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Merge branch 'develop' into dev-merge-rl

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This commit is contained in:
robcaulk
2022-09-04 11:23:25 +02:00
parent 27dce20b29 8545d74378
commit 69b3fcfd32
97 changed files with 1710 additions and 958 deletions
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2
freqtrade/__init__.py
View File

@@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2022.8.dev'
__version__ = '2022.9.dev'
if 'dev' in __version__:
try:

2
freqtrade/commands/arguments.py
View File

@@ -34,7 +34,7 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",
"hyperopt_loss", "disableparamexport",
"hyperopt_ignore_missing_space"]
"hyperopt_ignore_missing_space", "analyze_per_epoch"]
ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]

9
freqtrade/commands/cli_options.py
View File

@@ -255,6 +255,13 @@ AVAILABLE_CLI_OPTIONS = {
nargs='+',
default='default',
),
"analyze_per_epoch": Arg(
'--analyze-per-epoch',
help='Run populate_indicators once per epoch.',
action='store_true',
default=False,
),
"print_all": Arg(
'--print-all',
help='Print all results, not only the best ones.',
@@ -455,7 +462,7 @@ AVAILABLE_CLI_OPTIONS = {
),
"prepend_data": Arg(
'--prepend',
help='Allow data prepending.',
help='Allow data prepending. (Data-appending is disabled)',
action='store_true',
),
"erase": Arg(

3
freqtrade/commands/data_commands.py
View File

@@ -11,8 +11,7 @@ from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_oh
refresh_backtest_trades_data)
from freqtrade.enums import CandleType, RunMode, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.exchange.exchange import market_is_active
from freqtrade.exchange import market_is_active, timeframe_to_minutes
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
from freqtrade.resolvers import ExchangeResolver

3
freqtrade/configuration/configuration.py
View File

@@ -302,6 +302,9 @@ class Configuration:
self._args_to_config(config, argname='spaces',
logstring='Parameter -s/--spaces detected: {}')
self._args_to_config(config, argname='analyze_per_epoch',
logstring='Parameter --analyze-per-epoch detected.')
self._args_to_config(config, argname='print_all',
logstring='Parameter --print-all detected ...')

3
freqtrade/constants.py
View File

@@ -23,7 +23,8 @@ REQUIRED_ORDERTIF = ['entry', 'exit']
REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange']
PRICING_SIDES = ['ask', 'bid', 'same', 'other']
ORDERTYPE_POSSIBILITIES = ['limit', 'market']
ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
_ORDERTIF_POSSIBILITIES = ['GTC', 'FOK', 'IOC', 'PO']
ORDERTIF_POSSIBILITIES = _ORDERTIF_POSSIBILITIES + [t.lower() for t in _ORDERTIF_POSSIBILITIES]
HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',

21
freqtrade/data/dataprovider.py
View File

@@ -91,9 +91,9 @@ class DataProvider:
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
timerange.subtract_start(
timeframe_to_seconds(str(timeframe)) * self._config.get('startup_candle_count', 0)
)
startup_candles = self.get_required_startup(str(timeframe))
tf_seconds = timeframe_to_seconds(str(timeframe))
timerange.subtract_start(tf_seconds * startup_candles)
self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
pair=pair,
timeframe=timeframe or self._config['timeframe'],
@@ -105,6 +105,21 @@ class DataProvider:
)
return self.__cached_pairs_backtesting[saved_pair].copy()
def get_required_startup(self, timeframe: str) -> int:
freqai_config = self._config.get('freqai', {})
if not freqai_config.get('enabled', False):
return self._config.get('startup_candle_count', 0)
else:
startup_candles = self._config.get('startup_candle_count', 0)
indicator_periods = freqai_config['feature_parameters']['indicator_periods_candles']
# 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
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
def get_pair_dataframe(
self,
pair: str,

2
freqtrade/edge/edge_positioning.py
View File

@@ -15,7 +15,7 @@ from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
from freqtrade.data.history import get_timerange, load_data, refresh_data
from freqtrade.enums import CandleType, ExitType, RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange.exchange import timeframe_to_seconds
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.strategy.interface import IStrategy

1
freqtrade/enums/__init__.py
View File

@@ -3,6 +3,7 @@ from freqtrade.enums.backteststate import BacktestState
from freqtrade.enums.candletype import CandleType
from freqtrade.enums.exitchecktuple import ExitCheckTuple
from freqtrade.enums.exittype import ExitType
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.enums.marginmode import MarginMode
from freqtrade.enums.ordertypevalue import OrderTypeValues
from freqtrade.enums.rpcmessagetype import RPCMessageType

12
freqtrade/enums/hyperoptstate.py Normal file
View File

@@ -0,0 +1,12 @@
from enum import Enum
class HyperoptState(Enum):
""" Hyperopt states """
STARTUP = 1
DATALOAD = 2
INDICATORS = 3
OPTIMIZE = 4
def __str__(self):
return f"{self.name.lower()}"

10
freqtrade/exchange/__init__.py
View File

@@ -9,11 +9,11 @@ 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_contracts, amount_to_precision,
available_exchanges, ccxt_exchanges, contracts_to_amount,
date_minus_candles, is_exchange_known_ccxt,
is_exchange_officially_supported, market_is_active,
price_to_precision, timeframe_to_minutes,
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, is_exchange_officially_supported,
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)

24
freqtrade/exchange/binance.py
View File

@@ -23,8 +23,7 @@ class Binance(Exchange):
_ft_has: Dict = {
"stoploss_on_exchange": True,
"stoploss_order_types": {"limit": "stop_loss_limit"},
"order_time_in_force": ['gtc', 'fok', 'ioc'],
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['GTC', 'FOK', 'IOC'],
"ohlcv_candle_limit": 1000,
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
@@ -137,23 +136,27 @@ class Binance(Exchange):
pair: str,
open_rate: float, # Entry price of position
is_short: bool,
position: float, # Absolute value of position size
amount: float,
stake_amount: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
) -> Optional[float]:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
MARGIN: https://www.binance.com/en/support/faq/f6b010588e55413aa58b7d63ee0125ed
PERPETUAL: https://www.binance.com/en/support/faq/b3c689c1f50a44cabb3a84e663b81d93
:param exchange_name:
:param open_rate: (EP1) Entry price of position
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param position: Absolute value of position size (in base currency)
:param wallet_balance: (WB)
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade
Cross-Margin Mode: crossWalletBalance
Isolated-Margin Mode: isolatedWalletBalance
:param maintenance_amt:
# * Only required for Cross
:param mm_ex_1: (TMM)
@@ -165,12 +168,11 @@ class Binance(Exchange):
"""
side_1 = -1 if is_short else 1
position = abs(position)
cross_vars = upnl_ex_1 - mm_ex_1 if self.margin_mode == MarginMode.CROSS else 0.0
# mm_ratio: Binance's formula specifies maintenance margin rate which is mm_ratio * 100%
# maintenance_amt: (CUM) Maintenance Amount of position
mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, position)
mm_ratio, maintenance_amt = self.get_maintenance_ratio_and_amt(pair, stake_amount)
if (maintenance_amt is None):
raise OperationalException(
@@ -182,9 +184,9 @@ class Binance(Exchange):
return (
(
(wallet_balance + cross_vars + maintenance_amt) -
(side_1 * position * open_rate)
(side_1 * amount * open_rate)
) / (
(position * mm_ratio) - (side_1 * position)
(amount * mm_ratio) - (side_1 * amount)
)
)
else:

117
freqtrade/exchange/exchange.py
View File

@@ -62,7 +62,7 @@ class Exchange:
# or by specifying them in the configuration.
_ft_has_default: Dict = {
"stoploss_on_exchange": False,
"order_time_in_force": ["gtc"],
"order_time_in_force": ["GTC"],
"time_in_force_parameter": "timeInForce",
"ohlcv_params": {},
"ohlcv_candle_limit": 500,
@@ -611,7 +611,7 @@ class Exchange:
"""
Checks if order time in force configured in strategy/config are supported
"""
if any(v not in self._ft_has["order_time_in_force"]
if any(v.upper() not in self._ft_has["order_time_in_force"]
for k, v in order_time_in_force.items()):
raise OperationalException(
f'Time in force policies are not supported for {self.name} yet.')
@@ -989,12 +989,12 @@ class Exchange:
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
params = self._params.copy()
if time_in_force != 'gtc' and ordertype != 'market':
if time_in_force != 'GTC' and ordertype != 'market':
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: time_in_force})
params.update({param: time_in_force.upper()})
if reduceOnly:
params.update({'reduceOnly': True})
return params
@@ -1009,7 +1009,7 @@ class Exchange:
rate: float,
leverage: float,
reduceOnly: bool = False,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
if self._config['dry_run']:
dry_order = self.create_dry_run_order(
@@ -2316,10 +2316,10 @@ class Exchange:
def parse_leverage_tier(self, tier) -> Dict:
info = tier.get('info', {})
return {
'min': tier['minNotional'],
'max': tier['maxNotional'],
'mmr': tier['maintenanceMarginRate'],
'lev': tier['maxLeverage'],
'minNotional': tier['minNotional'],
'maxNotional': tier['maxNotional'],
'maintenanceMarginRate': tier['maintenanceMarginRate'],
'maxLeverage': tier['maxLeverage'],
'maintAmt': float(info['cum']) if 'cum' in info else None,
}
@@ -2348,18 +2348,18 @@ class Exchange:
pair_tiers = self._leverage_tiers[pair]
if stake_amount == 0:
return self._leverage_tiers[pair][0]['lev'] # Max lev for lowest amount
return self._leverage_tiers[pair][0]['maxLeverage'] # Max lev for lowest amount
for tier_index in range(len(pair_tiers)):
tier = pair_tiers[tier_index]
lev = tier['lev']
lev = tier['maxLeverage']
if tier_index < len(pair_tiers) - 1:
next_tier = pair_tiers[tier_index + 1]
next_floor = next_tier['min'] / next_tier['lev']
next_floor = next_tier['minNotional'] / next_tier['maxLeverage']
if next_floor > stake_amount: # Next tier min too high for stake amount
return min((tier['max'] / stake_amount), lev)
return min((tier['maxNotional'] / stake_amount), lev)
#
# With the two leverage tiers below,
# - a stake amount of 150 would mean a max leverage of (10000 / 150) = 66.66
@@ -2380,10 +2380,11 @@ class Exchange:
#
else: # if on the last tier
if stake_amount > tier['max']: # If stake is > than max tradeable amount
if stake_amount > tier['maxNotional']:
# If stake is > than max tradeable amount
raise InvalidOrderException(f'Amount {stake_amount} too high for {pair}')
else:
return tier['lev']
return tier['maxLeverage']
raise OperationalException(
'Looped through all tiers without finding a max leverage. Should never be reached'
@@ -2431,35 +2432,6 @@ class Exchange:
"""
return 0.0
def get_liquidation_price(
self,
pair: str,
open_rate: float,
amount: float, # quote currency, includes leverage
leverage: float,
is_short: bool
) -> Optional[float]:
if self.trading_mode in TradingMode.SPOT:
return None
elif (
self.trading_mode == TradingMode.FUTURES
):
wallet_balance = (amount * open_rate) / leverage
isolated_liq = self.get_or_calculate_liquidation_price(
pair=pair,
open_rate=open_rate,
is_short=is_short,
position=amount,
wallet_balance=wallet_balance,
mm_ex_1=0.0,
upnl_ex_1=0.0,
)
return isolated_liq
else:
raise OperationalException(
"Freqtrade currently only supports futures for leverage trading.")
def funding_fee_cutoff(self, open_date: datetime):
"""
:param open_date: The open date for a trade
@@ -2620,34 +2592,36 @@ class Exchange:
else:
return 0.0
def get_or_calculate_liquidation_price(
def get_liquidation_price(
self,
pair: str,
# Dry-run
open_rate: float, # Entry price of position
is_short: bool,
position: float, # Absolute value of position size
wallet_balance: float, # Or margin balance
amount: float, # Absolute value of position size
stake_amount: float,
wallet_balance: float,
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
) -> Optional[float]:
"""
Set's the margin mode on the exchange to cross or isolated for a specific pair
:param pair: base/quote currency pair (e.g. "ADA/USDT")
"""
if self.trading_mode == TradingMode.SPOT:
return None
elif (self.trading_mode != TradingMode.FUTURES):
raise OperationalException(
f"{self.name} does not support {self.margin_mode.value} {self.trading_mode.value}")
f"{self.name} does not support {self.margin_mode} {self.trading_mode}")
isolated_liq = None
if self._config['dry_run'] or not self.exchange_has("fetchPositions"):
isolated_liq = self.dry_run_liquidation_price(
pair=pair,
open_rate=open_rate,
is_short=is_short,
position=position,
amount=amount,
stake_amount=stake_amount,
wallet_balance=wallet_balance,
mm_ex_1=mm_ex_1,
upnl_ex_1=upnl_ex_1
@@ -2657,8 +2631,6 @@ class Exchange:
if len(positions) > 0:
pos = positions[0]
isolated_liq = pos['liquidationPrice']
else:
return None
if isolated_liq:
buffer_amount = abs(open_rate - isolated_liq) * self.liquidation_buffer
@@ -2676,22 +2648,24 @@ class Exchange:
pair: str,
open_rate: float, # Entry price of position
is_short: bool,
position: float, # Absolute value of position size
amount: float,
stake_amount: float,
wallet_balance: float, # Or margin balance
mm_ex_1: float = 0.0, # (Binance) Cross only
upnl_ex_1: float = 0.0, # (Binance) Cross only
) -> Optional[float]:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL:
gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price
okex: https://www.okex.com/support/hc/en-us/articles/
360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
Important: Must be fetching data from cached values as this is used by backtesting!
:param exchange_name:
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param position: Absolute value of position size incl. leverage (in base currency)
:param amount: Absolute value of position size incl. leverage (in base currency)
:param stake_amount: Stake amount - Collateral in settle currency.
:param trading_mode: SPOT, MARGIN, FUTURES, etc.
:param margin_mode: Either ISOLATED or CROSS
:param wallet_balance: Amount of margin_mode in the wallet being used to trade
@@ -2705,7 +2679,7 @@ class Exchange:
market = self.markets[pair]
taker_fee_rate = market['taker']
mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, position)
mm_ratio, _ = self.get_maintenance_ratio_and_amt(pair, stake_amount)
if self.trading_mode == TradingMode.FUTURES and self.margin_mode == MarginMode.ISOLATED:
@@ -2713,7 +2687,7 @@ class Exchange:
raise OperationalException(
"Freqtrade does not yet support inverse contracts")
value = wallet_balance / position
value = wallet_balance / amount
mm_ratio_taker = (mm_ratio + taker_fee_rate)
if is_short:
@@ -2749,8 +2723,8 @@ class Exchange:
pair_tiers = self._leverage_tiers[pair]
for tier in reversed(pair_tiers):
if nominal_value >= tier['min']:
return (tier['mmr'], tier['maintAmt'])
if nominal_value >= tier['minNotional']:
return (tier['maintenanceMarginRate'], tier['maintAmt'])
raise OperationalException("nominal value can not be lower than 0")
# The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it
@@ -2943,6 +2917,29 @@ def amount_to_precision(amount: float, amount_precision: Optional[float],
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:
"""

1
freqtrade/exchange/ftx.py
View File

@@ -19,6 +19,7 @@ logger = logging.getLogger(__name__)
class Ftx(Exchange):
_ft_has: Dict = {
"order_time_in_force": ['GTC', 'IOC', 'PO'],
"stoploss_on_exchange": True,
"ohlcv_candle_limit": 1500,
"ohlcv_require_since": True,

7
freqtrade/exchange/gateio.py
View File

@@ -25,8 +25,7 @@ class Gateio(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['gtc', 'ioc'],
"order_time_in_force": ['GTC', 'IOC'],
"stoploss_order_types": {"limit": "limit"},
"stoploss_on_exchange": True,
}
@@ -57,7 +56,7 @@ class Gateio(Exchange):
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
params = super()._get_params(
side=side,
@@ -69,7 +68,7 @@ class Gateio(Exchange):
if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES:
params['type'] = 'market'
param = self._ft_has.get('time_in_force_parameter', '')
params.update({param: 'ioc'})
params.update({param: 'IOC'})
return params
def get_trades_for_order(self, order_id: str, pair: str, since: datetime,

2
freqtrade/exchange/kraken.py
View File

@@ -171,7 +171,7 @@ class Kraken(Exchange):
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc'
time_in_force: str = 'GTC'
) -> Dict:
params = super()._get_params(
side=side,

3
freqtrade/exchange/kucoin.py
View File

@@ -23,8 +23,7 @@ class Kucoin(Exchange):
"stoploss_order_types": {"limit": "limit", "market": "market"},
"l2_limit_range": [20, 100],
"l2_limit_range_required": False,
"order_time_in_force": ['gtc', 'fok', 'ioc'],
"time_in_force_parameter": "timeInForce",
"order_time_in_force": ['GTC', 'FOK', 'IOC'],
"ohlcv_candle_limit": 1500,
}

4
freqtrade/exchange/okx.py
View File

@@ -98,7 +98,7 @@ class Okx(Exchange):
ordertype: str,
leverage: float,
reduceOnly: bool,
time_in_force: str = 'gtc',
time_in_force: str = 'GTC',
) -> Dict:
params = super()._get_params(
side=side,
@@ -146,4 +146,4 @@ class Okx(Exchange):
return float('inf')
pair_tiers = self._leverage_tiers[pair]
return pair_tiers[-1]['max'] / leverage
return pair_tiers[-1]['maxNotional'] / leverage

1
freqtrade/freqai/data_drawer.py
View File

@@ -579,7 +579,6 @@ class FreqaiDataDrawer:
for training according to user defined train_period_days
metadata: dict = strategy furnished pair metadata
"""
with self.history_lock:
corr_dataframes: Dict[Any, Any] = {}
base_dataframes: Dict[Any, Any] = {}

198
freqtrade/freqai/data_kitchen.py
View File

@@ -16,8 +16,6 @@ from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.strategy.interface import IStrategy
@@ -71,6 +69,8 @@ class FreqaiDataKitchen:
self.label_list: List = []
self.training_features_list: List = []
self.model_filename: str = ""
self.backtesting_results_path = Path()
self.backtest_predictions_folder: str = "backtesting_predictions"
self.live = live
self.pair = pair
@@ -168,9 +168,17 @@ class FreqaiDataKitchen:
train_labels = labels
train_weights = weights
return self.build_data_dictionary(
train_features, test_features, train_labels, test_labels, train_weights, test_weights
)
# Simplest way to reverse the order of training and test data:
if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
return self.build_data_dictionary(
test_features, train_features, test_labels,
train_labels, test_weights, train_weights
)
else:
return self.build_data_dictionary(
train_features, test_features, train_labels,
test_labels, train_weights, test_weights
)
def filter_features(
self,
@@ -281,6 +289,7 @@ class FreqaiDataKitchen:
:returns:
:data_dictionary: updated dictionary with standardized values.
"""
# standardize the data by training stats
train_max = data_dictionary["train_features"].max()
train_min = data_dictionary["train_features"].min()
@@ -314,10 +323,24 @@ class FreqaiDataKitchen:
- 1
)
self.data[f"{item}_max"] = train_labels_max # .to_dict()
self.data[f"{item}_min"] = train_labels_min # .to_dict()
self.data[f"{item}_max"] = train_labels_max
self.data[f"{item}_min"] = train_labels_min
return data_dictionary
def normalize_single_dataframe(self, df: DataFrame) -> DataFrame:
train_max = df.max()
train_min = df.min()
df = (
2 * (df - train_min) / (train_max - train_min) - 1
)
for item in train_max.keys():
self.data[item + "_max"] = train_max[item]
self.data[item + "_min"] = train_min[item]
return df
def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
"""
Normalize a set of data using the mean and standard deviation from
@@ -444,22 +467,23 @@ class FreqaiDataKitchen:
from sklearn.decomposition import PCA # avoid importing if we dont need it
n_components = self.data_dictionary["train_features"].shape[1]
pca = PCA(n_components=n_components)
pca = PCA(0.999)
pca = pca.fit(self.data_dictionary["train_features"])
n_keep_components = np.argmin(pca.explained_variance_ratio_.cumsum() < 0.999)
pca2 = PCA(n_components=n_keep_components)
n_keep_components = pca.n_components_
self.data["n_kept_components"] = n_keep_components
pca2 = pca2.fit(self.data_dictionary["train_features"])
n_components = self.data_dictionary["train_features"].shape[1]
logger.info("reduced feature dimension by %s", n_components - n_keep_components)
logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_))
train_components = pca2.transform(self.data_dictionary["train_features"])
logger.info("explained variance %f", np.sum(pca.explained_variance_ratio_))
train_components = pca.transform(self.data_dictionary["train_features"])
self.data_dictionary["train_features"] = pd.DataFrame(
data=train_components,
columns=["PC" + str(i) for i in range(0, n_keep_components)],
index=self.data_dictionary["train_features"].index,
)
# normalsing transformed training features
self.data_dictionary["train_features"] = self.normalize_single_dataframe(
self.data_dictionary["train_features"])
# keeping a copy of the non-transformed features so we can check for errors during
# model load from disk
@@ -467,15 +491,18 @@ class FreqaiDataKitchen:
self.training_features_list = self.data_dictionary["train_features"].columns
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
test_components = pca2.transform(self.data_dictionary["test_features"])
test_components = pca.transform(self.data_dictionary["test_features"])
self.data_dictionary["test_features"] = pd.DataFrame(
data=test_components,
columns=["PC" + str(i) for i in range(0, n_keep_components)],
index=self.data_dictionary["test_features"].index,
)
# normalise transformed test feature to transformed training features
self.data_dictionary["test_features"] = self.normalize_data_from_metadata(
self.data_dictionary["test_features"])
self.data["n_kept_components"] = n_keep_components
self.pca = pca2
self.pca = pca
logger.info(f"PCA reduced total features from {n_components} to {n_keep_components}")
@@ -496,6 +523,9 @@ class FreqaiDataKitchen:
columns=["PC" + str(i) for i in range(0, self.data["n_kept_components"])],
index=filtered_dataframe.index,
)
# normalise transformed predictions to transformed training features
self.data_dictionary["prediction_features"] = self.normalize_data_from_metadata(
self.data_dictionary["prediction_features"])
def compute_distances(self) -> float:
"""
@@ -513,6 +543,18 @@ class FreqaiDataKitchen:
return avg_mean_dist
def get_outlier_percentage(self, dropped_pts: npt.NDArray) -> float:
"""
Check if more than X% of points werer dropped during outlier detection.
"""
outlier_protection_pct = self.freqai_config["feature_parameters"].get(
"outlier_protection_percentage", 30)
outlier_pct = (dropped_pts.sum() / len(dropped_pts)) * 100
if outlier_pct >= outlier_protection_pct:
return outlier_pct
else:
return 0.0
def use_SVM_to_remove_outliers(self, predict: bool) -> None:
"""
Build/inference a Support Vector Machine to detect outliers
@@ -550,8 +592,17 @@ class FreqaiDataKitchen:
self.data_dictionary["train_features"]
)
y_pred = self.svm_model.predict(self.data_dictionary["train_features"])
dropped_points = np.where(y_pred == -1, 0, y_pred)
kept_points = np.where(y_pred == -1, 0, y_pred)
# keep_index = np.where(y_pred == 1)
outlier_pct = self.get_outlier_percentage(1 - kept_points)
if outlier_pct:
logger.warning(
f"SVM detected {outlier_pct:.2f}% of the points as outliers. "
f"Keeping original dataset."
)
self.svm_model = None
return
self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
(y_pred == 1)
]
@@ -563,7 +614,7 @@ class FreqaiDataKitchen:
]
logger.info(
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f"SVM tossed {len(y_pred) - kept_points.sum()}"
f" train points from {len(y_pred)} total points."
)
@@ -572,7 +623,7 @@ class FreqaiDataKitchen:
# to reduce code duplication
if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0:
y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
dropped_points = np.where(y_pred == -1, 0, y_pred)
kept_points = np.where(y_pred == -1, 0, y_pred)
self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
(y_pred == 1)
]
@@ -583,7 +634,7 @@ class FreqaiDataKitchen:
]
logger.info(
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f"SVM tossed {len(y_pred) - kept_points.sum()}"
f" test points from {len(y_pred)} total points."
)
@@ -604,6 +655,8 @@ class FreqaiDataKitchen:
from math import cos, sin
if predict:
if not self.data['DBSCAN_eps']:
return
train_ft_df = self.data_dictionary['train_features']
pred_ft_df = self.data_dictionary['prediction_features']
num_preds = len(pred_ft_df)
@@ -635,8 +688,8 @@ class FreqaiDataKitchen:
cos(angle) * (point[1] - origin[1])
return (x, y)
MinPts = len(self.data_dictionary['train_features'].columns) * 2
# measure pairwise distances to train_features.shape[1]*2 nearest neighbours
MinPts = int(len(self.data_dictionary['train_features'].index) * 0.25)
# measure pairwise distances to nearest neighbours
neighbors = NearestNeighbors(
n_neighbors=MinPts, n_jobs=self.thread_count)
neighbors_fit = neighbors.fit(self.data_dictionary['train_features'])
@@ -667,6 +720,15 @@ class FreqaiDataKitchen:
self.data['DBSCAN_min_samples'] = MinPts
dropped_points = np.where(clustering.labels_ == -1, 1, 0)
outlier_pct = self.get_outlier_percentage(dropped_points)
if outlier_pct:
logger.warning(
f"DBSCAN detected {outlier_pct:.2f}% of the points as outliers. "
f"Keeping original dataset."
)
self.data['DBSCAN_eps'] = 0
return
self.data_dictionary['train_features'] = self.data_dictionary['train_features'][
(clustering.labels_ != -1)
]
@@ -725,7 +787,7 @@ class FreqaiDataKitchen:
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
"being too far from training data"
"being too far from training data."
)
self.do_predict += do_predict
@@ -740,9 +802,10 @@ class FreqaiDataKitchen:
weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
return weights
def append_predictions(self, predictions: DataFrame, do_predict: npt.ArrayLike) -> None:
def get_predictions_to_append(self, predictions: DataFrame,
do_predict: npt.ArrayLike) -> DataFrame:
"""
Append backtest prediction from current backtest period to all previous periods
Get backtest prediction from current backtest period
"""
append_df = DataFrame()
@@ -757,13 +820,18 @@ class FreqaiDataKitchen:
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
append_df["DI_values"] = self.DI_values
return append_df
def append_predictions(self, append_df: DataFrame) -> None:
"""
Append backtest prediction from current backtest period to all previous periods
"""
if self.full_df.empty:
self.full_df = append_df
else:
self.full_df = pd.concat([self.full_df, append_df], axis=0)
return
def fill_predictions(self, dataframe):
"""
Back fill values to before the backtesting range so that the dataframe matches size
@@ -863,9 +931,7 @@ class FreqaiDataKitchen:
# We notice that users like to use exotic indicators where
# they do not know the required timeperiod. Here we include a factor
# of safety by multiplying the user considered "max" by 2.
max_period = self.freqai_config["feature_parameters"].get(
"indicator_max_period_candles", 20
) * 2
max_period = self.config.get('startup_candle_count', 20) * 2
additional_seconds = max_period * max_tf_seconds
if trained_timestamp != 0:
@@ -911,31 +977,6 @@ class FreqaiDataKitchen:
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
def download_all_data_for_training(self, timerange: TimeRange, dp: DataProvider) -> None:
"""
Called only once upon start of bot to download the necessary data for
populating indicators and training the model.
:param timerange: TimeRange = The full data timerange for populating the indicators
and training the model.
:param dp: DataProvider instance attached to the strategy
"""
new_pairs_days = int((timerange.stopts - timerange.startts) / SECONDS_IN_DAY)
if not dp._exchange:
# Not realistic - this is only called in live mode.
raise OperationalException("Dataprovider did not have an exchange attached.")
refresh_backtest_ohlcv_data(
dp._exchange,
pairs=self.all_pairs,
timeframes=self.freqai_config["feature_parameters"].get("include_timeframes"),
datadir=self.config["datadir"],
timerange=timerange,
new_pairs_days=new_pairs_days,
erase=False,
data_format=self.config.get("dataformat_ohlcv", "json"),
trading_mode=self.config.get("trading_mode", "spot"),
prepend=self.config.get("prepend_data", False),
)
def set_all_pairs(self) -> None:
self.all_pairs = copy.deepcopy(
@@ -1049,3 +1090,50 @@ class FreqaiDataKitchen:
if self.unique_classes:
for label in self.unique_classes:
self.unique_class_list += list(self.unique_classes[label])
def save_backtesting_prediction(
self, append_df: DataFrame
) -> None:
"""
Save prediction dataframe from backtesting to h5 file format
:param append_df: dataframe for backtesting period
"""
full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
if not full_predictions_folder.is_dir():
full_predictions_folder.mkdir(parents=True, exist_ok=True)
append_df.to_hdf(self.backtesting_results_path, key='append_df', mode='w')
def get_backtesting_prediction(
self
) -> DataFrame:
"""
Get prediction dataframe from h5 file format
"""
append_df = pd.read_hdf(self.backtesting_results_path)
return append_df
def check_if_backtest_prediction_exists(
self
) -> bool:
"""
Check if a backtesting prediction already exists
:param dk: FreqaiDataKitchen
:return:
:boolean: whether the prediction file exists or not.
"""
path_to_predictionfile = Path(self.full_path /
self.backtest_predictions_folder /
f"{self.model_filename}_prediction.h5")
self.backtesting_results_path = path_to_predictionfile
file_exists = path_to_predictionfile.is_file()
if file_exists:
logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
else:
logger.info(
f"Could not find backtesting prediction file at {path_to_predictionfile}"
)
return file_exists

66
freqtrade/freqai/freqai_interface.py
View File

@@ -71,6 +71,9 @@ 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)
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
@@ -125,10 +128,9 @@ class IFreqaiModel(ABC):
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
with self.analysis_lock:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
dk = self.start_backtesting(dataframe, metadata, self.dk)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
@@ -225,28 +227,39 @@ class IFreqaiModel(ABC):
"trains"
)
trained_timestamp_int = int(trained_timestamp.stopts)
dk.data_path = Path(
dk.full_path
/
f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
f"sub-train-{metadata['pair'].split('/')[0]}_{trained_timestamp_int}"
)
if not self.model_exists(
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
):
dk.find_features(dataframe_train)
self.model = self.train(dataframe_train, metadata["pair"], dk)
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
trained_timestamp.stopts)
dk.set_new_model_names(metadata["pair"], trained_timestamp)
self.dd.save_data(self.model, metadata["pair"], dk)
dk.set_new_model_names(metadata["pair"], trained_timestamp)
if dk.check_if_backtest_prediction_exists():
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
self.model = self.dd.load_data(metadata["pair"], dk)
if not self.model_exists(
metadata["pair"], dk, trained_timestamp=trained_timestamp_int
):
dk.find_features(dataframe_train)
self.model = self.train(dataframe_train, metadata["pair"], dk)
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
trained_timestamp.stopts)
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
if self.save_backtest_models:
logger.info('Saving backtest model to disk.')
self.dd.save_data(self.model, metadata["pair"], dk)
else:
self.model = self.dd.load_data(metadata["pair"], dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
dk.append_predictions(pred_df, do_preds)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
append_df = dk.get_predictions_to_append(pred_df, do_preds)
dk.append_predictions(append_df)
dk.save_backtesting_prediction(append_df)
dk.fill_predictions(dataframe)
@@ -291,14 +304,8 @@ class IFreqaiModel(ABC):
)
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
# download candle history if it is not already in memory
# load candle history into memory if it is not yet.
if not self.dd.historic_data:
logger.info(
"Downloading all training data for all pairs in whitelist and "
"corr_pairlist, this may take a while if you do not have the "
"data saved"
)
dk.download_all_data_for_training(data_load_timerange, strategy.dp)
self.dd.load_all_pair_histories(data_load_timerange, dk)
if not self.scanning:
@@ -463,11 +470,6 @@ class IFreqaiModel(ABC):
:return:
:boolean: whether the model file exists or not.
"""
coin, _ = pair.split("/")
if not self.live:
dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
file_exists = path_to_modelfile.is_file()
if file_exists and not scanning:
@@ -620,8 +622,8 @@ class IFreqaiModel(ABC):
logger.info(
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
if self.inference_time > 0.25 * self.base_tf_seconds:
logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to'
' avoid blinding open trades and degrading performance.')
logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
" avoid blinding open trades and degrading performance.")
self.pair_it = 0
self.inference_time = 0
return

134
freqtrade/freqai/utils.py Normal file
View File

@@ -0,0 +1,134 @@
import logging
from datetime import datetime, timezone
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange.exchange import market_is_active
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
logger = logging.getLogger(__name__)
def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
"""
Called only once upon start of bot to download the necessary data for
populating indicators and training the model.
:param timerange: TimeRange = The full data timerange for populating the indicators
and training the model.
:param dp: DataProvider instance attached to the strategy
"""
if dp._exchange is None:
raise OperationalException('No exchange object found.')
markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
or config.get('include_inactive')]
all_pairs = dynamic_expand_pairlist(config, markets)
timerange = get_required_data_timerange(config)
new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
refresh_backtest_ohlcv_data(
dp._exchange,
pairs=all_pairs,
timeframes=config["freqai"]["feature_parameters"].get("include_timeframes"),
datadir=config["datadir"],
timerange=timerange,
new_pairs_days=new_pairs_days,
erase=False,
data_format=config.get("dataformat_ohlcv", "json"),
trading_mode=config.get("trading_mode", "spot"),
prepend=config.get("prepend_data", False),
)
def get_required_data_timerange(
config: dict
) -> TimeRange:
"""
Used to compute the required data download time range
for auto data-download in FreqAI
"""
time = datetime.now(tz=timezone.utc).timestamp()
timeframes = config["freqai"]["feature_parameters"].get("include_timeframes")
max_tf_seconds = 0
for tf in timeframes:
secs = timeframe_to_seconds(tf)
if secs > max_tf_seconds:
max_tf_seconds = secs
startup_candles = config.get('startup_candle_count', 0)
indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
# factor the max_period as a factor of safety.
max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
config['startup_candle_count'] = max_period
logger.info(f'FreqAI auto-downloader using {max_period} startup candles.')
additional_seconds = max_period * max_tf_seconds
startts = int(
time
- config["freqai"].get("train_period_days", 0) * 86400
- additional_seconds
)
stopts = int(time)
data_load_timerange = TimeRange('date', 'date', startts, stopts)
return data_load_timerange
# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
# def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
# """
# Called only once upon start of bot to download the necessary data for
# populating indicators and training a FreqAI model.
# :param timerange: TimeRange = The full data timerange for populating the indicators
# and training the model.
# :param dp: DataProvider instance attached to the strategy
# """
# if dp._exchange is not None:
# markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
# or config.get('include_inactive')]
# else:
# # This should not occur:
# raise OperationalException('No exchange object found.')
# all_pairs = dynamic_expand_pairlist(config, markets)
# if not dp._exchange:
# # Not realistic - this is only called in live mode.
# raise OperationalException("Dataprovider did not have an exchange attached.")
# time = datetime.now(tz=timezone.utc).timestamp()
# for tf in config["freqai"]["feature_parameters"].get("include_timeframes"):
# timerange = TimeRange()
# timerange.startts = int(time)
# timerange.stopts = int(time)
# startup_candles = dp.get_required_startup(str(tf))
# tf_seconds = timeframe_to_seconds(str(tf))
# timerange.subtract_start(tf_seconds * startup_candles)
# new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
# # FIXME: now that we are looping on `refresh_backtest_ohlcv_data`, the function
# # redownloads the funding rate for each pair.
# refresh_backtest_ohlcv_data(
# dp._exchange,
# pairs=all_pairs,
# timeframes=[tf],
# datadir=config["datadir"],
# timerange=timerange,
# new_pairs_days=new_pairs_days,
# erase=False,
# data_format=config.get("dataformat_ohlcv", "json"),
# trading_mode=config.get("trading_mode", "spot"),
# prepend=config.get("prepend_data", False),
# )

65
freqtrade/freqtradebot.py
View File

@@ -21,8 +21,7 @@ from freqtrade.enums import (ExitCheckTuple, ExitType, RPCMessageType, RunMode,
State, TradingMode)
from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
from freqtrade.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.mixins import LoggingMixin
from freqtrade.persistence import Order, PairLocks, Trade, init_db
@@ -240,7 +239,7 @@ class FreqtradeBot(LoggingMixin):
'status':
f"{len(open_trades)} open trades active.\n\n"
f"Handle these trades manually on {self.exchange.name}, "
f"or '/start' the bot again and use '/stopbuy' "
f"or '/start' the bot again and use '/stopentry' "
f"to handle open trades gracefully. \n"
f"{'Note: Trades are simulated (dry run).' if self.config['dry_run'] else ''}",
}
@@ -1553,9 +1552,10 @@ class FreqtradeBot(LoggingMixin):
trade.close_rate_requested = limit
trade.exit_reason = exit_reason
# Lock pair for one candle to prevent immediate re-trading
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
if not sub_trade_amt:
# Lock pair for one candle to prevent immediate re-trading
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_exit(trade, order_type, sub_trade=bool(sub_trade_amt), order=order_obj)
# In case of market sell orders the order can be closed immediately
@@ -1732,11 +1732,12 @@ class FreqtradeBot(LoggingMixin):
# TODO: Margin will need to use interest_rate as well.
# interest_rate = self.exchange.get_interest_rate()
trade.set_liquidation_price(self.exchange.get_liquidation_price(
leverage=trade.leverage,
pair=trade.pair,
amount=trade.amount,
open_rate=trade.open_rate,
is_short=trade.is_short
is_short=trade.is_short,
amount=trade.amount,
stake_amount=trade.stake_amount,
wallet_balance=trade.stake_amount,
))
# Updating wallets when order is closed
@@ -1777,7 +1778,7 @@ class FreqtradeBot(LoggingMixin):
self.rpc.send_msg(msg)
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
amount: float, fee_abs: float) -> float:
amount: float, fee_abs: float, order_obj: Order) -> Optional[float]:
"""
Applies the fee to amount (either from Order or from Trades).
Can eat into dust if more than the required asset is available.
@@ -1785,40 +1786,42 @@ class FreqtradeBot(LoggingMixin):
never in base currency.
"""
self.wallets.update()
if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount:
amount_ = 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
if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount_:
# Eat into dust if we own more than base currency
logger.info(f"Fee amount for {trade} was in base currency - "
f"Eating Fee {fee_abs} into dust.")
elif fee_abs != 0:
real_amount = self.exchange.amount_to_precision(trade.pair, amount - fee_abs)
logger.info(f"Applying fee on amount for {trade} "
f"(from {amount} to {real_amount}).")
return real_amount
return amount
logger.info(f"Applying fee on amount for {trade}, fee={fee_abs}.")
return fee_abs
return None
def handle_order_fee(self, trade: Trade, order_obj: Order, order: Dict[str, Any]) -> None:
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order, order_obj)
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order_obj.ft_fee_base = trade.amount - new_amount
fee_abs = self.get_real_amount(trade, order, order_obj)
if fee_abs is not None:
order_obj.ft_fee_base = fee_abs
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> float:
def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> Optional[float]:
"""
Detect and update trade fee.
Calls trade.update_fee() upon correct detection.
Returns modified amount if the fee was taken from the destination currency.
Necessary for exchanges which charge fees in base currency (e.g. binance)
:return: identical (or new) amount for the trade
:return: Absolute fee to apply for this order or None
"""
# Init variables
order_amount = safe_value_fallback(order, 'filled', 'amount')
# Only run for closed orders
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
return order_amount
return None
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
# use fee from order-dict if possible
@@ -1835,13 +1838,14 @@ class FreqtradeBot(LoggingMixin):
if trade_base_currency == fee_currency:
# Apply fee to amount
return self.apply_fee_conditional(trade, trade_base_currency,
amount=order_amount, fee_abs=fee_cost)
return order_amount
amount=order_amount, fee_abs=fee_cost,
order_obj=order_obj)
return None
return self.fee_detection_from_trades(
trade, order, order_obj, order_amount, order.get('trades', []))
def fee_detection_from_trades(self, trade: Trade, order: Dict, order_obj: Order,
order_amount: float, trades: List) -> float:
order_amount: float, trades: List) -> Optional[float]:
"""
fee-detection fallback to Trades.
Either uses provided trades list or the result of fetch_my_trades to get correct fee.
@@ -1852,7 +1856,7 @@ class FreqtradeBot(LoggingMixin):
if len(trades) == 0:
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
return order_amount
return None
fee_currency = None
amount = 0
fee_abs = 0.0
@@ -1894,10 +1898,9 @@ class FreqtradeBot(LoggingMixin):
raise DependencyException("Half bought? Amounts don't match")
if fee_abs != 0:
return self.apply_fee_conditional(trade, trade_base_currency,
amount=amount, fee_abs=fee_abs)
else:
return amount
return self.apply_fee_conditional(
trade, trade_base_currency, amount=amount, fee_abs=fee_abs, order_obj=order_obj)
return None
def get_valid_price(self, custom_price: float, proposed_price: float) -> float:
"""

103
freqtrade/optimize/backtesting.py
View File

@@ -23,9 +23,8 @@ from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode,
TradingMode)
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import (amount_to_contracts, amount_to_precision,
contracts_to_amount)
from freqtrade.exchange import (amount_to_contract_precision, price_to_precision,
timeframe_to_minutes, timeframe_to_seconds)
from freqtrade.mixins import LoggingMixin
from freqtrade.optimize.backtest_caching import get_strategy_run_id
from freqtrade.optimize.bt_progress import BTProgress
@@ -213,21 +212,12 @@ class Backtesting:
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
if self.config.get('freqai', {}).get('enabled', False):
startup_candles = int(self.config.get('freqai', {}).get('startup_candles', 0))
if not startup_candles:
raise OperationalException('FreqAI backtesting module requires user set '
'startup_candles in config.')
self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 0))
logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
self.config['startup_candle_count'] = self.required_startup
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=self.timerange,
startup_candles=self.required_startup,
startup_candles=self.dataprovider.get_required_startup(self.timeframe),
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
@@ -535,12 +525,16 @@ class Backtesting:
# Check if we should increase our position
if stake_amount is not None and stake_amount > 0.0:
pos_trade = self._enter_trade(
trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade)
if pos_trade is not None:
self.wallets.update()
return pos_trade
check_adjust_entry = True
if self.strategy.max_entry_position_adjustment > -1:
entry_count = trade.nr_of_successful_entries
check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
if check_adjust_entry:
pos_trade = self._enter_trade(
trade.pair, row, 'short' if trade.is_short else 'long', stake_amount, trade)
if pos_trade is not None:
self.wallets.update()
return pos_trade
if stake_amount is not None and stake_amount < 0.0:
amount = abs(stake_amount) / current_rate
@@ -551,7 +545,8 @@ class Backtesting:
if remaining < min_stake:
# Remaining stake is too low to be sold.
return trade
pos_trade = self._exit_trade(trade, row, current_rate, amount)
exit_ = ExitCheckTuple(ExitType.PARTIAL_EXIT)
pos_trade = self._get_exit_for_signal(trade, row, exit_, amount)
if pos_trade is not None:
order = pos_trade.orders[-1]
if self._get_order_filled(order.price, row):
@@ -571,12 +566,7 @@ class Backtesting:
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
check_adjust_entry = True
if self.strategy.max_entry_position_adjustment > -1:
entry_count = trade.nr_of_successful_entries
check_adjust_entry = (entry_count <= self.strategy.max_entry_position_adjustment)
if check_adjust_entry:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
trade = self._get_adjust_trade_entry_for_candle(trade, row)
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
@@ -591,14 +581,15 @@ class Backtesting:
return t
return None
def _get_exit_for_signal(self, trade: LocalTrade, row: Tuple,
exit_: ExitCheckTuple) -> Optional[LocalTrade]:
def _get_exit_for_signal(
self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
amount: Optional[float] = None) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if exit_.exit_flag:
trade.close_date = exit_candle_time
exit_reason = exit_.exit_reason
amount_ = amount if amount is not None else trade.amount
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
try:
close_rate = self._get_close_rate(row, trade, exit_, trade_dur)
@@ -607,7 +598,8 @@ class Backtesting:
# call the custom exit price,with default value as previous close_rate
current_profit = trade.calc_profit_ratio(close_rate)
order_type = self.strategy.order_types['exit']
if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT):
if exit_.exit_type in (ExitType.EXIT_SIGNAL, ExitType.CUSTOM_EXIT,
ExitType.PARTIAL_EXIT):
# Checks and adds an exit tag, after checking that the length of the
# row has the length for an exit tag column
if (
@@ -635,22 +627,23 @@ class Backtesting:
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['exit']
if (exit_.exit_type != ExitType.LIQUIDATION and not strategy_safe_wrapper(
self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
order_type=order_type,
amount=trade.amount,
rate=close_rate,
time_in_force=time_in_force,
sell_reason=exit_reason, # deprecated
exit_reason=exit_reason,
current_time=exit_candle_time)):
if (exit_.exit_type not in (ExitType.LIQUIDATION, ExitType.PARTIAL_EXIT)
and not strategy_safe_wrapper(
self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair,
trade=trade, # type: ignore[arg-type]
order_type=order_type,
amount=amount_,
rate=close_rate,
time_in_force=time_in_force,
sell_reason=exit_reason, # deprecated
exit_reason=exit_reason,
current_time=exit_candle_time)):
return None
trade.exit_reason = exit_reason
return self._exit_trade(trade, row, close_rate, trade.amount)
return self._exit_trade(trade, row, close_rate, amount_)
return None
def _exit_trade(self, trade: LocalTrade, sell_row: Tuple,
@@ -658,7 +651,10 @@ class Backtesting:
self.order_id_counter += 1
exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
order_type = self.strategy.order_types['exit']
amount = amount or trade.amount
# amount = amount or trade.amount
amount = amount_to_contract_precision(amount or trade.amount, trade.amount_precision,
self.precision_mode, trade.contract_size)
rate = price_to_precision(close_rate, trade.price_precision, self.precision_mode)
order = Order(
id=self.order_id_counter,
ft_trade_id=trade.id,
@@ -672,12 +668,12 @@ class Backtesting:
side=trade.exit_side,
order_type=order_type,
status="open",
price=close_rate,
average=close_rate,
price=rate,
average=rate,
amount=amount,
filled=0,
remaining=amount,
cost=amount * close_rate,
cost=amount * rate,
)
trade.orders.append(order)
return trade
@@ -823,14 +819,14 @@ class Backtesting:
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
self.order_id_counter += 1
base_currency = self.exchange.get_pair_base_currency(pair)
precision_price = self.exchange.get_precision_price(pair)
propose_rate = price_to_precision(propose_rate, precision_price, self.precision_mode)
amount_p = (stake_amount / propose_rate) * leverage
contract_size = self.exchange.get_contract_size(pair)
precision_amount = self.exchange.get_precision_amount(pair)
amount = contracts_to_amount(
amount_to_precision(
amount_to_contracts(amount_p, contract_size),
precision_amount, self.precision_mode),
contract_size)
amount = amount_to_contract_precision(amount_p, precision_amount, self.precision_mode,
contract_size)
# Backcalculate actual stake amount.
stake_amount = amount * propose_rate / leverage
@@ -863,7 +859,7 @@ class Backtesting:
leverage=leverage,
# interest_rate=interest_rate,
amount_precision=precision_amount,
price_precision=self.exchange.get_precision_price(pair),
price_precision=precision_price,
precision_mode=self.precision_mode,
contract_size=contract_size,
orders=[],
@@ -875,7 +871,8 @@ class Backtesting:
pair=pair,
open_rate=propose_rate,
amount=amount,
leverage=leverage,
stake_amount=trade.stake_amount,
wallet_balance=trade.stake_amount,
is_short=is_short,
))

46
freqtrade/optimize/hyperopt.py
View File

@@ -24,13 +24,15 @@ from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.enums import HyperoptState
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.hyperopt_tools import (HyperoptStateContainer, HyperoptTools,
hyperopt_serializer)
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
@@ -74,10 +76,14 @@ class Hyperopt:
self.dimensions: List[Dimension] = []
self.config = config
self.min_date: datetime
self.max_date: datetime
self.backtesting = Backtesting(self.config)
self.pairlist = self.backtesting.pairlists.whitelist
self.custom_hyperopt: HyperOptAuto
self.analyze_per_epoch = self.config.get('analyze_per_epoch', False)
HyperoptStateContainer.set_state(HyperoptState.STARTUP)
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
@@ -290,6 +296,7 @@ class Hyperopt:
Called once per epoch to optimize whatever is configured.
Keep this function as optimized as possible!
"""
HyperoptStateContainer.set_state(HyperoptState.OPTIMIZE)
backtest_start_time = datetime.now(timezone.utc)
params_dict = self._get_params_dict(self.dimensions, raw_params)
@@ -321,6 +328,10 @@ class Hyperopt:
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
if self.analyze_per_epoch:
# Data is not yet analyzed, rerun populate_indicators.
processed = self.advise_and_trim(processed)
bt_results = self.backtesting.backtest(
processed=processed,
start_date=self.min_date,
@@ -406,22 +417,33 @@ class Hyperopt:
def _set_random_state(self, random_state: Optional[int]) -> int:
return random_state or random.randint(1, 2**16 - 1)
def prepare_hyperopt_data(self) -> None:
data, timerange = self.backtesting.load_bt_data()
self.backtesting.load_bt_data_detail()
logger.info("Dataload complete. Calculating indicators")
def advise_and_trim(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe to get correct dates for output.
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
processed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(processed)
return processed
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
def prepare_hyperopt_data(self) -> None:
HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
data, self.timerange = self.backtesting.load_bt_data()
self.backtesting.load_bt_data_detail()
logger.info("Dataload complete. Calculating indicators")
if not self.analyze_per_epoch:
HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
preprocessed = self.advise_and_trim(data)
logger.info(f'Hyperopting with data from '
f'{self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(self.max_date - self.min_date).days} days)..')
# Store non-trimmed data - will be trimmed after signal generation.
dump(preprocessed, self.data_pickle_file)
else:
dump(data, self.data_pickle_file)
def get_asked_points(self, n_points: int) -> Tuple[List[List[Any]], List[bool]]:
"""

10
freqtrade/optimize/hyperopt_tools.py
View File

@@ -13,6 +13,7 @@ from colorama import Fore, Style
from pandas import isna, json_normalize
from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
from freqtrade.enums import HyperoptState
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
@@ -32,6 +33,15 @@ def hyperopt_serializer(x):
return str(x)
class HyperoptStateContainer():
""" Singleton class to track state of hyperopt"""
state: HyperoptState = HyperoptState.OPTIMIZE
@classmethod
def set_state(cls, value: HyperoptState):
cls.state = value
class HyperoptTools():
@staticmethod

20
freqtrade/persistence/trade_model.py
View File

@@ -14,8 +14,7 @@ from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPE
BuySell, LongShort)
from freqtrade.enums import ExitType, TradingMode
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.exchange import amount_to_precision, price_to_precision
from freqtrade.exchange.exchange import amount_to_contracts, contracts_to_amount
from freqtrade.exchange import amount_to_contract_precision, price_to_precision
from freqtrade.leverage import interest
from freqtrade.persistence.base import _DECL_BASE
from freqtrade.util import FtPrecise
@@ -625,11 +624,8 @@ class LocalTrade():
else:
logger.warning(
f'Got different open_order_id {self.open_order_id} != {order.order_id}')
amount_tr = contracts_to_amount(
amount_to_precision(
amount_to_contracts(self.amount, self.contract_size),
self.amount_precision, self.precision_mode),
self.contract_size)
amount_tr = amount_to_contract_precision(self.amount, self.amount_precision,
self.precision_mode, self.contract_size)
if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC):
self.close(order.safe_price)
else:
@@ -652,7 +648,6 @@ class LocalTrade():
"""
self.close_rate = rate
self.close_date = self.close_date or datetime.utcnow()
self.close_profit_abs = self.calc_profit(rate) + self.realized_profit
self.is_open = False
self.exit_order_status = 'closed'
self.open_order_id = None
@@ -847,7 +842,7 @@ class LocalTrade():
avg_price = FtPrecise(0.0)
close_profit = 0.0
close_profit_abs = 0.0
profit = None
for o in self.orders:
if o.ft_is_open or not o.filled:
continue
@@ -874,8 +869,6 @@ class LocalTrade():
close_profit_abs += profit
close_profit = self.calc_profit_ratio(
exit_rate, amount=exit_amount, open_rate=avg_price)
if current_amount <= ZERO:
profit = close_profit_abs
else:
total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price)
@@ -884,8 +877,8 @@ class LocalTrade():
self.realized_profit = close_profit_abs
self.close_profit_abs = profit
current_amount_tr = amount_to_precision(float(current_amount),
self.amount_precision, self.precision_mode)
current_amount_tr = amount_to_contract_precision(
float(current_amount), self.amount_precision, self.precision_mode, self.contract_size)
if current_amount_tr > 0.0:
# Trade is still open
# Leverage not updated, as we don't allow changing leverage through DCA at the moment.
@@ -900,6 +893,7 @@ class LocalTrade():
# Close profit abs / maximum owned
# Fees are considered as they are part of close_profit_abs
self.close_profit = (close_profit_abs / total_stake) * self.leverage
self.close_profit_abs = close_profit_abs
def select_order_by_order_id(self, order_id: str) -> Optional[Order]:
"""

6
freqtrade/plugins/pairlist/PrecisionFilter.py
View File

@@ -52,7 +52,7 @@ class PrecisionFilter(IPairList):
:return: True if the pair can stay, false if it should be removed
"""
if ticker.get('last', None) is None:
self.log_once(f"Removed {ticker['symbol']} from whitelist, because "
self.log_once(f"Removed {pair} from whitelist, because "
"ticker['last'] is empty (Usually no trade in the last 24h).",
logger.info)
return False
@@ -62,10 +62,10 @@ class PrecisionFilter(IPairList):
sp = self._exchange.price_to_precision(pair, stop_price)
stop_gap_price = self._exchange.price_to_precision(pair, stop_price * 0.99)
logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
logger.debug(f"{pair} - {sp} : {stop_gap_price}")
if sp <= stop_gap_price:
self.log_once(f"Removed {ticker['symbol']} from whitelist, because "
self.log_once(f"Removed {pair} from whitelist, because "
f"stop price {sp} would be <= stop limit {stop_gap_price}", logger.info)
return False

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

@@ -186,6 +186,7 @@ class VolumePairList(IPairList):
needed_pairs, since_ms=since_ms, cache=False
)
for i, p in enumerate(filtered_tickers):
contract_size = self._exchange.markets[p['symbol']].get('contractSize', 1.0) or 1.0
pair_candles = candles[
(p['symbol'], self._lookback_timeframe, self._def_candletype)
] if (
@@ -199,6 +200,7 @@ class VolumePairList(IPairList):
pair_candles['quoteVolume'] = (
pair_candles['volume'] * pair_candles['typical_price']
* contract_size
)
else:
# Exchange ohlcv data is in quote volume already.

3
freqtrade/rpc/api_server/api_v1.py
View File

@@ -216,9 +216,10 @@ def stop(rpc: RPC = Depends(get_rpc)):
return rpc._rpc_stop()
@router.post('/stopentry', response_model=StatusMsg, tags=['botcontrol'])
@router.post('/stopbuy', response_model=StatusMsg, tags=['botcontrol'])
def stop_buy(rpc: RPC = Depends(get_rpc)):
return rpc._rpc_stopbuy()
return rpc._rpc_stopentry()
@router.post('/reload_config', response_model=StatusMsg, tags=['botcontrol'])

4
freqtrade/rpc/rpc.py
View File

@@ -657,7 +657,7 @@ class RPC:
self._freqtrade.state = State.RELOAD_CONFIG
return {'status': 'Reloading config ...'}
def _rpc_stopbuy(self) -> Dict[str, str]:
def _rpc_stopentry(self) -> Dict[str, str]:
"""
Handler to stop buying, but handle open trades gracefully.
"""
@@ -665,7 +665,7 @@ class RPC:
# Set 'max_open_trades' to 0
self._freqtrade.config['max_open_trades'] = 0
return {'status': 'No more buy will occur from now. Run /reload_config to reset.'}
return {'status': 'No more entries will occur from now. Run /reload_config to reset.'}
def __exec_force_exit(self, trade: Trade, ordertype: Optional[str],
amount: Optional[float] = None) -> None:

34
freqtrade/rpc/telegram.py
View File

@@ -114,18 +114,20 @@ class Telegram(RPCHandler):
# TODO: DRY! - its not good to list all valid cmds here. But otherwise
# this needs refactoring of the whole telegram module (same
# problem in _help()).
valid_keys: List[str] = [r'/start$', r'/stop$', r'/status$', r'/status table$',
r'/trades$', r'/performance$', r'/buys', r'/entries',
r'/sells', r'/exits', r'/mix_tags',
r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
r'/stats$', r'/count$', r'/locks$', r'/balance$',
r'/stopbuy$', r'/reload_config$', r'/show_config$',
r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$',
r'/blacklist$', r'/bl_delete$',
r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
r'/forcebuy$', r'/forcelong$', r'/forceshort$',
r'/forcesell$', r'/forceexit$',
r'/edge$', r'/health$', r'/help$', r'/version$']
valid_keys: List[str] = [
r'/start$', r'/stop$', r'/status$', r'/status table$',
r'/trades$', r'/performance$', r'/buys', r'/entries',
r'/sells', r'/exits', r'/mix_tags',
r'/daily$', r'/daily \d+$', r'/profit$', r'/profit \d+',
r'/stats$', r'/count$', r'/locks$', r'/balance$',
r'/stopbuy$', r'/stopentry$', r'/reload_config$', r'/show_config$',
r'/logs$', r'/whitelist$', r'/whitelist(\ssorted|\sbaseonly)+$',
r'/blacklist$', r'/bl_delete$',
r'/weekly$', r'/weekly \d+$', r'/monthly$', r'/monthly \d+$',
r'/forcebuy$', r'/forcelong$', r'/forceshort$',
r'/forcesell$', r'/forceexit$',
r'/edge$', r'/health$', r'/help$', r'/version$'
]
# Create keys for generation
valid_keys_print = [k.replace('$', '') for k in valid_keys]
@@ -182,7 +184,7 @@ class Telegram(RPCHandler):
CommandHandler(['unlock', 'delete_locks'], self._delete_locks),
CommandHandler(['reload_config', 'reload_conf'], self._reload_config),
CommandHandler(['show_config', 'show_conf'], self._show_config),
CommandHandler('stopbuy', self._stopbuy),
CommandHandler(['stopbuy', 'stopentry'], self._stopentry),
CommandHandler('whitelist', self._whitelist),
CommandHandler('blacklist', self._blacklist),
CommandHandler(['blacklist_delete', 'bl_delete'], self._blacklist_delete),
@@ -984,7 +986,7 @@ class Telegram(RPCHandler):
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
def _stopbuy(self, update: Update, context: CallbackContext) -> None:
def _stopentry(self, update: Update, context: CallbackContext) -> None:
"""
Handler for /stop_buy.
Sets max_open_trades to 0 and gracefully sells all open trades
@@ -992,7 +994,7 @@ class Telegram(RPCHandler):
:param update: message update
:return: None
"""
msg = self._rpc._rpc_stopbuy()
msg = self._rpc._rpc_stopentry()
self._send_msg(f"Status: `{msg['status']}`")
@authorized_only
@@ -1488,7 +1490,7 @@ class Telegram(RPCHandler):
"------------\n"
"*/start:* `Starts the trader`\n"
"*/stop:* Stops the trader\n"
"*/stopbuy:* `Stops buying, but handles open trades gracefully` \n"
"*/stopentry:* `Stops entering, but handles open trades gracefully` \n"
"*/forceexit <trade_id>|all:* `Instantly exits the given trade or all trades, "
"regardless of profit`\n"
"*/fx <trade_id>|all:* `Alias to /forceexit`\n"

15
freqtrade/strategy/interface.py
View File

@@ -78,8 +78,8 @@ class IStrategy(ABC, HyperStrategyMixin):
# Optional time in force
order_time_in_force: Dict = {
'entry': 'gtc',
'exit': 'gtc',
'entry': 'GTC',
'exit': 'GTC',
}
# run "populate_indicators" only for new candle
@@ -148,10 +148,19 @@ class IStrategy(ABC, HyperStrategyMixin):
def load_freqAI_model(self) -> None:
if self.config.get('freqai', {}).get('enabled', False):
# Import here to avoid importing this if freqAI is disabled
from freqtrade.freqai.utils import download_all_data_for_training
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
self.freqai_info = self.config["freqai"]
# download the desired data in dry/live
if self.config.get('runmode') in (RunMode.DRY_RUN, RunMode.LIVE):
logger.info(
"Downloading all training data for all pairs in whitelist and "
"corr_pairlist, this may take a while if the data is not "
"already on disk."
)
download_all_data_for_training(self.dp, self.config)
self.freqai.strategy = self
else:
# Gracious failures if freqAI is disabled but "start" is called.

16
freqtrade/strategy/parameters.py
View File

@@ -7,6 +7,9 @@ from abc import ABC, abstractmethod
from contextlib import suppress
from typing import Any, Optional, Sequence, Union
from freqtrade.enums.hyperoptstate import HyperoptState
from freqtrade.optimize.hyperopt_tools import HyperoptStateContainer
with suppress(ImportError):
from skopt.space import Integer, Real, Categorical
@@ -57,6 +60,13 @@ class BaseParameter(ABC):
Get-space - will be used by Hyperopt to get the hyperopt Space
"""
def can_optimize(self):
return (
self.in_space
and self.optimize
and HyperoptStateContainer.state != HyperoptState.OPTIMIZE
)
class NumericParameter(BaseParameter):
""" Internal parameter used for Numeric purposes """
@@ -133,7 +143,7 @@ class IntParameter(NumericParameter):
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
if self.can_optimize():
# Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
return range(self.low, self.high + 1)
else:
@@ -212,7 +222,7 @@ class DecimalParameter(NumericParameter):
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
if self.can_optimize():
low = int(self.low * pow(10, self._decimals))
high = int(self.high * pow(10, self._decimals)) + 1
return [round(n * pow(0.1, self._decimals), self._decimals) for n in range(low, high)]
@@ -261,7 +271,7 @@ class CategoricalParameter(BaseParameter):
Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
calculating 100ds of indicators.
"""
if self.in_space and self.optimize:
if self.can_optimize():
return self.opt_range
else:
return [self.value]

3
freqtrade/templates/FreqaiExampleStrategy.py
View File

@@ -43,7 +43,8 @@ class FreqaiExampleStrategy(IStrategy):
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
# this is the maximum period fed to talib (timeframe independent)
startup_candle_count: int = 40
can_short = False
linear_roi_offset = DecimalParameter(

258
freqtrade/templates/FreqaiHybridExampleStrategy.py Normal file
View File

@@ -0,0 +1,258 @@
import logging
import numpy as np
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
class FreqaiExampleHybridStrategy(IStrategy):
"""
Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
FreqAI to bolster a typical Freqtrade strategy.
Launching this strategy would be:
freqtrade trade --strategy FreqaiExampleHyridStrategy --strategy-path freqtrade/templates
--freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
or the user simply adds this to their config:
"freqai": {
"enabled": true,
"purge_old_models": true,
"train_period_days": 15,
"identifier": "uniqe-id",
"feature_parameters": {
"include_timeframes": [
"3m",
"15m",
"1h"
],
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
],
"label_period_candles": 20,
"include_shifted_candles": 2,
"DI_threshold": 0.9,
"weight_factor": 0.9,
"principal_component_analysis": false,
"use_SVM_to_remove_outliers": true,
"indicator_periods_candles": [10, 20]
},
"data_split_parameters": {
"test_size": 0,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 800
}
},
Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
"""
minimal_roi = {
"60": 0.01,
"30": 0.02,
"0": 0.04
}
plot_config = {
'main_plot': {
'tema': {},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
},
"Up_or_down": {
'&s-up_or_down': {'color': 'green'},
}
}
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = True
# Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# FreqAI required function, leave as is or add additional informatives to existing structure.
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
# FreqAI required function, user can add or remove indicators, but general structure
# must stay the same.
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
User feeds these indicators to FreqAI to train a classifier to decide
if the market will go up or down.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
: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)
# first loop is automatically duplicating indicators for time periods
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, window=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["volume"] / informative["volume"].rolling(t).mean()
)
# FreqAI needs the following lines in order to detect features and automatically
# expand upon them.
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# User can set the "target" here (in present case it is the
# "up" or "down")
if set_generalized_indicators:
# User "looks into the future" here to figure out if the future
# will be "up" or "down". This same column name is available to
# the user
df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
df["close"], 'up', 'down')
return df
# flake8: noqa: C901
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# User creates their own custom strat here. Present example is a supertrend
# based strategy.
dataframe = self.freqai.start(dataframe, metadata, self)
# TA indicators to combine with the Freqai targets
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[
(
# Signal: RSI crosses above 30
(qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) &
(df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle
(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
(df['volume'] > 0) & # Make sure Volume is not 0
(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
# Only enter trade if Freqai thinks the trend is in this direction
(df['&s-up_or_down'] == 'up')
),
'enter_long'] = 1
df.loc[
(
# Signal: RSI crosses above 70
(qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) &
(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
(df['volume'] > 0) & # Make sure Volume is not 0
(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
# Only enter trade if Freqai thinks the trend is in this direction
(df['&s-up_or_down'] == 'down')
),
'enter_short'] = 1
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[
(
# Signal: RSI crosses above 70
(qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) &
(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
(df['volume'] > 0) # Make sure Volume is not 0
),
'exit_long'] = 1
df.loc[
(
# Signal: RSI crosses above 30
(qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) &
# Guard: tema below BB middle
(df['tema'] <= df['bb_middleband']) &
(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
(df['volume'] > 0) # Make sure Volume is not 0
),
'exit_short'] = 1
return df

4
freqtrade/templates/base_strategy.py.j2
View File

@@ -88,8 +88,8 @@ class {{ strategy }}(IStrategy):
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
'entry': 'GTC',
'exit': 'GTC'
}
{{ plot_config | indent(4) }}

4
freqtrade/templates/sample_strategy.py
View File

@@ -88,8 +88,8 @@ class SampleStrategy(IStrategy):
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
'entry': 'GTC',
'exit': 'GTC'
}
plot_config = {