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Merge remote-tracking branch 'origin/develop' into dev-merge-rl

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This commit is contained in:
robcaulk
2022-10-01 17:48:47 +02:00
parent 09e834fa21 3e34f10e3d
commit 048cb95bd6
119 changed files with 5062 additions and 1694 deletions
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2
freqtrade/__init__.py
View File

@@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2022.9.dev'
__version__ = '2022.10.dev'
if 'dev' in __version__:
try:

1
freqtrade/commands/build_config_commands.py
View File

@@ -211,6 +211,7 @@ def ask_user_config() -> Dict[str, Any]:
)
# Force JWT token to be a random string
answers['api_server_jwt_key'] = secrets.token_hex()
answers['api_server_ws_token'] = secrets.token_urlsafe(25)
return answers

2
freqtrade/commands/cli_options.py
View File

@@ -440,7 +440,7 @@ AVAILABLE_CLI_OPTIONS = {
"dataformat_trades": Arg(
'--data-format-trades',
help='Storage format for downloaded trades data. (default: `jsongz`).',
choices=constants.AVAILABLE_DATAHANDLERS,
choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
),
"show_timerange": Arg(
'--show-timerange',

19
freqtrade/configuration/config_validation.py
View File

@@ -1,4 +1,5 @@
import logging
from collections import Counter
from copy import deepcopy
from typing import Any, Dict
@@ -85,6 +86,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_unlimited_amount(conf)
_validate_ask_orderbook(conf)
_validate_freqai_hyperopt(conf)
_validate_consumers(conf)
validate_migrated_strategy_settings(conf)
# validate configuration before returning
@@ -332,6 +334,23 @@ def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
def _validate_consumers(conf: Dict[str, Any]) -> None:
emc_conf = conf.get('external_message_consumer', {})
if emc_conf.get('enabled', False):
if len(emc_conf.get('producers', [])) < 1:
raise OperationalException("You must specify at least 1 Producer to connect to.")
producer_names = [p['name'] for p in emc_conf.get('producers', [])]
duplicates = [item for item, count in Counter(producer_names).items() if count > 1]
if duplicates:
raise OperationalException(
f"Producer names must be unique. Duplicate: {', '.join(duplicates)}")
if conf.get('process_only_new_candles', True):
# Warning here or require it?
logger.warning("To receive best performance with external data, "
"please set `process_only_new_candles` to False")
def _strategy_settings(conf: Dict[str, Any]) -> None:
process_deprecated_setting(conf, None, 'use_sell_signal', None, 'use_exit_signal')

53
freqtrade/constants.py
View File

@@ -31,12 +31,13 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'CalmarHyperOptLoss',
'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
'ProfitDrawDownHyperOptLoss']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList',
'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5']
AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['feather', 'parquet']
BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
BACKTEST_CACHE_DEFAULT = 'day'
@@ -243,6 +244,7 @@ CONF_SCHEMA = {
'exchange': {'$ref': '#/definitions/exchange'},
'edge': {'$ref': '#/definitions/edge'},
'freqai': {'$ref': '#/definitions/freqai'},
'external_message_consumer': {'$ref': '#/definitions/external_message_consumer'},
'experimental': {
'type': 'object',
'properties': {
@@ -404,6 +406,7 @@ CONF_SCHEMA = {
},
'username': {'type': 'string'},
'password': {'type': 'string'},
'ws_token': {'type': ['string', 'array'], 'items': {'type': 'string'}},
'jwt_secret_key': {'type': 'string'},
'CORS_origins': {'type': 'array', 'items': {'type': 'string'}},
'verbosity': {'type': 'string', 'enum': ['error', 'info']},
@@ -432,7 +435,7 @@ CONF_SCHEMA = {
},
'dataformat_trades': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'enum': AVAILABLE_DATAHANDLERS_TRADES,
'default': 'jsongz'
},
'position_adjustment_enable': {'type': 'boolean'},
@@ -488,6 +491,47 @@ CONF_SCHEMA = {
},
'required': ['process_throttle_secs', 'allowed_risk']
},
'external_message_consumer': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean', 'default': False},
'producers': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'name': {'type': 'string'},
'host': {'type': 'string'},
'port': {
'type': 'integer',
'default': 8080,
'minimum': 0,
'maximum': 65535
},
'ws_token': {'type': 'string'},
},
'required': ['name', 'host', 'ws_token']
}
},
'wait_timeout': {'type': 'integer', 'minimum': 0},
'sleep_time': {'type': 'integer', 'minimum': 0},
'ping_timeout': {'type': 'integer', 'minimum': 0},
'remove_entry_exit_signals': {'type': 'boolean', 'default': False},
'initial_candle_limit': {
'type': 'integer',
'minimum': 0,
'maximum': 1500,
'default': 1500
},
'message_size_limit': { # In megabytes
'type': 'integer',
'minimum': 1,
'maxmium': 20,
'default': 8,
}
},
'required': ['producers']
},
"freqai": {
"type": "object",
"properties": {
@@ -508,7 +552,7 @@ CONF_SCHEMA = {
"weight_factor": {"type": "number", "default": 0},
"principal_component_analysis": {"type": "boolean", "default": False},
"use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
"plot_feature_importance": {"type": "boolean", "default": False},
"plot_feature_importances": {"type": "integer", "default": 0},
"svm_params": {"type": "object",
"properties": {
"shuffle": {"type": "boolean", "default": False},
@@ -523,6 +567,7 @@ CONF_SCHEMA = {
"properties": {
"test_size": {"type": "number"},
"random_state": {"type": "integer"},
"shuffle": {"type": "boolean", "default": False}
},
},
"model_training_parameters": {

8
freqtrade/data/btanalysis.py
View File

@@ -284,7 +284,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1)
if 'orders' not in df.columns:
df.loc[:, 'orders'] = None
df['orders'] = None
else:
# old format - only with lists.
@@ -341,9 +341,9 @@ def trade_list_to_dataframe(trades: List[LocalTrade]) -> pd.DataFrame:
"""
df = pd.DataFrame.from_records([t.to_json(True) for t in trades], columns=BT_DATA_COLUMNS)
if len(df) > 0:
df.loc[:, 'close_date'] = pd.to_datetime(df['close_date'], utc=True)
df.loc[:, 'open_date'] = pd.to_datetime(df['open_date'], utc=True)
df.loc[:, 'close_rate'] = df['close_rate'].astype('float64')
df['close_date'] = pd.to_datetime(df['close_date'], utc=True)
df['open_date'] = pd.to_datetime(df['open_date'], utc=True)
df['close_rate'] = df['close_rate'].astype('float64')
return df

3
freqtrade/data/converter.py
View File

@@ -47,8 +47,7 @@ def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
fill_missing: bool, drop_incomplete: bool) -> DataFrame:
"""
Cleanse a OHLCV dataframe by
* Grouping it by date (removes duplicate tics)

138
freqtrade/data/dataprovider.py
View File

@@ -14,9 +14,10 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType, RunMode
from freqtrade.enums import CandleType, RPCMessageType, RunMode
from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange, timeframe_to_seconds
from freqtrade.rpc import RPCManager
from freqtrade.util import PeriodicCache
@@ -28,17 +29,33 @@ MAX_DATAFRAME_CANDLES = 1000
class DataProvider:
def __init__(self, config: Config, exchange: Optional[Exchange], pairlists=None) -> None:
def __init__(
self,
config: Config,
exchange: Optional[Exchange],
pairlists=None,
rpc: Optional[RPCManager] = None
) -> None:
self._config = config
self._exchange = exchange
self._pairlists = pairlists
self.__rpc = rpc
self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
self.__slice_index: Optional[int] = None
self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
self.__producer_pairs_df: Dict[str,
Dict[PairWithTimeframe, Tuple[DataFrame, datetime]]] = {}
self.__producer_pairs: Dict[str, List[str]] = {}
self._msg_queue: deque = deque()
self._default_candle_type = self._config.get('candle_type_def', CandleType.SPOT)
self._default_timeframe = self._config.get('timeframe', '1h')
self.__msg_cache = PeriodicCache(
maxsize=1000, ttl=timeframe_to_seconds(self._config.get('timeframe', '1h')))
maxsize=1000, ttl=timeframe_to_seconds(self._default_timeframe))
self.producers = self._config.get('external_message_consumer', {}).get('producers', [])
self.external_data_enabled = len(self.producers) > 0
def _set_dataframe_max_index(self, limit_index: int):
"""
@@ -63,9 +80,110 @@ class DataProvider:
:param dataframe: analyzed dataframe
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
self.__cached_pairs[(pair, timeframe, candle_type)] = (
pair_key = (pair, timeframe, candle_type)
self.__cached_pairs[pair_key] = (
dataframe, datetime.now(timezone.utc))
# For multiple producers we will want to merge the pairlists instead of overwriting
def _set_producer_pairs(self, pairlist: List[str], producer_name: str = "default"):
"""
Set the pairs received to later be used.
:param pairlist: List of pairs
"""
self.__producer_pairs[producer_name] = pairlist
def get_producer_pairs(self, producer_name: str = "default") -> List[str]:
"""
Get the pairs cached from the producer
:returns: List of pairs
"""
return self.__producer_pairs.get(producer_name, []).copy()
def _emit_df(
self,
pair_key: PairWithTimeframe,
dataframe: DataFrame
) -> None:
"""
Send this dataframe as an ANALYZED_DF message to RPC
:param pair_key: PairWithTimeframe tuple
:param data: Tuple containing the DataFrame and the datetime it was cached
"""
if self.__rpc:
self.__rpc.send_msg(
{
'type': RPCMessageType.ANALYZED_DF,
'data': {
'key': pair_key,
'df': dataframe,
'la': datetime.now(timezone.utc)
}
}
)
def _add_external_df(
self,
pair: str,
dataframe: DataFrame,
last_analyzed: datetime,
timeframe: str,
candle_type: CandleType,
producer_name: str = "default"
) -> None:
"""
Add the pair data to this class from an external source.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
pair_key = (pair, timeframe, candle_type)
if producer_name not in self.__producer_pairs_df:
self.__producer_pairs_df[producer_name] = {}
_last_analyzed = datetime.now(timezone.utc) if not last_analyzed else last_analyzed
self.__producer_pairs_df[producer_name][pair_key] = (dataframe, _last_analyzed)
logger.debug(f"External DataFrame for {pair_key} from {producer_name} added.")
def get_producer_df(
self,
pair: str,
timeframe: Optional[str] = None,
candle_type: Optional[CandleType] = None,
producer_name: str = "default"
) -> Tuple[DataFrame, datetime]:
"""
Get the pair data from producers.
:param pair: pair to get the data for
:param timeframe: Timeframe to get data for
:param candle_type: Any of the enum CandleType (must match trading mode!)
:returns: Tuple of the DataFrame and last analyzed timestamp
"""
_timeframe = self._default_timeframe if not timeframe else timeframe
_candle_type = self._default_candle_type if not candle_type else candle_type
pair_key = (pair, _timeframe, _candle_type)
# If we have no data from this Producer yet
if producer_name not in self.__producer_pairs_df:
# We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
# If we do have data from that Producer, but no data on this pair_key
if pair_key not in self.__producer_pairs_df[producer_name]:
# We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
# We have it, return this data
df, la = self.__producer_pairs_df[producer_name][pair_key]
return (df.copy(), la)
def add_pairlisthandler(self, pairlists) -> None:
"""
Allow adding pairlisthandler after initialization
@@ -90,8 +208,10 @@ class DataProvider:
if saved_pair not in self.__cached_pairs_backtesting:
timerange = TimeRange.parse_timerange(None if self._config.get(
'timerange') is None else str(self._config.get('timerange')))
# Move informative start time respecting startup_candle_count
startup_candles = self.get_required_startup(str(timeframe))
# It is not necessary to add the training candles, as they
# were already added at the beginning of the backtest.
startup_candles = self.get_required_startup(str(timeframe), False)
tf_seconds = timeframe_to_seconds(str(timeframe))
timerange.subtract_start(tf_seconds * startup_candles)
self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
@@ -105,7 +225,7 @@ class DataProvider:
)
return self.__cached_pairs_backtesting[saved_pair].copy()
def get_required_startup(self, timeframe: str) -> int:
def get_required_startup(self, timeframe: str, add_train_candles: bool = True) -> int:
freqai_config = self._config.get('freqai', {})
if not freqai_config.get('enabled', False):
return self._config.get('startup_candle_count', 0)
@@ -115,7 +235,9 @@ class DataProvider:
# make sure the startupcandles is at least the set maximum indicator periods
self._config['startup_candle_count'] = max(startup_candles, max(indicator_periods))
tf_seconds = timeframe_to_seconds(timeframe)
train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
train_candles = 0
if add_train_candles:
train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
total_candles = int(self._config['startup_candle_count'] + train_candles)
logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
return total_candles

130
freqtrade/data/history/featherdatahandler.py Normal file
View File

@@ -0,0 +1,130 @@
import logging
from typing import Optional
from pandas import DataFrame, read_feather, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
from freqtrade.enums import CandleType
from .idatahandler import IDataHandler
logger = logging.getLogger(__name__)
class FeatherDataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
def ohlcv_store(
self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
:param pair: Pair - used to generate filename
:param timeframe: Timeframe - used to generate filename
:param data: Dataframe containing OHLCV data
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: None
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
self.create_dir_if_needed(filename)
data.reset_index(drop=True).loc[:, self._columns].to_feather(
filename, compression_level=9, compression='lz4')
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange], candle_type: CandleType
) -> DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: DataFrame with ohlcv data, or empty DataFrame
"""
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type)
if not filename.exists():
# Fallback mode for 1M files
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
if not filename.exists():
return DataFrame(columns=self._columns)
pairdata = read_feather(filename)
pairdata.columns = self._columns
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return pairdata
def ohlcv_append(
self,
pair: str,
timeframe: str,
data: DataFrame,
candle_type: CandleType
) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
raise NotImplementedError()
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
# filename = self._pair_trades_filename(self._datadir, pair)
raise NotImplementedError()
# array = pa.array(data)
# array
# feather.write_feather(data, filename)
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
raise NotImplementedError()
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
raise NotImplementedError()
# filename = self._pair_trades_filename(self._datadir, pair)
# tradesdata = misc.file_load_json(filename)
# if not tradesdata:
# return []
# return tradesdata
@classmethod
def _get_file_extension(cls):
return "feather"

1
freqtrade/data/history/hdf5datahandler.py
View File

@@ -81,6 +81,7 @@ class HDF5DataHandler(IDataHandler):
raise ValueError("Wrong dataframe format")
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata = pairdata.reset_index(drop=True)
return pairdata
def ohlcv_append(

2
freqtrade/data/history/history_utils.py
View File

@@ -26,7 +26,7 @@ def load_pair_history(pair: str,
datadir: Path, *,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True,
drop_incomplete: bool = False,
startup_candles: int = 0,
data_format: str = None,
data_handler: IDataHandler = None,

10
freqtrade/data/history/idatahandler.py
View File

@@ -272,10 +272,10 @@ class IDataHandler(ABC):
return res
def ohlcv_load(self, pair, timeframe: str,
candle_type: CandleType,
candle_type: CandleType, *,
timerange: Optional[TimeRange] = None,
fill_missing: bool = True,
drop_incomplete: bool = True,
drop_incomplete: bool = False,
startup_candles: int = 0,
warn_no_data: bool = True,
) -> DataFrame:
@@ -375,6 +375,12 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
elif datatype == 'hdf5':
from .hdf5datahandler import HDF5DataHandler
return HDF5DataHandler
elif datatype == 'feather':
from .featherdatahandler import FeatherDataHandler
return FeatherDataHandler
elif datatype == 'parquet':
from .parquetdatahandler import ParquetDataHandler
return ParquetDataHandler
else:
raise ValueError(f"No datahandler for datatype {datatype} available.")

129
freqtrade/data/history/parquetdatahandler.py Normal file
View File

@@ -0,0 +1,129 @@
import logging
from typing import Optional
from pandas import DataFrame, read_parquet, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
from freqtrade.enums import CandleType
from .idatahandler import IDataHandler
logger = logging.getLogger(__name__)
class ParquetDataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
def ohlcv_store(
self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
:param pair: Pair - used to generate filename
:param timeframe: Timeframe - used to generate filename
:param data: Dataframe containing OHLCV data
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: None
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
self.create_dir_if_needed(filename)
data.reset_index(drop=True).loc[:, self._columns].to_parquet(filename)
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange], candle_type: CandleType
) -> DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: DataFrame with ohlcv data, or empty DataFrame
"""
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type)
if not filename.exists():
# Fallback mode for 1M files
filename = self._pair_data_filename(
self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
if not filename.exists():
return DataFrame(columns=self._columns)
pairdata = read_parquet(filename)
pairdata.columns = self._columns
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
pairdata['date'] = to_datetime(pairdata['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return pairdata
def ohlcv_append(
self,
pair: str,
timeframe: str,
data: DataFrame,
candle_type: CandleType
) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
:param candle_type: Any of the enum CandleType (must match trading mode!)
"""
raise NotImplementedError()
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
# filename = self._pair_trades_filename(self._datadir, pair)
raise NotImplementedError()
# array = pa.array(data)
# array
# feather.write_feather(data, filename)
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
raise NotImplementedError()
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from file, either .json.gz or .json
# TODO: respect timerange ...
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
raise NotImplementedError()
# filename = self._pair_trades_filename(self._datadir, pair)
# tradesdata = misc.file_load_json(filename)
# if not tradesdata:
# return []
# return tradesdata
@classmethod
def _get_file_extension(cls):
return "parquet"

2
freqtrade/enums/__init__.py
View File

@@ -6,7 +6,7 @@ from freqtrade.enums.exittype import ExitType
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.ordertypevalue import OrderTypeValues
from freqtrade.enums.rpcmessagetype import RPCMessageType
from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType
from freqtrade.enums.state import State

13
freqtrade/enums/rpcmessagetype.py
View File

@@ -1,7 +1,7 @@
from enum import Enum
class RPCMessageType(Enum):
class RPCMessageType(str, Enum):
STATUS = 'status'
WARNING = 'warning'
STARTUP = 'startup'
@@ -19,8 +19,19 @@ class RPCMessageType(Enum):
STRATEGY_MSG = 'strategy_msg'
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'
def __repr__(self):
return self.value
def __str__(self):
return self.value
# Enum for parsing requests from ws consumers
class RPCRequestType(str, Enum):
SUBSCRIBE = 'subscribe'
WHITELIST = 'whitelist'
ANALYZED_DF = 'analyzed_df'

31
freqtrade/exchange/binance.py
View File

@@ -68,6 +68,37 @@ class Binance(Exchange):
tickers = deep_merge_dicts(bidsasks, tickers, allow_null_overrides=False)
return tickers
@retrier
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
position_side = self._api.fapiPrivateGetPositionsideDual()
self._log_exchange_response('position_side_setting', position_side)
assets_margin = self._api.fapiPrivateGetMultiAssetsMargin()
self._log_exchange_response('multi_asset_margin', assets_margin)
msg = ""
if position_side.get('dualSidePosition') is True:
msg += (
"\nHedge Mode is not supported by freqtrade. "
"Please change 'Position Mode' on your binance futures account.")
if assets_margin.get('multiAssetsMargin') is True:
msg += ("\nMulti-Asset Mode is not supported by freqtrade. "
"Please change 'Asset Mode' on your binance futures account.")
if msg:
raise OperationalException(msg)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def _set_leverage(
self,

434
freqtrade/exchange/binance_leverage_tiers.json
View File

@@ -4485,6 +4485,120 @@
}
}
],
"BTCUSDT_221230": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 375000.0,
"maintenanceMarginRate": 0.02,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "375000",
"notionalFloor": "0",
"maintMarginRatio": "0.02",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 375000.0,
"maxNotional": 2000000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "2000000",
"notionalFloor": "375000",
"maintMarginRatio": "0.05",
"cum": "11250.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 2000000.0,
"maxNotional": 4000000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "4000000",
"notionalFloor": "2000000",
"maintMarginRatio": "0.1",
"cum": "111250.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 4000000.0,
"maxNotional": 10000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 4.0,
"info": {
"bracket": "4",
"initialLeverage": "4",
"notionalCap": "10000000",
"notionalFloor": "4000000",
"maintMarginRatio": "0.125",
"cum": "211250.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 10000000.0,
"maxNotional": 20000000.0,
"maintenanceMarginRate": 0.15,
"maxLeverage": 3.0,
"info": {
"bracket": "5",
"initialLeverage": "3",
"notionalCap": "20000000",
"notionalFloor": "10000000",
"maintMarginRatio": "0.15",
"cum": "461250.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 20000000.0,
"maxNotional": 40000000.0,
"maintenanceMarginRate": 0.25,
"maxLeverage": 2.0,
"info": {
"bracket": "6",
"initialLeverage": "2",
"notionalCap": "40000000",
"notionalFloor": "20000000",
"maintMarginRatio": "0.25",
"cum": "2461250.0"
}
},
{
"tier": 7.0,
"currency": "USDT",
"minNotional": 40000000.0,
"maxNotional": 400000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "7",
"initialLeverage": "1",
"notionalCap": "400000000",
"notionalFloor": "40000000",
"maintMarginRatio": "0.5",
"cum": "1.246125E7"
}
}
],
"BTS/USDT": [
{
"tier": 1.0,
@@ -5759,6 +5873,104 @@
}
}
],
"CVX/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"DAR/USDT": [
{
"tier": 1.0,
@@ -8105,6 +8317,120 @@
}
}
],
"ETHUSDT_221230": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 375000.0,
"maintenanceMarginRate": 0.02,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "375000",
"notionalFloor": "0",
"maintMarginRatio": "0.02",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 375000.0,
"maxNotional": 2000000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "2",
"initialLeverage": "10",
"notionalCap": "2000000",
"notionalFloor": "375000",
"maintMarginRatio": "0.05",
"cum": "11250.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 2000000.0,
"maxNotional": 4000000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "3",
"initialLeverage": "5",
"notionalCap": "4000000",
"notionalFloor": "2000000",
"maintMarginRatio": "0.1",
"cum": "111250.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 4000000.0,
"maxNotional": 10000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 4.0,
"info": {
"bracket": "4",
"initialLeverage": "4",
"notionalCap": "10000000",
"notionalFloor": "4000000",
"maintMarginRatio": "0.125",
"cum": "211250.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 10000000.0,
"maxNotional": 20000000.0,
"maintenanceMarginRate": 0.15,
"maxLeverage": 3.0,
"info": {
"bracket": "5",
"initialLeverage": "3",
"notionalCap": "20000000",
"notionalFloor": "10000000",
"maintMarginRatio": "0.15",
"cum": "461250.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 20000000.0,
"maxNotional": 40000000.0,
"maintenanceMarginRate": 0.25,
"maxLeverage": 2.0,
"info": {
"bracket": "6",
"initialLeverage": "2",
"notionalCap": "40000000",
"notionalFloor": "20000000",
"maintMarginRatio": "0.25",
"cum": "2461250.0"
}
},
{
"tier": 7.0,
"currency": "USDT",
"minNotional": 40000000.0,
"maxNotional": 400000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "7",
"initialLeverage": "1",
"notionalCap": "400000000",
"notionalFloor": "40000000",
"maintMarginRatio": "0.5",
"cum": "1.246125E7"
}
}
],
"FIL/BUSD": [
{
"tier": 1.0,
@@ -10138,10 +10464,10 @@
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 50.0,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "50",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
@@ -10216,13 +10542,13 @@
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 30000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "30000000",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
@@ -11389,6 +11715,104 @@
}
}
],
"LDO/USDT": [
{
"tier": 1.0,
"currency": "USDT",
"minNotional": 0.0,
"maxNotional": 5000.0,
"maintenanceMarginRate": 0.01,
"maxLeverage": 25.0,
"info": {
"bracket": "1",
"initialLeverage": "25",
"notionalCap": "5000",
"notionalFloor": "0",
"maintMarginRatio": "0.01",
"cum": "0.0"
}
},
{
"tier": 2.0,
"currency": "USDT",
"minNotional": 5000.0,
"maxNotional": 25000.0,
"maintenanceMarginRate": 0.025,
"maxLeverage": 20.0,
"info": {
"bracket": "2",
"initialLeverage": "20",
"notionalCap": "25000",
"notionalFloor": "5000",
"maintMarginRatio": "0.025",
"cum": "75.0"
}
},
{
"tier": 3.0,
"currency": "USDT",
"minNotional": 25000.0,
"maxNotional": 100000.0,
"maintenanceMarginRate": 0.05,
"maxLeverage": 10.0,
"info": {
"bracket": "3",
"initialLeverage": "10",
"notionalCap": "100000",
"notionalFloor": "25000",
"maintMarginRatio": "0.05",
"cum": "700.0"
}
},
{
"tier": 4.0,
"currency": "USDT",
"minNotional": 100000.0,
"maxNotional": 250000.0,
"maintenanceMarginRate": 0.1,
"maxLeverage": 5.0,
"info": {
"bracket": "4",
"initialLeverage": "5",
"notionalCap": "250000",
"notionalFloor": "100000",
"maintMarginRatio": "0.1",
"cum": "5700.0"
}
},
{
"tier": 5.0,
"currency": "USDT",
"minNotional": 250000.0,
"maxNotional": 1000000.0,
"maintenanceMarginRate": 0.125,
"maxLeverage": 2.0,
"info": {
"bracket": "5",
"initialLeverage": "2",
"notionalCap": "1000000",
"notionalFloor": "250000",
"maintMarginRatio": "0.125",
"cum": "11950.0"
}
},
{
"tier": 6.0,
"currency": "USDT",
"minNotional": 1000000.0,
"maxNotional": 5000000.0,
"maintenanceMarginRate": 0.5,
"maxLeverage": 1.0,
"info": {
"bracket": "6",
"initialLeverage": "1",
"notionalCap": "5000000",
"notionalFloor": "1000000",
"maintMarginRatio": "0.5",
"cum": "386950.0"
}
}
],
"LEVER/BUSD": [
{
"tier": 1.0,
@@ -19209,4 +19633,4 @@
}
}
]
}
}

4
freqtrade/exchange/exchange.py
View File

@@ -2891,7 +2891,7 @@ def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
:return: num-contracts
"""
if contract_size and contract_size != 1:
return amount / contract_size
return float(FtPrecise(amount) / FtPrecise(contract_size))
else:
return amount
@@ -2905,7 +2905,7 @@ def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) ->
"""
if contract_size and contract_size != 1:
return num_contracts * contract_size
return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
else:
return num_contracts

3
freqtrade/exchange/okx.py
View File

@@ -78,7 +78,8 @@ class Okx(Exchange):
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
) from e
except ccxt.BaseError as e:
raise OperationalException(e) from e

20
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any, Tuple
import numpy as np
@@ -32,7 +33,9 @@ class BaseClassifierModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
@@ -45,10 +48,10 @@ class BaseClassifierModel(IFreqaiModel):
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@@ -57,13 +60,16 @@ class BaseClassifierModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(data_dictionary["train_features"])} data points'
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model
@@ -86,7 +92,7 @@ class BaseClassifierModel(IFreqaiModel):
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk, filtered_df)
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

20
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any, Tuple
import numpy as np
@@ -31,7 +32,9 @@ class BaseRegressionModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
@@ -44,10 +47,10 @@ class BaseRegressionModel(IFreqaiModel):
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@@ -56,13 +59,16 @@ class BaseRegressionModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(data_dictionary["train_features"])} data points'
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model
@@ -86,7 +92,7 @@ class BaseRegressionModel(IFreqaiModel):
dk.data_dictionary["prediction_features"] = filtered_df
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_df)
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

18
freqtrade/freqai/base_models/BaseTensorFlowModel.py
View File

@@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any
from pandas import DataFrame
@@ -28,7 +29,9 @@ class BaseTensorFlowModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
@@ -41,10 +44,10 @@ class BaseTensorFlowModel(IFreqaiModel):
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------")
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@@ -53,12 +56,15 @@ class BaseTensorFlowModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(data_dictionary["train_features"])} data points'
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model

1
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
View File

@@ -1,4 +1,3 @@
from joblib import Parallel
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
from sklearn.utils.fixes import delayed

35
freqtrade/freqai/data_drawer.py
View File

@@ -314,6 +314,7 @@ class FreqaiDataDrawer:
"""
dk.find_features(dataframe)
dk.find_labels(dataframe)
full_labels = dk.label_list + dk.unique_class_list
@@ -377,7 +378,27 @@ class FreqaiDataDrawer:
if self.config.get("freqai", {}).get("purge_old_models", False):
self.purge_old_models()
# Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
def save_metadata(self, dk: FreqaiDataKitchen) -> None:
"""
Saves only metadata for backtesting studies if user prefers
not to save model data. This saves tremendous amounts of space
for users generating huge studies.
This is only active when `save_backtest_models`: false (not default)
"""
if not dk.data_path.is_dir():
dk.data_path.mkdir(parents=True, exist_ok=True)
save_path = Path(dk.data_path)
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
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:
rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
return
def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
"""
@@ -406,7 +427,7 @@ class FreqaiDataDrawer:
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
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:
@@ -434,6 +455,16 @@ class FreqaiDataDrawer:
return
def load_metadata(self, dk: FreqaiDataKitchen) -> None:
"""
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:
dk.data = json.load(fp)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
"""
loads all data required to make a prediction on a sub-train time range

54
freqtrade/freqai/data_kitchen.py
View File

@@ -138,20 +138,15 @@ class FreqaiDataKitchen:
"""
feat_dict = self.freqai_config["feature_parameters"]
if 'shuffle' not in self.freqai_config['data_split_parameters']:
self.freqai_config["data_split_parameters"].update({'shuffle': False})
weights: npt.ArrayLike
if feat_dict.get("weight_factor", 0) > 0:
weights = self.set_weights_higher_recent(len(filtered_dataframe))
else:
weights = np.ones(len(filtered_dataframe))
if feat_dict.get("stratify_training_data", 0) > 0:
stratification = np.zeros(len(filtered_dataframe))
for i in range(1, len(stratification)):
if i % feat_dict.get("stratify_training_data", 0) == 0:
stratification[i] = 1
else:
stratification = None
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(
train_features,
@@ -164,7 +159,6 @@ class FreqaiDataKitchen:
filtered_dataframe[: filtered_dataframe.shape[0]],
labels,
weights,
stratify=stratification,
**self.config["freqai"]["data_split_parameters"],
)
else:
@@ -214,7 +208,7 @@ class FreqaiDataKitchen:
filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(filtered_df).any(1) # get the rows that have NaNs,
drop_index = pd.isnull(filtered_df).any(axis=1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (training_filter):
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
@@ -225,7 +219,7 @@ class FreqaiDataKitchen:
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
labels = unfiltered_df.filter(label_list, axis=1)
drop_index_labels = pd.isnull(labels).any(1)
drop_index_labels = pd.isnull(labels).any(axis=1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
dates = unfiltered_df['date']
filtered_df = filtered_df[
@@ -253,7 +247,7 @@ class FreqaiDataKitchen:
else:
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_df).any(1)
drop_index = pd.isnull(filtered_df).any(axis=1)
self.data["filter_drop_index_prediction"] = drop_index
filtered_df.fillna(0, inplace=True)
# replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
@@ -470,27 +464,6 @@ class FreqaiDataKitchen:
return df
def remove_training_from_backtesting(
self
) -> DataFrame:
"""
Function which takes the backtesting time range and
remove training data from dataframe, keeping only the
startup_candle_count candles
"""
startup_candle_count = self.config.get('startup_candle_count', 0)
tf = self.config['timeframe']
tr = self.config["timerange"]
backtesting_timerange = TimeRange.parse_timerange(tr)
if startup_candle_count > 0 and backtesting_timerange:
backtesting_timerange.subtract_start(timeframe_to_seconds(tf) * startup_candle_count)
start = datetime.fromtimestamp(backtesting_timerange.startts, tz=timezone.utc)
df = self.return_dataframe
df = df.loc[df["date"] >= start, :]
return df
def principal_component_analysis(self) -> None:
"""
Performs Principal Component Analysis on the data for dimensionality reduction
@@ -833,7 +806,7 @@ class FreqaiDataKitchen:
:, :no_prev_pts
]
distances = distances.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(distances).any(1)
drop_index = pd.isnull(distances).any(axis=1)
distances = distances[drop_index == 0]
inliers = pd.DataFrame(index=distances.index)
@@ -856,7 +829,7 @@ class FreqaiDataKitchen:
inlier_metric = pd.DataFrame(
data=inliers.sum(axis=1) / no_prev_pts,
columns=['inlier_metric'],
columns=['%-inlier_metric'],
index=compute_df.index
)
@@ -906,11 +879,15 @@ class FreqaiDataKitchen:
"""
column_names = dataframe.columns
features = [c for c in column_names if "%" in c]
labels = [c for c in column_names if "&" in c]
if not features:
raise OperationalException("Could not find any features!")
self.training_features_list = features
def find_labels(self, dataframe: DataFrame) -> None:
column_names = dataframe.columns
labels = [c for c in column_names if "&" in c]
self.label_list = labels
def check_if_pred_in_training_spaces(self) -> None:
@@ -998,8 +975,6 @@ class FreqaiDataKitchen:
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
self.return_dataframe = self.remove_training_from_backtesting()
self.full_df = DataFrame()
return
@@ -1233,7 +1208,8 @@ class FreqaiDataKitchen:
def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
self.find_features(dataframe)
# self.find_features(dataframe)
self.find_labels(dataframe)
for key in self.label_list:
if dataframe[key].dtype == object:

82
freqtrade/freqai/freqai_interface.py
View File

@@ -66,7 +66,7 @@ class IFreqaiModel(ABC):
self.first = True
self.set_full_path()
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", False)
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
@@ -93,6 +93,7 @@ class IFreqaiModel(ABC):
self.begin_time_train: float = 0
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
self.continual_learning = self.freqai_info.get('continual_learning', False)
self.plot_features = self.ft_params.get("plot_feature_importances", 0)
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
@@ -258,7 +259,8 @@ 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(metadata["pair"])
pair = metadata["pair"]
(_, _, _) = self.dd.get_pair_dict_info(pair)
train_it += 1
total_trains = len(dk.backtesting_timeranges)
self.training_timerange = tr_train
@@ -273,39 +275,41 @@ class IFreqaiModel(ABC):
tr_train.stopts,
tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
logger.info(
f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
"trains"
)
trained_timestamp_int = int(trained_timestamp.stopts)
dk.data_path = Path(
dk.full_path
/
f"sub-train-{metadata['pair'].split('/')[0]}_{trained_timestamp_int}"
dk.full_path / f"sub-train-{pair.split('/')[0]}_{trained_timestamp_int}"
)
dk.set_new_model_names(metadata["pair"], trained_timestamp)
dk.set_new_model_names(pair, trained_timestamp)
if dk.check_if_backtest_prediction_exists():
self.dd.load_metadata(dk)
dk.find_features(dataframe_train)
self.check_if_feature_list_matches_strategy(dk)
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
if not self.model_exists(
metadata["pair"], dk, trained_timestamp=trained_timestamp_int
):
if not self.model_exists(dk):
dk.find_features(dataframe_train)
self.model = self.train(dataframe_train, metadata["pair"], dk)
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
dk.find_labels(dataframe_train)
self.model = self.train(dataframe_train, pair, dk)
self.dd.pair_dict[pair]["trained_timestamp"] = int(
trained_timestamp.stopts)
if self.plot_features:
plot_feature_importance(self.model, pair, dk, self.plot_features)
if self.save_backtest_models:
logger.info('Saving backtest model to disk.')
self.dd.save_data(self.model, metadata["pair"], dk)
self.dd.save_data(self.model, pair, dk)
else:
logger.info('Saving metadata to disk.')
self.dd.save_metadata(dk)
else:
self.model = self.dd.load_data(metadata["pair"], dk)
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
self.model = self.dd.load_data(pair, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
append_df = dk.get_predictions_to_append(pred_df, do_preds)
@@ -385,8 +389,7 @@ class IFreqaiModel(ABC):
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dataframe, dk)
dk.find_labels(dataframe)
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
@@ -431,7 +434,7 @@ class IFreqaiModel(ABC):
return
def check_if_feature_list_matches_strategy(
self, dataframe: DataFrame, dk: FreqaiDataKitchen
self, dk: FreqaiDataKitchen
) -> None:
"""
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
@@ -440,18 +443,21 @@ class IFreqaiModel(ABC):
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
current coin/bot loop
"""
dk.find_features(dataframe)
if "training_features_list_raw" in dk.data:
feature_list = dk.data["training_features_list_raw"]
else:
feature_list = dk.training_features_list
feature_list = dk.data['training_features_list']
if dk.training_features_list != feature_list:
raise OperationalException(
"Trying to access pretrained model with `identifier` "
"but found different features furnished by current strategy."
"Change `identifier` to train from scratch, or ensure the"
"strategy is furnishing the same features as the pretrained"
"model"
"model. In case of --strategy-list, please be aware that FreqAI "
"requires all strategies to maintain identical "
"populate_any_indicator() functions"
)
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
@@ -490,20 +496,23 @@ class IFreqaiModel(ABC):
if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
dk.add_noise_to_training_features()
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
def data_cleaning_predict(self, dk: FreqaiDataKitchen) -> None:
"""
Base data cleaning method for predict.
Functions here are complementary to the functions of data_cleaning_train.
"""
ft_params = self.freqai_info["feature_parameters"]
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk)
if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='predict')
if ft_params.get(
"principal_component_analysis", False
):
dk.pca_transform(self.dk.data_dictionary['prediction_features'])
dk.pca_transform(dk.data_dictionary['prediction_features'])
if ft_params.get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=True)
@@ -514,14 +523,7 @@ class IFreqaiModel(ABC):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
dk.use_DBSCAN_to_remove_outliers(predict=True)
def model_exists(
self,
pair: str,
dk: FreqaiDataKitchen,
trained_timestamp: int = None,
model_filename: str = "",
scanning: bool = False,
) -> bool:
def model_exists(self, dk: FreqaiDataKitchen) -> bool:
"""
Given a pair and path, check if a model already exists
:param pair: pair e.g. BTC/USD
@@ -529,11 +531,11 @@ class IFreqaiModel(ABC):
:return:
:boolean: whether the model file exists or not.
"""
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
file_exists = path_to_modelfile.is_file()
if file_exists and not scanning:
if file_exists:
logger.info("Found model at %s", dk.data_path / dk.model_filename)
elif not scanning:
else:
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
return file_exists
@@ -580,16 +582,16 @@ class IFreqaiModel(ABC):
# find the features indicated by strategy and store in datakitchen
dk.find_features(unfiltered_dataframe)
# import pytest
# pytest.set_trace()
dk.find_labels(unfiltered_dataframe)
model = self.train(unfiltered_dataframe, pair, dk)
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
dk.set_new_model_names(pair, new_trained_timerange)
self.dd.save_data(model, pair, dk)
if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
plot_feature_importance(model, pair, dk)
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()

2
freqtrade/freqai/utils.py
View File

@@ -170,7 +170,7 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
# Data preparation
fi_df = pd.DataFrame({
"feature_names": np.array(dk.training_features_list),
"feature_names": np.array(dk.data_dictionary['train_features'].columns),
"feature_importance": np.array(feature_importance)
})
fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]

56
freqtrade/freqtradebot.py
View File

@@ -29,6 +29,7 @@ from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager
from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.util import FtPrecise
@@ -72,6 +73,8 @@ class FreqtradeBot(LoggingMixin):
PairLocks.timeframe = self.config['timeframe']
self.pairlists = PairListManager(self.exchange, self.config)
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
@@ -79,9 +82,10 @@ class FreqtradeBot(LoggingMixin):
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
self.pairlists = PairListManager(self.exchange, self.config)
self.dataprovider = DataProvider(self.config, self.exchange, rpc=self.rpc)
self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
self.dataprovider.add_pairlisthandler(self.pairlists)
# Attach Dataprovider to strategy instance
self.strategy.dp = self.dataprovider
@@ -92,6 +96,10 @@ class FreqtradeBot(LoggingMixin):
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
# Init ExternalMessageConsumer if enabled
self.emc = ExternalMessageConsumer(self.config, self.dataprovider) if \
self.config.get('external_message_consumer', {}).get('enabled', False) else None
self.active_pair_whitelist = self._refresh_active_whitelist()
# Set initial bot state from config
@@ -151,9 +159,11 @@ class FreqtradeBot(LoggingMixin):
finally:
self.strategy.ft_bot_cleanup()
self.rpc.cleanup()
Trade.commit()
self.exchange.close()
self.rpc.cleanup()
if self.emc:
self.emc.shutdown()
Trade.commit()
self.exchange.close()
def startup(self) -> None:
"""
@@ -254,6 +264,7 @@ class FreqtradeBot(LoggingMixin):
pairs that have open trades.
"""
# Refresh whitelist
_prev_whitelist = self.pairlists.whitelist
self.pairlists.refresh_pairlist()
_whitelist = self.pairlists.whitelist
@@ -266,6 +277,11 @@ class FreqtradeBot(LoggingMixin):
# Extend active-pair whitelist with pairs of open trades
# It ensures that candle (OHLCV) data are downloaded for open trades as well
_whitelist.extend([trade.pair for trade in trades if trade.pair not in _whitelist])
# Called last to include the included pairs
if _prev_whitelist != _whitelist:
self.rpc.send_msg({'type': RPCMessageType.WHITELIST, 'data': _whitelist})
return _whitelist
def get_free_open_trades(self) -> int:
@@ -584,7 +600,7 @@ class FreqtradeBot(LoggingMixin):
# We should decrease our position
amount = self.exchange.amount_to_contract_precision(
trade.pair,
abs(float(FtPrecise(stake_amount) / FtPrecise(current_exit_rate))))
abs(float(FtPrecise(stake_amount * trade.leverage) / FtPrecise(current_exit_rate))))
if amount > trade.amount:
# This is currently ineffective as remaining would become < min tradable
# Fixing this would require checking for 0.0 there -
@@ -1327,11 +1343,12 @@ class FreqtradeBot(LoggingMixin):
replacing: Optional[bool] = False
) -> bool:
"""
Buy cancel - cancel order
entry cancel - cancel order
:param replacing: Replacing order - prevent trade deletion.
:return: True if order was fully cancelled
:return: True if trade was fully cancelled
"""
was_trade_fully_canceled = False
side = trade.entry_side.capitalize()
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in constants.NON_OPEN_EXCHANGE_STATES:
@@ -1358,7 +1375,6 @@ class FreqtradeBot(LoggingMixin):
corder = order
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
side = trade.entry_side.capitalize()
logger.info('%s order %s for %s.', side, reason, trade)
# Using filled to determine the filled amount
@@ -1372,24 +1388,13 @@ class FreqtradeBot(LoggingMixin):
was_trade_fully_canceled = True
reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
else:
# FIXME TODO: This could possibly reworked to not duplicate the code 15 lines below.
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info(f'{side} Order timeout for {trade}.')
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
# cancel_order may not contain the full order dict, so we need to fallback
# to the order dict acquired before cancelling.
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = filled_amount
# * Check edge cases, we don't want to make leverage > 1.0 if we don't have to
# * (for leverage modes which aren't isolated futures)
trade.stake_amount = trade.amount * trade.open_rate / trade.leverage
# update_trade_state (and subsequently recalc_trade_from_orders) will handle updates
# to the trade object
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info(f'Partial {trade.entry_side} order timeout for {trade}.')
reason += f", {constants.CANCEL_REASON['PARTIALLY_FILLED']}"
@@ -1426,8 +1431,6 @@ class FreqtradeBot(LoggingMixin):
trade.close_rate_requested = None
trade.close_profit = None
trade.close_profit_abs = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
trade.exit_reason = None
cancelled = True
@@ -1687,11 +1690,6 @@ class FreqtradeBot(LoggingMixin):
'stake_amount': trade.stake_amount,
}
if 'fiat_display_currency' in self.config:
msg.update({
'fiat_currency': self.config['fiat_display_currency'],
})
# Send the message
self.rpc.send_msg(msg)

40
freqtrade/misc.py
View File

@@ -10,9 +10,11 @@ from typing import Any, Iterator, List
from typing.io import IO
from urllib.parse import urlparse
import pandas
import rapidjson
from freqtrade.constants import DECIMAL_PER_COIN_FALLBACK, DECIMALS_PER_COIN
from freqtrade.enums import SignalTagType, SignalType
logger = logging.getLogger(__name__)
@@ -249,3 +251,41 @@ def parse_db_uri_for_logging(uri: str):
return uri
pwd = parsed_db_uri.netloc.split(':')[1].split('@')[0]
return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
def dataframe_to_json(dataframe: pandas.DataFrame) -> str:
"""
Serialize a DataFrame for transmission over the wire using JSON
:param dataframe: A pandas DataFrame
:returns: A JSON string of the pandas DataFrame
"""
return dataframe.to_json(orient='split')
def json_to_dataframe(data: str) -> pandas.DataFrame:
"""
Deserialize JSON into a DataFrame
:param data: A JSON string
:returns: A pandas DataFrame from the JSON string
"""
dataframe = pandas.read_json(data, orient='split')
if 'date' in dataframe.columns:
dataframe['date'] = pandas.to_datetime(dataframe['date'], unit='ms', utc=True)
return dataframe
def remove_entry_exit_signals(dataframe: pandas.DataFrame):
"""
Remove Entry and Exit signals from a DataFrame
:param dataframe: The DataFrame to remove signals from
"""
dataframe[SignalType.ENTER_LONG.value] = 0
dataframe[SignalType.EXIT_LONG.value] = 0
dataframe[SignalType.ENTER_SHORT.value] = 0
dataframe[SignalType.EXIT_SHORT.value] = 0
dataframe[SignalTagType.ENTER_TAG.value] = None
dataframe[SignalTagType.EXIT_TAG.value] = None
return dataframe

26
freqtrade/optimize/backtesting.py
View File

@@ -91,8 +91,8 @@ class Backtesting:
if self.config.get('strategy_list'):
if self.config.get('freqai', {}).get('enabled', False):
raise OperationalException(
"You can't use strategy_list and freqai at the same time.")
logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "
"to have identical populate_any_indicators.")
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@@ -110,10 +110,10 @@ class Backtesting:
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
self.init_backtest_detail()
self.pairlists = PairListManager(self.exchange, self.config)
self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting. "
"Please use StaticPairlist instead.")
"Please use StaticPairList instead.")
if 'PerformanceFilter' in self.pairlists.name_list:
raise OperationalException("PerformanceFilter not allowed for backtesting.")
@@ -139,9 +139,14 @@ class Backtesting:
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
if self.config.get('freqai', {}).get('enabled', False):
# For FreqAI, increase the required_startup to includes the training data
self.required_startup = self.dataprovider.get_required_startup(self.timeframe)
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
# strategies which define "can_short=True" will fail to load in Spot mode.
@@ -149,9 +154,6 @@ class Backtesting:
self.init_backtest()
def __del__(self):
self.cleanup()
@staticmethod
def cleanup():
LoggingMixin.show_output = True
@@ -217,7 +219,7 @@ class Backtesting:
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=self.timerange,
startup_candles=self.dataprovider.get_required_startup(self.timeframe),
startup_candles=self.config['startup_candle_count'],
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
@@ -368,10 +370,10 @@ class Backtesting:
for col in HEADERS[5:]:
tag_col = col in ('enter_tag', 'exit_tag')
if col in df_analyzed.columns:
df_analyzed.loc[:, col] = df_analyzed.loc[:, col].replace(
df_analyzed[col] = df_analyzed.loc[:, col].replace(
[nan], [0 if not tag_col else None]).shift(1)
elif not df_analyzed.empty:
df_analyzed.loc[:, col] = 0 if not tag_col else None
df_analyzed[col] = 0 if not tag_col else None
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
@@ -538,7 +540,7 @@ class Backtesting:
if stake_amount is not None and stake_amount < 0.0:
amount = amount_to_contract_precision(
abs(stake_amount) / current_rate, trade.amount_precision,
abs(stake_amount * trade.leverage) / current_rate, trade.amount_precision,
self.precision_mode, trade.contract_size)
if amount == 0.0:
return trade

2
freqtrade/optimize/hyperopt.py
View File

@@ -61,7 +61,7 @@ class Hyperopt:
"""
Hyperopt class, this class contains all the logic to run a hyperopt simulation
To run a backtest:
To start a hyperopt run:
hyperopt = Hyperopt(config)
hyperopt.start()
"""

6
freqtrade/optimize/optimize_reports.py
View File

@@ -173,7 +173,7 @@ def generate_tag_metrics(tag_type: str,
tabular_data = []
if tag_type in results.columns:
for tag, count in results[tag_type].value_counts().iteritems():
for tag, count in results[tag_type].value_counts().items():
result = results[results[tag_type] == tag]
if skip_nan and result['profit_abs'].isnull().all():
continue
@@ -199,7 +199,7 @@ def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List
"""
tabular_data = []
for reason, count in results['exit_reason'].value_counts().iteritems():
for reason, count in results['exit_reason'].value_counts().items():
result = results.loc[results['exit_reason'] == reason]
profit_mean = result['profit_ratio'].mean()
@@ -361,7 +361,7 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
winning_days = sum(daily_profit > 0)
draw_days = sum(daily_profit == 0)
losing_days = sum(daily_profit < 0)
daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.iteritems()]
daily_profit_list = [(str(idx.date()), val) for idx, val in daily_profit.items()]
return {
'backtest_best_day': best_rel,

90
freqtrade/plugins/pairlist/ProducerPairList.py Normal file
View File

@@ -0,0 +1,90 @@
"""
External Pair List provider
Provides pair list from Leader data
"""
import logging
from typing import Any, Dict, List, Optional
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class ProducerPairList(IPairList):
"""
PairList plugin for use with external_message_consumer.
Will use pairs given from leader data.
Usage:
"pairlists": [
{
"method": "ProducerPairList",
"number_assets": 5,
"producer_name": "default",
}
],
"""
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._num_assets: int = self._pairlistconfig.get('number_assets', 0)
self._producer_name = self._pairlistconfig.get('producer_name', 'default')
if not config.get('external_message_consumer', {}).get('enabled'):
raise OperationalException(
"ProducerPairList requires external_message_consumer to be enabled.")
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requires tickers, an empty Dict is passed
as tickers argument to filter_pairlist
"""
return False
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
-> Please overwrite in subclasses
"""
return f"{self.name} - {self._producer_name}"
def _filter_pairlist(self, pairlist: Optional[List[str]]):
upstream_pairlist = self._pairlistmanager._dataprovider.get_producer_pairs(
self._producer_name)
if pairlist is None:
pairlist = self._pairlistmanager._dataprovider.get_producer_pairs(self._producer_name)
pairs = list(dict.fromkeys(pairlist + upstream_pairlist))
if self._num_assets:
pairs = pairs[:self._num_assets]
return pairs
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: List of pairs
"""
pairs = self._filter_pairlist(None)
self.log_once(f"Received pairs: {pairs}", logger.debug)
pairs = self._whitelist_for_active_markets(self.verify_whitelist(pairs, logger.info))
return pairs
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new whitelist
"""
return self._filter_pairlist(pairlist)

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

@@ -232,6 +232,4 @@ class VolumePairList(IPairList):
# Limit pairlist to the requested number of pairs
pairs = pairs[:self._number_pairs]
self.log_once(f"Searching {self._number_pairs} pairs: {pairs}", logger.info)
return pairs

8
freqtrade/plugins/pairlistmanager.py
View File

@@ -3,11 +3,12 @@ PairList manager class
"""
import logging
from functools import partial
from typing import Dict, List
from typing import Dict, List, Optional
from cachetools import TTLCache, cached
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import CandleType
from freqtrade.exceptions import OperationalException
from freqtrade.mixins import LoggingMixin
@@ -21,13 +22,14 @@ logger = logging.getLogger(__name__)
class PairListManager(LoggingMixin):
def __init__(self, exchange, config: Config) -> None:
def __init__(self, exchange, config: Config, dataprovider: DataProvider = None) -> None:
self._exchange = exchange
self._config = config
self._whitelist = self._config['exchange'].get('pair_whitelist')
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
self._pairlist_handlers: List[IPairList] = []
self._tickers_needed = False
self._dataprovider: Optional[DataProvider] = dataprovider
for pairlist_handler_config in self._config.get('pairlists', []):
pairlist_handler = PairListResolver.load_pairlist(
pairlist_handler_config['method'],
@@ -96,6 +98,8 @@ class PairListManager(LoggingMixin):
# to ensure blacklist is respected.
pairlist = self.verify_blacklist(pairlist, logger.warning)
self.log_once(f"Whitelist with {len(pairlist)} pairs: {pairlist}", logger.info)
self._whitelist = pairlist
def verify_blacklist(self, pairlist: List[str], logmethod) -> List[str]:

47
freqtrade/rpc/api_server/api_auth.py
View File

@@ -1,8 +1,10 @@
import logging
import secrets
from datetime import datetime, timedelta
from typing import Any, Dict, Union
import jwt
from fastapi import APIRouter, Depends, HTTPException, status
from fastapi import APIRouter, Depends, HTTPException, Query, WebSocket, status
from fastapi.security import OAuth2PasswordBearer
from fastapi.security.http import HTTPBasic, HTTPBasicCredentials
@@ -10,6 +12,8 @@ from freqtrade.rpc.api_server.api_schemas import AccessAndRefreshToken, AccessTo
from freqtrade.rpc.api_server.deps import get_api_config
logger = logging.getLogger(__name__)
ALGORITHM = "HS256"
router_login = APIRouter()
@@ -25,7 +29,7 @@ httpbasic = HTTPBasic(auto_error=False)
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
def get_user_from_token(token, secret_key: str, token_type: str = "access"):
def get_user_from_token(token, secret_key: str, token_type: str = "access") -> str:
credentials_exception = HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Could not validate credentials",
@@ -44,6 +48,45 @@ def get_user_from_token(token, secret_key: str, token_type: str = "access"):
return username
# This should be reimplemented to better realign with the existing tools provided
# by FastAPI regarding API Tokens
# https://github.com/tiangolo/fastapi/blob/master/fastapi/security/api_key.py
async def validate_ws_token(
ws: WebSocket,
ws_token: Union[str, None] = Query(default=None, alias="token"),
api_config: Dict[str, Any] = Depends(get_api_config)
):
secret_ws_token = api_config.get('ws_token', None)
secret_jwt_key = api_config.get('jwt_secret_key', 'super-secret')
# Check if ws_token is/in secret_ws_token
if ws_token and secret_ws_token:
is_valid_ws_token = False
if isinstance(secret_ws_token, str):
is_valid_ws_token = secrets.compare_digest(secret_ws_token, ws_token)
elif isinstance(secret_ws_token, list):
is_valid_ws_token = any([
secrets.compare_digest(potential, ws_token)
for potential in secret_ws_token
])
if is_valid_ws_token:
return ws_token
# Check if ws_token is a JWT
try:
user = get_user_from_token(ws_token, secret_jwt_key)
return user
# If the token is a jwt, and it's valid return the user
except HTTPException:
pass
# No checks passed, deny the connection
logger.debug("Denying websocket request.")
# If it doesn't match, close the websocket connection
await ws.close(code=status.WS_1008_POLICY_VIOLATION)
def create_token(data: dict, secret_key: str, token_type: str = "access") -> str:
to_encode = data.copy()
if token_type == "access":

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

@@ -5,6 +5,7 @@ from datetime import datetime
from typing import Any, Dict, List
from fastapi import APIRouter, BackgroundTasks, Depends
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
@@ -31,6 +32,9 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
if ApiServer._bgtask_running:
raise RPCException('Bot Background task already running')
if ':' in bt_settings.strategy:
raise HTTPException(status_code=500, detail="base64 encoded strategies are not allowed.")
btconfig = deepcopy(config)
settings = dict(bt_settings)
# Pydantic models will contain all keys, but non-provided ones are None

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

@@ -38,7 +38,8 @@ logger = logging.getLogger(__name__)
# 2.15: Add backtest history endpoints
# 2.16: Additional daily metrics
# 2.17: Forceentry - leverage, partial force_exit
API_VERSION = 2.17
# 2.20: Add websocket endpoints
API_VERSION = 2.20
# Public API, requires no auth.
router_public = APIRouter()
@@ -264,6 +265,8 @@ def list_strategies(config=Depends(get_config)):
@router.get('/strategy/{strategy}', response_model=StrategyResponse, tags=['strategy'])
def get_strategy(strategy: str, config=Depends(get_config)):
if ":" in strategy:
raise HTTPException(status_code=500, detail="base64 encoded strategies are not allowed.")
config_ = deepcopy(config)
from freqtrade.resolvers.strategy_resolver import StrategyResolver

140
freqtrade/rpc/api_server/api_ws.py Normal file
View File

@@ -0,0 +1,140 @@
import logging
from typing import Any, Dict
from fastapi import APIRouter, Depends, WebSocketDisconnect
from fastapi.websockets import WebSocket, WebSocketState
from pydantic import ValidationError
from freqtrade.enums import RPCMessageType, RPCRequestType
from freqtrade.rpc.api_server.api_auth import validate_ws_token
from freqtrade.rpc.api_server.deps import get_channel_manager, get_rpc
from freqtrade.rpc.api_server.ws import WebSocketChannel
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
WSRequestSchema, WSWhitelistMessage)
from freqtrade.rpc.rpc import RPC
logger = logging.getLogger(__name__)
# Private router, protected by API Key authentication
router = APIRouter()
async def is_websocket_alive(ws: WebSocket) -> bool:
"""
Check if a FastAPI Websocket is still open
"""
if (
ws.application_state == WebSocketState.CONNECTED and
ws.client_state == WebSocketState.CONNECTED
):
return True
return False
async def _process_consumer_request(
request: Dict[str, Any],
channel: WebSocketChannel,
rpc: RPC
):
"""
Validate and handle a request from a websocket consumer
"""
# Validate the request, makes sure it matches the schema
try:
websocket_request = WSRequestSchema.parse_obj(request)
except ValidationError as e:
logger.error(f"Invalid request from {channel}: {e}")
return
type, data = websocket_request.type, websocket_request.data
response: WSMessageSchema
logger.debug(f"Request of type {type} from {channel}")
# If we have a request of type SUBSCRIBE, set the topics in this channel
if type == RPCRequestType.SUBSCRIBE:
# If the request is empty, do nothing
if not data:
return
# If all topics passed are a valid RPCMessageType, set subscriptions on channel
if all([any(x.value == topic for x in RPCMessageType) for topic in data]):
channel.set_subscriptions(data)
# We don't send a response for subscriptions
return
elif type == RPCRequestType.WHITELIST:
# Get whitelist
whitelist = rpc._ws_request_whitelist()
# Format response
response = WSWhitelistMessage(data=whitelist)
# Send it back
await channel.send(response.dict(exclude_none=True))
elif type == RPCRequestType.ANALYZED_DF:
limit = None
if data:
# Limit the amount of candles per dataframe to 'limit' or 1500
limit = max(data.get('limit', 1500), 1500)
# They requested the full historical analyzed dataframes
analyzed_df = rpc._ws_request_analyzed_df(limit)
# For every dataframe, send as a separate message
for _, message in analyzed_df.items():
response = WSAnalyzedDFMessage(data=message)
await channel.send(response.dict(exclude_none=True))
@router.websocket("/message/ws")
async def message_endpoint(
ws: WebSocket,
rpc: RPC = Depends(get_rpc),
channel_manager=Depends(get_channel_manager),
token: str = Depends(validate_ws_token)
):
"""
Message WebSocket endpoint, facilitates sending RPC messages
"""
try:
channel = await channel_manager.on_connect(ws)
if await is_websocket_alive(ws):
logger.info(f"Consumer connected - {channel}")
# Keep connection open until explicitly closed, and process requests
try:
while not channel.is_closed():
request = await channel.recv()
# Process the request here
await _process_consumer_request(request, channel, rpc)
except WebSocketDisconnect:
# Handle client disconnects
logger.info(f"Consumer disconnected - {channel}")
await channel_manager.on_disconnect(ws)
except Exception as e:
logger.info(f"Consumer connection failed - {channel}")
logger.exception(e)
# Handle cases like -
# RuntimeError('Cannot call "send" once a closed message has been sent')
await channel_manager.on_disconnect(ws)
else:
await ws.close()
except RuntimeError:
# WebSocket was closed
await channel_manager.on_disconnect(ws)
except Exception as e:
logger.error(f"Failed to serve - {ws.client}")
# Log tracebacks to keep track of what errors are happening
logger.exception(e)
await channel_manager.on_disconnect(ws)

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

@@ -41,6 +41,10 @@ def get_exchange(config=Depends(get_config)):
return ApiServer._exchange
def get_channel_manager():
return ApiServer._ws_channel_manager
def is_webserver_mode(config=Depends(get_config)):
if config['runmode'] != RunMode.WEBSERVER:
raise RPCException('Bot is not in the correct state')

77
freqtrade/rpc/api_server/webserver.py
View File

@@ -1,16 +1,21 @@
import asyncio
import logging
from ipaddress import IPv4Address
from threading import Thread
from typing import Any, Dict
import orjson
import uvicorn
from fastapi import Depends, FastAPI
from fastapi.middleware.cors import CORSMiddleware
# Look into alternatives
from janus import Queue as ThreadedQueue
from starlette.responses import JSONResponse
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer
from freqtrade.rpc.api_server.ws import ChannelManager
from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
@@ -44,6 +49,10 @@ class ApiServer(RPCHandler):
_config: Config = {}
# Exchange - only available in webserver mode.
_exchange = None
# websocket message queue stuff
_ws_channel_manager = None
_ws_thread = None
_ws_loop = None
def __new__(cls, *args, **kwargs):
"""
@@ -61,17 +70,21 @@ class ApiServer(RPCHandler):
return
self._standalone: bool = standalone
self._server = None
self._ws_queue = None
self._ws_background_task = None
ApiServer.__initialized = True
api_config = self._config['api_server']
ApiServer._ws_channel_manager = ChannelManager()
self.app = FastAPI(title="Freqtrade API",
docs_url='/docs' if api_config.get('enable_openapi', False) else None,
redoc_url=None,
default_response_class=FTJSONResponse,
)
self.configure_app(self.app, self._config)
self.start_api()
def add_rpc_handler(self, rpc: RPC):
@@ -93,6 +106,19 @@ class ApiServer(RPCHandler):
logger.info("Stopping API Server")
self._server.cleanup()
if self._ws_thread and self._ws_loop:
logger.info("Stopping API Server background tasks")
if self._ws_background_task:
# Cancel the queue task
self._ws_background_task.cancel()
self._ws_thread.join()
self._ws_thread = None
self._ws_loop = None
self._ws_background_task = None
@classmethod
def shutdown(cls):
cls.__initialized = False
@@ -102,7 +128,9 @@ class ApiServer(RPCHandler):
cls._rpc = None
def send_msg(self, msg: Dict[str, str]) -> None:
pass
if self._ws_queue:
sync_q = self._ws_queue.sync_q
sync_q.put(msg)
def handle_rpc_exception(self, request, exc):
logger.exception(f"API Error calling: {exc}")
@@ -116,6 +144,7 @@ class ApiServer(RPCHandler):
from freqtrade.rpc.api_server.api_backtest import router as api_backtest
from freqtrade.rpc.api_server.api_v1 import router as api_v1
from freqtrade.rpc.api_server.api_v1 import router_public as api_v1_public
from freqtrade.rpc.api_server.api_ws import router as ws_router
from freqtrade.rpc.api_server.web_ui import router_ui
app.include_router(api_v1_public, prefix="/api/v1")
@@ -126,6 +155,7 @@ class ApiServer(RPCHandler):
app.include_router(api_backtest, prefix="/api/v1",
dependencies=[Depends(http_basic_or_jwt_token)],
)
app.include_router(ws_router, prefix="/api/v1")
app.include_router(router_login, prefix="/api/v1", tags=["auth"])
# UI Router MUST be last!
app.include_router(router_ui, prefix='')
@@ -140,6 +170,48 @@ class ApiServer(RPCHandler):
app.add_exception_handler(RPCException, self.handle_rpc_exception)
def start_message_queue(self):
if self._ws_thread:
return
# Create a new loop, as it'll be just for the background thread
self._ws_loop = asyncio.new_event_loop()
# Start the thread
self._ws_thread = Thread(target=self._ws_loop.run_forever)
self._ws_thread.start()
# Finally, submit the coro to the thread
self._ws_background_task = asyncio.run_coroutine_threadsafe(
self._broadcast_queue_data(), loop=self._ws_loop)
async def _broadcast_queue_data(self):
# Instantiate the queue in this coroutine so it's attached to our loop
self._ws_queue = ThreadedQueue()
async_queue = self._ws_queue.async_q
try:
while True:
logger.debug("Getting queue messages...")
# Get data from queue
message = await async_queue.get()
logger.debug(f"Found message of type: {message.get('type')}")
# Broadcast it
await self._ws_channel_manager.broadcast(message)
# Sleep, make this configurable?
await asyncio.sleep(0.1)
except asyncio.CancelledError:
pass
# For testing, shouldn't happen when stable
except Exception as e:
logger.exception(f"Exception happened in background task: {e}")
finally:
# Disconnect channels and stop the loop on cancel
await self._ws_channel_manager.disconnect_all()
self._ws_loop.stop()
def start_api(self):
"""
Start API ... should be run in thread.
@@ -177,6 +249,7 @@ class ApiServer(RPCHandler):
if self._standalone:
self._server.run()
else:
self.start_message_queue()
self._server.run_in_thread()
except Exception:
logger.exception("Api server failed to start.")

6
freqtrade/rpc/api_server/ws/__init__.py Normal file
View File

@@ -0,0 +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 ChannelManager, WebSocketChannel

178
freqtrade/rpc/api_server/ws/channel.py Normal file
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@@ -0,0 +1,178 @@
import logging
from threading import RLock
from typing import List, Optional, Type
from uuid import uuid4
from fastapi import WebSocket as FastAPIWebSocket
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
from freqtrade.rpc.api_server.ws.serializer import (HybridJSONWebSocketSerializer,
WebSocketSerializer)
from freqtrade.rpc.api_server.ws.types import WebSocketType
logger = logging.getLogger(__name__)
class WebSocketChannel:
"""
Object to help facilitate managing a websocket connection
"""
def __init__(
self,
websocket: WebSocketType,
channel_id: Optional[str] = None,
serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
):
self.channel_id = channel_id if channel_id else uuid4().hex[:8]
# The WebSocket object
self._websocket = WebSocketProxy(websocket)
# The Serializing class for the WebSocket object
self._serializer_cls = serializer_cls
self._subscriptions: List[str] = []
# Internal event to signify a closed websocket
self._closed = False
# Wrap the WebSocket in the Serializing class
self._wrapped_ws = self._serializer_cls(self._websocket)
def __repr__(self):
return f"WebSocketChannel({self.channel_id}, {self.remote_addr})"
@property
def remote_addr(self):
return self._websocket.remote_addr
async def send(self, data):
"""
Send data on the wrapped websocket
"""
await self._wrapped_ws.send(data)
async def recv(self):
"""
Receive data on the wrapped websocket
"""
return await self._wrapped_ws.recv()
async def ping(self):
"""
Ping the websocket
"""
return await self._websocket.ping()
async def close(self):
"""
Close the WebSocketChannel
"""
self._closed = True
def is_closed(self) -> bool:
"""
Closed flag
"""
return self._closed
def set_subscriptions(self, subscriptions: List[str] = []) -> None:
"""
Set which subscriptions this channel is subscribed to
:param subscriptions: List of subscriptions, List[str]
"""
self._subscriptions = subscriptions
def subscribed_to(self, message_type: str) -> bool:
"""
Check if this channel is subscribed to the message_type
:param message_type: The message type to check
"""
return message_type in self._subscriptions
class ChannelManager:
def __init__(self):
self.channels = dict()
self._lock = RLock() # Re-entrant Lock
async def on_connect(self, websocket: WebSocketType):
"""
Wrap websocket connection into Channel and add to list
:param websocket: The WebSocket object to attach to the Channel
"""
if isinstance(websocket, FastAPIWebSocket):
try:
await websocket.accept()
except RuntimeError:
# The connection was closed before we could accept it
return
ws_channel = WebSocketChannel(websocket)
with self._lock:
self.channels[websocket] = ws_channel
return ws_channel
async def on_disconnect(self, websocket: WebSocketType):
"""
Call close on the channel if it's not, and remove from channel list
:param websocket: The WebSocket objet attached to the Channel
"""
with self._lock:
channel = self.channels.get(websocket)
if channel:
if not channel.is_closed():
await channel.close()
del self.channels[websocket]
async def disconnect_all(self):
"""
Disconnect all Channels
"""
with self._lock:
for websocket, channel in self.channels.copy().items():
if not channel.is_closed():
await channel.close()
self.channels = dict()
async def broadcast(self, data):
"""
Broadcast data on all Channels
:param data: The data to send
"""
with self._lock:
message_type = data.get('type')
for websocket, channel in self.channels.copy().items():
try:
if channel.subscribed_to(message_type):
await channel.send(data)
except RuntimeError:
# Handle cannot send after close cases
await self.on_disconnect(websocket)
async def send_direct(self, channel, data):
"""
Send data directly through direct_channel only
:param direct_channel: The WebSocketChannel object to send data through
:param data: The data to send
"""
await channel.send(data)
def has_channels(self):
"""
Flag for more than 0 channels
"""
return len(self.channels) > 0

69
freqtrade/rpc/api_server/ws/proxy.py Normal file
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@@ -0,0 +1,69 @@
from typing import Any, Tuple, Union
from fastapi import WebSocket as FastAPIWebSocket
from websockets.client import WebSocketClientProtocol as WebSocket
from freqtrade.rpc.api_server.ws.types import WebSocketType
class WebSocketProxy:
"""
WebSocketProxy object to bring the FastAPIWebSocket and websockets.WebSocketClientProtocol
under the same API
"""
def __init__(self, websocket: WebSocketType):
self._websocket: Union[FastAPIWebSocket, WebSocket] = websocket
@property
def remote_addr(self) -> Tuple[Any, ...]:
if isinstance(self._websocket, WebSocket):
return self._websocket.remote_address
elif isinstance(self._websocket, FastAPIWebSocket):
if self._websocket.client:
client, port = self._websocket.client.host, self._websocket.client.port
return (client, port)
return ("unknown", 0)
async def send(self, data):
"""
Send data on the wrapped websocket
"""
if hasattr(self._websocket, "send_text"):
await self._websocket.send_text(data)
else:
await self._websocket.send(data)
async def recv(self):
"""
Receive data on the wrapped websocket
"""
if hasattr(self._websocket, "receive_text"):
return await self._websocket.receive_text()
else:
return await self._websocket.recv()
async def ping(self):
"""
Ping the websocket, not supported by FastAPI WebSockets
"""
if hasattr(self._websocket, "ping"):
return await self._websocket.ping()
return False
async def close(self, code: int = 1000):
"""
Close the websocket connection, only supported by FastAPI WebSockets
"""
if hasattr(self._websocket, "close"):
try:
return await self._websocket.close(code)
except RuntimeError:
pass
async def accept(self):
"""
Accept the WebSocket connection, only support by FastAPI WebSockets
"""
if hasattr(self._websocket, "accept"):
return await self._websocket.accept()

62
freqtrade/rpc/api_server/ws/serializer.py Normal file
View File

@@ -0,0 +1,62 @@
import logging
from abc import ABC, abstractmethod
import orjson
import rapidjson
from pandas import DataFrame
from freqtrade.misc import dataframe_to_json, json_to_dataframe
from freqtrade.rpc.api_server.ws.proxy import WebSocketProxy
logger = logging.getLogger(__name__)
class WebSocketSerializer(ABC):
def __init__(self, websocket: WebSocketProxy):
self._websocket: WebSocketProxy = websocket
@abstractmethod
def _serialize(self, data):
raise NotImplementedError()
@abstractmethod
def _deserialize(self, data):
raise NotImplementedError()
async def send(self, data: bytes):
await self._websocket.send(self._serialize(data))
async def recv(self) -> bytes:
data = await self._websocket.recv()
return self._deserialize(data)
async def close(self, code: int = 1000):
await self._websocket.close(code)
class HybridJSONWebSocketSerializer(WebSocketSerializer):
def _serialize(self, data) -> str:
return str(orjson.dumps(data, default=_json_default), "utf-8")
def _deserialize(self, data: str):
# RapidJSON expects strings
return rapidjson.loads(data, object_hook=_json_object_hook)
# Support serializing pandas DataFrames
def _json_default(z):
if isinstance(z, DataFrame):
return {
'__type__': 'dataframe',
'__value__': dataframe_to_json(z)
}
raise TypeError
# Support deserializing JSON to pandas DataFrames
def _json_object_hook(z):
if z.get('__type__') == 'dataframe':
return json_to_dataframe(z.get('__value__'))
return z

8
freqtrade/rpc/api_server/ws/types.py Normal file
View File

@@ -0,0 +1,8 @@
from typing import Any, Dict, TypeVar
from fastapi import WebSocket as FastAPIWebSocket
from websockets.client import WebSocketClientProtocol as WebSocket
WebSocketType = TypeVar("WebSocketType", FastAPIWebSocket, WebSocket)
MessageType = Dict[str, Any]

63
freqtrade/rpc/api_server/ws_schemas.py Normal file
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@@ -0,0 +1,63 @@
from datetime import datetime
from typing import Any, Dict, List, Optional
from pandas import DataFrame
from pydantic import BaseModel
from freqtrade.constants import PairWithTimeframe
from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
class BaseArbitraryModel(BaseModel):
class Config:
arbitrary_types_allowed = True
class WSRequestSchema(BaseArbitraryModel):
type: RPCRequestType
data: Optional[Any] = None
class WSMessageSchema(BaseArbitraryModel):
type: RPCMessageType
data: Optional[Any] = None
class Config:
extra = 'allow'
# ------------------------------ REQUEST SCHEMAS ----------------------------
class WSSubscribeRequest(WSRequestSchema):
type: RPCRequestType = RPCRequestType.SUBSCRIBE
data: List[RPCMessageType]
class WSWhitelistRequest(WSRequestSchema):
type: RPCRequestType = RPCRequestType.WHITELIST
data: None = None
class WSAnalyzedDFRequest(WSRequestSchema):
type: RPCRequestType = RPCRequestType.ANALYZED_DF
data: Dict[str, Any] = {"limit": 1500}
# ------------------------------ MESSAGE SCHEMAS ----------------------------
class WSWhitelistMessage(WSMessageSchema):
type: RPCMessageType = RPCMessageType.WHITELIST
data: List[str]
class WSAnalyzedDFMessage(WSMessageSchema):
class AnalyzedDFData(BaseArbitraryModel):
key: PairWithTimeframe
df: DataFrame
la: datetime
type: RPCMessageType = RPCMessageType.ANALYZED_DF
data: AnalyzedDFData
# --------------------------------------------------------------------------

2
freqtrade/rpc/discord.py
View File

@@ -30,9 +30,9 @@ class Discord(Webhook):
pass
def send_msg(self, msg) -> None:
logger.info(f"Sending discord message: {msg}")
if msg['type'].value in self.config['discord']:
logger.info(f"Sending discord message: {msg}")
msg['strategy'] = self.strategy
msg['timeframe'] = self.timeframe

335
freqtrade/rpc/external_message_consumer.py Normal file
View File

@@ -0,0 +1,335 @@
"""
ExternalMessageConsumer module
Main purpose is to connect to external bot's message websocket to consume data
from it
"""
import asyncio
import logging
import socket
from threading import Thread
from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypedDict
import websockets
from pydantic import ValidationError
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RPCMessageType
from freqtrade.misc import remove_entry_exit_signals
from freqtrade.rpc.api_server.ws import WebSocketChannel
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSAnalyzedDFRequest,
WSMessageSchema, WSRequestSchema,
WSSubscribeRequest, WSWhitelistMessage,
WSWhitelistRequest)
if TYPE_CHECKING:
import websockets.connect
class Producer(TypedDict):
name: str
host: str
port: int
ws_token: str
logger = logging.getLogger(__name__)
class ExternalMessageConsumer:
"""
The main controller class for consuming external messages from
other freqtrade bot's
"""
def __init__(
self,
config: Dict[str, Any],
dataprovider: DataProvider
):
self._config = config
self._dp = dataprovider
self._running = False
self._thread = None
self._loop = None
self._main_task = None
self._sub_tasks = None
self._emc_config = self._config.get('external_message_consumer', {})
self.enabled = self._emc_config.get('enabled', False)
self.producers: List[Producer] = self._emc_config.get('producers', [])
self.wait_timeout = self._emc_config.get('wait_timeout', 300) # in seconds
self.ping_timeout = self._emc_config.get('ping_timeout', 10) # in seconds
self.sleep_time = self._emc_config.get('sleep_time', 10) # in seconds
# The amount of candles per dataframe on the initial request
self.initial_candle_limit = self._emc_config.get('initial_candle_limit', 1500)
# Message size limit, in megabytes. Default 8mb, Use bitwise operator << 20 to convert
# as the websockets client expects bytes.
self.message_size_limit = (self._emc_config.get('message_size_limit', 8) << 20)
# Setting these explicitly as they probably shouldn't be changed by a user
# Unless we somehow integrate this with the strategy to allow creating
# callbacks for the messages
self.topics = [RPCMessageType.WHITELIST, RPCMessageType.ANALYZED_DF]
# Allow setting data for each initial request
self._initial_requests: List[WSRequestSchema] = [
WSSubscribeRequest(data=self.topics),
WSWhitelistRequest(),
WSAnalyzedDFRequest()
]
# Specify which function to use for which RPCMessageType
self._message_handlers: Dict[str, Callable[[str, WSMessageSchema], None]] = {
RPCMessageType.WHITELIST: self._consume_whitelist_message,
RPCMessageType.ANALYZED_DF: self._consume_analyzed_df_message,
}
self.start()
def start(self):
"""
Start the main internal loop in another thread to run coroutines
"""
if self._thread and self._loop:
return
logger.info("Starting ExternalMessageConsumer")
self._loop = asyncio.new_event_loop()
self._thread = Thread(target=self._loop.run_forever)
self._running = True
self._thread.start()
self._main_task = asyncio.run_coroutine_threadsafe(self._main(), loop=self._loop)
def shutdown(self):
"""
Shutdown the loop, thread, and tasks
"""
if self._thread and self._loop:
logger.info("Stopping ExternalMessageConsumer")
self._running = False
if self._sub_tasks:
# Cancel sub tasks
for task in self._sub_tasks:
task.cancel()
if self._main_task:
# Cancel the main task
self._main_task.cancel()
self._thread.join()
self._thread = None
self._loop = None
self._sub_tasks = None
self._main_task = None
async def _main(self):
"""
The main task coroutine
"""
lock = asyncio.Lock()
try:
# Create a connection to each producer
self._sub_tasks = [
self._loop.create_task(self._handle_producer_connection(producer, lock))
for producer in self.producers
]
await asyncio.gather(*self._sub_tasks)
except asyncio.CancelledError:
pass
finally:
# Stop the loop once we are done
self._loop.stop()
async def _handle_producer_connection(self, producer: Producer, lock: asyncio.Lock):
"""
Main connection loop for the consumer
:param producer: Dictionary containing producer info
:param lock: An asyncio Lock
"""
try:
await self._create_connection(producer, lock)
except asyncio.CancelledError:
# Exit silently
pass
async def _create_connection(self, producer: Producer, lock: asyncio.Lock):
"""
Actually creates and handles the websocket connection, pinging on timeout
and handling connection errors.
:param producer: Dictionary containing producer info
:param lock: An asyncio Lock
"""
while self._running:
try:
host, port = producer['host'], producer['port']
token = producer['ws_token']
name = producer['name']
ws_url = f"ws://{host}:{port}/api/v1/message/ws?token={token}"
# This will raise InvalidURI if the url is bad
async with websockets.connect(ws_url, max_size=self.message_size_limit) as ws:
channel = WebSocketChannel(ws, channel_id=name)
logger.info(f"Producer connection success - {channel}")
# Now request the initial data from this Producer
for request in self._initial_requests:
await channel.send(
request.dict(exclude_none=True)
)
# Now receive data, if none is within the time limit, ping
await self._receive_messages(channel, producer, lock)
except (websockets.exceptions.InvalidURI, ValueError) as e:
logger.error(f"{ws_url} is an invalid WebSocket URL - {e}")
break
except (
socket.gaierror,
ConnectionRefusedError,
websockets.exceptions.InvalidStatusCode,
websockets.exceptions.InvalidMessage
) as e:
logger.error(f"Connection Refused - {e} retrying in {self.sleep_time}s")
await asyncio.sleep(self.sleep_time)
continue
except (
websockets.exceptions.ConnectionClosedError,
websockets.exceptions.ConnectionClosedOK
):
# Just keep trying to connect again indefinitely
await asyncio.sleep(self.sleep_time)
continue
except Exception as e:
# An unforseen error has occurred, log and continue
logger.error("Unexpected error has occurred:")
logger.exception(e)
continue
async def _receive_messages(
self,
channel: WebSocketChannel,
producer: Producer,
lock: asyncio.Lock
):
"""
Loop to handle receiving messages from a Producer
:param channel: The WebSocketChannel object for the WebSocket
:param producer: Dictionary containing producer info
:param lock: An asyncio Lock
"""
while self._running:
try:
message = await asyncio.wait_for(
channel.recv(),
timeout=self.wait_timeout
)
try:
async with lock:
# Handle the message
self.handle_producer_message(producer, message)
except Exception as e:
logger.exception(f"Error handling producer message: {e}")
except (asyncio.TimeoutError, websockets.exceptions.ConnectionClosed):
# We haven't received data yet. Check the connection and continue.
try:
# ping
ping = await channel.ping()
await asyncio.wait_for(ping, timeout=self.ping_timeout)
logger.debug(f"Connection to {channel} still alive...")
continue
except Exception as e:
logger.warning(f"Ping error {channel} - retrying in {self.sleep_time}s")
logger.debug(e, exc_info=e)
await asyncio.sleep(self.sleep_time)
break
def handle_producer_message(self, producer: Producer, message: Dict[str, Any]):
"""
Handles external messages from a Producer
"""
producer_name = producer.get('name', 'default')
try:
producer_message = WSMessageSchema.parse_obj(message)
except ValidationError as e:
logger.error(f"Invalid message from `{producer_name}`: {e}")
return
if not producer_message.data:
logger.error(f"Empty message received from `{producer_name}`")
return
logger.debug(f"Received message of type `{producer_message.type}` from `{producer_name}`")
message_handler = self._message_handlers.get(producer_message.type)
if not message_handler:
logger.info(f"Received unhandled message: `{producer_message.data}`, ignoring...")
return
message_handler(producer_name, producer_message)
def _consume_whitelist_message(self, producer_name: str, message: WSMessageSchema):
try:
# Validate the message
whitelist_message = WSWhitelistMessage.parse_obj(message)
except ValidationError as e:
logger.error(f"Invalid message from `{producer_name}`: {e}")
return
# Add the pairlist data to the DataProvider
self._dp._set_producer_pairs(whitelist_message.data, producer_name=producer_name)
logger.debug(f"Consumed message from `{producer_name}` of type `RPCMessageType.WHITELIST`")
def _consume_analyzed_df_message(self, producer_name: str, message: WSMessageSchema):
try:
df_message = WSAnalyzedDFMessage.parse_obj(message)
except ValidationError as e:
logger.error(f"Invalid message from `{producer_name}`: {e}")
return
key = df_message.data.key
df = df_message.data.df
la = df_message.data.la
pair, timeframe, candle_type = key
# If set, remove the Entry and Exit signals from the Producer
if self._emc_config.get('remove_entry_exit_signals', False):
df = remove_entry_exit_signals(df)
# Add the dataframe to the dataprovider
self._dp._add_external_df(pair, df,
last_analyzed=la,
timeframe=timeframe,
candle_type=candle_type,
producer_name=producer_name)
logger.debug(
f"Consumed message from `{producer_name}` of type `RPCMessageType.ANALYZED_DF`")

53
freqtrade/rpc/rpc.py
View File

@@ -25,7 +25,7 @@ from freqtrade.exceptions import ExchangeError, PricingError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
from freqtrade.loggers import bufferHandler
from freqtrade.misc import decimals_per_coin, shorten_date
from freqtrade.persistence import PairLocks, Trade
from freqtrade.persistence import Order, PairLocks, Trade
from freqtrade.persistence.models import PairLock
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
@@ -166,9 +166,9 @@ class RPC:
else:
results = []
for trade in trades:
order = None
order: Optional[Order] = None
if trade.open_order_id:
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
order = trade.select_order_by_order_id(trade.open_order_id)
# calculate profit and send message to user
if trade.is_open:
try:
@@ -219,7 +219,7 @@ class RPC:
stoploss_entry_dist=stoploss_entry_dist,
stoploss_entry_dist_ratio=round(stoploss_entry_dist_ratio, 8),
open_order='({} {} rem={:.8f})'.format(
order['type'], order['side'], order['remaining']
order.order_type, order.side, order.remaining
) if order else None,
))
results.append(trade_dict)
@@ -773,6 +773,9 @@ class RPC:
is_short = trade.is_short
if not self._freqtrade.strategy.position_adjustment_enable:
raise RPCException(f'position for {pair} already open - id: {trade.id}')
else:
if Trade.get_open_trade_count() >= self._config['max_open_trades']:
raise RPCException("Maximum number of trades is reached.")
if not stake_amount:
# gen stake amount
@@ -1039,14 +1042,52 @@ class RPC:
def _rpc_analysed_dataframe(self, pair: str, timeframe: str,
limit: Optional[int]) -> Dict[str, Any]:
""" Analyzed dataframe in Dict form """
_data, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
return self._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
pair, timeframe, _data, last_analyzed)
def __rpc_analysed_dataframe_raw(self, pair: str, timeframe: str,
limit: Optional[int]) -> Tuple[DataFrame, datetime]:
""" Get the dataframe and last analyze from the dataprovider """
_data, last_analyzed = self._freqtrade.dataprovider.get_analyzed_dataframe(
pair, timeframe)
_data = _data.copy()
if limit:
_data = _data.iloc[-limit:]
return self._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
pair, timeframe, _data, last_analyzed)
return _data, last_analyzed
def _ws_all_analysed_dataframes(
self,
pairlist: List[str],
limit: Optional[int]
) -> Dict[str, Any]:
""" Get the analysed dataframes of each pair in the pairlist """
timeframe = self._freqtrade.config['timeframe']
candle_type = self._freqtrade.config.get('candle_type_def', CandleType.SPOT)
_data = {}
for pair in pairlist:
dataframe, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
_data[pair] = {
"key": (pair, timeframe, candle_type),
"df": dataframe,
"la": last_analyzed
}
return _data
def _ws_request_analyzed_df(self, limit: Optional[int]):
""" Historical Analyzed Dataframes for WebSocket """
whitelist = self._freqtrade.active_pair_whitelist
return self._ws_all_analysed_dataframes(whitelist, limit)
def _ws_request_whitelist(self):
""" Whitelist data for WebSocket """
return self._freqtrade.active_pair_whitelist
@staticmethod
def _rpc_analysed_history_full(config, pair: str, timeframe: str,

3
freqtrade/rpc/rpc_manager.py
View File

@@ -67,7 +67,8 @@ class RPCManager:
'status': 'stopping bot'
}
"""
logger.info('Sending rpc message: %s', msg)
if msg.get('type') not in (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST):
logger.info('Sending rpc message: %s', msg)
if 'pair' in msg:
msg.update({
'base_currency': self._rpc._freqtrade.exchange.get_pair_base_currency(msg['pair'])

8
freqtrade/rpc/webhook.py
View File

@@ -61,6 +61,14 @@ class Webhook(RPCHandler):
RPCMessageType.STARTUP,
RPCMessageType.WARNING):
valuedict = whconfig.get('webhookstatus')
elif msg['type'] in (
RPCMessageType.PROTECTION_TRIGGER,
RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
RPCMessageType.WHITELIST,
RPCMessageType.ANALYZED_DF,
RPCMessageType.STRATEGY_MSG):
# Don't fail for non-implemented types
return
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
if not valuedict:

18
freqtrade/strategy/interface.py
View File

@@ -16,6 +16,7 @@ from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, Sign
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
from freqtrade.persistence import Order, PairLocks, Trade
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
@@ -742,20 +743,19 @@ class IStrategy(ABC, HyperStrategyMixin):
# always run if process_only_new_candles is set to false
if (not self.process_only_new_candles or
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
# Defs that only make change on new candle data.
dataframe = self.analyze_ticker(dataframe, metadata)
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
self.dp._set_cached_df(
pair, self.timeframe, dataframe,
candle_type=self.config.get('candle_type_def', CandleType.SPOT))
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
self.dp._set_cached_df(pair, self.timeframe, dataframe, candle_type=candle_type)
self.dp._emit_df((pair, self.timeframe, candle_type), dataframe)
else:
logger.debug("Skipping TA Analysis for already analyzed candle")
dataframe[SignalType.ENTER_LONG.value] = 0
dataframe[SignalType.EXIT_LONG.value] = 0
dataframe[SignalType.ENTER_SHORT.value] = 0
dataframe[SignalType.EXIT_SHORT.value] = 0
dataframe[SignalTagType.ENTER_TAG.value] = None
dataframe[SignalTagType.EXIT_TAG.value] = None
dataframe = remove_entry_exit_signals(dataframe)
logger.debug("Loop Analysis Launched")

1
freqtrade/templates/base_config.json.j2
View File

@@ -67,6 +67,7 @@
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "{{ api_server_jwt_key }}",
"ws_token": "{{ api_server_ws_token }}",
"CORS_origins": [],
"username": "{{ api_server_username }}",
"password": "{{ api_server_password }}"