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Merge branch 'develop' into backtest_live_models

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This commit is contained in:
Wagner Costa Santos
2022-11-03 13:29:25 -03:00
parent 356d79b38a 0aff8c4823
commit 17798b3397
68 changed files with 1232 additions and 606 deletions
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4
freqtrade/__init__.py
View File

@@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2022.10.dev'
__version__ = '2022.11.dev'
if 'dev' in __version__:
try:
@@ -16,6 +16,6 @@ if 'dev' in __version__:
from pathlib import Path
versionfile = Path('./freqtrade_commit')
if versionfile.is_file():
__version__ = f"docker-{versionfile.read_text()[:8]}"
__version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"
except Exception:
pass

2
freqtrade/commands/cli_options.py
View File

@@ -49,7 +49,7 @@ AVAILABLE_CLI_OPTIONS = {
default=0,
),
"logfile": Arg(
'--logfile',
'--logfile', '--log-file',
help="Log to the file specified. Special values are: 'syslog', 'journald'. "
"See the documentation for more details.",
metavar='FILE',

21
freqtrade/data/history/idatahandler.py
View File

@@ -303,7 +303,7 @@ class IDataHandler(ABC):
timerange=timerange_startup,
candle_type=candle_type
)
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data):
if self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data, True):
return pairdf
else:
enddate = pairdf.iloc[-1]['date']
@@ -323,8 +323,9 @@ class IDataHandler(ABC):
self._check_empty_df(pairdf, pair, timeframe, candle_type, warn_no_data)
return pairdf
def _check_empty_df(self, pairdf: DataFrame, pair: str, timeframe: str,
candle_type: CandleType, warn_no_data: bool):
def _check_empty_df(
self, pairdf: DataFrame, pair: str, timeframe: str, candle_type: CandleType,
warn_no_data: bool, warn_price: bool = False) -> bool:
"""
Warn on empty dataframe
"""
@@ -335,6 +336,20 @@ class IDataHandler(ABC):
"Use `freqtrade download-data` to download the data"
)
return True
elif warn_price:
candle_price_gap = 0
if (candle_type in (CandleType.SPOT, CandleType.FUTURES) and
not pairdf.empty
and 'close' in pairdf.columns and 'open' in pairdf.columns):
# Detect gaps between prior close and open
gaps = ((pairdf['open'] - pairdf['close'].shift(1)) / pairdf['close'].shift(1))
gaps = gaps.dropna()
if len(gaps):
candle_price_gap = max(abs(gaps))
if candle_price_gap > 0.1:
logger.info(f"Price jump in {pair}, {timeframe}, {candle_type} between two candles "
f"of {candle_price_gap:.2%} detected.")
return False
def _validate_pairdata(self, pair, pairdata: DataFrame, timeframe: str,

17
freqtrade/exchange/__init__.py
View File

@@ -9,14 +9,15 @@ from freqtrade.exchange.bitpanda import Bitpanda
from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.bybit import Bybit
from freqtrade.exchange.coinbasepro import Coinbasepro
from freqtrade.exchange.exchange import (amount_to_contract_precision, amount_to_contracts,
amount_to_precision, available_exchanges, ccxt_exchanges,
contracts_to_amount, date_minus_candles,
is_exchange_known_ccxt, market_is_active,
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds,
validate_exchange, validate_exchanges)
from freqtrade.exchange.exchange_utils import (amount_to_contract_precision, amount_to_contracts,
amount_to_precision, available_exchanges,
ccxt_exchanges, contracts_to_amount,
date_minus_candles, is_exchange_known_ccxt,
market_is_active, price_to_precision,
timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.gateio import Gateio
from freqtrade.exchange.hitbtc import Hitbtc

18
freqtrade/exchange/binance.py
View File

@@ -42,24 +42,6 @@ class Binance(Exchange):
(TradingMode.FUTURES, MarginMode.ISOLATED)
]
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
:param side: "buy" or "sell"
"""
order_types = ('stop_loss_limit', 'stop', 'stop_market')
return (
order.get('stopPrice', None) is None
or (
order['type'] in order_types
and (
(side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)
))
def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
tickers = super().get_tickers(symbols=symbols, cached=cached)
if self.trading_mode == TradingMode.FUTURES:

277
freqtrade/exchange/exchange.py
View File

@@ -8,7 +8,6 @@ import inspect
import logging
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from math import ceil
from threading import Lock
from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
@@ -16,7 +15,7 @@ import arrow
import ccxt
import ccxt.async_support as ccxt_async
from cachetools import TTLCache
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
from ccxt import TICK_SIZE
from dateutil import parser
from pandas import DataFrame, concat
@@ -28,17 +27,19 @@ from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, PricingError,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
remove_credentials, retrier, retrier_async)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_credentials, retrier,
retrier_async)
from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contract_precision,
amount_to_contracts, amount_to_precision,
contracts_to_amount, date_minus_candles,
is_exchange_known_ccxt, market_is_active,
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.exchange.types import Ticker, Tickers
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
safe_value_fallback2)
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.util import FtPrecise
CcxtModuleType = Any
logger = logging.getLogger(__name__)
@@ -1076,7 +1077,14 @@ class Exchange:
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
raise OperationalException(f"stoploss is not implemented for {self.name}.")
if not self._ft_has.get('stoploss_on_exchange'):
raise OperationalException(f"stoploss is not implemented for {self.name}.")
return (
order.get('stopPrice', None) is None
or ((side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice'])))
)
def _get_stop_order_type(self, user_order_type) -> Tuple[str, str]:
@@ -1106,7 +1114,7 @@ class Exchange:
'In stoploss limit order, stop price should be more than limit price')
return limit_rate
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
# Verify if stopPrice works for your exchange!
params.update({'stopPrice': stop_price})
@@ -1155,7 +1163,8 @@ class Exchange:
return dry_order
try:
params = self._get_stop_params(ordertype=ordertype, stop_price=stop_price_norm)
params = self._get_stop_params(side=side, ordertype=ordertype,
stop_price=stop_price_norm)
if self.trading_mode == TradingMode.FUTURES:
params['reduceOnly'] = True
@@ -1995,11 +2004,8 @@ class Exchange:
def _now_is_time_to_refresh(self, pair: str, timeframe: str, candle_type: CandleType) -> bool:
# Timeframe in seconds
interval_in_sec = timeframe_to_seconds(timeframe)
return not (
(self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0)
+ interval_in_sec) >= arrow.utcnow().int_timestamp
)
plr = self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0) + interval_in_sec
return plr < arrow.utcnow().int_timestamp
@retrier_async
async def _async_get_candle_history(
@@ -2802,240 +2808,3 @@ class Exchange:
# describes the min amt for a tier, and the lowest tier will always go down to 0
else:
raise OperationalException(f"Cannot get maintenance ratio using {self.name}")
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
exchanges = ccxt_exchanges(ccxt_module)
return [x for x in exchanges if validate_exchange(x)[0]]
def validate_exchange(exchange: str) -> Tuple[bool, str]:
ex_mod = getattr(ccxt, exchange.lower())()
if not ex_mod or not ex_mod.has:
return False, ''
missing = [k for k in EXCHANGE_HAS_REQUIRED if ex_mod.has.get(k) is not True]
if missing:
return False, f"missing: {', '.join(missing)}"
missing_opt = [k for k in EXCHANGE_HAS_OPTIONAL if not ex_mod.has.get(k)]
if exchange.lower() in BAD_EXCHANGES:
return False, BAD_EXCHANGES.get(exchange.lower(), '')
if missing_opt:
return True, f"missing opt: {', '.join(missing_opt)}"
return True, ''
def validate_exchanges(all_exchanges: bool) -> List[Tuple[str, bool, str]]:
"""
:return: List of tuples with exchangename, valid, reason.
"""
exchanges = ccxt_exchanges() if all_exchanges else available_exchanges()
exchanges_valid = [
(e, *validate_exchange(e)) for e in exchanges
]
return exchanges_valid
def timeframe_to_seconds(timeframe: str) -> int:
"""
Translates the timeframe interval value written in the human readable
form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
of seconds for one timeframe interval.
"""
return ccxt.Exchange.parse_timeframe(timeframe)
def timeframe_to_minutes(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns minutes.
"""
return ccxt.Exchange.parse_timeframe(timeframe) // 60
def timeframe_to_msecs(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns milliseconds.
"""
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of previous candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_DOWN) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of next candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_UP) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def date_minus_candles(
timeframe: str, candle_count: int, date: Optional[datetime] = None) -> datetime:
"""
subtract X candles from a date.
:param timeframe: timeframe in string format (e.g. "5m")
:param candle_count: Amount of candles to subtract.
:param date: date to use. Defaults to now(utc)
"""
if not date:
date = datetime.now(timezone.utc)
tf_min = timeframe_to_minutes(timeframe)
new_date = timeframe_to_prev_date(timeframe, date) - timedelta(minutes=tf_min * candle_count)
return new_date
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.
"""
# "It's active, if the active flag isn't explicitly set to false. If it's missing or
# true then it's true. If it's undefined, then it's most likely true, but not 100% )"
# See https://github.com/ccxt/ccxt/issues/4874,
# https://github.com/ccxt/ccxt/issues/4075#issuecomment-434760520
return market.get('active', True) is not False
def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
"""
Convert amount to contracts.
:param amount: amount to convert
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: num-contracts
"""
if contract_size and contract_size != 1:
return float(FtPrecise(amount) / FtPrecise(contract_size))
else:
return amount
def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) -> float:
"""
Takes num-contracts and converts it to contract size
:param num_contracts: number of contracts
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: Amount
"""
if contract_size and contract_size != 1:
return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
else:
return num_contracts
def amount_to_precision(amount: float, amount_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
precision = int(amount_precision) if precisionMode != TICK_SIZE else amount_precision
# precision must be an int for non-ticksize inputs.
amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE,
precision=precision,
counting_mode=precisionMode,
))
return amount
def amount_to_contract_precision(
amount, amount_precision: Optional[float], precisionMode: Optional[int],
contract_size: Optional[float]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
including calculation to and from contracts.
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
contracts = amount_to_contracts(amount, contract_size)
amount_p = amount_to_precision(contracts, amount_precision, precisionMode)
return contracts_to_amount(amount_p, contract_size)
return amount
def price_to_precision(price: float, price_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the price rounded up to the precision the Exchange accepts.
Partial Re-implementation of ccxt internal method decimal_to_precision(),
which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision().
!!! Rounds up
:param price: price to convert
:param price_precision: price precision to use. Used from markets[pair]['precision']['price']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: price rounded up to the precision the Exchange accepts
"""
if price_precision is not None and precisionMode is not None:
# price = float(decimal_to_precision(price, rounding_mode=ROUND,
# precision=price_precision,
# counting_mode=self.precisionMode,
# ))
if precisionMode == TICK_SIZE:
precision = FtPrecise(price_precision)
price_str = FtPrecise(price)
missing = price_str % precision
if not missing == FtPrecise("0"):
price = round(float(str(price_str - missing + precision)), 14)
else:
symbol_prec = price_precision
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
return price

252
freqtrade/exchange/exchange_utils.py Normal file
View File

@@ -0,0 +1,252 @@
"""
Exchange support utils
"""
from datetime import datetime, timedelta, timezone
from math import ceil
from typing import Any, Dict, List, Optional, Tuple
import ccxt
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
from freqtrade.exchange.common import BAD_EXCHANGES, EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED
from freqtrade.util import FtPrecise
CcxtModuleType = Any
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
exchanges = ccxt_exchanges(ccxt_module)
return [x for x in exchanges if validate_exchange(x)[0]]
def validate_exchange(exchange: str) -> Tuple[bool, str]:
ex_mod = getattr(ccxt, exchange.lower())()
if not ex_mod or not ex_mod.has:
return False, ''
missing = [k for k in EXCHANGE_HAS_REQUIRED if ex_mod.has.get(k) is not True]
if missing:
return False, f"missing: {', '.join(missing)}"
missing_opt = [k for k in EXCHANGE_HAS_OPTIONAL if not ex_mod.has.get(k)]
if exchange.lower() in BAD_EXCHANGES:
return False, BAD_EXCHANGES.get(exchange.lower(), '')
if missing_opt:
return True, f"missing opt: {', '.join(missing_opt)}"
return True, ''
def validate_exchanges(all_exchanges: bool) -> List[Tuple[str, bool, str]]:
"""
:return: List of tuples with exchangename, valid, reason.
"""
exchanges = ccxt_exchanges() if all_exchanges else available_exchanges()
exchanges_valid = [
(e, *validate_exchange(e)) for e in exchanges
]
return exchanges_valid
def timeframe_to_seconds(timeframe: str) -> int:
"""
Translates the timeframe interval value written in the human readable
form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
of seconds for one timeframe interval.
"""
return ccxt.Exchange.parse_timeframe(timeframe)
def timeframe_to_minutes(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns minutes.
"""
return ccxt.Exchange.parse_timeframe(timeframe) // 60
def timeframe_to_msecs(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns milliseconds.
"""
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of previous candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_DOWN) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")
:param date: date to use. Defaults to now(utc)
:returns: date of next candle (with utc timezone)
"""
if not date:
date = datetime.now(timezone.utc)
new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
ROUND_UP) // 1000
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def date_minus_candles(
timeframe: str, candle_count: int, date: Optional[datetime] = None) -> datetime:
"""
subtract X candles from a date.
:param timeframe: timeframe in string format (e.g. "5m")
:param candle_count: Amount of candles to subtract.
:param date: date to use. Defaults to now(utc)
"""
if not date:
date = datetime.now(timezone.utc)
tf_min = timeframe_to_minutes(timeframe)
new_date = timeframe_to_prev_date(timeframe, date) - timedelta(minutes=tf_min * candle_count)
return new_date
def market_is_active(market: Dict) -> bool:
"""
Return True if the market is active.
"""
# "It's active, if the active flag isn't explicitly set to false. If it's missing or
# true then it's true. If it's undefined, then it's most likely true, but not 100% )"
# See https://github.com/ccxt/ccxt/issues/4874,
# https://github.com/ccxt/ccxt/issues/4075#issuecomment-434760520
return market.get('active', True) is not False
def amount_to_contracts(amount: float, contract_size: Optional[float]) -> float:
"""
Convert amount to contracts.
:param amount: amount to convert
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: num-contracts
"""
if contract_size and contract_size != 1:
return float(FtPrecise(amount) / FtPrecise(contract_size))
else:
return amount
def contracts_to_amount(num_contracts: float, contract_size: Optional[float]) -> float:
"""
Takes num-contracts and converts it to contract size
:param num_contracts: number of contracts
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: Amount
"""
if contract_size and contract_size != 1:
return float(FtPrecise(num_contracts) * FtPrecise(contract_size))
else:
return num_contracts
def amount_to_precision(amount: float, amount_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
precision = int(amount_precision) if precisionMode != TICK_SIZE else amount_precision
# precision must be an int for non-ticksize inputs.
amount = float(decimal_to_precision(amount, rounding_mode=TRUNCATE,
precision=precision,
counting_mode=precisionMode,
))
return amount
def amount_to_contract_precision(
amount, amount_precision: Optional[float], precisionMode: Optional[int],
contract_size: Optional[float]) -> float:
"""
Returns the amount to buy or sell to a precision the Exchange accepts
including calculation to and from contracts.
Re-implementation of ccxt internal methods - ensuring we can test the result is correct
based on our definitions.
:param amount: amount to truncate
:param amount_precision: amount precision to use.
should be retrieved from markets[pair]['precision']['amount']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:param contract_size: contract size - taken from exchange.get_contract_size(pair)
:return: truncated amount
"""
if amount_precision is not None and precisionMode is not None:
contracts = amount_to_contracts(amount, contract_size)
amount_p = amount_to_precision(contracts, amount_precision, precisionMode)
return contracts_to_amount(amount_p, contract_size)
return amount
def price_to_precision(price: float, price_precision: Optional[float],
precisionMode: Optional[int]) -> float:
"""
Returns the price rounded up to the precision the Exchange accepts.
Partial Re-implementation of ccxt internal method decimal_to_precision(),
which does not support rounding up
TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
align with amount_to_precision().
!!! Rounds up
:param price: price to convert
:param price_precision: price precision to use. Used from markets[pair]['precision']['price']
:param precisionMode: precision mode to use. Should be used from precisionMode
one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE
:return: price rounded up to the precision the Exchange accepts
"""
if price_precision is not None and precisionMode is not None:
# price = float(decimal_to_precision(price, rounding_mode=ROUND,
# precision=price_precision,
# counting_mode=self.precisionMode,
# ))
if precisionMode == TICK_SIZE:
precision = FtPrecise(price_precision)
price_str = FtPrecise(price)
missing = price_str % precision
if not missing == FtPrecise("0"):
price = round(float(str(price_str - missing + precision)), 14)
else:
symbol_prec = price_precision
big_price = price * pow(10, symbol_prec)
price = ceil(big_price) / pow(10, symbol_prec)
return price

10
freqtrade/exchange/gateio.py
View File

@@ -126,13 +126,3 @@ class Gateio(Exchange):
pair=pair,
params={'stop': True}
)
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return (order.get('stopPrice', None) is None or (
side == "sell" and stop_loss > float(order['stopPrice'])) or
(side == "buy" and stop_loss < float(order['stopPrice']))
)

16
freqtrade/exchange/huobi.py
View File

@@ -2,6 +2,7 @@
import logging
from typing import Dict
from freqtrade.constants import BuySell
from freqtrade.exchange import Exchange
@@ -22,20 +23,7 @@ class Huobi(Exchange):
"l2_limit_range_required": False,
}
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return (
order.get('stopPrice', None) is None
or (
order['type'] == 'stop'
and stop_loss > float(order['stopPrice'])
)
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
params.update({

13
freqtrade/exchange/kucoin.py
View File

@@ -2,6 +2,7 @@
import logging
from typing import Dict
from freqtrade.constants import BuySell
from freqtrade.exchange import Exchange
@@ -27,17 +28,7 @@ class Kucoin(Exchange):
"ohlcv_candle_limit": 1500,
}
def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
Returns True if adjustment is necessary.
"""
return (
order.get('stopPrice', None) is None
or stop_loss > float(order['stopPrice'])
)
def _get_stop_params(self, ordertype: str, stop_price: float) -> Dict:
def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict:
params = self._params.copy()
params.update({

2
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@@ -51,7 +51,7 @@ class BaseClassifierModel(IFreqaiModel):
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_candles", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)

2
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@@ -50,7 +50,7 @@ class BaseRegressionModel(IFreqaiModel):
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_candles", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)

2
freqtrade/freqai/base_models/BaseTensorFlowModel.py
View File

@@ -47,7 +47,7 @@ class BaseTensorFlowModel(IFreqaiModel):
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_candles", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)

116
freqtrade/freqai/data_kitchen.py
View File

@@ -214,7 +214,10 @@ class FreqaiDataKitchen:
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
if const_cols:
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
self.data['constant_features_list'] = const_cols
logger.warning(f"Removed features {const_cols} with constant values.")
else:
self.data['constant_features_list'] = []
# we don't care about total row number (total no. datapoints) in training, we only care
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
@@ -245,7 +248,8 @@ class FreqaiDataKitchen:
self.data["filter_drop_index_training"] = drop_index
else:
filtered_df = self.check_pred_labels(filtered_df)
if len(self.data['constant_features_list']):
filtered_df = self.check_pred_labels(filtered_df)
# 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(axis=1)
@@ -354,13 +358,19 @@ class FreqaiDataKitchen:
:param df: Dataframe to be standardized
"""
for item in df.keys():
df[item] = (
2
* (df[item] - self.data[f"{item}_min"])
/ (self.data[f"{item}_max"] - self.data[f"{item}_min"])
- 1
)
train_max = [None] * len(df.keys())
train_min = [None] * len(df.keys())
for i, item in enumerate(df.keys()):
train_max[i] = self.data[f"{item}_max"]
train_min[i] = self.data[f"{item}_min"]
train_max_series = pd.Series(train_max, index=df.keys())
train_min_series = pd.Series(train_min, index=df.keys())
df = (
2 * (df - train_min_series) / (train_max_series - train_min_series) - 1
)
return df
@@ -491,18 +501,16 @@ class FreqaiDataKitchen:
def check_pred_labels(self, df_predictions: DataFrame) -> DataFrame:
"""
Check that prediction feature labels match training feature labels.
:params:
:df_predictions: incoming predictions
:param df_predictions: incoming predictions
"""
train_labels = self.data_dictionary["train_features"].columns
pred_labels = df_predictions.columns
num_diffs = len(pred_labels.difference(train_labels))
if num_diffs != 0:
df_predictions = df_predictions[train_labels]
logger.warning(
f"Removed {num_diffs} features from prediction features, "
f"these were likely considered constant values during most recent training."
)
constant_labels = self.data['constant_features_list']
df_predictions = df_predictions.filter(
df_predictions.columns.difference(constant_labels)
)
logger.warning(
f"Removed {len(constant_labels)} features from prediction features, "
f"these were considered constant values during most recent training."
)
return df_predictions
@@ -986,6 +994,9 @@ class FreqaiDataKitchen:
if "labels_std" in self.data:
append_df[f"{label}_std"] = self.data["labels_std"][label]
for extra_col in self.data["extra_returns_per_train"]:
append_df[f"{extra_col}"] = self.data["extra_returns_per_train"][extra_col]
append_df["do_predict"] = do_predict
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values
@@ -1150,6 +1161,51 @@ class FreqaiDataKitchen:
if pair not in self.all_pairs:
self.all_pairs.append(pair)
def extract_corr_pair_columns_from_populated_indicators(
self,
dataframe: DataFrame
) -> Dict[str, DataFrame]:
"""
Find the columns of the dataframe corresponding to the corr_pairlist, save them
in a dictionary to be reused and attached to other pairs.
:param dataframe: fully populated dataframe (current pair + corr_pairs)
:return: corr_dataframes, dictionary of dataframes to be attached
to other pairs in same candle.
"""
corr_dataframes: Dict[str, DataFrame] = {}
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
pair_cols = [col for col in dataframe.columns if
any(substr in col for substr in valid_strs)]
pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
return corr_dataframes
def attach_corr_pair_columns(self, dataframe: DataFrame,
corr_dataframes: Dict[str, DataFrame],
current_pair: str) -> DataFrame:
"""
Attach the existing corr_pair dataframes to the current pair dataframe before training
:param dataframe: current pair strategy dataframe, indicators populated already
:param corr_dataframes: dictionary of saved dataframes from earlier in the same candle
:param current_pair: current pair to which we will attach corr pair dataframe
:return:
:dataframe: current pair dataframe of populated indicators, concatenated with corr_pairs
ready for training
"""
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
if current_pair != pair:
dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
return dataframe
def use_strategy_to_populate_indicators(
self,
strategy: IStrategy,
@@ -1157,6 +1213,7 @@ class FreqaiDataKitchen:
base_dataframes: dict = {},
pair: str = "",
prediction_dataframe: DataFrame = pd.DataFrame(),
do_corr_pairs: bool = True,
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during retrain
@@ -1166,15 +1223,15 @@ class FreqaiDataKitchen:
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param metadata: dict = strategy furnished pair metadata
:returns:
:return:
dataframe: DataFrame = dataframe containing populated indicators
"""
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
# so we create empty dictionaries, which allows us to pass None to
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
tfs = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
for tf in tfs:
@@ -1197,15 +1254,18 @@ class FreqaiDataKitchen:
informative=base_dataframes[tf],
set_generalized_indicators=sgi
)
if pairs:
for i in pairs:
if pair in i:
continue # dont repeat anything from whitelist
# ensure corr pairs are always last
for corr_pair in pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
for tf in tfs:
if pairs and do_corr_pairs:
dataframe = strategy.populate_any_indicators(
i,
corr_pair,
dataframe.copy(),
tf,
informative=corr_dataframes[i][tf]
informative=corr_dataframes[corr_pair][tf]
)
self.get_unique_classes_from_labels(dataframe)

68
freqtrade/freqai/freqai_interface.py
View File

@@ -1,12 +1,10 @@
import logging
import shutil
import threading
import time
from abc import ABC, abstractmethod
from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from threading import Lock
from typing import Any, Dict, List, Literal, Tuple
import numpy as np
@@ -21,7 +19,7 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.utils import plot_feature_importance
from freqtrade.freqai.utils import plot_feature_importance, record_params
from freqtrade.strategy.interface import IStrategy
@@ -61,6 +59,7 @@ class IFreqaiModel(ABC):
"data_split_parameters", {})
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
"model_training_parameters", {})
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.retrain = False
self.first = True
self.set_full_path()
@@ -69,9 +68,9 @@ class IFreqaiModel(ABC):
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)
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"]
self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
self.keras: bool = self.freqai_info.get("keras", False)
if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
@@ -83,9 +82,6 @@ class IFreqaiModel(ABC):
self.pair_it_train = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
self.train_queue = self._set_train_queue()
self.last_trade_database_summary: DataFrame = {}
self.current_trade_database_summary: DataFrame = {}
self.analysis_lock = Lock()
self.inference_time: float = 0
self.train_time: float = 0
self.begin_time: float = 0
@@ -93,10 +89,16 @@ class IFreqaiModel(ABC):
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.corr_dataframes: Dict[str, DataFrame] = {}
# get_corr_dataframes is controlling the caching of corr_dataframes
# for improved performance. Careful with this boolean.
self.get_corr_dataframes: bool = True
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
record_params(config, self.full_path)
def __getstate__(self):
"""
Return an empty state to be pickled in hyperopt
@@ -385,10 +387,10 @@ class IFreqaiModel(ABC):
# load the model and associated data into the data kitchen
self.model = self.dd.load_data(metadata["pair"], dk)
with self.analysis_lock:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
dataframe = dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"],
do_corr_pairs=self.get_corr_dataframes
)
if not self.model:
logger.warning(
@@ -397,6 +399,9 @@ class IFreqaiModel(ABC):
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
if self.corr_pairlist:
dataframe = self.cache_corr_pairlist_dfs(dataframe, dk)
dk.find_labels(dataframe)
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
@@ -548,14 +553,13 @@ class IFreqaiModel(ABC):
return file_exists
def set_full_path(self) -> None:
"""
Creates and sets the full path for the identifier
"""
self.full_path = Path(
self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}"
self.config["user_data_dir"] / "models" / f"{self.identifier}"
)
self.full_path.mkdir(parents=True, exist_ok=True)
shutil.copy(
self.config["config_files"][0],
Path(self.full_path, Path(self.config["config_files"][0]).name),
)
def extract_data_and_train_model(
self,
@@ -581,10 +585,9 @@ class IFreqaiModel(ABC):
data_load_timerange, pair, dk
)
with self.analysis_lock:
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
strategy, corr_dataframes, base_dataframes, pair
)
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
strategy, corr_dataframes, base_dataframes, pair
)
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
@@ -702,6 +705,8 @@ class IFreqaiModel(ABC):
" avoid blinding open trades and degrading performance.")
self.pair_it = 0
self.inference_time = 0
if self.corr_pairlist:
self.get_corr_dataframes = True
return
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
@@ -760,6 +765,29 @@ class IFreqaiModel(ABC):
f'Best approximation queue: {best_queue}')
return best_queue
def cache_corr_pairlist_dfs(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
Cache the corr_pairlist dfs to speed up performance for subsequent pairs during the
current candle.
:param dataframe: strategy fed dataframe
:param dk: datakitchen object for current asset
:return: dataframe to attach/extract cached corr_pair dfs to/from.
"""
if self.get_corr_dataframes:
self.corr_dataframes = dk.extract_corr_pair_columns_from_populated_indicators(dataframe)
if not self.corr_dataframes:
logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
"Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
"is included in the column names when you are creating features "
"in `populate_any_indicators()`.")
self.get_corr_dataframes = not bool(self.corr_dataframes)
else:
dataframe = dk.attach_corr_pair_columns(
dataframe, self.corr_dataframes, dk.pair)
return dataframe
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

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

@@ -26,9 +26,8 @@ class XGBoostRFClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
@@ -65,7 +64,7 @@ class XGBoostRFClassifier(BaseClassifierModel):
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

1
freqtrade/freqai/prediction_models/XGBoostRFRegressor.py
View File

@@ -29,6 +29,7 @@ class XGBoostRFRegressor(BaseRegressionModel):
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
eval_weights = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]

1
freqtrade/freqai/prediction_models/XGBoostRegressor.py
View File

@@ -29,6 +29,7 @@ class XGBoostRegressor(BaseRegressionModel):
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
eval_weights = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]

29
freqtrade/freqai/utils.py
View File

@@ -1,9 +1,11 @@
import logging
from datetime import datetime, timezone
from typing import Any
from pathlib import Path
from typing import Any, Dict
import numpy as np
import pandas as pd
import rapidjson
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
@@ -193,6 +195,31 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path)
def record_params(config: Dict[str, Any], full_path: Path) -> None:
"""
Records run params in the full path for reproducibility
"""
params_record_path = full_path / "run_params.json"
run_params = {
"freqai": config.get('freqai', {}),
"timeframe": config.get('timeframe'),
"stake_amount": config.get('stake_amount'),
"stake_currency": config.get('stake_currency'),
"max_open_trades": config.get('max_open_trades'),
"pairs": config.get('exchange', {}).get('pair_whitelist')
}
with open(params_record_path, "w") as handle:
rapidjson.dump(
run_params,
handle,
indent=4,
default=str,
number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN
)
def get_timerange_backtest_live_models(config: Config):
"""
Returns a formated timerange for backtest live/ready models

8
freqtrade/freqtradebot.py
View File

@@ -1471,12 +1471,13 @@ class FreqtradeBot(LoggingMixin):
)
return cancelled
def _safe_exit_amount(self, pair: str, amount: float) -> float:
def _safe_exit_amount(self, trade: Trade, pair: str, amount: float) -> float:
"""
Get sellable amount.
Should be trade.amount - but will fall back to the available amount if necessary.
This should cover cases where get_real_amount() was not able to update the amount
for whatever reason.
:param trade: Trade we're working with
:param pair: Pair we're trying to sell
:param amount: amount we expect to be available
:return: amount to sell
@@ -1495,6 +1496,7 @@ class FreqtradeBot(LoggingMixin):
return amount
elif wallet_amount > amount * 0.98:
logger.info(f"{pair} - Falling back to wallet-amount {wallet_amount} -> {amount}.")
trade.amount = wallet_amount
return wallet_amount
else:
raise DependencyException(
@@ -1553,7 +1555,7 @@ class FreqtradeBot(LoggingMixin):
# Emergency sells (default to market!)
order_type = self.strategy.order_types.get("emergency_exit", "market")
amount = self._safe_exit_amount(trade.pair, sub_trade_amt or trade.amount)
amount = self._safe_exit_amount(trade, trade.pair, sub_trade_amt or trade.amount)
time_in_force = self.strategy.order_time_in_force['exit']
if (exit_check.exit_type != ExitType.LIQUIDATION
@@ -1828,7 +1830,7 @@ class FreqtradeBot(LoggingMixin):
never in base currency.
"""
self.wallets.update()
amount_ = amount
amount_ = trade.amount
if order_obj.ft_order_side == trade.exit_side or order_obj.ft_order_side == 'stoploss':
# check against remaining amount!
amount_ = trade.amount - amount

84
freqtrade/persistence/trade_model.py
View File

@@ -1520,3 +1520,87 @@ class Trade(_DECL_BASE, LocalTrade):
Order.status == 'closed'
).scalar()
return trading_volume
@staticmethod
def from_json(json_str: str) -> 'Trade':
"""
Create a Trade instance from a json string.
Used for debugging purposes - please keep.
:param json_str: json string to parse
:return: Trade instance
"""
import rapidjson
data = rapidjson.loads(json_str)
trade = Trade(
id=data["trade_id"],
pair=data["pair"],
base_currency=data["base_currency"],
stake_currency=data["quote_currency"],
is_open=data["is_open"],
exchange=data["exchange"],
amount=data["amount"],
amount_requested=data["amount_requested"],
stake_amount=data["stake_amount"],
strategy=data["strategy"],
enter_tag=data["enter_tag"],
timeframe=data["timeframe"],
fee_open=data["fee_open"],
fee_open_cost=data["fee_open_cost"],
fee_open_currency=data["fee_open_currency"],
fee_close=data["fee_close"],
fee_close_cost=data["fee_close_cost"],
fee_close_currency=data["fee_close_currency"],
open_date=datetime.fromtimestamp(data["open_timestamp"] // 1000, tz=timezone.utc),
open_rate=data["open_rate"],
open_rate_requested=data["open_rate_requested"],
open_trade_value=data["open_trade_value"],
close_date=(datetime.fromtimestamp(data["close_timestamp"] // 1000, tz=timezone.utc)
if data["close_timestamp"] else None),
realized_profit=data["realized_profit"],
close_rate=data["close_rate"],
close_rate_requested=data["close_rate_requested"],
close_profit=data["close_profit"],
close_profit_abs=data["close_profit_abs"],
exit_reason=data["exit_reason"],
exit_order_status=data["exit_order_status"],
stop_loss=data["stop_loss_abs"],
stop_loss_pct=data["stop_loss_ratio"],
stoploss_order_id=data["stoploss_order_id"],
stoploss_last_update=(datetime.fromtimestamp(data["stoploss_last_update"] // 1000,
tz=timezone.utc) if data["stoploss_last_update"] else None),
initial_stop_loss=data["initial_stop_loss_abs"],
initial_stop_loss_pct=data["initial_stop_loss_ratio"],
min_rate=data["min_rate"],
max_rate=data["max_rate"],
leverage=data["leverage"],
interest_rate=data["interest_rate"],
liquidation_price=data["liquidation_price"],
is_short=data["is_short"],
trading_mode=data["trading_mode"],
funding_fees=data["funding_fees"],
open_order_id=data["open_order_id"],
)
for order in data["orders"]:
order_obj = Order(
amount=order["amount"],
ft_order_side=order["ft_order_side"],
ft_pair=order["pair"],
ft_is_open=order["is_open"],
order_id=order["order_id"],
status=order["status"],
average=order["average"],
cost=order["cost"],
filled=order["filled"],
order_date=datetime.strptime(order["order_date"], DATETIME_PRINT_FORMAT),
order_filled_date=(datetime.fromtimestamp(
order["order_filled_timestamp"] // 1000, tz=timezone.utc)
if order["order_filled_timestamp"] else None),
order_type=order["order_type"],
price=order["price"],
remaining=order["remaining"],
)
trade.orders.append(order_obj)
return trade

1
freqtrade/plugins/pairlist/IPairList.py
View File

@@ -36,7 +36,6 @@ class IPairList(LoggingMixin, ABC):
self._pairlistconfig = pairlistconfig
self._pairlist_pos = pairlist_pos
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._last_refresh = 0
LoggingMixin.__init__(self, logger, self.refresh_period)
@property

17
freqtrade/plugins/pairlist/ShuffleFilter.py
View File

@@ -3,16 +3,20 @@ Shuffle pair list filter
"""
import logging
import random
from typing import Any, Dict, List
from typing import Any, Dict, List, Literal
from freqtrade.constants import Config
from freqtrade.enums import RunMode
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange.types import Tickers
from freqtrade.plugins.pairlist.IPairList import IPairList
from freqtrade.util.periodic_cache import PeriodicCache
logger = logging.getLogger(__name__)
ShuffleValues = Literal['candle', 'iteration']
class ShuffleFilter(IPairList):
@@ -31,6 +35,9 @@ class ShuffleFilter(IPairList):
logger.info(f"Backtesting mode detected, applying seed value: {self._seed}")
self._random = random.Random(self._seed)
self._shuffle_freq: ShuffleValues = pairlistconfig.get('shuffle_frequency', 'candle')
self.__pairlist_cache = PeriodicCache(
maxsize=1000, ttl=timeframe_to_seconds(self._config['timeframe']))
@property
def needstickers(self) -> bool:
@@ -45,7 +52,7 @@ class ShuffleFilter(IPairList):
"""
Short whitelist method description - used for startup-messages
"""
return (f"{self.name} - Shuffling pairs" +
return (f"{self.name} - Shuffling pairs every {self._shuffle_freq}" +
(f", seed = {self._seed}." if self._seed is not None else "."))
def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
@@ -56,7 +63,13 @@ class ShuffleFilter(IPairList):
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
pairlist_bef = tuple(pairlist)
pairlist_new = self.__pairlist_cache.get(pairlist_bef)
if pairlist_new and self._shuffle_freq == 'candle':
# Use cached pairlist.
return pairlist_new
# Shuffle is done inplace
self._random.shuffle(pairlist)
self.__pairlist_cache[pairlist_bef] = pairlist
return pairlist

23
freqtrade/rpc/api_server/api_ws.py
View File

@@ -1,4 +1,3 @@
import asyncio
import logging
from typing import Any, Dict
@@ -11,6 +10,7 @@ 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.channel import ChannelManager
from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSMessageSchema,
WSRequestSchema, WSWhitelistMessage)
from freqtrade.rpc.rpc import RPC
@@ -37,7 +37,8 @@ async def is_websocket_alive(ws: WebSocket) -> bool:
async def _process_consumer_request(
request: Dict[str, Any],
channel: WebSocketChannel,
rpc: RPC
rpc: RPC,
channel_manager: ChannelManager
):
"""
Validate and handle a request from a websocket consumer
@@ -74,7 +75,7 @@ async def _process_consumer_request(
# Format response
response = WSWhitelistMessage(data=whitelist)
# Send it back
await channel.send(response.dict(exclude_none=True))
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
elif type == RPCRequestType.ANALYZED_DF:
limit = None
@@ -89,9 +90,7 @@ async def _process_consumer_request(
# 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))
# Throttle the messages to 50/s
await asyncio.sleep(0.02)
await channel_manager.send_direct(channel, response.dict(exclude_none=True))
@router.websocket("/message/ws")
@@ -116,7 +115,7 @@ async def message_endpoint(
request = await channel.recv()
# Process the request here
await _process_consumer_request(request, channel, rpc)
await _process_consumer_request(request, channel, rpc, channel_manager)
except (WebSocketDisconnect, WebSocketException):
# Handle client disconnects
@@ -128,13 +127,6 @@ async def message_endpoint(
except Exception as e:
logger.info(f"Consumer connection failed - {channel}: {e}")
logger.debug(e, exc_info=e)
finally:
await channel_manager.on_disconnect(ws)
else:
if channel:
await channel_manager.on_disconnect(ws)
await ws.close()
except RuntimeError:
# WebSocket was closed
@@ -145,4 +137,5 @@ async def message_endpoint(
# Log tracebacks to keep track of what errors are happening
logger.exception(e)
finally:
await channel_manager.on_disconnect(ws)
if channel:
await channel_manager.on_disconnect(ws)

13
freqtrade/rpc/api_server/webserver.py
View File

@@ -16,6 +16,7 @@ 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.api_server.ws_schemas import WSMessageSchemaType
from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler
@@ -127,7 +128,7 @@ class ApiServer(RPCHandler):
cls._has_rpc = False
cls._rpc = None
def send_msg(self, msg: Dict[str, str]) -> None:
def send_msg(self, msg: Dict[str, Any]) -> None:
if self._ws_queue:
sync_q = self._ws_queue.sync_q
sync_q.put(msg)
@@ -194,14 +195,11 @@ class ApiServer(RPCHandler):
while True:
logger.debug("Getting queue messages...")
# Get data from queue
message = await async_queue.get()
message: WSMessageSchemaType = await async_queue.get()
logger.debug(f"Found message of type: {message.get('type')}")
async_queue.task_done()
# Broadcast it
await self._ws_channel_manager.broadcast(message)
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
await asyncio.sleep(0.001)
except asyncio.CancelledError:
pass
@@ -213,6 +211,9 @@ class ApiServer(RPCHandler):
# Disconnect channels and stop the loop on cancel
await self._ws_channel_manager.disconnect_all()
self._ws_loop.stop()
# Avoid adding more items to the queue if they aren't
# going to get broadcasted.
self._ws_queue = None
def start_api(self):
"""

93
freqtrade/rpc/api_server/ws/channel.py
View File

@@ -1,7 +1,8 @@
import asyncio
import logging
import time
from threading import RLock
from typing import Any, Dict, List, Optional, Type
from typing import Any, Dict, List, Optional, Type, Union
from uuid import uuid4
from fastapi import WebSocket as FastAPIWebSocket
@@ -10,6 +11,7 @@ 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
from freqtrade.rpc.api_server.ws_schemas import WSMessageSchemaType
logger = logging.getLogger(__name__)
@@ -24,6 +26,8 @@ class WebSocketChannel:
self,
websocket: WebSocketType,
channel_id: Optional[str] = None,
drain_timeout: int = 3,
throttle: float = 0.01,
serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
):
@@ -34,12 +38,16 @@ class WebSocketChannel:
# The Serializing class for the WebSocket object
self._serializer_cls = serializer_cls
self.drain_timeout = drain_timeout
self.throttle = throttle
self._subscriptions: List[str] = []
# 32 is the size of the receiving queue in websockets package
self.queue: asyncio.Queue[Dict[str, Any]] = asyncio.Queue(maxsize=32)
self._relay_task = asyncio.create_task(self.relay())
# Internal event to signify a closed websocket
self._closed = False
self._closed = asyncio.Event()
# Wrap the WebSocket in the Serializing class
self._wrapped_ws = self._serializer_cls(self._websocket)
@@ -47,6 +55,10 @@ class WebSocketChannel:
def __repr__(self):
return f"WebSocketChannel({self.channel_id}, {self.remote_addr})"
@property
def raw_websocket(self):
return self._websocket.raw_websocket
@property
def remote_addr(self):
return self._websocket.remote_addr
@@ -57,11 +69,30 @@ class WebSocketChannel:
"""
await self._wrapped_ws.send(data)
async def send(self, data):
async def send(self, data) -> bool:
"""
Add the data to the queue to be sent
Add the data to the queue to be sent.
:returns: True if data added to queue, False otherwise
"""
self.queue.put_nowait(data)
# This block only runs if the queue is full, it will wait
# until self.drain_timeout for the relay to drain the outgoing queue
# We can't use asyncio.wait_for here because the queue may have been created with a
# different eventloop
start = time.time()
while self.queue.full():
await asyncio.sleep(1)
if (time.time() - start) > self.drain_timeout:
return False
# If for some reason the queue is still full, just return False
try:
self.queue.put_nowait(data)
except asyncio.QueueFull:
return False
# If we got here everything is ok
return True
async def recv(self):
"""
@@ -80,14 +111,19 @@ class WebSocketChannel:
Close the WebSocketChannel
"""
self._closed = True
try:
await self.raw_websocket.close()
except Exception:
pass
self._closed.set()
self._relay_task.cancel()
def is_closed(self) -> bool:
"""
Closed flag
"""
return self._closed
return self._closed.is_set()
def set_subscriptions(self, subscriptions: List[str] = []) -> None:
"""
@@ -110,7 +146,7 @@ class WebSocketChannel:
Relay messages from the channel's queue and send them out. This is started
as a task.
"""
while True:
while not self._closed.is_set():
message = await self.queue.get()
try:
await self._send(message)
@@ -119,8 +155,8 @@ class WebSocketChannel:
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
# 0.001 = 1000/s
await asyncio.sleep(0.001)
# 0.01 = 100/s
await asyncio.sleep(self.throttle)
except RuntimeError:
# The connection was closed, just exit the task
return
@@ -160,6 +196,7 @@ class ChannelManager:
with self._lock:
channel = self.channels.get(websocket)
if channel:
logger.info(f"Disconnecting channel {channel}")
if not channel.is_closed():
await channel.close()
@@ -170,36 +207,30 @@ class ChannelManager:
Disconnect all Channels
"""
with self._lock:
for websocket, channel in self.channels.copy().items():
if not channel.is_closed():
await channel.close()
for websocket in self.channels.copy().keys():
await self.on_disconnect(websocket)
self.channels = dict()
async def broadcast(self, data):
async def broadcast(self, message: WSMessageSchemaType):
"""
Broadcast data on all Channels
Broadcast a message on all Channels
:param data: The data to send
:param message: The message to send
"""
with self._lock:
message_type = data.get('type')
for websocket, channel in self.channels.copy().items():
if channel.subscribed_to(message_type):
if not channel.queue.full():
await channel.send(data)
else:
logger.info(f"Channel {channel} is too far behind, disconnecting")
await self.on_disconnect(websocket)
for channel in self.channels.copy().values():
if channel.subscribed_to(message.get('type')):
await self.send_direct(channel, message)
async def send_direct(self, channel, data):
async def send_direct(
self, channel: WebSocketChannel, message: Union[WSMessageSchemaType, Dict[str, Any]]):
"""
Send data directly through direct_channel only
Send a message directly through direct_channel only
:param direct_channel: The WebSocketChannel object to send data through
:param data: The data to send
:param direct_channel: The WebSocketChannel object to send the message through
:param message: The message to send
"""
await channel.send(data)
if not await channel.send(message):
await self.on_disconnect(channel.raw_websocket)
def has_channels(self):
"""

4
freqtrade/rpc/api_server/ws/proxy.py
View File

@@ -15,6 +15,10 @@ class WebSocketProxy:
def __init__(self, websocket: WebSocketType):
self._websocket: Union[FastAPIWebSocket, WebSocket] = websocket
@property
def raw_websocket(self):
return self._websocket
@property
def remote_addr(self) -> Tuple[Any, ...]:
if isinstance(self._websocket, WebSocket):

8
freqtrade/rpc/api_server/ws_schemas.py
View File

@@ -1,5 +1,5 @@
from datetime import datetime
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, TypedDict
from pandas import DataFrame
from pydantic import BaseModel
@@ -18,6 +18,12 @@ class WSRequestSchema(BaseArbitraryModel):
data: Optional[Any] = None
class WSMessageSchemaType(TypedDict):
# Type for typing to avoid doing pydantic typechecks.
type: RPCMessageType
data: Optional[Dict[str, Any]]
class WSMessageSchema(BaseArbitraryModel):
type: RPCMessageType
data: Optional[Any] = None

13
freqtrade/rpc/external_message_consumer.py
View File

@@ -264,14 +264,19 @@ class ExternalMessageConsumer:
# We haven't received data yet. Check the connection and continue.
try:
# ping
ping = await channel.ping()
pong = await channel.ping()
latency = (await asyncio.wait_for(pong, timeout=self.ping_timeout) * 1000)
await asyncio.wait_for(ping, timeout=self.ping_timeout)
logger.debug(f"Connection to {channel} still alive...")
logger.info(f"Connection to {channel} still alive, latency: {latency}ms")
continue
except (websockets.exceptions.ConnectionClosed):
# Just eat the error and continue reconnecting
logger.warning(f"Disconnection in {channel} - retrying in {self.sleep_time}s")
await asyncio.sleep(self.sleep_time)
break
except Exception as e:
logger.warning(f"Ping error {channel} - retrying in {self.sleep_time}s")
logger.warning(f"Ping error {channel} - {e} - retrying in {self.sleep_time}s")
logger.debug(e, exc_info=e)
await asyncio.sleep(self.sleep_time)

20
freqtrade/strategy/interface.py
View File

@@ -1072,28 +1072,26 @@ class IStrategy(ABC, HyperStrategyMixin):
trade.stop_loss > (high or current_rate)
)
# Make sure current_profit is calculated using high for backtesting.
bound = (low if trade.is_short else high)
bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
if self.use_custom_stoploss and dir_correct:
stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None
)(pair=trade.pair, trade=trade,
current_time=current_time,
current_rate=current_rate,
current_profit=current_profit)
current_rate=(bound or current_rate),
current_profit=bound_profit)
# Sanity check - error cases will return None
if stop_loss_value:
# logger.info(f"{trade.pair} {stop_loss_value=} {current_profit=}")
trade.adjust_stop_loss(current_rate, stop_loss_value)
# logger.info(f"{trade.pair} {stop_loss_value=} {bound_profit=}")
trade.adjust_stop_loss(bound or current_rate, stop_loss_value)
else:
logger.warning("CustomStoploss function did not return valid stoploss")
sl_lower_long = (trade.stop_loss < (low or current_rate) and not trade.is_short)
sl_higher_short = (trade.stop_loss > (high or current_rate) and trade.is_short)
if self.trailing_stop and (sl_lower_long or sl_higher_short):
if self.trailing_stop and dir_correct:
# trailing stoploss handling
sl_offset = self.trailing_stop_positive_offset
# Make sure current_profit is calculated using high for backtesting.
bound = low if trade.is_short else high
bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
# Don't update stoploss if trailing_only_offset_is_reached is true.
if not (self.trailing_only_offset_is_reached and bound_profit < sl_offset):
@@ -1101,7 +1099,7 @@ class IStrategy(ABC, HyperStrategyMixin):
if self.trailing_stop_positive is not None and bound_profit > sl_offset:
stop_loss_value = self.trailing_stop_positive
logger.debug(f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
f"offset: {sl_offset:.4g} profit: {current_profit:.2%}")
f"offset: {sl_offset:.4g} profit: {bound_profit:.2%}")
trade.adjust_stop_loss(bound or current_rate, stop_loss_value)

16
freqtrade/templates/FreqaiHybridExampleStrategy.py → freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

@@ -110,8 +110,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
:param informative: the dataframe associated with the informative pair
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@@ -119,13 +117,13 @@ class FreqaiExampleHybridStrategy(IStrategy):
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)

42
freqtrade/templates/FreqaiExampleStrategy.py
View File

@@ -53,7 +53,7 @@ class FreqaiExampleStrategy(IStrategy):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + coin `
passed to the training/prediction by prepending indicators with `f'%-{pair}`
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
@@ -63,8 +63,6 @@ class FreqaiExampleStrategy(IStrategy):
:param informative: the dataframe associated with the informative pair
"""
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@@ -72,36 +70,36 @@ class FreqaiExampleStrategy(IStrategy):
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}raw_volume"] = informative["volume"]
informative[f"%-{pair}raw_price"] = informative["close"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data

35
freqtrade/worker.py
View File

@@ -14,6 +14,7 @@ from freqtrade.configuration import Configuration
from freqtrade.constants import PROCESS_THROTTLE_SECS, RETRY_TIMEOUT, Config
from freqtrade.enums import State
from freqtrade.exceptions import OperationalException, TemporaryError
from freqtrade.exchange import timeframe_to_next_date
from freqtrade.freqtradebot import FreqtradeBot
@@ -35,7 +36,6 @@ class Worker:
self._config = config
self._init(False)
self.last_throttle_start_time: float = 0
self._heartbeat_msg: float = 0
# Tell systemd that we completed initialization phase
@@ -112,7 +112,10 @@ class Worker:
# Ping systemd watchdog before throttling
self._notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs)
# Use an offset of 1s to ensure a new candle has been issued
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs,
timeframe=self._config['timeframe'] if self._config else None,
timeframe_offset=1)
if self._heartbeat_interval:
now = time.time()
@@ -127,24 +130,42 @@ class Worker:
return state
def _throttle(self, func: Callable[..., Any], throttle_secs: float, *args, **kwargs) -> Any:
def _throttle(self, func: Callable[..., Any], throttle_secs: float,
timeframe: Optional[str] = None, timeframe_offset: float = 1.0,
*args, **kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param throttle_secs: throttling interation execution time limit in seconds
:param timeframe: ensure iteration is executed at the beginning of the next candle.
:param timeframe_offset: offset in seconds to apply to the next candle time.
:return: Any (result of execution of func)
"""
self.last_throttle_start_time = time.time()
last_throttle_start_time = time.time()
logger.debug("========================================")
result = func(*args, **kwargs)
time_passed = time.time() - self.last_throttle_start_time
sleep_duration = max(throttle_secs - time_passed, 0.0)
time_passed = time.time() - last_throttle_start_time
sleep_duration = throttle_secs - time_passed
if timeframe:
next_tf = timeframe_to_next_date(timeframe)
# Maximum throttling should be until new candle arrives
# Offset of 0.2s is added to ensure a new candle has been issued.
next_tf_with_offset = next_tf.timestamp() - time.time() + timeframe_offset
sleep_duration = min(sleep_duration, next_tf_with_offset)
sleep_duration = max(sleep_duration, 0.0)
# next_iter = datetime.now(timezone.utc) + timedelta(seconds=sleep_duration)
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
f"last iteration took {time_passed:.2f} s.")
time.sleep(sleep_duration)
self._sleep(sleep_duration)
return result
@staticmethod
def _sleep(sleep_duration: float) -> None:
"""Local sleep method - to improve testability"""
time.sleep(sleep_duration)
def _process_stopped(self) -> None:
self.freqtrade.process_stopped()