += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
LocalTrade.add_bt_trade(trade)
self.wallets.update()
if (self.trade_slot_available(open_trade_count_start)):
trade = self._enter_trade(pair, row, trade_dir)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behavior - not sure if this is correct
# Prevents entering if the trade-slot was freed in this candle
open_trade_count_start += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
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)
logger.info(f"Running backtesting for Strategy {strat.get_strategy_name()}")
strategy_name = 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(
self.config['exportfilename'], self.processed_dfs, dt_appendix)
store_backtest_analysis_results(
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:

69
freqtrade/optimize/optimize_reports.py
View File

@@ -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(
recordfilename: Path, candles: Dict[str, Dict], dtappendix: str) -> Path:
def _store_backtest_analysis_data(
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())

5
freqtrade/rpc/api_server/api_v1.py
View File

@@ -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'])

14
freqtrade/rpc/rpc.py
View File

@@ -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]:
timerange_parsed = TimeRange.parse_timerange(timerange)
exchange) -> Dict[str, Any]:
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)

2
pyproject.toml
View File

@@ -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]

4
requirements-dev.txt
View File

@@ -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

11
requirements-freqai-rl.txt
View File

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

13
requirements.txt
View File

@@ -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

4
scripts/rest_client.py
View File

@@ -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 '',
})

26
setup.py
View File

@@ -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,

3
setup.sh
View File

@@ -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==23.0.1 wheel==0.38.4 setuptools==65.5.1
${PYTHON} -m pip install --upgrade pip wheel setuptools
REQUIREMENTS_HYPEROPT=""
REQUIREMENTS_PLOT=""
REQUIREMENTS_FREQAI=""

11
tests/data/test_entryexitanalysis.py
View File

@@ -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

2
tests/exchange/test_exchange.py
View File

@@ -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)

2
tests/optimize/test_backtesting.py
View File

@@ -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']))

27
tests/optimize/test_optimize_reports.py
View File

@@ -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')
assert dump_mock.call_count == 1
store_backtest_analysis_results(filename, candle_dict, {}, '2022_01_01_15_05_13')
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')
scp = stored_file.open("rb")
pickled_signal_candles = joblib.load(scp)
scp.close()
sample_date = '2022_01_01_15_05_13'
store_backtest_analysis_results(Path(tmpdir), candle_dict, {}, sample_date)
stored_file = Path(tmpdir / f'backtest-result-{sample_date}_signals.pkl')
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')
scp = stored_file.open("rb")
pickled_signal_candles = joblib.load(scp)
scp.close()
store_backtest_analysis_results(filename, candle_dict, {}, sample_date)
stored_file = Path(tmpdir / f'testresult-{sample_date}_signals.pkl')
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()

3
tests/rpc/test_rpc_telegram.py
View File

@@ -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()