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Merge branch 'freqtrade:develop' into strategy_utils

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This commit is contained in:
hippocritical
2023-03-03 18:56:00 +01:00
committed by GitHub
parent 87b7513401 9573974c47
commit d92971cca1
123 changed files with 1855 additions and 1721 deletions
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2
freqtrade/__init__.py
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@@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2023.2.dev'
__version__ = '2023.3.dev'
if 'dev' in __version__:
from pathlib import Path

0
freqtrade/__main__.py Normal file → Executable file
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0
freqtrade/commands/analyze_commands.py Executable file → Normal file
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22
freqtrade/commands/data_commands.py
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@@ -5,7 +5,7 @@ from datetime import datetime, timedelta
from typing import Any, Dict, List
from freqtrade.configuration import TimeRange, setup_utils_configuration
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config
from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
@@ -20,15 +20,24 @@ from freqtrade.util.binance_mig import migrate_binance_futures_data
logger = logging.getLogger(__name__)
def _data_download_sanity(config: Config) -> None:
if 'days' in config and 'timerange' in config:
raise OperationalException("--days and --timerange are mutually exclusive. "
"You can only specify one or the other.")
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
def start_download_data(args: Dict[str, Any]) -> None:
"""
Download data (former download_backtest_data.py script)
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
if 'days' in config and 'timerange' in config:
raise OperationalException("--days and --timerange are mutually exclusive. "
"You can only specify one or the other.")
_data_download_sanity(config)
timerange = TimeRange()
if 'days' in config:
time_since = (datetime.now() - timedelta(days=config['days'])).strftime("%Y%m%d")
@@ -40,11 +49,6 @@ def start_download_data(args: Dict[str, Any]) -> None:
# Remove stake-currency to skip checks which are not relevant for datadownload
config['stake_currency'] = ''
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
pairs_not_available: List[str] = []
# Init exchange

0
freqtrade/commands/hyperopt_commands.py Executable file → Normal file
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2
freqtrade/configuration/load_config.py
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@@ -58,7 +58,7 @@ def load_config_file(path: str) -> Dict[str, Any]:
"""
try:
# Read config from stdin if requested in the options
with open(path) if path != '-' else sys.stdin as file:
with Path(path).open() if path != '-' else sys.stdin as file:
config = rapidjson.load(file, parse_mode=CONFIG_PARSE_MODE)
except FileNotFoundError:
raise OperationalException(

6
freqtrade/constants.py
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@@ -546,7 +546,7 @@ CONF_SCHEMA = {
"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},
"purge_old_models": {"type": ["boolean", "number"], "default": 2},
"conv_width": {"type": "integer", "default": 1},
"train_period_days": {"type": "integer", "default": 0},
"backtest_period_days": {"type": "number", "default": 7},
@@ -568,7 +568,9 @@ CONF_SCHEMA = {
"shuffle": {"type": "boolean", "default": False},
"nu": {"type": "number", "default": 0.1}
},
}
},
"shuffle_after_split": {"type": "boolean", "default": False},
"buffer_train_data_candles": {"type": "integer", "default": 0}
},
"required": ["include_timeframes", "include_corr_pairlist", ]
},

2
freqtrade/data/btanalysis.py
View File

@@ -346,7 +346,7 @@ def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
return df_final[df_final['open_trades'] > max_open_trades]
def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
def trade_list_to_dataframe(trades: Union[List[Trade], List[LocalTrade]]) -> pd.DataFrame:
"""
Convert list of Trade objects to pandas Dataframe
:param trades: List of trade objects

6
freqtrade/data/dataprovider.py
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@@ -424,10 +424,8 @@ class DataProvider:
"""
if self._exchange is None:
raise OperationalException(NO_EXCHANGE_EXCEPTION)
if helping_pairs:
self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
else:
self._exchange.refresh_latest_ohlcv(pairlist)
final_pairs = (pairlist + helping_pairs) if helping_pairs else pairlist
self._exchange.refresh_latest_ohlcv(final_pairs)
@property
def available_pairs(self) -> ListPairsWithTimeframes:

6
freqtrade/data/entryexitanalysis.py Executable file → Normal file
View File

@@ -24,9 +24,9 @@ def _load_signal_candles(backtest_dir: Path):
scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
try:
scp = open(scpf, "rb")
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
with scpf.open("rb") as scp:
signal_candles = joblib.load(scp)
logger.info(f"Loaded signal candles: {str(scpf)}")
except Exception as e:
logger.error("Cannot load signal candles from pickled results: ", e)

1
freqtrade/enums/__init__.py
View File

@@ -5,6 +5,7 @@ from freqtrade.enums.exitchecktuple import ExitCheckTuple
from freqtrade.enums.exittype import ExitType
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.marketstatetype import MarketDirection
from freqtrade.enums.ordertypevalue import OrderTypeValues
from freqtrade.enums.pricetype import PriceType
from freqtrade.enums.rpcmessagetype import NO_ECHO_MESSAGES, RPCMessageType, RPCRequestType

3
freqtrade/enums/candletype.py
View File

@@ -13,6 +13,9 @@ class CandleType(str, Enum):
FUNDING_RATE = "funding_rate"
# BORROW_RATE = "borrow_rate" # * unimplemented
def __str__(self):
return f"{self.name.lower()}"
@staticmethod
def from_string(value: str) -> 'CandleType':
if not value:

15
freqtrade/enums/marketstatetype.py Normal file
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@@ -0,0 +1,15 @@
from enum import Enum
class MarketDirection(Enum):
"""
Enum for various market directions.
"""
LONG = "long"
SHORT = "short"
EVEN = "even"
NONE = "none"
def __str__(self):
# convert to string
return self.value

3
freqtrade/enums/rpcmessagetype.py
View File

@@ -37,5 +37,8 @@ class RPCRequestType(str, Enum):
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'
def __str__(self):
return self.value
NO_ECHO_MESSAGES = (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST, RPCMessageType.NEW_CANDLE)

9
freqtrade/enums/signaltype.py
View File

@@ -10,6 +10,9 @@ class SignalType(Enum):
ENTER_SHORT = "enter_short"
EXIT_SHORT = "exit_short"
def __str__(self):
return f"{self.name.lower()}"
class SignalTagType(Enum):
"""
@@ -18,7 +21,13 @@ class SignalTagType(Enum):
ENTER_TAG = "enter_tag"
EXIT_TAG = "exit_tag"
def __str__(self):
return f"{self.name.lower()}"
class SignalDirection(str, Enum):
LONG = 'long'
SHORT = 'short'
def __str__(self):
return f"{self.name.lower()}"

2
freqtrade/exchange/binance.py
View File

@@ -195,7 +195,7 @@ class Binance(Exchange):
leverage_tiers_path = (
Path(__file__).parent / 'binance_leverage_tiers.json'
)
with open(leverage_tiers_path) as json_file:
with leverage_tiers_path.open() as json_file:
return json_load(json_file)
else:
try:

11
freqtrade/exchange/exchange.py
View File

@@ -1961,7 +1961,8 @@ class Exchange:
cache: bool, drop_incomplete: bool) -> DataFrame:
# keeping last candle time as last refreshed time of the pair
if ticks and cache:
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
idx = -2 if drop_incomplete and len(ticks) > 1 else -1
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[idx][0] // 1000
# keeping parsed dataframe in cache
ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=drop_incomplete)
@@ -2015,9 +2016,9 @@ class Exchange:
continue
# Deconstruct tuple (has 5 elements)
pair, timeframe, c_type, ticks, drop_hint = res
drop_incomplete = drop_hint if drop_incomplete is None else drop_incomplete
drop_incomplete_ = drop_hint if drop_incomplete is None else drop_incomplete
ohlcv_df = self._process_ohlcv_df(
pair, timeframe, c_type, ticks, cache, drop_incomplete)
pair, timeframe, c_type, ticks, cache, drop_incomplete_)
results_df[(pair, timeframe, c_type)] = ohlcv_df
@@ -2034,7 +2035,9 @@ class Exchange:
# Timeframe in seconds
interval_in_sec = timeframe_to_seconds(timeframe)
plr = self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0) + interval_in_sec
return plr < arrow.utcnow().int_timestamp
# current,active candle open date
now = int(timeframe_to_prev_date(timeframe).timestamp())
return plr < now
@retrier_async
async def _async_get_candle_history(

1
freqtrade/exchange/hitbtc.py
View File

@@ -19,5 +19,4 @@ class Hitbtc(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"ohlcv_params": {"sort": "DESC"}
}

5
freqtrade/exchange/kucoin.py
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@@ -64,6 +64,7 @@ class Kucoin(Exchange):
# ccxt returns status = 'closed' at the moment - which is information ccxt invented.
# Since we rely on status heavily, we must set it to 'open' here.
# ref: https://github.com/ccxt/ccxt/pull/16674, (https://github.com/ccxt/ccxt/pull/16553)
res['type'] = ordertype
res['status'] = 'open'
if not self._config['dry_run']:
res['type'] = ordertype
res['status'] = 'open'
return res

97
freqtrade/freqai/data_drawer.py
View File

@@ -72,12 +72,7 @@ class FreqaiDataDrawer:
self.model_return_values: Dict[str, DataFrame] = {}
self.historic_data: Dict[str, Dict[str, DataFrame]] = {}
self.historic_predictions: Dict[str, DataFrame] = {}
self.follower_dict: Dict[str, pair_info] = {}
self.full_path = full_path
self.follower_name: str = self.config.get("bot_name", "follower1")
self.follower_dict_path = Path(
self.full_path / f"follower_dictionary-{self.follower_name}.json"
)
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
self.historic_predictions_bkp_path = Path(
self.full_path / "historic_predictions.backup.pkl")
@@ -131,7 +126,7 @@ class FreqaiDataDrawer:
"""
exists = self.global_metadata_path.is_file()
if exists:
with open(self.global_metadata_path, "r") as fp:
with self.global_metadata_path.open("r") as fp:
metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
return metatada_dict
return {}
@@ -144,7 +139,7 @@ class FreqaiDataDrawer:
"""
exists = self.pair_dictionary_path.is_file()
if exists:
with open(self.pair_dictionary_path, "r") as fp:
with self.pair_dictionary_path.open("r") as fp:
self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
else:
logger.info("Could not find existing datadrawer, starting from scratch")
@@ -157,7 +152,7 @@ class FreqaiDataDrawer:
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:
with self.metric_tracker_path.open("r") as fp:
self.metric_tracker = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
logger.info("Loading existing metric tracker from disk.")
else:
@@ -171,7 +166,7 @@ class FreqaiDataDrawer:
exists = self.historic_predictions_path.is_file()
if exists:
try:
with open(self.historic_predictions_path, "rb") as fp:
with self.historic_predictions_path.open("rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.info(
f"Found existing historic predictions at {self.full_path}, but beware "
@@ -181,7 +176,7 @@ class FreqaiDataDrawer:
except EOFError:
logger.warning(
'Historical prediction file was corrupted. Trying to load backup file.')
with open(self.historic_predictions_bkp_path, "rb") as fp:
with self.historic_predictions_bkp_path.open("rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
@@ -194,7 +189,7 @@ class FreqaiDataDrawer:
"""
Save historic predictions pickle to disk
"""
with open(self.historic_predictions_path, "wb") as fp:
with self.historic_predictions_path.open("wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
# create a backup
@@ -205,33 +200,25 @@ class FreqaiDataDrawer:
Save metric tracker of all pair metrics collected.
"""
with self.save_lock:
with open(self.metric_tracker_path, 'w') as fp:
with self.metric_tracker_path.open('w') as fp:
rapidjson.dump(self.metric_tracker, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_drawer_to_disk(self):
def save_drawer_to_disk(self) -> None:
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with self.save_lock:
with open(self.pair_dictionary_path, 'w') as fp:
with self.pair_dictionary_path.open('w') as fp:
rapidjson.dump(self.pair_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_follower_dict_to_disk(self):
"""
Save follower dictionary to disk (used by strategy for persistent prediction targets)
"""
with open(self.follower_dict_path, "w") as fp:
rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
"""
Save global metadata json to disk
"""
with self.save_lock:
with open(self.global_metadata_path, 'w') as fp:
with self.global_metadata_path.open('w') as fp:
rapidjson.dump(metadata, fp, default=self.np_encoder,
number_mode=rapidjson.NM_NATIVE)
@@ -239,7 +226,7 @@ class FreqaiDataDrawer:
if isinstance(object, np.generic):
return object.item()
def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool]:
def get_pair_dict_info(self, pair: str) -> Tuple[str, int]:
"""
Locate and load existing model metadata from persistent storage. If not located,
create a new one and append the current pair to it and prepare it for its first
@@ -248,12 +235,9 @@ class FreqaiDataDrawer:
:return:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
return_null_array: bool = Follower could not find pair metadata
"""
pair_dict = self.pair_dict.get(pair)
# data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
return_null_array = False
if pair_dict:
model_filename = pair_dict["model_filename"]
@@ -263,7 +247,7 @@ class FreqaiDataDrawer:
model_filename = ""
trained_timestamp = 0
return model_filename, trained_timestamp, return_null_array
return model_filename, trained_timestamp
def set_pair_dict_info(self, metadata: dict) -> None:
pair_in_dict = self.pair_dict.get(metadata["pair"])
@@ -382,6 +366,12 @@ class FreqaiDataDrawer:
def purge_old_models(self) -> None:
num_keep = self.freqai_info["purge_old_models"]
if not num_keep:
return
elif type(num_keep) == bool:
num_keep = 2
model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
@@ -404,11 +394,11 @@ class FreqaiDataDrawer:
delete_dict[coin]["timestamps"][int(timestamp)] = dir
for coin in delete_dict:
if delete_dict[coin]["num_folders"] > 2:
if delete_dict[coin]["num_folders"] > num_keep:
sorted_dict = collections.OrderedDict(
sorted(delete_dict[coin]["timestamps"].items())
)
num_delete = len(sorted_dict) - 2
num_delete = len(sorted_dict) - num_keep
deleted = 0
for k, v in sorted_dict.items():
if deleted >= num_delete:
@@ -417,12 +407,6 @@ class FreqaiDataDrawer:
shutil.rmtree(v)
deleted += 1
def update_follower_metadata(self):
# follower needs to load from disk to get any changes made by leader to pair_dict
self.load_drawer_from_disk()
if self.config.get("freqai", {}).get("purge_old_models", False):
self.purge_old_models()
def save_metadata(self, dk: FreqaiDataKitchen) -> None:
"""
Saves only metadata for backtesting studies if user prefers
@@ -440,7 +424,7 @@ class FreqaiDataDrawer:
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["label_list"] = dk.label_list
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
return
@@ -473,7 +457,7 @@ class FreqaiDataDrawer:
dk.data["training_features_list"] = dk.training_features_list
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
with (save_path / f"{dk.model_filename}_metadata.json").open("w") as fp:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
# save the train data to file so we can check preds for area of applicability later
@@ -487,7 +471,7 @@ class FreqaiDataDrawer:
if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
cloudpickle.dump(
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
dk.pca, (dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("wb")
)
self.model_dictionary[coin] = model
@@ -507,7 +491,7 @@ class FreqaiDataDrawer:
Load only metadata into datakitchen to increase performance during
presaved backtesting (prediction file loading).
"""
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as 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"]
@@ -530,7 +514,7 @@ class FreqaiDataDrawer:
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:
with (dk.data_path / f"{dk.model_filename}_metadata.json").open("r") as fp:
dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
dk.data_dictionary["train_features"] = pd.read_pickle(
@@ -568,7 +552,7 @@ class FreqaiDataDrawer:
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
(dk.data_path / f"{dk.model_filename}_pca_object.pkl").open("rb")
)
return model
@@ -586,12 +570,12 @@ class FreqaiDataDrawer:
for pair in dk.all_pairs:
for tf in feat_params.get("include_timeframes"):
hist_df = history_data[pair][tf]
# check if newest candle is already appended
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
if len(df_dp.index) == 0:
continue
if str(history_data[pair][tf].iloc[-1]["date"]) == str(
if str(hist_df.iloc[-1]["date"]) == str(
df_dp.iloc[-1:]["date"].iloc[-1]
):
continue
@@ -599,21 +583,30 @@ class FreqaiDataDrawer:
try:
index = (
df_dp.loc[
df_dp["date"] == history_data[pair][tf].iloc[-1]["date"]
df_dp["date"] == hist_df.iloc[-1]["date"]
].index[0]
+ 1
)
except IndexError:
logger.warning(
f"Unable to update pair history for {pair}. "
"If this does not resolve itself after 1 additional candle, "
"please report the error to #freqai discord channel"
)
return
if hist_df.iloc[-1]['date'] < df_dp['date'].iloc[0]:
raise OperationalException("In memory historical data is older than "
f"oldest DataProvider candle for {pair} on "
f"timeframe {tf}")
else:
index = -1
logger.warning(
f"No common dates in historical data and dataprovider for {pair}. "
f"Appending latest dataprovider candle to historical data "
"but please be aware that there is likely a gap in the historical "
"data. \n"
f"Historical data ends at {hist_df.iloc[-1]['date']} "
f"while dataprovider starts at {df_dp['date'].iloc[0]} and"
f"ends at {df_dp['date'].iloc[0]}."
)
history_data[pair][tf] = pd.concat(
[
history_data[pair][tf],
hist_df,
df_dp.iloc[index:],
],
ignore_index=True,

175
freqtrade/freqai/data_kitchen.py
View File

@@ -1,6 +1,7 @@
import copy
import inspect
import logging
import random
import shutil
from datetime import datetime, timezone
from math import cos, sin
@@ -170,6 +171,19 @@ class FreqaiDataKitchen:
train_labels = labels
train_weights = weights
if feat_dict["shuffle_after_split"]:
rint1 = random.randint(0, 100)
rint2 = random.randint(0, 100)
train_features = train_features.sample(
frac=1, random_state=rint1).reset_index(drop=True)
train_labels = train_labels.sample(frac=1, random_state=rint1).reset_index(drop=True)
train_weights = pd.DataFrame(train_weights).sample(
frac=1, random_state=rint1).reset_index(drop=True).to_numpy()[:, 0]
test_features = test_features.sample(frac=1, random_state=rint2).reset_index(drop=True)
test_labels = test_labels.sample(frac=1, random_state=rint2).reset_index(drop=True)
test_weights = pd.DataFrame(test_weights).sample(
frac=1, random_state=rint2).reset_index(drop=True).to_numpy()[:, 0]
# Simplest way to reverse the order of training and test data:
if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
return self.build_data_dictionary(
@@ -1301,123 +1315,54 @@ class FreqaiDataKitchen:
dataframe: DataFrame = dataframe containing populated indicators
"""
# this is a hack to check if the user is using the populate_any_indicators function
# check if the user is using the deprecated populate_any_indicators function
new_version = inspect.getsource(strategy.populate_any_indicators) == (
inspect.getsource(IStrategy.populate_any_indicators))
if new_version:
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
if not new_version:
raise OperationalException(
"You are using the `populate_any_indicators()` function"
" which was deprecated on March 1, 2023. Please refer "
"to the strategy migration guide to use the new "
"feature_engineering_* methods: \n"
"https://www.freqtrade.io/en/stable/strategy_migration/#freqai-strategy \n"
"And the feature_engineering_* documentation: \n"
"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
)
for tf in tfs:
if tf not in base_dataframes:
base_dataframes[tf] = pd.DataFrame()
for p in pairs:
if p not in corr_dataframes:
corr_dataframes[p] = {}
if tf not in corr_dataframes[p]:
corr_dataframes[p][tf] = pd.DataFrame()
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
else:
dataframe = base_dataframes[self.config["timeframe"]].copy()
corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
dataframe = self.populate_features(dataframe.copy(), pair, strategy,
corr_dataframes, base_dataframes)
metadata = {"pair": pair}
dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
# ensure corr pairs are always last
for corr_pair in corr_pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
if corr_pairs and do_corr_pairs:
dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
corr_dataframes, base_dataframes, True)
dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
self.get_unique_classes_from_labels(dataframe)
dataframe = self.remove_special_chars_from_feature_names(dataframe)
if self.config.get('reduce_df_footprint', False):
dataframe = reduce_dataframe_footprint(dataframe)
return dataframe
else:
# the user is using the populate_any_indicators functions which is deprecated
df = self.use_strategy_to_populate_indicators_old_version(
strategy, corr_dataframes, base_dataframes, pair,
prediction_dataframe, do_corr_pairs)
return df
def use_strategy_to_populate_indicators_old_version(
self,
strategy: IStrategy,
corr_dataframes: dict = {},
base_dataframes: dict = {},
pair: str = "",
prediction_dataframe: DataFrame = pd.DataFrame(),
do_corr_pairs: bool = True,
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during retrain
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param metadata: dict = strategy furnished pair metadata
:return:
dataframe: DataFrame = dataframe containing populated indicators
"""
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
# so we create empty dictionaries, which allows us to pass None to
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
for tf in tfs:
if tf not in base_dataframes:
base_dataframes[tf] = pd.DataFrame()
for p in pairs:
if p not in corr_dataframes:
corr_dataframes[p] = {}
if tf not in corr_dataframes[p]:
corr_dataframes[p][tf] = pd.DataFrame()
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
for tf in tfs:
base_dataframes[tf] = None
for p in pairs:
if p not in corr_dataframes:
corr_dataframes[p] = {}
corr_dataframes[p][tf] = None
else:
dataframe = base_dataframes[self.config["timeframe"]].copy()
sgi = False
for tf in tfs:
if tf == tfs[-1]:
sgi = True # doing this last allows user to use all tf raw prices in labels
dataframe = strategy.populate_any_indicators(
pair,
dataframe.copy(),
tf,
informative=base_dataframes[tf],
set_generalized_indicators=sgi
)
corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
dataframe = self.populate_features(dataframe.copy(), pair, strategy,
corr_dataframes, base_dataframes)
metadata = {"pair": pair}
dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata)
# ensure corr pairs are always last
for corr_pair in pairs:
for corr_pair in corr_pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
for tf in tfs:
if pairs and do_corr_pairs:
dataframe = strategy.populate_any_indicators(
corr_pair,
dataframe.copy(),
tf,
informative=corr_dataframes[corr_pair][tf]
)
if corr_pairs and do_corr_pairs:
dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
corr_dataframes, base_dataframes, True)
dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
self.get_unique_classes_from_labels(dataframe)
@@ -1548,3 +1493,25 @@ class FreqaiDataKitchen:
dataframe.columns = dataframe.columns.str.replace(c, "")
return dataframe
def buffer_timerange(self, timerange: TimeRange):
"""
Buffer the start and end of the timerange. This is used *after* the indicators
are populated.
The main example use is when predicting maxima and minima, the argrelextrema
function cannot know the maxima/minima at the edges of the timerange. To improve
model accuracy, it is best to compute argrelextrema on the full timerange
and then use this function to cut off the edges (buffer) by the kernel.
In another case, if the targets are set to a shifted price movement, this
buffer is unnecessary because the shifted candles at the end of the timerange
will be NaN and FreqAI will automatically cut those off of the training
dataset.
"""
buffer = self.freqai_config["feature_parameters"]["buffer_train_data_candles"]
if buffer:
timerange.stopts -= buffer * timeframe_to_seconds(self.config["timeframe"])
timerange.startts += buffer * timeframe_to_seconds(self.config["timeframe"])
return timerange

47
freqtrade/freqai/freqai_interface.py
View File

@@ -1,4 +1,3 @@
import inspect
import logging
import threading
import time
@@ -106,8 +105,6 @@ class IFreqaiModel(ABC):
self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
self.can_short = True # overridden in start() with strategy.can_short
self.warned_deprecated_populate_any_indicators = False
record_params(config, self.full_path)
def __getstate__(self):
@@ -138,9 +135,6 @@ class IFreqaiModel(ABC):
self.data_provider = strategy.dp
self.can_short = strategy.can_short
# check if the strategy has deprecated populate_any_indicators function
self.check_deprecated_populate_any_indicators(strategy)
if self.live:
self.inference_timer('start')
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
@@ -227,7 +221,7 @@ class IFreqaiModel(ABC):
logger.warning(f'{pair} not in current whitelist, removing from train queue.')
continue
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
(_, trained_timestamp) = self.dd.get_pair_dict_info(pair)
dk = FreqaiDataKitchen(self.config, self.live, pair)
(
@@ -285,7 +279,7 @@ class IFreqaiModel(ABC):
# following tr_train. Both of these windows slide through the
# entire backtest
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
(_, _, _) = self.dd.get_pair_dict_info(pair)
(_, _) = self.dd.get_pair_dict_info(pair)
train_it += 1
total_trains = len(dk.backtesting_timeranges)
self.training_timerange = tr_train
@@ -330,6 +324,8 @@ class IFreqaiModel(ABC):
dataframe_base_backtest = strategy.set_freqai_targets(
dataframe_base_backtest, metadata=metadata)
tr_train = dk.buffer_timerange(tr_train)
dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
@@ -382,7 +378,7 @@ class IFreqaiModel(ABC):
"""
# get the model metadata associated with the current pair
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
(_, trained_timestamp) = self.dd.get_pair_dict_info(metadata["pair"])
# append the historic data once per round
if self.dd.historic_data:
@@ -489,7 +485,7 @@ class IFreqaiModel(ABC):
"strategy is furnishing the same features as the pretrained"
"model. In case of --strategy-list, please be aware that FreqAI "
"requires all strategies to maintain identical "
"populate_any_indicator() functions"
"feature_engineering_* functions"
)
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
@@ -569,7 +565,7 @@ class IFreqaiModel(ABC):
file_type = ".h5"
elif 'stable_baselines' in self.dd.model_type or 'sb3_contrib' == self.dd.model_type:
file_type = ".zip"
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.{file_type}")
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}")
file_exists = path_to_modelfile.is_file()
if file_exists:
logger.info("Found model at %s", dk.data_path / dk.model_filename)
@@ -601,7 +597,7 @@ class IFreqaiModel(ABC):
:param strategy: IStrategy = user defined strategy object
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param data_load_timerange: TimeRange = the amount of data to be loaded
for populate_any_indicators
for populating indicators
(larger than new_trained_timerange so that
new_trained_timerange does not contain any NaNs)
"""
@@ -614,6 +610,8 @@ class IFreqaiModel(ABC):
strategy, corr_dataframes, base_dataframes, pair
)
new_trained_timerange = dk.buffer_timerange(new_trained_timerange)
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
# find the features indicated by strategy and store in datakitchen
@@ -629,8 +627,7 @@ class IFreqaiModel(ABC):
if self.plot_features:
plot_feature_importance(model, pair, dk, self.plot_features)
if self.freqai_info.get("purge_old_models", False):
self.dd.purge_old_models()
self.dd.purge_old_models()
def set_initial_historic_predictions(
self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str, strat_df: DataFrame
@@ -806,7 +803,7 @@ class IFreqaiModel(ABC):
logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
"Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
"is included in the column names when you are creating features "
"in `populate_any_indicators()`.")
"in `feature_engineering_*` functions.")
self.get_corr_dataframes = not bool(self.corr_dataframes)
elif self.corr_dataframes:
dataframe = dk.attach_corr_pair_columns(
@@ -933,26 +930,6 @@ class IFreqaiModel(ABC):
dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
return dk
def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
"""
Check and warn if the deprecated populate_any_indicators function is used.
:param strategy: strategy object
"""
if not self.warned_deprecated_populate_any_indicators:
self.warned_deprecated_populate_any_indicators = True
old_version = inspect.getsource(strategy.populate_any_indicators) != (
inspect.getsource(IStrategy.populate_any_indicators))
if old_version:
logger.warning("DEPRECATION WARNING: "
"You are using the deprecated populate_any_indicators function. "
"This function will raise an error on March 1 2023. "
"Please update your strategy by using "
"the new feature_engineering functions. See \n"
"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
"for details.")
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

5
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@@ -34,6 +34,11 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
if self.train_env:
self.train_env.close()
if self.eval_env:
self.eval_env.close()
env_info = self.pack_env_dict(dk.pair)
env_id = "train_env"

2
freqtrade/freqai/utils.py
View File

@@ -211,7 +211,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
"pairs": config.get('exchange', {}).get('pair_whitelist')
}
with open(params_record_path, "w") as handle:
with params_record_path.open("w") as handle:
rapidjson.dump(
run_params,
handle,

28
freqtrade/freqtradebot.py
View File

@@ -127,7 +127,7 @@ class FreqtradeBot(LoggingMixin):
for minutes in [0, 15, 30, 45]:
t = str(time(time_slot, minutes, 2))
self._schedule.every().day.at(t).do(update)
self.last_process = datetime(1970, 1, 1, tzinfo=timezone.utc)
self.last_process: Optional[datetime] = None
self.strategy.ft_bot_start()
# Initialize protections AFTER bot start - otherwise parameters are not loaded.
@@ -633,7 +633,7 @@ class FreqtradeBot(LoggingMixin):
return
remaining = (trade.amount - amount) * current_exit_rate
if remaining < min_exit_stake:
if min_exit_stake and remaining < min_exit_stake:
logger.info(f"Remaining amount of {remaining} would be smaller "
f"than the minimum of {min_exit_stake}.")
return
@@ -841,7 +841,7 @@ class FreqtradeBot(LoggingMixin):
def cancel_stoploss_on_exchange(self, trade: Trade) -> Trade:
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
if trade.stoploss_order_id:
try:
logger.info(f"Canceling stoploss on exchange for {trade}")
co = self.exchange.cancel_stoploss_order_with_result(
@@ -1275,8 +1275,7 @@ class FreqtradeBot(LoggingMixin):
if order['side'] == trade.entry_side:
self.handle_cancel_enter(trade, order, reason)
else:
canceled = self.handle_cancel_exit(
trade, order, reason)
canceled = self.handle_cancel_exit(trade, order, reason)
canceled_count = trade.get_exit_order_count()
max_timeouts = self.config.get('unfilledtimeout', {}).get('exit_timeout_count', 0)
if canceled and max_timeouts > 0 and canceled_count >= max_timeouts:
@@ -1315,7 +1314,7 @@ class FreqtradeBot(LoggingMixin):
default_retval=order_obj.price)(
trade=trade, order=order_obj, pair=trade.pair,
current_time=datetime.now(timezone.utc), proposed_rate=proposed_rate,
current_order_rate=order_obj.price, entry_tag=trade.enter_tag,
current_order_rate=order_obj.safe_price, entry_tag=trade.enter_tag,
side=trade.entry_side)
replacing = True
@@ -1331,7 +1330,8 @@ class FreqtradeBot(LoggingMixin):
# place new order only if new price is supplied
self.execute_entry(
pair=trade.pair,
stake_amount=(order_obj.remaining * order_obj.price / trade.leverage),
stake_amount=(
order_obj.safe_remaining * order_obj.safe_price / trade.leverage),
price=adjusted_entry_price,
trade=trade,
is_short=trade.is_short,
@@ -1345,6 +1345,8 @@ class FreqtradeBot(LoggingMixin):
"""
for trade in Trade.get_open_order_trades():
if not trade.open_order_id:
continue
try:
order = self.exchange.fetch_order(trade.open_order_id, trade.pair)
except (ExchangeError):
@@ -1369,6 +1371,9 @@ class FreqtradeBot(LoggingMixin):
"""
was_trade_fully_canceled = False
side = trade.entry_side.capitalize()
if not trade.open_order_id:
logger.warning(f"No open order for {trade}.")
return False
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in constants.NON_OPEN_EXCHANGE_STATES:
@@ -1455,7 +1460,7 @@ class FreqtradeBot(LoggingMixin):
return False
try:
co = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
co = self.exchange.cancel_order_with_result(order['id'], trade.pair,
trade.amount)
except InvalidOrderException:
logger.exception(
@@ -1640,7 +1645,7 @@ class FreqtradeBot(LoggingMixin):
profit = trade.calc_profit(rate=order_rate, amount=amount, open_rate=trade.open_rate)
profit_ratio = trade.calc_profit_ratio(order_rate, amount, trade.open_rate)
else:
order_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
order_rate = trade.safe_close_rate
profit = trade.calc_profit(rate=order_rate) + (0.0 if fill else trade.realized_profit)
profit_ratio = trade.calc_profit_ratio(order_rate)
amount = trade.amount
@@ -1695,7 +1700,7 @@ class FreqtradeBot(LoggingMixin):
raise DependencyException(
f"Order_obj not found for {order_id}. This should not have happened.")
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_rate: float = trade.safe_close_rate
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.exchange.get_rate(
trade.pair, side='exit', is_short=trade.is_short, refresh=False)
@@ -1738,7 +1743,8 @@ class FreqtradeBot(LoggingMixin):
#
def update_trade_state(
self, trade: Trade, order_id: str, action_order: Optional[Dict[str, Any]] = None,
self, trade: Trade, order_id: Optional[str],
action_order: Optional[Dict[str, Any]] = None,
stoploss_order: bool = False, send_msg: bool = True) -> bool:
"""
Checks trades with open orders and updates the amount if necessary

3
freqtrade/leverage/__init__.py
View File

@@ -1,2 +1 @@
# flake8: noqa: F401
from freqtrade.leverage.interest import interest
from freqtrade.leverage.interest import interest # noqa: F401

4
freqtrade/loggers.py
View File

@@ -103,9 +103,9 @@ def setup_logging(config: Config) -> None:
logging.root.addHandler(handler_sl)
elif s[0] == 'journald': # pragma: no cover
try:
from systemd.journal import JournaldLogHandler
from cysystemd.journal import JournaldLogHandler
except ImportError:
raise OperationalException("You need the systemd python package be installed in "
raise OperationalException("You need the cysystemd python package be installed in "
"order to use logging to journald.")
handler_jd = get_existing_handlers(JournaldLogHandler)
if handler_jd:

8
freqtrade/misc.py
View File

@@ -81,7 +81,7 @@ def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool =
else:
if log:
logger.info(f'dumping json to "{filename}"')
with open(filename, 'w') as fp:
with filename.open('w') as fp:
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
logger.debug(f'done json to "{filename}"')
@@ -98,7 +98,7 @@ def file_dump_joblib(filename: Path, data: Any, log: bool = True) -> None:
if log:
logger.info(f'dumping joblib to "{filename}"')
with open(filename, 'wb') as fp:
with filename.open('wb') as fp:
joblib.dump(data, fp)
logger.debug(f'done joblib dump to "{filename}"')
@@ -112,7 +112,7 @@ def json_load(datafile: IO) -> Any:
return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)
def file_load_json(file):
def file_load_json(file: Path):
if file.suffix != ".gz":
gzipfile = file.with_suffix(file.suffix + '.gz')
@@ -125,7 +125,7 @@ def file_load_json(file):
pairdata = json_load(datafile)
elif file.is_file():
logger.debug(f"Loading historical data from file {file}")
with open(file) as datafile:
with file.open() as datafile:
pairdata = json_load(datafile)
else:
return None

3
freqtrade/mixins/__init__.py
View File

@@ -1,2 +1 @@
# flake8: noqa: F401
from freqtrade.mixins.logging_mixin import LoggingMixin
from freqtrade.mixins.logging_mixin import LoggingMixin # noqa: F401

2
freqtrade/optimize/backtest_caching.py
View File

@@ -29,7 +29,7 @@ def get_strategy_run_id(strategy) -> str:
# Include _ft_params_from_file - so changing parameter files cause cache eviction
digest.update(rapidjson.dumps(
strategy._ft_params_from_file, default=str, number_mode=rapidjson.NM_NAN).encode('utf-8'))
with open(strategy.__file__, 'rb') as fp:
with Path(strategy.__file__).open('rb') as fp:
digest.update(fp.read())
return digest.hexdigest().lower()

28
freqtrade/optimize/backtesting.py
View File

@@ -93,7 +93,7 @@ class Backtesting:
if self.config.get('strategy_list'):
if self.config.get('freqai', {}).get('enabled', False):
logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
"to have identical populate_any_indicators.")
"to have identical feature_engineering_* functions.")
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@@ -440,7 +440,8 @@ class Backtesting:
side_1 * abs(self.strategy.trailing_stop_positive / leverage)))
else:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = row[OPEN_IDX] * (1 - side_1 * abs(trade.stop_loss_pct / leverage))
stop_rate = row[OPEN_IDX] * (1 - side_1 * abs(
(trade.stop_loss_pct or 0.0) / leverage))
if is_short:
assert stop_rate > row[LOW_IDX]
else:
@@ -472,7 +473,7 @@ class Backtesting:
# - (Expected abs profit - open_rate - open_fee) / (fee_close -1)
roi_rate = trade.open_rate * roi / leverage
open_fee_rate = side_1 * trade.open_rate * (1 + side_1 * trade.fee_open)
close_rate = -(roi_rate + open_fee_rate) / (trade.fee_close - side_1 * 1)
close_rate = -(roi_rate + open_fee_rate) / ((trade.fee_close or 0.0) - side_1 * 1)
if is_short:
is_new_roi = row[OPEN_IDX] < close_rate
else:
@@ -563,7 +564,7 @@ class Backtesting:
pos_trade = self._get_exit_for_signal(trade, row, exit_, amount)
if pos_trade is not None:
order = pos_trade.orders[-1]
if self._get_order_filled(order.price, row):
if self._get_order_filled(order.ft_price, row):
order.close_bt_order(current_date, trade)
trade.recalc_trade_from_orders()
self.wallets.update()
@@ -664,6 +665,7 @@ class Backtesting:
side=trade.exit_side,
order_type=order_type,
status="open",
ft_price=close_rate,
price=close_rate,
average=close_rate,
amount=amount,
@@ -887,6 +889,7 @@ class Backtesting:
order_date=current_time,
order_filled_date=current_time,
order_update_date=current_time,
ft_price=propose_rate,
price=propose_rate,
average=propose_rate,
amount=amount,
@@ -895,7 +898,7 @@ class Backtesting:
cost=stake_amount + trade.fee_open,
)
trade.orders.append(order)
if pos_adjust and self._get_order_filled(order.price, row):
if pos_adjust and self._get_order_filled(order.ft_price, row):
order.close_bt_order(current_time, trade)
else:
trade.open_order_id = str(self.order_id_counter)
@@ -1008,15 +1011,15 @@ class Backtesting:
# only check on new candles for open entry orders
if order.side == trade.entry_side and current_time > order.order_date_utc:
requested_rate = strategy_safe_wrapper(self.strategy.adjust_entry_price,
default_retval=order.price)(
default_retval=order.ft_price)(
trade=trade, # type: ignore[arg-type]
order=order, pair=trade.pair, current_time=current_time,
proposed_rate=row[OPEN_IDX], current_order_rate=order.price,
proposed_rate=row[OPEN_IDX], current_order_rate=order.ft_price,
entry_tag=trade.enter_tag, side=trade.trade_direction
) # default value is current order price
# cancel existing order whenever a new rate is requested (or None)
if requested_rate == order.price:
if requested_rate == order.ft_price:
# assumption: there can't be multiple open entry orders at any given time
return False
else:
@@ -1028,7 +1031,8 @@ class Backtesting:
if requested_rate:
self._enter_trade(pair=trade.pair, row=row, trade=trade,
requested_rate=requested_rate,
requested_stake=(order.remaining * order.price / trade.leverage),
requested_stake=(
order.safe_remaining * order.ft_price / trade.leverage),
direction='short' if trade.is_short else 'long')
self.replaced_entry_orders += 1
else:
@@ -1095,7 +1099,7 @@ class Backtesting:
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):
if order and self._get_order_filled(order.ft_price, row):
order.close_bt_order(current_time, trade)
trade.open_order_id = None
self.wallets.update()
@@ -1106,7 +1110,7 @@ class Backtesting:
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
if order and self._get_order_filled(order.price, row):
if order and self._get_order_filled(order.ft_price, row):
order.close_bt_order(current_time, trade)
trade.open_order_id = None
sub_trade = order.safe_amount_after_fee != trade.amount
@@ -1115,7 +1119,7 @@ class Backtesting:
trade.recalc_trade_from_orders()
else:
trade.close_date = current_time
trade.close(order.price, show_msg=False)
trade.close(order.ft_price, show_msg=False)
# logger.debug(f"{pair} - Backtesting exit {trade}")
LocalTrade.close_bt_trade(trade)

0
freqtrade/optimize/hyperopt_tools.py Executable file → Normal file
View File

5
freqtrade/optimize/space/__init__.py
View File

@@ -1,4 +1,3 @@
# flake8: noqa: F401
from skopt.space import Categorical, Dimension, Integer, Real
from skopt.space import Categorical, Dimension, Integer, Real # noqa: F401
from .decimalspace import SKDecimal
from .decimalspace import SKDecimal # noqa: F401

10
freqtrade/persistence/base.py
View File

@@ -1,7 +1,9 @@
from typing import Any
from sqlalchemy.orm import declarative_base
from sqlalchemy.orm import DeclarativeBase, Session, scoped_session
_DECL_BASE: Any = declarative_base()
SessionType = scoped_session[Session]
class ModelBase(DeclarativeBase):
pass

11
freqtrade/persistence/models.py
View File

@@ -2,6 +2,7 @@
This module contains the class to persist trades into SQLite
"""
import logging
from typing import Any, Dict
from sqlalchemy import create_engine, inspect
from sqlalchemy.exc import NoSuchModuleError
@@ -9,7 +10,7 @@ from sqlalchemy.orm import scoped_session, sessionmaker
from sqlalchemy.pool import StaticPool
from freqtrade.exceptions import OperationalException
from freqtrade.persistence.base import _DECL_BASE
from freqtrade.persistence.base import ModelBase
from freqtrade.persistence.migrations import check_migrate
from freqtrade.persistence.pairlock import PairLock
from freqtrade.persistence.trade_model import Order, Trade
@@ -29,7 +30,7 @@ def init_db(db_url: str) -> None:
:param db_url: Database to use
:return: None
"""
kwargs = {}
kwargs: Dict[str, Any] = {}
if db_url == 'sqlite:///':
raise OperationalException(
@@ -54,10 +55,12 @@ def init_db(db_url: str) -> None:
# Scoped sessions proxy requests to the appropriate thread-local session.
# We should use the scoped_session object - not a seperately initialized version
Trade._session = scoped_session(sessionmaker(bind=engine, autoflush=False))
Order._session = Trade._session
PairLock._session = Trade._session
Trade.query = Trade._session.query_property()
Order.query = Trade._session.query_property()
PairLock.query = Trade._session.query_property()
previous_tables = inspect(engine).get_table_names()
_DECL_BASE.metadata.create_all(engine)
check_migrate(engine, decl_base=_DECL_BASE, previous_tables=previous_tables)
ModelBase.metadata.create_all(engine)
check_migrate(engine, decl_base=ModelBase, previous_tables=previous_tables)

29
freqtrade/persistence/pairlock.py
View File

@@ -1,33 +1,36 @@
from datetime import datetime, timezone
from typing import Any, Dict, Optional
from typing import Any, ClassVar, Dict, Optional
from sqlalchemy import Boolean, Column, DateTime, Integer, String, or_
from sqlalchemy.orm import Query
from sqlalchemy import String, or_
from sqlalchemy.orm import Mapped, Query, mapped_column
from sqlalchemy.orm.scoping import _QueryDescriptorType
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.persistence.base import _DECL_BASE
from freqtrade.persistence.base import ModelBase, SessionType
class PairLock(_DECL_BASE):
class PairLock(ModelBase):
"""
Pair Locks database model.
"""
__tablename__ = 'pairlocks'
query: ClassVar[_QueryDescriptorType]
_session: ClassVar[SessionType]
id = Column(Integer, primary_key=True)
id: Mapped[int] = mapped_column(primary_key=True)
pair = Column(String(25), nullable=False, index=True)
pair: Mapped[str] = mapped_column(String(25), nullable=False, index=True)
# lock direction - long, short or * (for both)
side = Column(String(25), nullable=False, default="*")
reason = Column(String(255), nullable=True)
side: Mapped[str] = mapped_column(String(25), nullable=False, default="*")
reason: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
# Time the pair was locked (start time)
lock_time = Column(DateTime(), nullable=False)
lock_time: Mapped[datetime] = mapped_column(nullable=False)
# Time until the pair is locked (end time)
lock_end_time = Column(DateTime(), nullable=False, index=True)
lock_end_time: Mapped[datetime] = mapped_column(nullable=False, index=True)
active = Column(Boolean, nullable=False, default=True, index=True)
active: Mapped[bool] = mapped_column(nullable=False, default=True, index=True)
def __repr__(self):
def __repr__(self) -> str:
lock_time = self.lock_time.strftime(DATETIME_PRINT_FORMAT)
lock_end_time = self.lock_end_time.strftime(DATETIME_PRINT_FORMAT)
return (

4
freqtrade/persistence/pairlock_middleware.py
View File

@@ -133,8 +133,8 @@ class PairLocks():
PairLock.query.session.commit()
else:
# used in backtesting mode; don't show log messages for speed
locks = PairLocks.get_pair_locks(None)
for lock in locks:
locksb = PairLocks.get_pair_locks(None)
for lock in locksb:
if lock.reason == reason:
lock.active = False

259
freqtrade/persistence/trade_model.py
View File

@@ -5,11 +5,11 @@ import logging
from collections import defaultdict
from datetime import datetime, timedelta, timezone
from math import isclose
from typing import Any, Dict, List, Optional
from typing import Any, ClassVar, Dict, List, Optional, cast
from sqlalchemy import (Boolean, Column, DateTime, Enum, Float, ForeignKey, Integer, String,
UniqueConstraint, desc, func)
from sqlalchemy.orm import Query, lazyload, relationship
from sqlalchemy import Enum, Float, ForeignKey, Integer, String, UniqueConstraint, desc, func
from sqlalchemy.orm import Mapped, Query, lazyload, mapped_column, relationship
from sqlalchemy.orm.scoping import _QueryDescriptorType
from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPEN_EXCHANGE_STATES,
BuySell, LongShort)
@@ -17,14 +17,14 @@ from freqtrade.enums import ExitType, TradingMode
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import amount_to_contract_precision, price_to_precision
from freqtrade.leverage import interest
from freqtrade.persistence.base import _DECL_BASE
from freqtrade.persistence.base import ModelBase, SessionType
from freqtrade.util import FtPrecise
logger = logging.getLogger(__name__)
class Order(_DECL_BASE):
class Order(ModelBase):
"""
Order database model
Keeps a record of all orders placed on the exchange
@@ -36,41 +36,44 @@ class Order(_DECL_BASE):
Mirrors CCXT Order structure
"""
__tablename__ = 'orders'
query: ClassVar[_QueryDescriptorType]
_session: ClassVar[SessionType]
# Uniqueness should be ensured over pair, order_id
# its likely that order_id is unique per Pair on some exchanges.
__table_args__ = (UniqueConstraint('ft_pair', 'order_id', name="_order_pair_order_id"),)
id = Column(Integer, primary_key=True)
ft_trade_id = Column(Integer, ForeignKey('trades.id'), index=True)
id: Mapped[int] = mapped_column(Integer, primary_key=True)
ft_trade_id: Mapped[int] = mapped_column(Integer, ForeignKey('trades.id'), index=True)
trade = relationship("Trade", back_populates="orders")
trade: Mapped[List["Trade"]] = relationship("Trade", back_populates="orders")
# order_side can only be 'buy', 'sell' or 'stoploss'
ft_order_side = Column(String(25), nullable=False)
ft_pair = Column(String(25), nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
ft_amount = Column(Float(), nullable=False)
ft_price = Column(Float(), nullable=False)
ft_order_side: Mapped[str] = mapped_column(String(25), nullable=False)
ft_pair: Mapped[str] = mapped_column(String(25), nullable=False)
ft_is_open: Mapped[bool] = mapped_column(nullable=False, default=True, index=True)
ft_amount: Mapped[float] = mapped_column(Float(), nullable=False)
ft_price: Mapped[float] = mapped_column(Float(), nullable=False)
order_id = Column(String(255), nullable=False, index=True)
status = Column(String(255), nullable=True)
symbol = Column(String(25), nullable=True)
order_type = Column(String(50), nullable=True)
side = Column(String(25), nullable=True)
price = Column(Float(), nullable=True)
average = Column(Float(), nullable=True)
amount = Column(Float(), nullable=True)
filled = Column(Float(), nullable=True)
remaining = Column(Float(), nullable=True)
cost = Column(Float(), nullable=True)
stop_price = Column(Float(), nullable=True)
order_date = Column(DateTime(), nullable=True, default=datetime.utcnow)
order_filled_date = Column(DateTime(), nullable=True)
order_update_date = Column(DateTime(), nullable=True)
order_id: Mapped[str] = mapped_column(String(255), nullable=False, index=True)
status: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
symbol: Mapped[Optional[str]] = mapped_column(String(25), nullable=True)
# TODO: type: order_type type is Optional[str]
order_type: Mapped[str] = mapped_column(String(50), nullable=True)
side: Mapped[str] = mapped_column(String(25), nullable=True)
price: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
average: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
amount: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
filled: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
remaining: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
cost: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
stop_price: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
order_date: Mapped[datetime] = mapped_column(nullable=True, default=datetime.utcnow)
order_filled_date: Mapped[Optional[datetime]] = mapped_column(nullable=True)
order_update_date: Mapped[Optional[datetime]] = mapped_column(nullable=True)
funding_fee: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
funding_fee = Column(Float(), nullable=True)
ft_fee_base = Column(Float(), nullable=True)
ft_fee_base: Mapped[Optional[float]] = mapped_column(Float(), nullable=True)
@property
def order_date_utc(self) -> datetime:
@@ -96,6 +99,10 @@ class Order(_DECL_BASE):
def safe_filled(self) -> float:
return self.filled if self.filled is not None else self.amount or 0.0
@property
def safe_cost(self) -> float:
return self.cost or 0.0
@property
def safe_remaining(self) -> float:
return (
@@ -151,7 +158,7 @@ class Order(_DECL_BASE):
self.order_update_date = datetime.now(timezone.utc)
def to_ccxt_object(self) -> Dict[str, Any]:
return {
order: Dict[str, Any] = {
'id': self.order_id,
'symbol': self.ft_pair,
'price': self.price,
@@ -169,6 +176,9 @@ class Order(_DECL_BASE):
'fee': None,
'info': {},
}
if self.ft_order_side == 'stoploss':
order['ft_order_type'] = 'stoploss'
return order
def to_json(self, entry_side: str, minified: bool = False) -> Dict[str, Any]:
resp = {
@@ -210,7 +220,7 @@ class Order(_DECL_BASE):
# Assumes backtesting will use date_last_filled_utc to calculate future funding fees.
self.funding_fee = trade.funding_fees
if (self.ft_order_side == trade.entry_side):
if (self.ft_order_side == trade.entry_side and self.price):
trade.open_rate = self.price
trade.recalc_trade_from_orders()
trade.adjust_stop_loss(trade.open_rate, trade.stop_loss_pct, refresh=True)
@@ -290,15 +300,15 @@ class LocalTrade():
exchange: str = ''
pair: str = ''
base_currency: str = ''
stake_currency: str = ''
base_currency: Optional[str] = ''
stake_currency: Optional[str] = ''
is_open: bool = True
fee_open: float = 0.0
fee_open_cost: Optional[float] = None
fee_open_currency: str = ''
fee_close: float = 0.0
fee_open_currency: Optional[str] = ''
fee_close: Optional[float] = 0.0
fee_close_cost: Optional[float] = None
fee_close_currency: str = ''
fee_close_currency: Optional[str] = ''
open_rate: float = 0.0
open_rate_requested: Optional[float] = None
# open_trade_value - calculated via _calc_open_trade_value
@@ -308,7 +318,7 @@ class LocalTrade():
close_profit: Optional[float] = None
close_profit_abs: Optional[float] = None
stake_amount: float = 0.0
max_stake_amount: float = 0.0
max_stake_amount: Optional[float] = 0.0
amount: float = 0.0
amount_requested: Optional[float] = None
open_date: datetime
@@ -317,9 +327,9 @@ class LocalTrade():
# absolute value of the stop loss
stop_loss: float = 0.0
# percentage value of the stop loss
stop_loss_pct: float = 0.0
stop_loss_pct: Optional[float] = 0.0
# absolute value of the initial stop loss
initial_stop_loss: float = 0.0
initial_stop_loss: Optional[float] = 0.0
# percentage value of the initial stop loss
initial_stop_loss_pct: Optional[float] = None
# stoploss order id which is on exchange
@@ -327,12 +337,12 @@ class LocalTrade():
# last update time of the stoploss order on exchange
stoploss_last_update: Optional[datetime] = None
# absolute value of the highest reached price
max_rate: float = 0.0
max_rate: Optional[float] = None
# Lowest price reached
min_rate: float = 0.0
exit_reason: str = ''
exit_order_status: str = ''
strategy: str = ''
min_rate: Optional[float] = None
exit_reason: Optional[str] = ''
exit_order_status: Optional[str] = ''
strategy: Optional[str] = ''
enter_tag: Optional[str] = None
timeframe: Optional[int] = None
@@ -589,7 +599,7 @@ class LocalTrade():
self.stop_loss_pct = -1 * abs(percent)
def adjust_stop_loss(self, current_price: float, stoploss: float,
def adjust_stop_loss(self, current_price: float, stoploss: Optional[float],
initial: bool = False, refresh: bool = False) -> None:
"""
This adjusts the stop loss to it's most recently observed setting
@@ -598,7 +608,7 @@ class LocalTrade():
:param initial: Called to initiate stop_loss.
Skips everything if self.stop_loss is already set.
"""
if initial and not (self.stop_loss is None or self.stop_loss == 0):
if stoploss is None or (initial and not (self.stop_loss is None or self.stop_loss == 0)):
# Don't modify if called with initial and nothing to do
return
refresh = True if refresh and self.nr_of_successful_entries == 1 else False
@@ -637,7 +647,7 @@ class LocalTrade():
f"initial_stop_loss={self.initial_stop_loss:.8f}, "
f"stop_loss={self.stop_loss:.8f}. "
f"Trailing stoploss saved us: "
f"{float(self.stop_loss) - float(self.initial_stop_loss):.8f}.")
f"{float(self.stop_loss) - float(self.initial_stop_loss or 0.0):.8f}.")
def update_trade(self, order: Order) -> None:
"""
@@ -789,10 +799,10 @@ class LocalTrade():
return interest(exchange_name=self.exchange, borrowed=borrowed, rate=rate, hours=hours)
def _calc_base_close(self, amount: FtPrecise, rate: float, fee: float) -> FtPrecise:
def _calc_base_close(self, amount: FtPrecise, rate: float, fee: Optional[float]) -> FtPrecise:
close_trade = amount * FtPrecise(rate)
fees = close_trade * FtPrecise(fee)
fees = close_trade * FtPrecise(fee or 0.0)
if self.is_short:
return close_trade + fees
@@ -1056,10 +1066,14 @@ class LocalTrade():
return len(self.select_filled_orders('sell'))
@property
def sell_reason(self) -> str:
def sell_reason(self) -> Optional[str]:
""" DEPRECATED! Please use exit_reason instead."""
return self.exit_reason
@property
def safe_close_rate(self) -> float:
return self.close_rate or self.close_rate_requested or 0.0
@staticmethod
def get_trades_proxy(*, pair: Optional[str] = None, is_open: Optional[bool] = None,
open_date: Optional[datetime] = None,
@@ -1121,7 +1135,7 @@ class LocalTrade():
@staticmethod
def get_open_trades() -> List[Any]:
"""
Query trades from persistence layer
Retrieve open trades
"""
return Trade.get_trades_proxy(is_open=True)
@@ -1156,7 +1170,7 @@ class LocalTrade():
logger.info(f"New stoploss: {trade.stop_loss}.")
class Trade(_DECL_BASE, LocalTrade):
class Trade(ModelBase, LocalTrade):
"""
Trade database model.
Also handles updating and querying trades
@@ -1164,79 +1178,98 @@ class Trade(_DECL_BASE, LocalTrade):
Note: Fields must be aligned with LocalTrade class
"""
__tablename__ = 'trades'
query: ClassVar[_QueryDescriptorType]
_session: ClassVar[SessionType]
use_db: bool = True
id = Column(Integer, primary_key=True)
id: Mapped[int] = mapped_column(Integer, primary_key=True) # type: ignore
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan",
lazy="selectin", innerjoin=True)
orders: Mapped[List[Order]] = relationship(
"Order", order_by="Order.id", cascade="all, delete-orphan", lazy="selectin",
innerjoin=True) # type: ignore
exchange = Column(String(25), nullable=False)
pair = Column(String(25), nullable=False, index=True)
base_currency = Column(String(25), nullable=True)
stake_currency = Column(String(25), nullable=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
fee_open = Column(Float(), nullable=False, default=0.0)
fee_open_cost = Column(Float(), nullable=True)
fee_open_currency = Column(String(25), nullable=True)
fee_close = Column(Float(), nullable=False, default=0.0)
fee_close_cost = Column(Float(), nullable=True)
fee_close_currency = Column(String(25), nullable=True)
open_rate: float = Column(Float())
open_rate_requested = Column(Float())
exchange: Mapped[str] = mapped_column(String(25), nullable=False) # type: ignore
pair: Mapped[str] = mapped_column(String(25), nullable=False, index=True) # type: ignore
base_currency: Mapped[Optional[str]] = mapped_column(String(25), nullable=True) # type: ignore
stake_currency: Mapped[Optional[str]] = mapped_column(String(25), nullable=True) # type: ignore
is_open: Mapped[bool] = mapped_column(nullable=False, default=True, index=True) # type: ignore
fee_open: Mapped[float] = mapped_column(Float(), nullable=False, default=0.0) # type: ignore
fee_open_cost: Mapped[Optional[float]] = mapped_column(Float(), nullable=True) # type: ignore
fee_open_currency: Mapped[Optional[str]] = mapped_column(
String(25), nullable=True) # type: ignore
fee_close: Mapped[Optional[float]] = mapped_column(
Float(), nullable=False, default=0.0) # type: ignore
fee_close_cost: Mapped[Optional[float]] = mapped_column(Float(), nullable=True) # type: ignore
fee_close_currency: Mapped[Optional[str]] = mapped_column(
String(25), nullable=True) # type: ignore
open_rate: Mapped[float] = mapped_column(Float()) # type: ignore
open_rate_requested: Mapped[Optional[float]] = mapped_column(
Float(), nullable=True) # type: ignore
# open_trade_value - calculated via _calc_open_trade_value
open_trade_value = Column(Float())
close_rate: Optional[float] = Column(Float())
close_rate_requested = Column(Float())
realized_profit = Column(Float(), default=0.0)
close_profit = Column(Float())
close_profit_abs = Column(Float())
stake_amount = Column(Float(), nullable=False)
max_stake_amount = Column(Float())
amount = Column(Float())
amount_requested = Column(Float())
open_date = Column(DateTime(), nullable=False, default=datetime.utcnow)
close_date = Column(DateTime())
open_order_id = Column(String(255))
open_trade_value: Mapped[float] = mapped_column(Float(), nullable=True) # type: ignore
close_rate: Mapped[Optional[float]] = mapped_column(Float()) # type: ignore
close_rate_requested: Mapped[Optional[float]] = mapped_column(Float()) # type: ignore
realized_profit: Mapped[float] = mapped_column(
Float(), default=0.0, nullable=True) # type: ignore
close_profit: Mapped[Optional[float]] = mapped_column(Float()) # type: ignore
close_profit_abs: Mapped[Optional[float]] = mapped_column(Float()) # type: ignore
stake_amount: Mapped[float] = mapped_column(Float(), nullable=False) # type: ignore
max_stake_amount: Mapped[Optional[float]] = mapped_column(Float()) # type: ignore
amount: Mapped[float] = mapped_column(Float()) # type: ignore
amount_requested: Mapped[Optional[float]] = mapped_column(Float()) # type: ignore
open_date: Mapped[datetime] = mapped_column(
nullable=False, default=datetime.utcnow) # type: ignore
close_date: Mapped[Optional[datetime]] = mapped_column() # type: ignore
open_order_id: Mapped[Optional[str]] = mapped_column(String(255), nullable=True) # type: ignore
# absolute value of the stop loss
stop_loss = Column(Float(), nullable=True, default=0.0)
stop_loss: Mapped[float] = mapped_column(Float(), nullable=True, default=0.0) # type: ignore
# percentage value of the stop loss
stop_loss_pct = Column(Float(), nullable=True)
stop_loss_pct: Mapped[Optional[float]] = mapped_column(Float(), nullable=True) # type: ignore
# absolute value of the initial stop loss
initial_stop_loss = Column(Float(), nullable=True, default=0.0)
initial_stop_loss: Mapped[Optional[float]] = mapped_column(
Float(), nullable=True, default=0.0) # type: ignore
# percentage value of the initial stop loss
initial_stop_loss_pct = Column(Float(), nullable=True)
initial_stop_loss_pct: Mapped[Optional[float]] = mapped_column(
Float(), nullable=True) # type: ignore
# stoploss order id which is on exchange
stoploss_order_id = Column(String(255), nullable=True, index=True)
stoploss_order_id: Mapped[Optional[str]] = mapped_column(
String(255), nullable=True, index=True) # type: ignore
# last update time of the stoploss order on exchange
stoploss_last_update = Column(DateTime(), nullable=True)
stoploss_last_update: Mapped[Optional[datetime]] = mapped_column(nullable=True) # type: ignore
# absolute value of the highest reached price
max_rate = Column(Float(), nullable=True, default=0.0)
max_rate: Mapped[Optional[float]] = mapped_column(
Float(), nullable=True, default=0.0) # type: ignore
# Lowest price reached
min_rate = Column(Float(), nullable=True)
exit_reason = Column(String(100), nullable=True)
exit_order_status = Column(String(100), nullable=True)
strategy = Column(String(100), nullable=True)
enter_tag = Column(String(100), nullable=True)
timeframe = Column(Integer, nullable=True)
min_rate: Mapped[Optional[float]] = mapped_column(Float(), nullable=True) # type: ignore
exit_reason: Mapped[Optional[str]] = mapped_column(String(100), nullable=True) # type: ignore
exit_order_status: Mapped[Optional[str]] = mapped_column(
String(100), nullable=True) # type: ignore
strategy: Mapped[Optional[str]] = mapped_column(String(100), nullable=True) # type: ignore
enter_tag: Mapped[Optional[str]] = mapped_column(String(100), nullable=True) # type: ignore
timeframe: Mapped[Optional[int]] = mapped_column(Integer, nullable=True) # type: ignore
trading_mode = Column(Enum(TradingMode), nullable=True)
amount_precision = Column(Float(), nullable=True)
price_precision = Column(Float(), nullable=True)
precision_mode = Column(Integer, nullable=True)
contract_size = Column(Float(), nullable=True)
trading_mode: Mapped[TradingMode] = mapped_column(
Enum(TradingMode), nullable=True) # type: ignore
amount_precision: Mapped[Optional[float]] = mapped_column(
Float(), nullable=True) # type: ignore
price_precision: Mapped[Optional[float]] = mapped_column(Float(), nullable=True) # type: ignore
precision_mode: Mapped[Optional[int]] = mapped_column(Integer, nullable=True) # type: ignore
contract_size: Mapped[Optional[float]] = mapped_column(Float(), nullable=True) # type: ignore
# Leverage trading properties
leverage = Column(Float(), nullable=True, default=1.0)
is_short = Column(Boolean, nullable=False, default=False)
liquidation_price = Column(Float(), nullable=True)
leverage: Mapped[float] = mapped_column(Float(), nullable=True, default=1.0) # type: ignore
is_short: Mapped[bool] = mapped_column(nullable=False, default=False) # type: ignore
liquidation_price: Mapped[Optional[float]] = mapped_column(
Float(), nullable=True) # type: ignore
# Margin Trading Properties
interest_rate = Column(Float(), nullable=False, default=0.0)
interest_rate: Mapped[float] = mapped_column(
Float(), nullable=False, default=0.0) # type: ignore
# Futures properties
funding_fees = Column(Float(), nullable=True, default=None)
funding_fees: Mapped[Optional[float]] = mapped_column(
Float(), nullable=True, default=None) # type: ignore
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -1282,7 +1315,7 @@ class Trade(_DECL_BASE, LocalTrade):
trade_filter.append(Trade.close_date > close_date)
if is_open is not None:
trade_filter.append(Trade.is_open.is_(is_open))
return Trade.get_trades(trade_filter).all()
return cast(List[LocalTrade], Trade.get_trades(trade_filter).all())
else:
return LocalTrade.get_trades_proxy(
pair=pair, is_open=is_open,
@@ -1291,7 +1324,7 @@ class Trade(_DECL_BASE, LocalTrade):
)
@staticmethod
def get_trades(trade_filter=None, include_orders: bool = True) -> Query:
def get_trades(trade_filter=None, include_orders: bool = True) -> Query['Trade']:
"""
Helper function to query Trades using filters.
NOTE: Not supported in Backtesting.
@@ -1378,7 +1411,7 @@ class Trade(_DECL_BASE, LocalTrade):
Returns List of dicts containing all Trades, including profit and trade count
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
filters: List = [Trade.is_open.is_(False)]
if minutes:
start_date = datetime.now(timezone.utc) - timedelta(minutes=minutes)
filters.append(Trade.close_date >= start_date)
@@ -1411,7 +1444,7 @@ class Trade(_DECL_BASE, LocalTrade):
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
filters: List = [Trade.is_open.is_(False)]
if (pair is not None):
filters.append(Trade.pair == pair)
@@ -1444,7 +1477,7 @@ class Trade(_DECL_BASE, LocalTrade):
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
filters: List = [Trade.is_open.is_(False)]
if (pair is not None):
filters.append(Trade.pair == pair)
@@ -1477,7 +1510,7 @@ class Trade(_DECL_BASE, LocalTrade):
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
filters: List = [Trade.is_open.is_(False)]
if (pair is not None):
filters.append(Trade.pair == pair)

2
freqtrade/plugins/pairlist/RemotePairList.py
View File

@@ -157,7 +157,7 @@ class RemotePairList(IPairList):
file_path = Path(filename)
if file_path.exists():
with open(filename) as json_file:
with file_path.open() as json_file:
# Load the JSON data into a dictionary
jsonparse = json.load(json_file)

5
freqtrade/rpc/__init__.py
View File

@@ -1,3 +1,2 @@
# flake8: noqa: F401
from .rpc import RPC, RPCException, RPCHandler
from .rpc_manager import RPCManager
from .rpc import RPC, RPCException, RPCHandler # noqa: F401
from .rpc_manager import RPCManager # noqa: F401

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

@@ -1,2 +1 @@
# flake8: noqa: F401
from .webserver import ApiServer
from .webserver import ApiServer # noqa: F401

97
freqtrade/rpc/api_server/api_backtest.py
View File

@@ -10,7 +10,7 @@ from fastapi.exceptions import HTTPException
from freqtrade.configuration.config_validation import validate_config_consistency
from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
from freqtrade.enums import BacktestState
from freqtrade.exceptions import DependencyException
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.misc import deep_merge_dicts
from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
BacktestResponse)
@@ -26,9 +26,10 @@ router = APIRouter()
@router.post('/backtest', response_model=BacktestResponse, tags=['webserver', 'backtest'])
# flake8: noqa: C901
async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: BackgroundTasks,
config=Depends(get_config), ws_mode=Depends(is_webserver_mode)):
async def api_start_backtest( # noqa: C901
bt_settings: BacktestRequest, background_tasks: BackgroundTasks,
config=Depends(get_config), ws_mode=Depends(is_webserver_mode)):
ApiServer._bt['bt_error'] = None
"""Start backtesting if not done so already"""
if ApiServer._bgtask_running:
raise RPCException('Bot Background task already running')
@@ -60,30 +61,31 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
asyncio.set_event_loop(asyncio.new_event_loop())
try:
# Reload strategy
lastconfig = ApiServer._bt_last_config
lastconfig = ApiServer._bt['last_config']
strat = StrategyResolver.load_strategy(btconfig)
validate_config_consistency(btconfig)
if (
not ApiServer._bt
not ApiServer._bt['bt']
or lastconfig.get('timeframe') != strat.timeframe
or lastconfig.get('timeframe_detail') != btconfig.get('timeframe_detail')
or lastconfig.get('timerange') != btconfig['timerange']
):
from freqtrade.optimize.backtesting import Backtesting
ApiServer._bt = Backtesting(btconfig)
ApiServer._bt.load_bt_data_detail()
ApiServer._bt['bt'] = Backtesting(btconfig)
ApiServer._bt['bt'].load_bt_data_detail()
else:
ApiServer._bt.config = btconfig
ApiServer._bt.init_backtest()
ApiServer._bt['bt'].config = btconfig
ApiServer._bt['bt'].init_backtest()
# Only reload data if timeframe changed.
if (
not ApiServer._bt_data
or not ApiServer._bt_timerange
not ApiServer._bt['data']
or not ApiServer._bt['timerange']
or lastconfig.get('timeframe') != strat.timeframe
or lastconfig.get('timerange') != btconfig['timerange']
):
ApiServer._bt_data, ApiServer._bt_timerange = ApiServer._bt.load_bt_data()
ApiServer._bt['data'], ApiServer._bt['timerange'] = ApiServer._bt[
'bt'].load_bt_data()
lastconfig['timerange'] = btconfig['timerange']
lastconfig['timeframe'] = strat.timeframe
@@ -91,34 +93,35 @@ 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()
ApiServer._bt['bt'].enable_protections = btconfig.get('enable_protections', False)
ApiServer._bt['bt'].strategylist = [strat]
ApiServer._bt['bt'].results = {}
ApiServer._bt['bt'].load_prior_backtest()
ApiServer._bt.abort = False
if (ApiServer._bt.results and
strat.get_strategy_name() in ApiServer._bt.results['strategy']):
ApiServer._bt['bt'].abort = False
if (ApiServer._bt['bt'].results and
strat.get_strategy_name() in ApiServer._bt['bt'].results['strategy']):
# When previous result hash matches - reuse that result and skip backtesting.
logger.info(f'Reusing result of previous backtest for {strat.get_strategy_name()}')
else:
min_date, max_date = ApiServer._bt.backtest_one_strategy(
strat, ApiServer._bt_data, ApiServer._bt_timerange)
min_date, max_date = ApiServer._bt['bt'].backtest_one_strategy(
strat, ApiServer._bt['data'], ApiServer._bt['timerange'])
ApiServer._bt.results = generate_backtest_stats(
ApiServer._bt_data, ApiServer._bt.all_results,
ApiServer._bt['bt'].results = generate_backtest_stats(
ApiServer._bt['data'], ApiServer._bt['bt'].all_results,
min_date=min_date, max_date=max_date)
if btconfig.get('export', 'none') == 'trades':
store_backtest_stats(
btconfig['exportfilename'], ApiServer._bt.results,
btconfig['exportfilename'], ApiServer._bt['bt'].results,
datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)
logger.info("Backtest finished.")
except DependencyException as e:
logger.info(f"Backtesting caused an error: {e}")
except (Exception, OperationalException, DependencyException) as e:
logger.exception(f"Backtesting caused an error: {e}")
ApiServer._bt['bt_error'] = str(e)
pass
finally:
ApiServer._bgtask_running = False
@@ -146,13 +149,14 @@ def api_get_backtest(ws_mode=Depends(is_webserver_mode)):
return {
"status": "running",
"running": True,
"step": ApiServer._bt.progress.action if ApiServer._bt else str(BacktestState.STARTUP),
"progress": ApiServer._bt.progress.progress if ApiServer._bt else 0,
"step": (ApiServer._bt['bt'].progress.action if ApiServer._bt['bt']
else str(BacktestState.STARTUP)),
"progress": ApiServer._bt['bt'].progress.progress if ApiServer._bt['bt'] else 0,
"trade_count": len(LocalTrade.trades),
"status_msg": "Backtest running",
}
if not ApiServer._bt:
if not ApiServer._bt['bt']:
return {
"status": "not_started",
"running": False,
@@ -160,6 +164,14 @@ def api_get_backtest(ws_mode=Depends(is_webserver_mode)):
"progress": 0,
"status_msg": "Backtest not yet executed"
}
if ApiServer._bt['bt_error']:
return {
"status": "error",
"running": False,
"step": "",
"progress": 0,
"status_msg": f"Backtest failed with {ApiServer._bt['bt_error']}"
}
return {
"status": "ended",
@@ -167,7 +179,7 @@ def api_get_backtest(ws_mode=Depends(is_webserver_mode)):
"status_msg": "Backtest ended",
"step": "finished",
"progress": 1,
"backtest_result": ApiServer._bt.results,
"backtest_result": ApiServer._bt['bt'].results,
}
@@ -182,12 +194,12 @@ def api_delete_backtest(ws_mode=Depends(is_webserver_mode)):
"progress": 0,
"status_msg": "Backtest running",
}
if ApiServer._bt:
ApiServer._bt.cleanup()
del ApiServer._bt
ApiServer._bt = None
del ApiServer._bt_data
ApiServer._bt_data = None
if ApiServer._bt['bt']:
ApiServer._bt['bt'].cleanup()
del ApiServer._bt['bt']
ApiServer._bt['bt'] = None
del ApiServer._bt['data']
ApiServer._bt['data'] = None
logger.info("Backtesting reset")
return {
"status": "reset",
@@ -208,7 +220,7 @@ def api_backtest_abort(ws_mode=Depends(is_webserver_mode)):
"progress": 0,
"status_msg": "Backtest ended",
}
ApiServer._bt.abort = True
ApiServer._bt['bt'].abort = True
return {
"status": "stopping",
"running": False,
@@ -218,14 +230,17 @@ def api_backtest_abort(ws_mode=Depends(is_webserver_mode)):
}
@router.get('/backtest/history', response_model=List[BacktestHistoryEntry], tags=['webserver', 'backtest'])
@router.get('/backtest/history', response_model=List[BacktestHistoryEntry],
tags=['webserver', 'backtest'])
def api_backtest_history(config=Depends(get_config), ws_mode=Depends(is_webserver_mode)):
# Get backtest result history, read from metadata files
return get_backtest_resultlist(config['user_data_dir'] / 'backtest_results')
@router.get('/backtest/history/result', response_model=BacktestResponse, tags=['webserver', 'backtest'])
def api_backtest_history_result(filename: str, strategy: str, config=Depends(get_config), ws_mode=Depends(is_webserver_mode)):
@router.get('/backtest/history/result', response_model=BacktestResponse,
tags=['webserver', 'backtest'])
def api_backtest_history_result(filename: str, strategy: str, config=Depends(get_config),
ws_mode=Depends(is_webserver_mode)):
# Get backtest result history, read from metadata files
fn = config['user_data_dir'] / 'backtest_results' / filename
results: Dict[str, Any] = {

19
freqtrade/rpc/api_server/api_schemas.py
View File

@@ -228,24 +228,32 @@ class TradeSchema(BaseModel):
fee_close: Optional[float]
fee_close_cost: Optional[float]
fee_close_currency: Optional[str]
open_date: str
open_timestamp: int
open_rate: float
open_rate_requested: Optional[float]
open_trade_value: float
close_date: Optional[str]
close_timestamp: Optional[int]
close_rate: Optional[float]
close_rate_requested: Optional[float]
close_profit: Optional[float]
close_profit_pct: Optional[float]
close_profit_abs: Optional[float]
profit_ratio: Optional[float]
profit_pct: Optional[float]
profit_abs: Optional[float]
profit_fiat: Optional[float]
realized_profit: float
exit_reason: Optional[str]
exit_order_status: Optional[str]
stop_loss_abs: Optional[float]
stop_loss_ratio: Optional[float]
stop_loss_pct: Optional[float]
@@ -255,6 +263,7 @@ class TradeSchema(BaseModel):
initial_stop_loss_abs: Optional[float]
initial_stop_loss_ratio: Optional[float]
initial_stop_loss_pct: Optional[float]
min_rate: Optional[float]
max_rate: Optional[float]
open_order_id: Optional[str]
@@ -273,10 +282,10 @@ class OpenTradeSchema(TradeSchema):
stoploss_current_dist_ratio: Optional[float]
stoploss_entry_dist: Optional[float]
stoploss_entry_dist_ratio: Optional[float]
current_profit: float
current_profit_abs: float
current_profit_pct: float
current_rate: float
total_profit_abs: float
total_profit_fiat: Optional[float]
open_order: Optional[str]
@@ -456,5 +465,5 @@ class SysInfo(BaseModel):
class Health(BaseModel):
last_process: datetime
last_process_ts: int
last_process: Optional[datetime]
last_process_ts: Optional[int]

2
freqtrade/rpc/api_server/api_v1.py
View File

@@ -346,4 +346,4 @@ def sysinfo():
@router.get('/health', response_model=Health, tags=['info'])
def health(rpc: RPC = Depends(get_rpc)):
return rpc._health()
return rpc.health()

11
freqtrade/rpc/api_server/webserver.py
View File

@@ -36,10 +36,13 @@ class ApiServer(RPCHandler):
_rpc: RPC
# Backtesting type: Backtesting
_bt = None
_bt_data = None
_bt_timerange = None
_bt_last_config: Config = {}
_bt: Dict[str, Any] = {
'bt': None,
'data': None,
'timerange': None,
'last_config': {},
'bt_error': None,
}
_has_rpc: bool = False
_bgtask_running: bool = False
_config: Config = {}

11
freqtrade/rpc/api_server/ws/__init__.py
View File

@@ -1,7 +1,6 @@
# flake8: noqa: F401
# isort: off
from freqtrade.rpc.api_server.ws.types import WebSocketType
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws.serializer import HybridJSONWebSocketSerializer
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel
from freqtrade.rpc.api_server.ws.message_stream import MessageStream
from freqtrade.rpc.api_server.ws.types import WebSocketType # noqa: F401
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy # noqa: F401
from freqtrade.rpc.api_server.ws.serializer import HybridJSONWebSocketSerializer # noqa: F401
from freqtrade.rpc.api_server.ws.channel import WebSocketChannel # noqa: F401
from freqtrade.rpc.api_server.ws.message_stream import MessageStream # noqa: F401

60
freqtrade/rpc/rpc.py
View File

@@ -19,8 +19,8 @@ from freqtrade.configuration.timerange import TimeRange
from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT, Config
from freqtrade.data.history import load_data
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, SignalDirection, State,
TradingMode)
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, SignalDirection,
State, TradingMode)
from freqtrade.exceptions import ExchangeError, PricingError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
from freqtrade.loggers import bufferHandler
@@ -169,6 +169,7 @@ class RPC:
for trade in trades:
order: Optional[Order] = None
current_profit_fiat: Optional[float] = None
total_profit_fiat: Optional[float] = None
if trade.open_order_id:
order = trade.select_order_by_order_id(trade.open_order_id)
# calculate profit and send message to user
@@ -188,8 +189,9 @@ class RPC:
else:
# Closed trade ...
current_rate = trade.close_rate
current_profit = trade.close_profit
current_profit_abs = trade.close_profit_abs
current_profit = trade.close_profit or 0.0
current_profit_abs = trade.close_profit_abs or 0.0
total_profit_abs = trade.realized_profit + current_profit_abs
# Calculate fiat profit
if not isnan(current_profit_abs) and self._fiat_converter:
@@ -198,6 +200,11 @@ class RPC:
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
total_profit_fiat = self._fiat_converter.convert_amount(
total_profit_abs,
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
# Calculate guaranteed profit (in case of trailing stop)
stoploss_entry_dist = trade.calc_profit(trade.stop_loss)
@@ -210,14 +217,13 @@ class RPC:
trade_dict.update(dict(
close_profit=trade.close_profit if not trade.is_open else None,
current_rate=current_rate,
current_profit=current_profit, # Deprecated
current_profit_pct=round(current_profit * 100, 2), # Deprecated
current_profit_abs=current_profit_abs, # Deprecated
profit_ratio=current_profit,
profit_pct=round(current_profit * 100, 2),
profit_abs=current_profit_abs,
profit_fiat=current_profit_fiat,
total_profit_abs=total_profit_abs,
total_profit_fiat=total_profit_fiat,
stoploss_current_dist=stoploss_current_dist,
stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
stoploss_current_dist_pct=round(stoploss_current_dist_ratio * 100, 2),
@@ -367,13 +373,13 @@ class RPC:
def _rpc_trade_history(self, limit: int, offset: int = 0, order_by_id: bool = False) -> Dict:
""" Returns the X last trades """
order_by = Trade.id if order_by_id else Trade.close_date.desc()
order_by: Any = Trade.id if order_by_id else Trade.close_date.desc()
if limit:
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
order_by).limit(limit).offset(offset)
else:
trades = Trade.get_trades([Trade.is_open.is_(False)]).order_by(
Trade.close_date.desc()).all()
Trade.close_date.desc())
output = [trade.to_json() for trade in trades]
@@ -395,7 +401,7 @@ class RPC:
return 'losses'
else:
return 'draws'
trades: List[Trade] = Trade.get_trades([Trade.is_open.is_(False)], include_orders=False)
trades = Trade.get_trades([Trade.is_open.is_(False)], include_orders=False)
# Sell reason
exit_reasons = {}
for trade in trades:
@@ -404,7 +410,7 @@ class RPC:
exit_reasons[trade.exit_reason][trade_win_loss(trade)] += 1
# Duration
dur: Dict[str, List[int]] = {'wins': [], 'draws': [], 'losses': []}
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()
@@ -443,11 +449,11 @@ class RPC:
durations.append((trade.close_date - trade.open_date).total_seconds())
if not trade.is_open:
profit_ratio = trade.close_profit
profit_abs = trade.close_profit_abs
profit_ratio = trade.close_profit or 0.0
profit_abs = trade.close_profit_abs or 0.0
profit_closed_coin.append(profit_abs)
profit_closed_ratio.append(profit_ratio)
if trade.close_profit >= 0:
if profit_ratio >= 0:
winning_trades += 1
winning_profit += profit_abs
else:
@@ -500,7 +506,7 @@ class RPC:
trades_df = DataFrame([{'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
'profit_abs': trade.close_profit_abs}
for trade in trades if not trade.is_open])
for trade in trades if not trade.is_open and trade.close_date])
max_drawdown_abs = 0.0
max_drawdown = 0.0
if len(trades_df) > 0:
@@ -779,7 +785,8 @@ class RPC:
# check if valid pair
# check if pair already has an open pair
trade: Trade = Trade.get_trades([Trade.is_open.is_(True), Trade.pair == pair]).first()
trade: Optional[Trade] = Trade.get_trades(
[Trade.is_open.is_(True), Trade.pair == pair]).first()
is_short = (order_side == SignalDirection.SHORT)
if trade:
is_short = trade.is_short
@@ -1198,10 +1205,23 @@ class RPC:
"ram_pct": psutil.virtual_memory().percent
}
def _health(self) -> Dict[str, Union[str, int]]:
def health(self) -> Dict[str, Optional[Union[str, int]]]:
last_p = self._freqtrade.last_process
if last_p is None:
return {
"last_process": None,
"last_process_loc": None,
"last_process_ts": None,
}
return {
'last_process': str(last_p),
'last_process_loc': last_p.astimezone(tzlocal()).strftime(DATETIME_PRINT_FORMAT),
'last_process_ts': int(last_p.timestamp()),
"last_process": str(last_p),
"last_process_loc": last_p.astimezone(tzlocal()).strftime(DATETIME_PRINT_FORMAT),
"last_process_ts": int(last_p.timestamp()),
}
def _update_market_direction(self, direction: MarketDirection) -> None:
self._freqtrade.strategy.market_direction = direction
def _get_market_direction(self) -> MarketDirection:
return self._freqtrade.strategy.market_direction

113
freqtrade/rpc/telegram.py
View File

@@ -25,7 +25,7 @@ from telegram.utils.helpers import escape_markdown
from freqtrade.__init__ import __version__
from freqtrade.constants import DUST_PER_COIN, Config
from freqtrade.enums import RPCMessageType, SignalDirection, TradingMode
from freqtrade.enums import MarketDirection, RPCMessageType, SignalDirection, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.misc import chunks, plural, round_coin_value
from freqtrade.persistence import Trade
@@ -129,7 +129,8 @@ class Telegram(RPCHandler):
r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
r'/forcebuy$', r'/forcelong$', r'/forceshort$',
r'/forcesell$', r'/forceexit$',
r'/edge$', r'/health$', r'/help$', r'/version$'
r'/edge$', r'/health$', r'/help$', r'/version$', r'/marketdir (long|short|even|none)$',
r'/marketdir$'
]
# Create keys for generation
valid_keys_print = [k.replace('$', '') for k in valid_keys]
@@ -197,6 +198,7 @@ class Telegram(RPCHandler):
CommandHandler('health', self._health),
CommandHandler('help', self._help),
CommandHandler('version', self._version),
CommandHandler('marketdir', self._changemarketdir)
]
callbacks = [
CallbackQueryHandler(self._status_table, pattern='update_status_table'),
@@ -469,42 +471,47 @@ class Telegram(RPCHandler):
lines_detail: List[str] = []
if len(filled_orders) > 0:
first_avg = filled_orders[0]["safe_price"]
for x, order in enumerate(filled_orders):
order_nr = 0
for order in filled_orders:
lines: List[str] = []
if order['is_open'] is True:
continue
order_nr += 1
wording = 'Entry' if order['ft_is_entry'] else 'Exit'
cur_entry_datetime = arrow.get(order["order_filled_date"])
cur_entry_amount = order["filled"] or order["amount"]
cur_entry_average = order["safe_price"]
lines.append(" ")
if x == 0:
lines.append(f"*{wording} #{x+1}:*")
if order_nr == 1:
lines.append(f"*{wording} #{order_nr}:*")
lines.append(
f"*Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})")
lines.append(f"*Average Price:* {cur_entry_average}")
else:
sumA = 0
sumB = 0
for y in range(x):
amount = filled_orders[y]["filled"] or filled_orders[y]["amount"]
sumA += amount * filled_orders[y]["safe_price"]
sumB += amount
prev_avg_price = sumA / sumB
sum_stake = 0
sum_amount = 0
for y in range(order_nr):
loc_order = filled_orders[y]
if loc_order['is_open'] is True:
# Skip open orders (e.g. stop orders)
continue
amount = loc_order["filled"] or loc_order["amount"]
sum_stake += amount * loc_order["safe_price"]
sum_amount += amount
prev_avg_price = sum_stake / sum_amount
# TODO: This calculation ignores fees.
price_to_1st_entry = ((cur_entry_average - first_avg) / first_avg)
minus_on_entry = 0
if prev_avg_price:
minus_on_entry = (cur_entry_average - prev_avg_price) / prev_avg_price
lines.append(f"*{wording} #{x+1}:* at {minus_on_entry:.2%} avg profit")
lines.append(f"*{wording} #{order_nr}:* at {minus_on_entry:.2%} avg profit")
if is_open:
lines.append("({})".format(cur_entry_datetime
.humanize(granularity=["day", "hour", "minute"])))
lines.append(
f"*Amount:* {cur_entry_amount} ({order['cost']:.8f} {quote_currency})")
lines.append(f"*Amount:* {cur_entry_amount} "
f"({round_coin_value(order['cost'], quote_currency)})")
lines.append(f"*Average {wording} Price:* {cur_entry_average} "
f"({price_to_1st_entry:.2%} from 1st entry rate)")
lines.append(f"*Order filled:* {order['order_filled_date']}")
@@ -518,6 +525,7 @@ class Telegram(RPCHandler):
# lines.append(
# f"({days}d {hours}h {minutes}m {seconds}s from previous {wording.lower()})")
lines_detail.append("\n".join(lines))
return lines_detail
@authorized_only
@@ -553,14 +561,21 @@ class Telegram(RPCHandler):
for r in results:
r['open_date_hum'] = arrow.get(r['open_date']).humanize()
r['num_entries'] = len([o for o in r['orders'] if o['ft_is_entry']])
r['num_exits'] = len([o for o in r['orders'] if not o['ft_is_entry']
and not o['ft_order_side'] == 'stoploss'])
r['exit_reason'] = r.get('exit_reason', "")
r['stake_amount_r'] = round_coin_value(r['stake_amount'], r['quote_currency'])
r['profit_abs_r'] = round_coin_value(r['profit_abs'], r['quote_currency'])
r['realized_profit_r'] = round_coin_value(r['realized_profit'], r['quote_currency'])
r['total_profit_abs_r'] = round_coin_value(
r['total_profit_abs'], r['quote_currency'])
lines = [
"*Trade ID:* `{trade_id}`" +
(" `(since {open_date_hum})`" if r['is_open'] else ""),
"*Current Pair:* {pair}",
"*Direction:* " + ("`Short`" if r.get('is_short') else "`Long`"),
"*Leverage:* `{leverage}`" if r.get('leverage') else "",
"*Amount:* `{amount} ({stake_amount} {quote_currency})`",
f"*Direction:* {'`Short`' if r.get('is_short') else '`Long`'}"
+ " ` ({leverage}x)`" if r.get('leverage') else "",
"*Amount:* `{amount} ({stake_amount_r})`",
"*Enter Tag:* `{enter_tag}`" if r['enter_tag'] else "",
"*Exit Reason:* `{exit_reason}`" if r['exit_reason'] else "",
]
@@ -568,6 +583,7 @@ class Telegram(RPCHandler):
if position_adjust:
max_buy_str = (f"/{max_entries + 1}" if (max_entries > 0) else "")
lines.append("*Number of Entries:* `{num_entries}`" + max_buy_str)
lines.append("*Number of Exits:* `{num_exits}`")
lines.extend([
"*Open Rate:* `{open_rate:.8f}`",
@@ -575,13 +591,15 @@ class Telegram(RPCHandler):
"*Open Date:* `{open_date}`",
"*Close Date:* `{close_date}`" if r['close_date'] else "",
"*Current Rate:* `{current_rate:.8f}`" if r['is_open'] else "",
("*Current Profit:* " if r['is_open'] else "*Close Profit: *")
+ "`{profit_ratio:.2%}`",
("*Unrealized Profit:* " if r['is_open'] else "*Close Profit: *")
+ "`{profit_ratio:.2%}` `({profit_abs_r})`",
])
if r['is_open']:
if r.get('realized_profit'):
lines.append("*Realized Profit:* `{realized_profit:.8f}`")
lines.append("*Realized Profit:* `{realized_profit_r}`")
lines.append("*Total Profit:* `{total_profit_abs_r}` ")
if (r['stop_loss_abs'] != r['initial_stop_loss_abs']
and r['initial_stop_loss_ratio'] is not None):
# Adding initial stoploss only if it is different from stoploss
@@ -1040,10 +1058,14 @@ class Telegram(RPCHandler):
query.answer()
query.edit_message_text(text="Force exit canceled.")
return
trade: Trade = Trade.get_trades(trade_filter=Trade.id == trade_id).first()
trade: Optional[Trade] = Trade.get_trades(trade_filter=Trade.id == trade_id).first()
query.answer()
query.edit_message_text(text=f"Manually exiting Trade #{trade_id}, {trade.pair}")
self._force_exit_action(trade_id)
if trade:
query.edit_message_text(
text=f"Manually exiting Trade #{trade_id}, {trade.pair}")
self._force_exit_action(trade_id)
else:
query.edit_message_text(text=f"Trade {trade_id} not found.")
def _force_enter_action(self, pair, price: Optional[float], order_side: SignalDirection):
if pair != 'cancel':
@@ -1494,6 +1516,9 @@ class Telegram(RPCHandler):
"*/count:* `Show number of active trades compared to allowed number of trades`\n"
"*/edge:* `Shows validated pairs by Edge if it is enabled` \n"
"*/health* `Show latest process timestamp - defaults to 1970-01-01 00:00:00` \n"
"*/marketdir [long | short | even | none]:* `Updates the user managed variable "
"that represents the current market direction. If no direction is provided `"
"`the currently set market direction will be output.` \n"
"_Statistics_\n"
"------------\n"
@@ -1527,7 +1552,7 @@ class Telegram(RPCHandler):
Handler for /health
Shows the last process timestamp
"""
health = self._rpc._health()
health = self._rpc.health()
message = f"Last process: `{health['last_process_loc']}`"
self._send_msg(message)
@@ -1677,3 +1702,39 @@ class Telegram(RPCHandler):
'TelegramError: %s! Giving up on that message.',
telegram_err.message
)
@authorized_only
def _changemarketdir(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /marketdir.
Updates the bot's market_direction
:param bot: telegram bot
:param update: message update
:return: None
"""
if context.args and len(context.args) == 1:
new_market_dir_arg = context.args[0]
old_market_dir = self._rpc._get_market_direction()
new_market_dir = None
if new_market_dir_arg == "long":
new_market_dir = MarketDirection.LONG
elif new_market_dir_arg == "short":
new_market_dir = MarketDirection.SHORT
elif new_market_dir_arg == "even":
new_market_dir = MarketDirection.EVEN
elif new_market_dir_arg == "none":
new_market_dir = MarketDirection.NONE
if new_market_dir is not None:
self._rpc._update_market_direction(new_market_dir)
self._send_msg("Successfully updated market direction"
f" from *{old_market_dir}* to *{new_market_dir}*.")
else:
raise RPCException("Invalid market direction provided. \n"
"Valid market directions: *long, short, even, none*")
elif context.args is not None and len(context.args) == 0:
old_market_dir = self._rpc._get_market_direction()
self._send_msg(f"Currently set market direction: *{old_market_dir}*")
else:
raise RPCException("Invalid usage of command /marketdir. \n"
"Usage: */marketdir [short | long | even | none]*")

7
freqtrade/strategy/interface.py
View File

@@ -12,8 +12,8 @@ from pandas import DataFrame
from freqtrade.constants import Config, IntOrInf, ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
SignalTagType, SignalType, TradingMode)
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, RunMode,
SignalDirection, SignalTagType, SignalType, TradingMode)
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
from freqtrade.misc import remove_entry_exit_signals
@@ -122,6 +122,9 @@ class IStrategy(ABC, HyperStrategyMixin):
# Definition of plot_config. See plotting documentation for more details.
plot_config: Dict = {}
# A self set parameter that represents the market direction. filled from configuration
market_direction: MarketDirection = MarketDirection.NONE
def __init__(self, config: Config) -> None:
self.config = config
# Dict to determine if analysis is necessary

13
freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

@@ -1,13 +1,13 @@
import logging
from typing import Dict
import numpy as np
import pandas as pd
import numpy as np # noqa
import pandas as pd # noqa
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair
from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair # noqa
logger = logging.getLogger(__name__)
@@ -27,7 +27,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
"freqai": {
"enabled": true,
"purge_old_models": true,
"purge_old_models": 2,
"train_period_days": 15,
"identifier": "uniqe-id",
"feature_parameters": {
@@ -224,12 +224,11 @@ class FreqaiExampleHybridStrategy(IStrategy):
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
dataframe["close"], 'up', 'down')
dataframe["close"], 'up', 'down')
return dataframe
# flake8: noqa: C901
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # noqa: C901
# User creates their own custom strat here. Present example is a supertrend
# based strategy.

5
freqtrade/util/__init__.py
View File

@@ -1,3 +1,2 @@
# flake8: noqa: F401
from freqtrade.util.ft_precise import FtPrecise
from freqtrade.util.periodic_cache import PeriodicCache
from freqtrade.util.ft_precise import FtPrecise # noqa: F401
from freqtrade.util.periodic_cache import PeriodicCache # noqa: F401

1
freqtrade/vendor/qtpylib/indicators.py vendored
View File

@@ -1,4 +1,3 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# QTPyLib: Quantitative Trading Python Library

0
freqtrade/worker.py Executable file → Normal file
View File