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Merge branch 'develop' into backtest_live_models

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This commit is contained in:
Wagner Costa Santos 2022-10-20 11:59:37 -03:00
commit 52b60c5cbb
43 changed files with 650 additions and 236 deletions
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2
.pre-commit-config.yaml
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@ -17,7 +17,7 @@ repos:
- types-filelock==3.2.7
- types-requests==2.28.11.2
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19
- types-python-dateutil==2.8.19.1
# stages: [push]
- repo: https://github.com/pycqa/isort

1
docs/freqai-parameter-table.md
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@ -37,6 +37,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
| | **Data split parameters**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.

2
docs/requirements-docs.txt
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@ -1,5 +1,5 @@
markdown==3.3.7
mkdocs==1.4.0
mkdocs==1.4.1
mkdocs-material==8.5.6
mdx_truly_sane_lists==1.3
pymdown-extensions==9.6

2
docs/stoploss.md
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@ -87,7 +87,7 @@ At this stage the bot contains the following stoploss support modes:
2. Trailing stop loss.
3. Trailing stop loss, custom positive loss.
4. Trailing stop loss only once the trade has reached a certain offset.
5. [Custom stoploss function](strategy-advanced.md#custom-stoploss)
5. [Custom stoploss function](strategy-callbacks.md#custom-stoploss)
### Static Stop Loss

8
docs/strategy-customization.md
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@ -659,9 +659,9 @@ informative = self.dp.get_pair_dataframe(pair=inf_pair,
```
!!! Warning "Warning about backtesting"
Be careful when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
for the backtesting runmode) provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode.
In backtesting, `dp.get_pair_dataframe()` behavior differs depending on where it's called.
Within `populate_*()` methods, `dp.get_pair_dataframe()` returns the full timerange. Please make sure to not "look into the future" to avoid surprises when running in dry/live mode.
Within [callbacks](strategy-callbacks.md), you'll get the full timerange up to the current (simulated) candle.
### *get_analyzed_dataframe(pair, timeframe)*
@ -670,13 +670,13 @@ It can also be used in specific callbacks to get the signal that caused the acti
``` python
# fetch current dataframe
if self.dp.runmode.value in ('live', 'dry_run'):
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.timeframe)
```
!!! Note "No data available"
Returns an empty dataframe if the requested pair was not cached.
You can check for this with `if dataframe.empty:` and handle this case accordingly.
This should not happen when using whitelisted pairs.
### *orderbook(pair, maximum)*

37
docs/utils.md
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@ -169,6 +169,43 @@ Example: Search dedicated strategy path.
freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
```
## List freqAI models
Use the `list-freqaimodels` subcommand to see all freqAI models available.
This subcommand is useful for finding problems in your environment with loading freqAI models: modules with models that contain errors and failed to load are printed in red (LOAD FAILED), while models with duplicate names are printed in yellow (DUPLICATE NAME).
```
usage: freqtrade list-freqaimodels [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[--freqaimodel-path PATH] [-1] [--no-color]
optional arguments:
-h, --help show this help message and exit
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
-1, --one-column Print output in one column.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
## List Exchanges
Use the `list-exchanges` subcommand to see the exchanges available for the bot.

6
freqtrade/commands/__init__.py
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@ -15,9 +15,9 @@ from freqtrade.commands.db_commands import start_convert_db
from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
start_new_strategy)
from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
start_list_strategies, start_list_timeframes,
start_show_trades)
from freqtrade.commands.list_commands import (start_list_exchanges, start_list_freqAI_models,
start_list_markets, start_list_strategies,
start_list_timeframes, start_show_trades)
from freqtrade.commands.optimize_commands import (start_backtesting, start_backtesting_show,
start_edge, start_hyperopt)
from freqtrade.commands.pairlist_commands import start_test_pairlist

24
freqtrade/commands/arguments.py
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@ -42,6 +42,8 @@ ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
ARGS_LIST_STRATEGIES = ["strategy_path", "print_one_column", "print_colorized",
"recursive_strategy_search"]
ARGS_LIST_FREQAIMODELS = ["freqaimodel_path", "print_one_column", "print_colorized"]
ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"]
ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"]
@ -107,8 +109,8 @@ ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason
"exit_reason_list", "indicator_list"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-data",
"hyperopt-list", "hyperopt-show", "backtest-filter",
"list-markets", "list-pairs", "list-strategies", "list-freqaimodels",
"list-data", "hyperopt-list", "hyperopt-show", "backtest-filter",
"plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
@ -193,10 +195,11 @@ class Arguments:
start_create_userdir, start_download_data, start_edge,
start_hyperopt, start_hyperopt_list, start_hyperopt_show,
start_install_ui, start_list_data, start_list_exchanges,
start_list_markets, start_list_strategies,
start_list_timeframes, start_new_config, start_new_strategy,
start_plot_dataframe, start_plot_profit, start_show_trades,
start_test_pairlist, start_trading, start_webserver)
start_list_freqAI_models, start_list_markets,
start_list_strategies, start_list_timeframes,
start_new_config, start_new_strategy, start_plot_dataframe,
start_plot_profit, start_show_trades, start_test_pairlist,
start_trading, start_webserver)
subparsers = self.parser.add_subparsers(dest='command',
# Use custom message when no subhandler is added
@ -363,6 +366,15 @@ class Arguments:
list_strategies_cmd.set_defaults(func=start_list_strategies)
self._build_args(optionlist=ARGS_LIST_STRATEGIES, parser=list_strategies_cmd)
# Add list-freqAI Models subcommand
list_freqaimodels_cmd = subparsers.add_parser(
'list-freqaimodels',
help='Print available freqAI models.',
parents=[_common_parser],
)
list_freqaimodels_cmd.set_defaults(func=start_list_freqAI_models)
self._build_args(optionlist=ARGS_LIST_FREQAIMODELS, parser=list_freqaimodels_cmd)
# Add list-timeframes subcommand
list_timeframes_cmd = subparsers.add_parser(
'list-timeframes',

26
freqtrade/commands/list_commands.py
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@ -1,7 +1,6 @@
import csv
import logging
import sys
from pathlib import Path
from typing import Any, Dict, List
import rapidjson
@ -10,7 +9,6 @@ from colorama import init as colorama_init
from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_STRATEGIES
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active, validate_exchanges
@ -41,7 +39,7 @@ def start_list_exchanges(args: Dict[str, Any]) -> None:
print(tabulate(exchanges, headers=['Exchange name', 'Valid', 'reason']))
def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> None:
def _print_objs_tabular(objs: List, print_colorized: bool) -> None:
if print_colorized:
colorama_init(autoreset=True)
red = Fore.RED
@ -55,7 +53,7 @@ def _print_objs_tabular(objs: List, print_colorized: bool, base_dir: Path) -> No
names = [s['name'] for s in objs]
objs_to_print = [{
'name': s['name'] if s['name'] else "--",
'location': s['location'].relative_to(base_dir),
'location': s['location_rel'],
'status': (red + "LOAD FAILED" + reset if s['class'] is None
else "OK" if names.count(s['name']) == 1
else yellow + "DUPLICATE NAME" + reset)
@ -76,9 +74,8 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
strategy_objs = StrategyResolver.search_all_objects(
directory, not args['print_one_column'], config.get('recursive_strategy_search', False))
config, not args['print_one_column'], config.get('recursive_strategy_search', False))
# Sort alphabetically
strategy_objs = sorted(strategy_objs, key=lambda x: x['name'])
for obj in strategy_objs:
@ -90,7 +87,22 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
if args['print_one_column']:
print('\n'.join([s['name'] for s in strategy_objs]))
else:
_print_objs_tabular(strategy_objs, config.get('print_colorized', False), directory)
_print_objs_tabular(strategy_objs, config.get('print_colorized', False))
def start_list_freqAI_models(args: Dict[str, Any]) -> None:
"""
Print files with FreqAI models custom classes available in the directory
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
model_objs = FreqaiModelResolver.search_all_objects(config, not args['print_one_column'])
# Sort alphabetically
model_objs = sorted(model_objs, key=lambda x: x['name'])
if args['print_one_column']:
print('\n'.join([s['name'] for s in model_objs]))
else:
_print_objs_tabular(model_objs, config.get('print_colorized', False))
def start_list_timeframes(args: Dict[str, Any]) -> None:

2
freqtrade/constants.py
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@ -540,6 +540,8 @@ CONF_SCHEMA = {
"properties": {
"enabled": {"type": "boolean", "default": False},
"keras": {"type": "boolean", "default": False},
"write_metrics_to_disk": {"type": "boolean", "default": False},
"purge_old_models": {"type": "boolean", "default": True},
"conv_width": {"type": "integer", "default": 2},
"train_period_days": {"type": "integer", "default": 0},
"backtest_period_days": {"type": "number", "default": 7},

9
freqtrade/exchange/exchange.py
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@ -410,11 +410,13 @@ class Exchange:
else:
return DataFrame()
def get_contract_size(self, pair: str) -> float:
def get_contract_size(self, pair: str) -> Optional[float]:
if self.trading_mode == TradingMode.FUTURES:
market = self.markets[pair]
market = self.markets.get(pair, {})
contract_size: float = 1.0
if market['contractSize'] is not None:
if not market:
return None
if market.get('contractSize') is not None:
# ccxt has contractSize in markets as string
contract_size = float(market['contractSize'])
return contract_size
@ -1934,6 +1936,7 @@ class Exchange:
candle_limit = self.ohlcv_candle_limit(timeframe, self._config['candle_type_def'])
# Age out old candles
ohlcv_df = ohlcv_df.tail(candle_limit + self._startup_candle_count)
ohlcv_df = ohlcv_df.reset_index(drop=True)
self._klines[(pair, timeframe, c_type)] = ohlcv_df
else:
self._klines[(pair, timeframe, c_type)] = ohlcv_df

100
freqtrade/freqai/data_drawer.py
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@ -1,14 +1,15 @@
import collections
import json
import logging
import re
import shutil
import threading
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict
import numpy as np
import pandas as pd
import psutil
import rapidjson
from joblib import dump, load
from joblib.externals import cloudpickle
@ -65,6 +66,8 @@ class FreqaiDataDrawer:
self.pair_dict: Dict[str, pair_info] = {}
# dictionary holding all actively inferenced models in memory given a model filename
self.model_dictionary: Dict[str, Any] = {}
# all additional metadata that we want to keep in ram
self.meta_data_dictionary: Dict[str, Dict[str, Any]] = {}
self.model_return_values: Dict[str, DataFrame] = {}
self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
self.historic_predictions: Dict[str, DataFrame] = {}
@ -78,30 +81,60 @@ class FreqaiDataDrawer:
self.historic_predictions_bkp_path = Path(
self.full_path / "historic_predictions.backup.pkl")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
self.follow_mode = follow_mode
if follow_mode:
self.create_follower_dict()
self.load_drawer_from_disk()
self.load_historic_predictions_from_disk()
self.load_metric_tracker_from_disk()
self.training_queue: Dict[str, int] = {}
self.history_lock = threading.Lock()
self.save_lock = threading.Lock()
self.pair_dict_lock = threading.Lock()
self.metric_tracker_lock = threading.Lock()
self.old_DBSCAN_eps: Dict[str, float] = {}
self.empty_pair_dict: pair_info = {
"model_filename": "", "trained_timestamp": 0,
"data_path": "", "extras": {}}
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
def update_metric_tracker(self, metric: str, value: float, pair: str) -> None:
"""
General utility for adding and updating custom metrics. Typically used
for adding training performance, train timings, inferenc timings, cpu loads etc.
"""
with self.metric_tracker_lock:
if pair not in self.metric_tracker:
self.metric_tracker[pair] = {}
if metric not in self.metric_tracker[pair]:
self.metric_tracker[pair][metric] = {'timestamp': [], 'value': []}
timestamp = int(datetime.now(timezone.utc).timestamp())
self.metric_tracker[pair][metric]['value'].append(value)
self.metric_tracker[pair][metric]['timestamp'].append(timestamp)
def collect_metrics(self, time_spent: float, pair: str):
"""
Add metrics to the metric tracker dictionary
"""
load1, load5, load15 = psutil.getloadavg()
cpus = psutil.cpu_count()
self.update_metric_tracker('train_time', time_spent, pair)
self.update_metric_tracker('cpu_load1min', load1 / cpus, pair)
self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
def load_drawer_from_disk(self):
"""
Locate and load a previously saved data drawer full of all pair model metadata in
present model folder.
:return: bool - whether or not the drawer was located
Load any existing metric tracker that may be present.
"""
exists = self.pair_dictionary_path.is_file()
if exists:
with open(self.pair_dictionary_path, "r") as fp:
self.pair_dict = json.load(fp)
self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
else:
@ -110,7 +143,18 @@ class FreqaiDataDrawer:
"sending null values back to strategy"
)
return exists
def load_metric_tracker_from_disk(self):
"""
Tries to load an existing metrics dictionary if the user
wants to collect metrics.
"""
if self.freqai_info.get('write_metrics_to_disk', False):
exists = self.metric_tracker_path.is_file()
if exists:
with open(self.metric_tracker_path, "r") as fp:
self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
else:
logger.info("Could not find existing metric tracker, starting from scratch")
def load_historic_predictions_from_disk(self):
"""
@ -146,7 +190,7 @@ class FreqaiDataDrawer:
def save_historic_predictions_to_disk(self):
"""
Save data drawer full of all pair model metadata in present model folder.
Save historic predictions pickle to disk
"""
with open(self.historic_predictions_path, "wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
@ -154,6 +198,15 @@ class FreqaiDataDrawer:
# create a backup
shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
def save_metric_tracker_to_disk(self):
"""
Save metric tracker of all pair metrics collected.
"""
with self.save_lock:
with open(self.metric_tracker_path, 'w') as fp:
rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_drawer_to_disk(self):
"""
Save data drawer full of all pair model metadata in present model folder.
@ -453,9 +506,14 @@ class FreqaiDataDrawer:
)
# if self.live:
# store as much in ram as possible to increase performance
self.model_dictionary[coin] = model
self.pair_dict[coin]["model_filename"] = dk.model_filename
self.pair_dict[coin]["data_path"] = str(dk.data_path)
if coin not in self.meta_data_dictionary:
self.meta_data_dictionary[coin] = {}
self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
self.meta_data_dictionary[coin]["meta_data"] = dk.data
self.save_drawer_to_disk()
return
@ -466,7 +524,7 @@ class FreqaiDataDrawer:
presaved backtesting (prediction file loading).
"""
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
@ -492,15 +550,20 @@ class FreqaiDataDrawer:
/ dk.data_path.parts[-1]
)
if coin in self.meta_data_dictionary:
dk.data = self.meta_data_dictionary[coin]["meta_data"]
dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
else:
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.data_dictionary["train_features"] = pd.read_pickle(
dk.data_path / f"{dk.model_filename}_trained_df.pkl"
)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
# try to access model in memory instead of loading object from disk to save time
if dk.live and coin in self.model_dictionary:
model = self.model_dictionary[coin]
@ -627,22 +690,3 @@ class FreqaiDataDrawer:
).reset_index(drop=True)
return corr_dataframes, base_dataframes
# to be used if we want to send predictions directly to the follower instead of forcing
# follower to load models and inference
# def save_model_return_values_to_disk(self) -> None:
# with open(self.full_path / str('model_return_values.json'), "w") as fp:
# json.dump(self.model_return_values, fp, default=self.np_encoder)
# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
# if exists:
# with open(self.full_path / str('model_return_values.json'), "r") as fp:
# self.model_return_values = json.load(fp)
# elif not self.follow_mode:
# logger.info("Could not find existing datadrawer, starting from scratch")
# else:
# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
# 'sending null values back to strategy')
# return exists, dk

23
freqtrade/freqai/freqai_interface.py
View File

@ -7,7 +7,7 @@ from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from threading import Lock
from typing import Any, Dict, List, Tuple
from typing import Any, Dict, List, Literal, Tuple
import numpy as np
import pandas as pd
@ -148,7 +148,7 @@ class IFreqaiModel(ABC):
dataframe = dk.remove_features_from_df(dk.return_dataframe)
self.clean_up()
if self.live:
self.inference_timer('stop')
self.inference_timer('stop', metadata["pair"])
return dataframe
def clean_up(self):
@ -217,12 +217,14 @@ class IFreqaiModel(ABC):
logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
f"Message: {msg}, skipping.")
self.train_timer('stop')
self.train_timer('stop', pair)
# only rotate the queue after the first has been trained.
self.train_queue.rotate(-1)
self.dd.save_historic_predictions_to_disk()
if self.freqai_info.get('write_metrics_to_disk', False):
self.dd.save_metric_tracker_to_disk()
def start_backtesting(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
@ -677,7 +679,7 @@ class IFreqaiModel(ABC):
return
def inference_timer(self, do='start'):
def inference_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
"""
Timer designed to track the cumulative time spent in FreqAI for one pass through
the whitelist. This will check if the time spent is more than 1/4 the time
@ -688,7 +690,10 @@ class IFreqaiModel(ABC):
self.begin_time = time.time()
elif do == 'stop':
end = time.time()
self.inference_time += (end - self.begin_time)
time_spent = (end - self.begin_time)
if self.freqai_info.get('write_metrics_to_disk', False):
self.dd.update_metric_tracker('inference_time', time_spent, pair)
self.inference_time += time_spent
if self.pair_it == self.total_pairs:
logger.info(
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
@ -699,7 +704,7 @@ class IFreqaiModel(ABC):
self.inference_time = 0
return
def train_timer(self, do='start'):
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
"""
Timer designed to track the cumulative time spent training the full pairlist in
FreqAI.
@ -709,7 +714,11 @@ class IFreqaiModel(ABC):
self.begin_time_train = time.time()
elif do == 'stop':
end = time.time()
self.train_time += (end - self.begin_time_train)
time_spent = (end - self.begin_time_train)
if self.freqai_info.get('write_metrics_to_disk', False):
self.dd.collect_metrics(time_spent, pair)
self.train_time += time_spent
if self.pair_it_train == self.total_pairs:
logger.info(
f'Total time spent training pairlist {self.train_time:.2f} seconds')

12
freqtrade/freqai/prediction_models/CatboostClassifier.py
View File

@ -1,4 +1,5 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
@ -30,6 +31,14 @@ class CatboostClassifier(BaseClassifierModel):
label=data_dictionary["train_labels"],
weight=data_dictionary["train_weights"],
)
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
test_data = None
else:
test_data = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"],
weight=data_dictionary["test_weights"],
)
cbr = CatBoostClassifier(
allow_writing_files=True,
@ -40,6 +49,7 @@ class CatboostClassifier(BaseClassifierModel):
init_model = self.get_init_model(dk.pair)
cbr.fit(train_data, init_model=init_model)
cbr.fit(X=train_data, eval_set=test_data, init_model=init_model,
log_cout=sys.stdout, log_cerr=sys.stderr)
return cbr

4
freqtrade/freqai/prediction_models/CatboostRegressor.py
View File

@ -1,4 +1,5 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
@ -47,6 +48,7 @@ class CatboostRegressor(BaseRegressionModel):
**self.model_training_parameters,
)
model.fit(X=train_data, eval_set=test_data, init_model=init_model)
model.fit(X=train_data, eval_set=test_data, init_model=init_model,
log_cout=sys.stdout, log_cerr=sys.stderr)
return model

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

@ -1,4 +1,5 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
@ -58,8 +59,10 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'init_model': init_models[i]})
fit_params.append({
'eval_set': eval_sets[i], 'init_model': init_models[i],
'log_cout': sys.stdout, 'log_cerr': sys.stderr,
})
model = FreqaiMultiOutputRegressor(estimator=cbr)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)

85
freqtrade/freqai/prediction_models/XGBoostRFClassifier.py Normal file
View File

@ -0,0 +1,85 @@
import logging
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRFClassifier
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRFClassifier(BaseClassifierModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
y = data_dictionary["train_labels"].to_numpy()[:, 0]
le = LabelEncoder()
if not is_integer_dtype(y):
y = pd.Series(le.fit_transform(y), dtype="int64")
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
eval_set = None
else:
test_features = data_dictionary["test_features"].to_numpy()
test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
if not is_integer_dtype(test_labels):
test_labels = pd.Series(le.transform(test_labels), dtype="int64")
eval_set = [(test_features, test_labels)]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = XGBRFClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
xgb_model=init_model)
return model
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
(pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
le = LabelEncoder()
label = dk.label_list[0]
labels_before = list(dk.data['labels_std'].keys())
labels_after = le.fit_transform(labels_before).tolist()
pred_df[label] = le.inverse_transform(pred_df[label])
pred_df = pred_df.rename(
columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
return (pred_df, dk.do_predict)

45
freqtrade/freqai/prediction_models/XGBoostRFRegressor.py Normal file
View File

@ -0,0 +1,45 @@
import logging
from typing import Any, Dict
from xgboost import XGBRFRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRFRegressor(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]
sample_weight = data_dictionary["train_weights"]
xgb_model = self.get_init_model(dk.pair)
model = XGBRFRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
return model

144
freqtrade/optimize/backtesting.py
View File

@ -155,6 +155,8 @@ class Backtesting:
self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
# strategies which define "can_short=True" will fail to load in Spot mode.
self._can_short = self.trading_mode != TradingMode.SPOT
self._position_stacking: bool = self.config.get('position_stacking', False)
self.enable_protections: bool = self.config.get('enable_protections', False)
self.init_backtest()
@ -923,14 +925,12 @@ class Backtesting:
return trade
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
data: Dict[str, List[Tuple]]) -> None:
"""
Handling of left open trades at the end of backtesting
"""
trades = []
for pair in open_trades.keys():
if len(open_trades[pair]) > 0:
for trade in open_trades[pair]:
for trade in list(open_trades[pair]):
if trade.open_order_id and trade.nr_of_successful_entries == 0:
# Ignore trade if entry-order did not fill yet
continue
@ -942,11 +942,6 @@ class Backtesting:
trade.exit_reason = ExitType.FORCE_EXIT.value
trade.close(exit_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
@ -970,9 +965,8 @@ class Backtesting:
return 'short'
return None
def run_protections(
self, enable_protections, pair: str, current_time: datetime, side: LongShort):
if enable_protections:
def run_protections(self, pair: str, current_time: datetime, side: LongShort):
if self.enable_protections:
self.protections.stop_per_pair(pair, current_time, side)
self.protections.global_stop(current_time, side)
@ -1078,65 +1072,20 @@ class Backtesting:
return None
return row
def backtest(self, processed: Dict, # noqa: max-complexity: 13
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False,
enable_protections: bool = False) -> Dict[str, Any]:
def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
max_open_trades: int, open_trade_count_start: int) -> int:
"""
Implement backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:param position_stacking: do we allow position stacking?
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
Backtesting processing for one candle/pair.
"""
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Ensure wallets are uptodate (important for --strategy-list)
self.wallets.update()
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = defaultdict(int)
current_time = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while current_time <= end_date:
open_trade_count_start = open_trade_count
self.check_abort()
for i, pair in enumerate(data):
row_index = indexes[pair]
row = self.validate_row(data, pair, row_index, current_time)
if not row:
continue
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
for t in list(open_trades[pair]):
for t in list(LocalTrade.bt_trades_open_pp[pair]):
# 1. Manage currently open orders of active trades
if self.manage_open_orders(t, current_time, row):
# Close trade
open_trade_count -= 1
open_trades[pair].remove(t)
LocalTrade.trades_open.remove(t)
open_trade_count_start -= 1
LocalTrade.remove_bt_trade(t)
self.wallets.update()
# 2. Process entries.
@ -1145,7 +1094,7 @@ class Backtesting:
# don't open on the last row
trade_dir = self.check_for_trade_entry(row)
if (
(position_stacking or len(open_trades[pair]) == 0)
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and current_time != end_date
and trade_dir is not None
@ -1157,13 +1106,11 @@ class Backtesting:
# 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
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
self.wallets.update()
for trade in list(open_trades[pair]):
for trade in list(LocalTrade.bt_trades_open_pp[pair]):
# 3. Process entry orders.
order = trade.select_order(trade.entry_side, is_open=True)
if order and self._get_order_filled(order.price, row):
@ -1189,22 +1136,67 @@ class Backtesting:
trade.close(order.price, show_msg=False)
# logger.debug(f"{pair} - Backtesting exit {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
LocalTrade.close_bt_trade(trade)
trades.append(trade)
self.wallets.update()
self.run_protections(
enable_protections, pair, current_time, trade.trade_direction)
self.run_protections(pair, current_time, trade.trade_direction)
return open_trade_count_start
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0) -> Dict[str, Any]:
"""
Implement backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:return: DataFrame with trades (results of backtesting)
"""
self.prepare_backtest(self.enable_protections)
# Ensure wallets are uptodate (important for --strategy-list)
self.wallets.update()
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = defaultdict(int)
current_time = start_date + timedelta(minutes=self.timeframe_min)
self.progress.init_step(BacktestState.BACKTEST, int(
(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while current_time <= end_date:
open_trade_count_start = LocalTrade.bt_open_open_trade_count
self.check_abort()
for i, pair in enumerate(data):
row_index = indexes[pair]
row = self.validate_row(data, pair, row_index, current_time)
if not row:
continue
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
# Move time one configured time_interval ahead.
self.progress.increment()
current_time += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.handle_left_open(LocalTrade.bt_trades_open_pp, data=data)
self.wallets.update()
results = trade_list_to_dataframe(trades)
results = trade_list_to_dataframe(LocalTrade.trades)
return {
'results': results,
'config': self.strategy.config,
@ -1257,8 +1249,6 @@ class Backtesting:
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
results.update({

4
freqtrade/optimize/hyperopt.py
View File

@ -122,7 +122,6 @@ class Hyperopt:
else:
logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
self.max_open_trades = 0
self.position_stacking = self.config.get('position_stacking', False)
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_exit_signal is enabled
@ -258,6 +257,7 @@ class Hyperopt:
logger.debug("Hyperopt has 'protection' space")
# Enable Protections if protection space is selected.
self.config['enable_protections'] = True
self.backtesting.enable_protections = True
self.protection_space = self.custom_hyperopt.protection_space()
if HyperoptTools.has_space(self.config, 'buy'):
@ -339,8 +339,6 @@ class Hyperopt:
start_date=self.min_date,
end_date=self.max_date,
max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking,
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({

5
freqtrade/optimize/hyperopt_tools.py
View File

@ -12,7 +12,7 @@ import tabulate
from colorama import Fore, Style
from pandas import isna, json_normalize
from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES, Config
from freqtrade.constants import FTHYPT_FILEVERSION, Config
from freqtrade.enums import HyperoptState
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
@ -50,9 +50,8 @@ class HyperoptTools():
Get Strategy-location (filename) from strategy_name
"""
from freqtrade.resolvers.strategy_resolver import StrategyResolver
directory = Path(config.get('strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
strategy_objs = StrategyResolver.search_all_objects(
directory, False, config.get('recursive_strategy_search', False))
config, False, config.get('recursive_strategy_search', False))
strategies = [s for s in strategy_objs if s['name'] == strategy_name]
if strategies:
strategy = strategies[0]

8
freqtrade/optimize/optimize_reports.py
View File

@ -408,10 +408,10 @@ def generate_strategy_stats(pairlist: List[str],
exit_reason_stats = generate_exit_reason_stats(max_open_trades=max_open_trades,
results=results)
left_open_results = generate_pair_metrics(pairlist, stake_currency=stake_currency,
starting_balance=start_balance,
results=results.loc[results['is_open']],
skip_nan=True)
left_open_results = generate_pair_metrics(
pairlist, stake_currency=stake_currency, starting_balance=start_balance,
results=results.loc[results['exit_reason'] == 'force_exit'], skip_nan=True)
daily_stats = generate_daily_stats(results)
trade_stats = generate_trading_stats(results)
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],

18
freqtrade/persistence/trade_model.py
View File

@ -2,6 +2,7 @@
This module contains the class to persist trades into SQLite
"""
import logging
from collections import defaultdict
from datetime import datetime, timedelta, timezone
from math import isclose
from typing import Any, Dict, List, Optional
@ -255,6 +256,9 @@ class LocalTrade():
# Trades container for backtesting
trades: List['LocalTrade'] = []
trades_open: List['LocalTrade'] = []
# Copy of trades_open - but indexed by pair
bt_trades_open_pp: Dict[str, List['LocalTrade']] = defaultdict(list)
bt_open_open_trade_count: int = 0
total_profit: float = 0
realized_profit: float = 0
@ -538,6 +542,8 @@ class LocalTrade():
"""
LocalTrade.trades = []
LocalTrade.trades_open = []
LocalTrade.bt_trades_open_pp = defaultdict(list)
LocalTrade.bt_open_open_trade_count = 0
LocalTrade.total_profit = 0
def adjust_min_max_rates(self, current_price: float, current_price_low: float) -> None:
@ -1067,6 +1073,8 @@ class LocalTrade():
@staticmethod
def close_bt_trade(trade):
LocalTrade.trades_open.remove(trade)
LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
LocalTrade.bt_open_open_trade_count -= 1
LocalTrade.trades.append(trade)
LocalTrade.total_profit += trade.close_profit_abs
@ -1074,9 +1082,17 @@ class LocalTrade():
def add_bt_trade(trade):
if trade.is_open:
LocalTrade.trades_open.append(trade)
LocalTrade.bt_trades_open_pp[trade.pair].append(trade)
LocalTrade.bt_open_open_trade_count += 1
else:
LocalTrade.trades.append(trade)
@staticmethod
def remove_bt_trade(trade):
LocalTrade.trades_open.remove(trade)
LocalTrade.bt_trades_open_pp[trade.pair].remove(trade)
LocalTrade.bt_open_open_trade_count -= 1
@staticmethod
def get_open_trades() -> List[Any]:
"""
@ -1092,7 +1108,7 @@ class LocalTrade():
if Trade.use_db:
return Trade.query.filter(Trade.is_open.is_(True)).count()
else:
return len(LocalTrade.trades_open)
return LocalTrade.bt_open_open_trade_count
@staticmethod
def stoploss_reinitialization(desired_stoploss):

2
freqtrade/resolvers/freqaimodel_resolver.py
View File

@ -26,6 +26,7 @@ class FreqaiModelResolver(IResolver):
initial_search_path = (
Path(__file__).parent.parent.joinpath("freqai/prediction_models").resolve()
)
extra_path = "freqaimodel_path"
@staticmethod
def load_freqaimodel(config: Config) -> IFreqaiModel:
@ -50,7 +51,6 @@ class FreqaiModelResolver(IResolver):
freqaimodel_name,
config,
kwargs={"config": config},
extra_dir=config.get("freqaimodel_path"),
)
return freqaimodel

37
freqtrade/resolvers/iresolver.py
View File

@ -42,6 +42,8 @@ class IResolver:
object_type_str: str
user_subdir: Optional[str] = None
initial_search_path: Optional[Path]
# Optional config setting containing a path (strategy_path, freqaimodel_path)
extra_path: Optional[str] = None
@classmethod
def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
@ -58,6 +60,9 @@ class IResolver:
for dir in extra_dirs:
abs_paths.insert(0, Path(dir).resolve())
if cls.extra_path and (extra := config.get(cls.extra_path)):
abs_paths.insert(0, Path(extra).resolve())
return abs_paths
@classmethod
@ -183,9 +188,35 @@ class IResolver:
)
@classmethod
def search_all_objects(cls, directory: Path, enum_failed: bool,
def search_all_objects(cls, config: Config, enum_failed: bool,
recursive: bool = False) -> List[Dict[str, Any]]:
"""
Searches for valid objects
:param config: Config object
:param enum_failed: If True, will return None for modules which fail.
Otherwise, failing modules are skipped.
:param recursive: Recursively walk directory tree searching for strategies
:return: List of dicts containing 'name', 'class' and 'location' entries
"""
result = []
abs_paths = cls.build_search_paths(config, user_subdir=cls.user_subdir)
for path in abs_paths:
result.extend(cls._search_all_objects(path, enum_failed, recursive))
return result
@classmethod
def _build_rel_location(cls, directory: Path, entry: Path) -> str:
builtin = cls.initial_search_path == directory
return f"<builtin>/{entry.relative_to(directory)}" if builtin else str(
entry.relative_to(directory))
@classmethod
def _search_all_objects(
cls, directory: Path, enum_failed: bool, recursive: bool = False,
basedir: Optional[Path] = None) -> List[Dict[str, Any]]:
"""
Searches a directory for valid objects
:param directory: Path to search
:param enum_failed: If True, will return None for modules which fail.
@ -204,7 +235,8 @@ class IResolver:
and not entry.name.startswith('__')
and not entry.name.startswith('.')
):
objects.extend(cls.search_all_objects(entry, enum_failed, recursive=recursive))
objects.extend(cls._search_all_objects(
entry, enum_failed, recursive, basedir or directory))
# Only consider python files
if entry.suffix != '.py':
logger.debug('Ignoring %s', entry)
@ -217,5 +249,6 @@ class IResolver:
{'name': obj[0].__name__ if obj is not None else '',
'class': obj[0] if obj is not None else None,
'location': entry,
'location_rel': cls._build_rel_location(basedir or directory, entry),
})
return objects

1
freqtrade/resolvers/strategy_resolver.py
View File

@ -30,6 +30,7 @@ class StrategyResolver(IResolver):
object_type_str = "Strategy"
user_subdir = USERPATH_STRATEGIES
initial_search_path = None
extra_path = "strategy_path"
@staticmethod
def load_strategy(config: Config = None) -> IStrategy:

1
freqtrade/rpc/api_server/api_backtest.py
View File

@ -89,6 +89,7 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
lastconfig['enable_protections'] = btconfig.get('enable_protections')
lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
ApiServer._bt.enable_protections = btconfig.get('enable_protections', False)
ApiServer._bt.strategylist = [strat]
ApiServer._bt.results = {}
ApiServer._bt.load_prior_backtest()

6
freqtrade/rpc/api_server/api_v1.py
View File

@ -1,13 +1,11 @@
import logging
from copy import deepcopy
from pathlib import Path
from typing import List, Optional
from fastapi import APIRouter, Depends, Query
from fastapi.exceptions import HTTPException
from freqtrade import __version__
from freqtrade.constants import USERPATH_STRATEGIES
from freqtrade.data.history import get_datahandler
from freqtrade.enums import CandleType, TradingMode
from freqtrade.exceptions import OperationalException
@ -253,11 +251,9 @@ def plot_config(rpc: RPC = Depends(get_rpc)):
@router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])
def list_strategies(config=Depends(get_config)):
directory = Path(config.get(
'strategy_path', config['user_data_dir'] / USERPATH_STRATEGIES))
from freqtrade.resolvers.strategy_resolver import StrategyResolver
strategies = StrategyResolver.search_all_objects(
directory, False, config.get('recursive_strategy_search', False))
config, False, config.get('recursive_strategy_search', False))
strategies = sorted(strategies, key=lambda x: x['name'])
return {'strategies': [x['name'] for x in strategies]}

3
freqtrade/rpc/api_server/api_ws.py
View File

@ -1,3 +1,4 @@
import asyncio
import logging
from typing import Any, Dict
@ -89,6 +90,8 @@ async def _process_consumer_request(
for _, message in analyzed_df.items():
response = WSAnalyzedDFMessage(data=message)
await channel.send(response.dict(exclude_none=True))
# Throttle the messages to 50/s
await asyncio.sleep(0.02)
@router.websocket("/message/ws")

4
freqtrade/rpc/api_server/webserver.py
View File

@ -198,6 +198,10 @@ class ApiServer(RPCHandler):
logger.debug(f"Found message of type: {message.get('type')}")
# Broadcast it
await self._ws_channel_manager.broadcast(message)
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
await asyncio.sleep(0.001)
except asyncio.CancelledError:
pass

2
requirements-dev.txt
View File

@ -27,4 +27,4 @@ types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.11.2
types-tabulate==0.9.0.0
types-python-dateutil==2.8.19
types-python-dateutil==2.8.19.1

2
requirements-freqai.txt
View File

@ -5,6 +5,6 @@
scikit-learn==1.1.2
joblib==1.2.0
catboost==1.1; platform_machine != 'aarch64'
lightgbm==3.3.2
lightgbm==3.3.3
xgboost==1.6.2
tensorboard==2.10.1

8
requirements.txt
View File

@ -1,14 +1,14 @@
numpy==1.23.3
numpy==1.23.4
pandas==1.5.0; platform_machine != 'armv7l'
# Piwheels doesn't have 1.5.0 yet.
pandas==1.4.3; platform_machine == 'armv7l'
pandas-ta==0.3.14b
ccxt==1.95.30
ccxt==2.0.25
# Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1
aiohttp==3.8.3
SQLAlchemy==1.4.41
SQLAlchemy==1.4.42
python-telegram-bot==13.14
arrow==1.2.3
cachetools==4.2.2
@ -37,7 +37,7 @@ orjson==3.8.0
sdnotify==0.3.2
# API Server
fastapi==0.85.0
fastapi==0.85.1
pydantic>=1.8.0
uvicorn==0.18.3
pyjwt==2.5.0

29
tests/commands/test_commands.py
View File

@ -18,6 +18,7 @@ from freqtrade.commands import (start_backtesting_show, start_convert_data, star
from freqtrade.commands.db_commands import start_convert_db
from freqtrade.commands.deploy_commands import (clean_ui_subdir, download_and_install_ui,
get_ui_download_url, read_ui_version)
from freqtrade.commands.list_commands import start_list_freqAI_models
from freqtrade.configuration import setup_utils_configuration
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
@ -944,6 +945,34 @@ def test_start_list_strategies(capsys):
assert str(Path("broken_strats/broken_futures_strategies.py")) in captured.out
def test_start_list_freqAI_models(capsys):
args = [
"list-freqaimodels",
"-1"
]
pargs = get_args(args)
pargs['config'] = None
start_list_freqAI_models(pargs)
captured = capsys.readouterr()
assert "LightGBMClassifier" in captured.out
assert "LightGBMRegressor" in captured.out
assert "XGBoostRegressor" in captured.out
assert "<builtin>/LightGBMRegressor.py" not in captured.out
args = [
"list-freqaimodels",
]
pargs = get_args(args)
pargs['config'] = None
start_list_freqAI_models(pargs)
captured = capsys.readouterr()
assert "LightGBMClassifier" in captured.out
assert "LightGBMRegressor" in captured.out
assert "XGBoostRegressor" in captured.out
assert "<builtin>/LightGBMRegressor.py" in captured.out
def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
patch_exchange(mocker, mock_markets=True)
mocker.patch.multiple('freqtrade.exchange.Exchange',

23
tests/exchange/test_exchange.py
View File

@ -2196,6 +2196,9 @@ def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_mach
time_machine.move_to(start + timedelta(hours=99, minutes=30))
exchange = get_patched_exchange(mocker, default_conf)
mocker.patch("freqtrade.exchange.Exchange.ohlcv_candle_limit", return_value=100)
assert exchange._startup_candle_count == 0
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair1 = ('IOTA/ETH', '1h', candle_type)
pair2 = ('XRP/ETH', '1h', candle_type)
@ -2236,30 +2239,36 @@ def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_mach
assert len(res) == 2
assert len(res[pair1]) == 99
assert len(res[pair2]) == 99
assert res[pair2].at[0, 'open']
assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
refresh_pior = exchange._pairs_last_refresh_time[pair1]
# New candle on exchange - only return 50 candles (but one candle further)
new_startdate = (start + timedelta(hours=51)).strftime('%Y-%m-%d %H:%M')
ohlcv = generate_test_data_raw('1h', 50, new_startdate)
# New candle on exchange - return 100 candles - but skip one candle so we actually get 2 candles
# in one go
new_startdate = (start + timedelta(hours=2)).strftime('%Y-%m-%d %H:%M')
# mocker.patch("freqtrade.exchange.Exchange.ohlcv_candle_limit", return_value=100)
ohlcv = generate_test_data_raw('1h', 100, new_startdate)
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert len(res) == 2
assert len(res[pair1]) == 100
assert len(res[pair2]) == 100
# Verify index starts at 0
assert res[pair2].at[0, 'open']
assert refresh_pior != exchange._pairs_last_refresh_time[pair1]
assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
assert exchange._pairs_last_refresh_time[pair2] == ohlcv[-1][0] // 1000
exchange._api_async.fetch_ohlcv.reset_mock()
# Retry same call - no action.
# Retry same call - from cache
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 0
assert len(res) == 2
assert len(res[pair1]) == 100
assert len(res[pair2]) == 100
assert res[pair2].at[0, 'open']
# Move to distant future (so a 1 call would cause a hole in the data)
time_machine.move_to(start + timedelta(hours=2000))
@ -2272,6 +2281,7 @@ def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_mach
# Cache eviction - new data.
assert len(res[pair1]) == 99
assert len(res[pair2]) == 99
assert res[pair2].at[0, 'open']
@pytest.mark.asyncio
@ -4341,9 +4351,10 @@ def test__fetch_and_calculate_funding_fees_datetime_called(
('XLTCUSDT', 1, 'spot'),
('LTC/USD', 1, 'futures'),
('XLTCUSDT', 0.01, 'futures'),
('ETH/USDT:USDT', 10, 'futures')
('ETH/USDT:USDT', 10, 'futures'),
('TORN/USDT:USDT', None, 'futures'), # Don't fail for unavailable pairs.
])
def est__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode):
def test__get_contract_size(mocker, default_conf, pair, expected_size, trading_mode):
api_mock = MagicMock()
default_conf['trading_mode'] = trading_mode
default_conf['margin_mode'] = 'isolated'

11
tests/freqai/test_freqai_interface.py
View File

@ -30,6 +30,7 @@ def is_mac() -> bool:
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'XGBoostRFRegressor',
'CatboostRegressor',
])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
@ -55,10 +56,17 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
data_load_timerange = TimeRange.parse_timerange("20180125-20180130")
new_timerange = TimeRange.parse_timerange("20180127-20180130")
freqai.dk.set_paths('ADA/BTC', None)
freqai.train_timer("start", "ADA/BTC")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
freqai.train_timer("stop", "ADA/BTC")
freqai.dd.save_metric_tracker_to_disk()
freqai.dd.save_drawer_to_disk()
assert Path(freqai.dk.full_path / "metric_tracker.json").is_file()
assert Path(freqai.dk.full_path / "pair_dictionary.json").is_file()
assert Path(freqai.dk.data_path /
f"{freqai.dk.model_filename}_model.{model_save_ext}").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
@ -93,6 +101,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.dk.set_paths('ADA/BTC', None)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
@ -111,6 +120,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
'LightGBMClassifier',
'CatboostClassifier',
'XGBoostClassifier',
'XGBoostRFClassifier',
])
def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostClassifier':
@ -134,6 +144,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.dk.set_paths('ADA/BTC', None)
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)

39
tests/optimize/test_backtesting.py
View File

@ -97,7 +97,6 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
'start_date': min_date,
'end_date': max_date,
'max_open_trades': 10,
'position_stacking': False,
}
@ -735,7 +734,6 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
start_date=min_date,
end_date=max_date,
max_open_trades=10,
position_stacking=False,
)
results = result['results']
assert not results.empty
@ -799,6 +797,34 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:
# This strategy intentionally places unfillable orders.
default_conf['strategy'] = 'StrategyTestV3CustomEntryPrice'
default_conf['startup_candle_count'] = 0
# Cancel unfilled order after 4 minutes on 5m timeframe.
default_conf["unfilledtimeout"] = {"entry": 4}
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
# Testing dataframe contains 11 candles. Expecting 10 timed out orders.
timerange = TimeRange('date', 'date', 1517227800, 1517231100)
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
timerange=timerange)
min_date, max_date = get_timerange(data)
result = backtesting.backtest(
processed=deepcopy(data),
start_date=min_date,
end_date=max_date,
max_open_trades=1,
)
assert result['timedout_entry_orders'] == 10
def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None:
default_conf['use_exit_signal'] = False
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
@ -819,7 +845,6 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
start_date=min_date,
end_date=max_date,
max_open_trades=1,
position_stacking=False,
)
assert not results['results'].empty
assert len(results['results']) == 1
@ -851,7 +876,6 @@ def test_backtest_trim_no_data_left(default_conf, fee, mocker, testdatadir) -> N
start_date=min_date,
end_date=max_date,
max_open_trades=10,
position_stacking=False,
)
@ -906,7 +930,6 @@ def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadi
start_date=min_date,
end_date=max_date,
max_open_trades=10,
position_stacking=False,
)
assert count == 5
@ -950,8 +973,6 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
start_date=min_date,
end_date=max_date,
max_open_trades=1,
position_stacking=False,
enable_protections=default_conf.get('enable_protections', False),
)
assert len(results['results']) == numres
@ -994,8 +1015,6 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
start_date=min_date,
end_date=max_date,
max_open_trades=1,
position_stacking=False,
enable_protections=default_conf.get('enable_protections', False),
)
assert len(results['results']) == expected
@ -1107,7 +1126,6 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
'start_date': min_date,
'end_date': max_date,
'max_open_trades': 3,
'position_stacking': False,
}
results = backtesting.backtest(**backtest_conf)
@ -1130,7 +1148,6 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
'start_date': min_date,
'end_date': max_date,
'max_open_trades': 1,
'position_stacking': False,
}
results = backtesting.backtest(**backtest_conf)
assert len(evaluate_result_multi(results['results'], '5m', 1)) == 0

1
tests/optimize/test_backtesting_adjust_position.py
View File

@ -42,7 +42,6 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
start_date=min_date,
end_date=max_date,
max_open_trades=10,
position_stacking=False,
)
results = result['results']
assert not results.empty

8
tests/optimize/test_hyperopt.py
View File

@ -336,7 +336,7 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
assert hasattr(hyperopt.backtesting, "_position_stacking")
def test_hyperopt_format_results(hyperopt):
@ -704,7 +704,7 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
assert hasattr(hyperopt.backtesting, "_position_stacking")
def test_simplified_interface_all_failed(mocker, hyperopt_conf, caplog) -> None:
@ -778,7 +778,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
assert hasattr(hyperopt.backtesting, "_position_stacking")
def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
@ -821,7 +821,7 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
assert hasattr(hyperopt.backtesting.strategy, "advise_entry")
assert hasattr(hyperopt, "max_open_trades")
assert hyperopt.max_open_trades == hyperopt_conf['max_open_trades']
assert hasattr(hyperopt, "position_stacking")
assert hasattr(hyperopt.backtesting, "_position_stacking")
@pytest.mark.parametrize("space", [

4
tests/rpc/test_rpc_apiserver.py
View File

@ -1443,8 +1443,9 @@ def test_api_plot_config(botclient):
assert isinstance(rc.json()['subplots'], dict)
def test_api_strategies(botclient):
def test_api_strategies(botclient, tmpdir):
ftbot, client = botclient
ftbot.config['user_data_dir'] = Path(tmpdir)
rc = client_get(client, f"{BASE_URI}/strategies")
@ -1456,6 +1457,7 @@ def test_api_strategies(botclient):
'InformativeDecoratorTest',
'StrategyTestV2',
'StrategyTestV3',
'StrategyTestV3CustomEntryPrice',
'StrategyTestV3Futures',
'freqai_test_classifier',
'freqai_test_multimodel_strat',

37
tests/strategy/strats/strategy_test_v3_custom_entry_price.py Normal file
View File

@ -0,0 +1,37 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from datetime import datetime
from typing import Optional
from pandas import DataFrame
from strategy_test_v3 import StrategyTestV3
class StrategyTestV3CustomEntryPrice(StrategyTestV3):
"""
Strategy used by tests freqtrade bot.
Please do not modify this strategy, it's intended for internal use only.
Please look at the SampleStrategy in the user_data/strategy directory
or strategy repository https://github.com/freqtrade/freqtrade-strategies
for samples and inspiration.
"""
new_entry_price: float = 0.001
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
dataframe['volume'] > 0,
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
return self.new_entry_price

18
tests/strategy/test_strategy_loading.py
View File

@ -32,24 +32,25 @@ def test_search_strategy():
def test_search_all_strategies_no_failed():
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
assert isinstance(strategies, list)
assert len(strategies) == 9
assert len(strategies) == 10
assert isinstance(strategies[0], dict)
def test_search_all_strategies_with_failed():
directory = Path(__file__).parent / "strats"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
assert isinstance(strategies, list)
assert len(strategies) == 10
assert len(strategies) == 11
# with enum_failed=True search_all_objects() shall find 2 good strategies
# and 1 which fails to load
assert len([x for x in strategies if x['class'] is not None]) == 9
assert len([x for x in strategies if x['class'] is not None]) == 10
assert len([x for x in strategies if x['class'] is None]) == 1
directory = Path(__file__).parent / "strats_nonexistingdir"
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
assert len(strategies) == 0
@ -77,10 +78,9 @@ def test_load_strategy_base64(dataframe_1m, caplog, default_conf):
def test_load_strategy_invalid_directory(caplog, default_conf):
default_conf['strategy'] = 'StrategyTestV3'
extra_dir = Path.cwd() / 'some/path'
with pytest.raises(OperationalException):
StrategyResolver._load_strategy(CURRENT_TEST_STRATEGY, config=default_conf,
with pytest.raises(OperationalException, match=r"Impossible to load Strategy.*"):
StrategyResolver._load_strategy('StrategyTestV333', config=default_conf,
extra_dir=extra_dir)
assert log_has_re(r'Path .*' + r'some.*path.*' + r'.* does not exist', caplog)

4
tests/test_persistence.py
View File

@ -2406,6 +2406,8 @@ def test_Trade_object_idem():
'get_trading_volume',
)
EXCLUDES2 = ('trades', 'trades_open', 'bt_trades_open_pp', 'bt_open_open_trade_count',
'total_profit')
# Parent (LocalTrade) should have the same attributes
for item in trade:
@ -2416,7 +2418,7 @@ def test_Trade_object_idem():
# Fails if only a column is added without corresponding parent field
for item in localtrade:
if (not item.startswith('__')
and item not in ('trades', 'trades_open', 'total_profit')
and item not in EXCLUDES2
and type(getattr(LocalTrade, item)) not in (property, FunctionType)):
assert item in trade