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fix logger, debug some flake8 appeasements

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robcaulk
2022-05-05 14:37:37 +02:00
parent 29c2d1d189
commit 764f9449b4
5 changed files with 479 additions and 415 deletions
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837
freqtrade/constants.py
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@@ -8,88 +8,133 @@ from typing import List, Literal, Tuple
from freqtrade.enums import CandleType
DEFAULT_CONFIG = 'config.json'
DEFAULT_EXCHANGE = 'bittrex'
DEFAULT_CONFIG = "config.json"
DEFAULT_EXCHANGE = "bittrex"
PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
TIMEOUT_UNITS = ['minutes', 'seconds']
EXPORT_OPTIONS = ['none', 'trades', 'signals']
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
TIMEOUT_UNITS = ["minutes", "seconds"]
EXPORT_OPTIONS = ["none", "trades", "signals"]
DEFAULT_DB_PROD_URL = "sqlite:///tradesv3.sqlite"
DEFAULT_DB_DRYRUN_URL = "sqlite:///tradesv3.dryrun.sqlite"
UNLIMITED_STAKE_AMOUNT = "unlimited"
DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
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']
HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
'CalmarHyperOptLoss',
'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
'ProfitDrawDownHyperOptLoss']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
BACKTEST_CACHE_DEFAULT = 'day'
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"]
HYPEROPT_LOSS_BUILTIN = [
"ShortTradeDurHyperOptLoss",
"OnlyProfitHyperOptLoss",
"SharpeHyperOptLoss",
"SharpeHyperOptLossDaily",
"SortinoHyperOptLoss",
"SortinoHyperOptLossDaily",
"CalmarHyperOptLoss",
"MaxDrawDownHyperOptLoss",
"MaxDrawDownRelativeHyperOptLoss",
"ProfitDrawDownHyperOptLoss",
]
AVAILABLE_PAIRLISTS = [
"StaticPairList",
"VolumePairList",
"AgeFilter",
"OffsetFilter",
"PerformanceFilter",
"PrecisionFilter",
"PriceFilter",
"RangeStabilityFilter",
"ShuffleFilter",
"SpreadFilter",
"VolatilityFilter",
]
AVAILABLE_PROTECTIONS = ["CooldownPeriod", "LowProfitPairs", "MaxDrawdown", "StoplossGuard"]
AVAILABLE_DATAHANDLERS = ["json", "jsongz", "hdf5"]
BACKTEST_BREAKDOWNS = ["day", "week", "month"]
BACKTEST_CACHE_AGE = ["none", "day", "week", "month"]
BACKTEST_CACHE_DEFAULT = "day"
DRY_RUN_WALLET = 1000
DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
DATETIME_PRINT_FORMAT = "%Y-%m-%d %H:%M:%S"
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
DEFAULT_DATAFRAME_COLUMNS = ["date", "open", "high", "low", "close", "volume"]
# Don't modify sequence of DEFAULT_TRADES_COLUMNS
# it has wide consequences for stored trades files
DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
TRADING_MODES = ['spot', 'margin', 'futures']
MARGIN_MODES = ['cross', 'isolated', '']
DEFAULT_TRADES_COLUMNS = ["timestamp", "id", "type", "side", "price", "amount", "cost"]
TRADING_MODES = ["spot", "margin", "futures"]
MARGIN_MODES = ["cross", "isolated", ""]
LAST_BT_RESULT_FN = '.last_result.json'
FTHYPT_FILEVERSION = 'fthypt_fileversion'
LAST_BT_RESULT_FN = ".last_result.json"
FTHYPT_FILEVERSION = "fthypt_fileversion"
USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
USERPATH_FREQAIMODELS = 'freqaimodels'
USERPATH_HYPEROPTS = "hyperopts"
USERPATH_STRATEGIES = "strategies"
USERPATH_NOTEBOOKS = "notebooks"
USERPATH_FREQAIMODELS = "freqaimodels"
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
TELEGRAM_SETTING_OPTIONS = ["on", "off", "silent"]
WEBHOOK_FORMAT_OPTIONS = ["form", "json", "raw"]
ENV_VAR_PREFIX = 'FREQTRADE__'
ENV_VAR_PREFIX = "FREQTRADE__"
NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired')
NON_OPEN_EXCHANGE_STATES = ("cancelled", "canceled", "closed", "expired")
# Define decimals per coin for outputs
# Only used for outputs.
DECIMAL_PER_COIN_FALLBACK = 3 # Should be low to avoid listing all possible FIAT's
DECIMALS_PER_COIN = {
'BTC': 8,
'ETH': 5,
"BTC": 8,
"ETH": 5,
}
DUST_PER_COIN = {
'BTC': 0.0001,
'ETH': 0.01
}
DUST_PER_COIN = {"BTC": 0.0001, "ETH": 0.01}
# Source files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGIES,
'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
"sample_strategy.py": USERPATH_STRATEGIES,
"sample_hyperopt_loss.py": USERPATH_HYPEROPTS,
"strategy_analysis_example.ipynb": USERPATH_NOTEBOOKS,
}
SUPPORTED_FIAT = [
"AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
"EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
"KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
"RUB", "UAH", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR",
"USD", "BTC", "ETH", "XRP", "LTC", "BCH"
"AUD",
"BRL",
"CAD",
"CHF",
"CLP",
"CNY",
"CZK",
"DKK",
"EUR",
"GBP",
"HKD",
"HUF",
"IDR",
"ILS",
"INR",
"JPY",
"KRW",
"MXN",
"MYR",
"NOK",
"NZD",
"PHP",
"PKR",
"PLN",
"RUB",
"UAH",
"SEK",
"SGD",
"THB",
"TRY",
"TWD",
"ZAR",
"USD",
"BTC",
"ETH",
"XRP",
"LTC",
"BCH",
]
MINIMAL_CONFIG = {
@@ -100,380 +145,416 @@ MINIMAL_CONFIG = {
"key": "",
"secret": "",
"pair_whitelist": [],
"ccxt_async_config": {
}
}
"ccxt_async_config": {},
},
}
# Required json-schema for user specified config
CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
'new_pairs_days': {'type': 'integer', 'default': 30},
'timeframe': {'type': 'string'},
'stake_currency': {'type': 'string'},
'stake_amount': {
'type': ['number', 'string'],
'minimum': 0.0001,
'pattern': UNLIMITED_STAKE_AMOUNT
"type": "object",
"properties": {
"max_open_trades": {"type": ["integer", "number"], "minimum": -1},
"new_pairs_days": {"type": "integer", "default": 30},
"timeframe": {"type": "string"},
"stake_currency": {"type": "string"},
"stake_amount": {
"type": ["number", "string"],
"minimum": 0.0001,
"pattern": UNLIMITED_STAKE_AMOUNT,
},
'tradable_balance_ratio': {
'type': 'number',
'minimum': 0.0,
'maximum': 1,
'default': 0.99
"tradable_balance_ratio": {"type": "number", "minimum": 0.0, "maximum": 1, "default": 0.99},
"available_capital": {
"type": "number",
"minimum": 0,
},
'available_capital': {
'type': 'number',
'minimum': 0,
"amend_last_stake_amount": {"type": "boolean", "default": False},
"last_stake_amount_min_ratio": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0,
"default": 0.5,
},
'amend_last_stake_amount': {'type': 'boolean', 'default': False},
'last_stake_amount_min_ratio': {
'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5
"fiat_display_currency": {"type": "string", "enum": SUPPORTED_FIAT},
"dry_run": {"type": "boolean"},
"dry_run_wallet": {"type": "number", "default": DRY_RUN_WALLET},
"cancel_open_orders_on_exit": {"type": "boolean", "default": False},
"process_only_new_candles": {"type": "boolean"},
"minimal_roi": {
"type": "object",
"patternProperties": {"^[0-9.]+$": {"type": "number"}},
"minProperties": 1,
},
'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
'dry_run': {'type': 'boolean'},
'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
'cancel_open_orders_on_exit': {'type': 'boolean', 'default': False},
'process_only_new_candles': {'type': 'boolean'},
'minimal_roi': {
'type': 'object',
'patternProperties': {
'^[0-9.]+$': {'type': 'number'}
"amount_reserve_percent": {"type": "number", "minimum": 0.0, "maximum": 0.5},
"stoploss": {"type": "number", "maximum": 0, "exclusiveMaximum": True, "minimum": -1},
"trailing_stop": {"type": "boolean"},
"trailing_stop_positive": {"type": "number", "minimum": 0, "maximum": 1},
"trailing_stop_positive_offset": {"type": "number", "minimum": 0, "maximum": 1},
"trailing_only_offset_is_reached": {"type": "boolean"},
"use_exit_signal": {"type": "boolean"},
"exit_profit_only": {"type": "boolean"},
"exit_profit_offset": {"type": "number"},
"ignore_roi_if_entry_signal": {"type": "boolean"},
"ignore_buying_expired_candle_after": {"type": "number"},
"trading_mode": {"type": "string", "enum": TRADING_MODES},
"margin_mode": {"type": "string", "enum": MARGIN_MODES},
"liquidation_buffer": {"type": "number", "minimum": 0.0, "maximum": 0.99},
"backtest_breakdown": {
"type": "array",
"items": {"type": "string", "enum": BACKTEST_BREAKDOWNS},
},
"bot_name": {"type": "string"},
"unfilledtimeout": {
"type": "object",
"properties": {
"entry": {"type": "number", "minimum": 1},
"exit": {"type": "number", "minimum": 1},
"exit_timeout_count": {"type": "number", "minimum": 0, "default": 0},
"unit": {"type": "string", "enum": TIMEOUT_UNITS, "default": "minutes"},
},
'minProperties': 1
},
'amount_reserve_percent': {'type': 'number', 'minimum': 0.0, 'maximum': 0.5},
'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True, 'minimum': -1},
'trailing_stop': {'type': 'boolean'},
'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
'trailing_only_offset_is_reached': {'type': 'boolean'},
'use_exit_signal': {'type': 'boolean'},
'exit_profit_only': {'type': 'boolean'},
'exit_profit_offset': {'type': 'number'},
'ignore_roi_if_entry_signal': {'type': 'boolean'},
'ignore_buying_expired_candle_after': {'type': 'number'},
'trading_mode': {'type': 'string', 'enum': TRADING_MODES},
'margin_mode': {'type': 'string', 'enum': MARGIN_MODES},
'liquidation_buffer': {'type': 'number', 'minimum': 0.0, 'maximum': 0.99},
'backtest_breakdown': {
'type': 'array',
'items': {'type': 'string', 'enum': BACKTEST_BREAKDOWNS}
},
'bot_name': {'type': 'string'},
'unfilledtimeout': {
'type': 'object',
'properties': {
'entry': {'type': 'number', 'minimum': 1},
'exit': {'type': 'number', 'minimum': 1},
'exit_timeout_count': {'type': 'number', 'minimum': 0, 'default': 0},
'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
}
},
'entry_pricing': {
'type': 'object',
'properties': {
'price_last_balance': {
'type': 'number',
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
"entry_pricing": {
"type": "object",
"properties": {
"price_last_balance": {
"type": "number",
"minimum": 0,
"maximum": 1,
"exclusiveMaximum": False,
},
'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'},
'use_order_book': {'type': 'boolean'},
'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, },
'check_depth_of_market': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
}
"price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"},
"use_order_book": {"type": "boolean"},
"order_book_top": {
"type": "integer",
"minimum": 1,
"maximum": 50,
},
"check_depth_of_market": {
"type": "object",
"properties": {
"enabled": {"type": "boolean"},
"bids_to_ask_delta": {"type": "number", "minimum": 0},
},
},
},
'required': ['price_side']
"required": ["price_side"],
},
'exit_pricing': {
'type': 'object',
'properties': {
'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'},
'price_last_balance': {
'type': 'number',
'minimum': 0,
'maximum': 1,
'exclusiveMaximum': False,
"exit_pricing": {
"type": "object",
"properties": {
"price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"},
"price_last_balance": {
"type": "number",
"minimum": 0,
"maximum": 1,
"exclusiveMaximum": False,
},
'use_order_book': {'type': 'boolean'},
'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, },
},
'required': ['price_side']
},
'custom_price_max_distance_ratio': {
'type': 'number', 'minimum': 0.0
},
'order_types': {
'type': 'object',
'properties': {
'entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'force_exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'force_entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'emergency_exit': {
'type': 'string',
'enum': ORDERTYPE_POSSIBILITIES,
'default': 'market'},
'stoploss': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
'stoploss_on_exchange': {'type': 'boolean'},
'stoploss_on_exchange_interval': {'type': 'number'},
'stoploss_on_exchange_limit_ratio': {'type': 'number', 'minimum': 0.0,
'maximum': 1.0}
},
'required': ['entry', 'exit', 'stoploss', 'stoploss_on_exchange']
},
'order_time_in_force': {
'type': 'object',
'properties': {
'entry': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES},
'exit': {'type': 'string', 'enum': ORDERTIF_POSSIBILITIES}
},
'required': REQUIRED_ORDERTIF
},
'exchange': {'$ref': '#/definitions/exchange'},
'edge': {'$ref': '#/definitions/edge'},
'experimental': {
'type': 'object',
'properties': {
'block_bad_exchanges': {'type': 'boolean'}
}
},
'pairlists': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'method': {'type': 'string', 'enum': AVAILABLE_PAIRLISTS},
"use_order_book": {"type": "boolean"},
"order_book_top": {
"type": "integer",
"minimum": 1,
"maximum": 50,
},
'required': ['method'],
}
},
"required": ["price_side"],
},
'protections': {
'type': 'array',
'items': {
'type': 'object',
'properties': {
'method': {'type': 'string', 'enum': AVAILABLE_PROTECTIONS},
'stop_duration': {'type': 'number', 'minimum': 0.0},
'stop_duration_candles': {'type': 'number', 'minimum': 0},
'trade_limit': {'type': 'number', 'minimum': 1},
'lookback_period': {'type': 'number', 'minimum': 1},
'lookback_period_candles': {'type': 'number', 'minimum': 1},
"custom_price_max_distance_ratio": {"type": "number", "minimum": 0.0},
"order_types": {
"type": "object",
"properties": {
"entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES},
"exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES},
"force_exit": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES},
"force_entry": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES},
"emergency_exit": {
"type": "string",
"enum": ORDERTYPE_POSSIBILITIES,
"default": "market",
},
'required': ['method'],
}
"stoploss": {"type": "string", "enum": ORDERTYPE_POSSIBILITIES},
"stoploss_on_exchange": {"type": "boolean"},
"stoploss_on_exchange_interval": {"type": "number"},
"stoploss_on_exchange_limit_ratio": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0,
},
},
"required": ["entry", "exit", "stoploss", "stoploss_on_exchange"],
},
'telegram': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'token': {'type': 'string'},
'chat_id': {'type': 'string'},
'balance_dust_level': {'type': 'number', 'minimum': 0.0},
'notification_settings': {
'type': 'object',
'default': {},
'properties': {
'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'entry_fill': {'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'exit': {
'type': ['string', 'object'],
'additionalProperties': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS
}
"order_time_in_force": {
"type": "object",
"properties": {
"entry": {"type": "string", "enum": ORDERTIF_POSSIBILITIES},
"exit": {"type": "string", "enum": ORDERTIF_POSSIBILITIES},
},
"required": REQUIRED_ORDERTIF,
},
"exchange": {"$ref": "#/definitions/exchange"},
"edge": {"$ref": "#/definitions/edge"},
"experimental": {
"type": "object",
"properties": {"block_bad_exchanges": {"type": "boolean"}},
},
"pairlists": {
"type": "array",
"items": {
"type": "object",
"properties": {
"method": {"type": "string", "enum": AVAILABLE_PAIRLISTS},
},
"required": ["method"],
},
},
"protections": {
"type": "array",
"items": {
"type": "object",
"properties": {
"method": {"type": "string", "enum": AVAILABLE_PROTECTIONS},
"stop_duration": {"type": "number", "minimum": 0.0},
"stop_duration_candles": {"type": "number", "minimum": 0},
"trade_limit": {"type": "number", "minimum": 1},
"lookback_period": {"type": "number", "minimum": 1},
"lookback_period_candles": {"type": "number", "minimum": 1},
},
"required": ["method"],
},
},
"telegram": {
"type": "object",
"properties": {
"enabled": {"type": "boolean"},
"token": {"type": "string"},
"chat_id": {"type": "string"},
"balance_dust_level": {"type": "number", "minimum": 0.0},
"notification_settings": {
"type": "object",
"default": {},
"properties": {
"status": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS},
"warning": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS},
"startup": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS},
"entry": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS},
"entry_cancel": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS},
"entry_fill": {
"type": "string",
"enum": TELEGRAM_SETTING_OPTIONS,
"default": "off",
},
'exit_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'exit_fill': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
"exit": {
"type": ["string", "object"],
"additionalProperties": {
"type": "string",
"enum": TELEGRAM_SETTING_OPTIONS,
},
},
'protection_trigger': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
"exit_cancel": {"type": "string", "enum": TELEGRAM_SETTING_OPTIONS},
"exit_fill": {
"type": "string",
"enum": TELEGRAM_SETTING_OPTIONS,
"default": "off",
},
'protection_trigger_global': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
"protection_trigger": {
"type": "string",
"enum": TELEGRAM_SETTING_OPTIONS,
"default": "off",
},
}
"protection_trigger_global": {
"type": "string",
"enum": TELEGRAM_SETTING_OPTIONS,
},
},
},
'reload': {'type': 'boolean'},
"reload": {"type": "boolean"},
},
'required': ['enabled', 'token', 'chat_id'],
"required": ["enabled", "token", "chat_id"],
},
'webhook': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'url': {'type': 'string'},
'format': {'type': 'string', 'enum': WEBHOOK_FORMAT_OPTIONS, 'default': 'form'},
'retries': {'type': 'integer', 'minimum': 0},
'retry_delay': {'type': 'number', 'minimum': 0},
'webhookentry': {'type': 'object'},
'webhookentrycancel': {'type': 'object'},
'webhookentryfill': {'type': 'object'},
'webhookexit': {'type': 'object'},
'webhookexitcancel': {'type': 'object'},
'webhookexitfill': {'type': 'object'},
'webhookstatus': {'type': 'object'},
"webhook": {
"type": "object",
"properties": {
"enabled": {"type": "boolean"},
"url": {"type": "string"},
"format": {"type": "string", "enum": WEBHOOK_FORMAT_OPTIONS, "default": "form"},
"retries": {"type": "integer", "minimum": 0},
"retry_delay": {"type": "number", "minimum": 0},
"webhookentry": {"type": "object"},
"webhookentrycancel": {"type": "object"},
"webhookentryfill": {"type": "object"},
"webhookexit": {"type": "object"},
"webhookexitcancel": {"type": "object"},
"webhookexitfill": {"type": "object"},
"webhookstatus": {"type": "object"},
},
},
'api_server': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'listen_ip_address': {'format': 'ipv4'},
'listen_port': {
'type': 'integer',
'minimum': 1024,
'maximum': 65535
},
'username': {'type': 'string'},
'password': {'type': 'string'},
'jwt_secret_key': {'type': 'string'},
'CORS_origins': {'type': 'array', 'items': {'type': 'string'}},
'verbosity': {'type': 'string', 'enum': ['error', 'info']},
"api_server": {
"type": "object",
"properties": {
"enabled": {"type": "boolean"},
"listen_ip_address": {"format": "ipv4"},
"listen_port": {"type": "integer", "minimum": 1024, "maximum": 65535},
"username": {"type": "string"},
"password": {"type": "string"},
"jwt_secret_key": {"type": "string"},
"CORS_origins": {"type": "array", "items": {"type": "string"}},
"verbosity": {"type": "string", "enum": ["error", "info"]},
},
'required': ['enabled', 'listen_ip_address', 'listen_port', 'username', 'password']
"required": ["enabled", "listen_ip_address", "listen_port", "username", "password"],
},
'db_url': {'type': 'string'},
'export': {'type': 'string', 'enum': EXPORT_OPTIONS, 'default': 'trades'},
'disableparamexport': {'type': 'boolean'},
'initial_state': {'type': 'string', 'enum': ['running', 'stopped']},
'force_entry_enable': {'type': 'boolean'},
'disable_dataframe_checks': {'type': 'boolean'},
'internals': {
'type': 'object',
'default': {},
'properties': {
'process_throttle_secs': {'type': 'integer'},
'interval': {'type': 'integer'},
'sd_notify': {'type': 'boolean'},
}
"db_url": {"type": "string"},
"export": {"type": "string", "enum": EXPORT_OPTIONS, "default": "trades"},
"disableparamexport": {"type": "boolean"},
"initial_state": {"type": "string", "enum": ["running", "stopped"]},
"force_entry_enable": {"type": "boolean"},
"disable_dataframe_checks": {"type": "boolean"},
"internals": {
"type": "object",
"default": {},
"properties": {
"process_throttle_secs": {"type": "integer"},
"interval": {"type": "integer"},
"sd_notify": {"type": "boolean"},
},
},
'dataformat_ohlcv': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'default': 'json'
"dataformat_ohlcv": {"type": "string", "enum": AVAILABLE_DATAHANDLERS, "default": "json"},
"dataformat_trades": {
"type": "string",
"enum": AVAILABLE_DATAHANDLERS,
"default": "jsongz",
},
'dataformat_trades': {
'type': 'string',
'enum': AVAILABLE_DATAHANDLERS,
'default': 'jsongz'
},
'position_adjustment_enable': {'type': 'boolean'},
'max_entry_position_adjustment': {'type': ['integer', 'number'], 'minimum': -1},
"position_adjustment_enable": {"type": "boolean"},
"max_entry_position_adjustment": {"type": ["integer", "number"], "minimum": -1},
},
'definitions': {
'exchange': {
'type': 'object',
'properties': {
'name': {'type': 'string'},
'sandbox': {'type': 'boolean', 'default': False},
'key': {'type': 'string', 'default': ''},
'secret': {'type': 'string', 'default': ''},
'password': {'type': 'string', 'default': ''},
'uid': {'type': 'string'},
'pair_whitelist': {
'type': 'array',
'items': {
'type': 'string',
"definitions": {
"exchange": {
"type": "object",
"properties": {
"name": {"type": "string"},
"sandbox": {"type": "boolean", "default": False},
"key": {"type": "string", "default": ""},
"secret": {"type": "string", "default": ""},
"password": {"type": "string", "default": ""},
"uid": {"type": "string"},
"pair_whitelist": {
"type": "array",
"items": {
"type": "string",
},
'uniqueItems': True
"uniqueItems": True,
},
'pair_blacklist': {
'type': 'array',
'items': {
'type': 'string',
"pair_blacklist": {
"type": "array",
"items": {
"type": "string",
},
'uniqueItems': True
"uniqueItems": True,
},
'unknown_fee_rate': {'type': 'number'},
'outdated_offset': {'type': 'integer', 'minimum': 1},
'markets_refresh_interval': {'type': 'integer'},
'ccxt_config': {'type': 'object'},
'ccxt_async_config': {'type': 'object'}
"unknown_fee_rate": {"type": "number"},
"outdated_offset": {"type": "integer", "minimum": 1},
"markets_refresh_interval": {"type": "integer"},
"ccxt_config": {"type": "object"},
"ccxt_async_config": {"type": "object"},
},
'required': ['name']
"required": ["name"],
},
'edge': {
'type': 'object',
'properties': {
'enabled': {'type': 'boolean'},
'process_throttle_secs': {'type': 'integer', 'minimum': 600},
'calculate_since_number_of_days': {'type': 'integer'},
'allowed_risk': {'type': 'number'},
'stoploss_range_min': {'type': 'number'},
'stoploss_range_max': {'type': 'number'},
'stoploss_range_step': {'type': 'number'},
'minimum_winrate': {'type': 'number'},
'minimum_expectancy': {'type': 'number'},
'min_trade_number': {'type': 'number'},
'max_trade_duration_minute': {'type': 'integer'},
'remove_pumps': {'type': 'boolean'}
"edge": {
"type": "object",
"properties": {
"enabled": {"type": "boolean"},
"process_throttle_secs": {"type": "integer", "minimum": 600},
"calculate_since_number_of_days": {"type": "integer"},
"allowed_risk": {"type": "number"},
"stoploss_range_min": {"type": "number"},
"stoploss_range_max": {"type": "number"},
"stoploss_range_step": {"type": "number"},
"minimum_winrate": {"type": "number"},
"minimum_expectancy": {"type": "number"},
"min_trade_number": {"type": "number"},
"max_trade_duration_minute": {"type": "integer"},
"remove_pumps": {"type": "boolean"},
},
'required': ['process_throttle_secs', 'allowed_risk']
}
"required": ["process_throttle_secs", "allowed_risk"],
},
"freqai": {
"type": "object",
"properties": {
"timeframes": {"type": "list"},
"full_timerange": {"type": "str"},
"train_period": {"type": "integer", "default": 0},
"backtest_period": {"type": "integer", "default": 7},
"identifier": {"type": "str", "default": "example"},
"base_features": {"type": "list"},
"corr_pairlist": {"type": "list"},
"training_timerange": {"type": "string", "default": None},
"feature_parameters": {
"type": "object",
"properties": {
"period": {"type": "integer"},
"shift": {"type": "integer", "default": 0},
"DI_threshold": {"type": "integer", "default": 0},
"weight_factor": {"type": "number", "default": 0},
"principal_component_analysis": {"type": "boolean", "default": False},
"remove_outliers": {"type": "boolean", "default": False},
},
},
"data_split_parameters": {
"type": "object",
"properties": {
"test_size": {"type": "number"},
"random_state": {"type": "integer"},
},
},
"model_training_parameters": {
"type": "object",
"properties": {
"n_estimators": {"type": "integer", "default": 2000},
"random_state": {"type": "integer", "default": 1},
"learning_rate": {"type": "number", "default": 0.02},
"task_type": {"type": "string", "default": "CPU"},
},
},
},
},
},
}
SCHEMA_TRADE_REQUIRED = [
'exchange',
'timeframe',
'max_open_trades',
'stake_currency',
'stake_amount',
'tradable_balance_ratio',
'last_stake_amount_min_ratio',
'dry_run',
'dry_run_wallet',
'exit_pricing',
'entry_pricing',
'stoploss',
'minimal_roi',
'internals',
'dataformat_ohlcv',
'dataformat_trades',
"exchange",
"timeframe",
"max_open_trades",
"stake_currency",
"stake_amount",
"tradable_balance_ratio",
"last_stake_amount_min_ratio",
"dry_run",
"dry_run_wallet",
"exit_pricing",
"entry_pricing",
"stoploss",
"minimal_roi",
"internals",
"dataformat_ohlcv",
"dataformat_trades",
]
SCHEMA_BACKTEST_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run_wallet',
'dataformat_ohlcv',
'dataformat_trades',
"exchange",
"max_open_trades",
"stake_currency",
"stake_amount",
"dry_run_wallet",
"dataformat_ohlcv",
"dataformat_trades",
]
SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
'stoploss',
'minimal_roi',
"stoploss",
"minimal_roi",
]
SCHEMA_MINIMAL_REQUIRED = [
'exchange',
'dry_run',
'dataformat_ohlcv',
'dataformat_trades',
"exchange",
"dry_run",
"dataformat_ohlcv",
"dataformat_trades",
]
CANCEL_REASON = {

29
freqtrade/freqai/data_handler.py
View File

@@ -36,6 +36,7 @@ class DataHandler:
config["freqai"]["backtest_period"],
)
self.data: Dict[Any, Any] = {}
self.data_dictionary: Dict[Any, Any] = {}
self.config = config
self.freq_config = config["freqai"]
self.predictions = np.array([])
@@ -58,10 +59,6 @@ class DataHandler:
save_path = Path(self.model_path)
# if not os.path.exists(self.model_path):
# os.mkdir(self.model_path)
# save_path = self.model_path + self.model_filename
# Save the trained model
dump(model, save_path / str(self.model_filename + "_model.joblib"))
self.data["model_path"] = self.model_path
@@ -179,10 +176,8 @@ class DataHandler:
(drop_index == 0) & (drop_index_labels == 0)
] # assuming the labels depend entirely on the dataframe here.
logger.info(
"dropped",
"dropped %s training points due to NaNs, ensure all historical data downloaded",
len(unfiltered_dataframe) - len(filtered_dataframe),
"training data points due to NaNs, ensure you have downloaded",
"all historical training data",
)
self.data["filter_drop_index_training"] = drop_index
@@ -197,12 +192,9 @@ class DataHandler:
drop_index = ~drop_index
self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
logger.info(
"dropped",
"dropped %s of %s prediction data points due to NaNs.",
len(self.do_predict) - self.do_predict.sum(),
"of",
len(filtered_dataframe),
"prediction data points due to NaNs. These are protected from prediction",
"with do_predict vector returned to strategy.",
)
return filtered_dataframe, labels
@@ -353,8 +345,8 @@ class DataHandler:
pca2 = PCA(n_components=n_keep_components)
self.data["n_kept_components"] = n_keep_components
pca2 = pca2.fit(self.data_dictionary["train_features"])
logger.info("reduced feature dimension by", n_components - n_keep_components)
logger.info("explained variance", np.sum(pca2.explained_variance_ratio_))
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"])
test_components = pca2.transform(self.data_dictionary["test_features"])
@@ -383,7 +375,7 @@ class DataHandler:
logger.info("computing average mean distance for all training points")
pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=-1)
avg_mean_dist = pairwise.mean(axis=1).mean()
logger.info("avg_mean_dist", avg_mean_dist)
logger.info("avg_mean_dist %s", avg_mean_dist)
return avg_mean_dist
@@ -411,9 +403,8 @@ class DataHandler:
do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
logger.info(
"remove_outliers() tossed",
"remove_outliers() tossed %s predictions",
len(do_predict) - do_predict.sum(),
"predictions because they were beyond 3 std deviations from training data.",
)
self.do_predict += do_predict
self.do_predict -= 1
@@ -475,7 +466,7 @@ class DataHandler:
for p in config["freqai"]["corr_pairlist"]:
features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
logger.info("number of features", len(features))
logger.info("number of features %s", len(features))
return features
def check_if_pred_in_training_spaces(self) -> None:
@@ -486,7 +477,6 @@ class DataHandler:
from the training data set.
"""
logger.info("checking if prediction features are in AOA")
distance = pairwise_distances(
self.data_dictionary["train_features"],
self.data_dictionary["prediction_features"],
@@ -501,9 +491,8 @@ class DataHandler:
)
logger.info(
"Distance checker tossed",
"Distance checker tossed %s predictions for being too far from training data",
len(do_predict) - do_predict.sum(),
"predictions for being too far from training data",
)
self.do_predict += do_predict

15
freqtrade/freqai/freqai_interface.py
View File

@@ -69,12 +69,7 @@ class IFreqaiModel(ABC):
self.pair = metadata["pair"]
self.dh = DataHandler(self.config, dataframe)
logger.info(
"going to train",
len(self.dh.training_timeranges),
"timeranges:",
self.dh.training_timeranges,
)
logger.info("going to train %s timeranges", len(self.dh.training_timeranges))
# Loop enforcing the sliding window training/backtesting paragigm
# tr_train is the training time range e.g. 1 historical month
@@ -90,14 +85,14 @@ class IFreqaiModel(ABC):
self.freqai_info["training_timerange"] = tr_train
dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
logger.info("training", self.pair, "for", tr_train)
logger.info("training %s for %s", self.pair, tr_train)
# self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/"
self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train)))
if not self.model_exists(self.pair, training_timerange=tr_train):
self.model = self.train(dataframe_train, metadata)
self.dh.save_data(self.model)
else:
self.model = self.dh.load_data(self.dh.model_path)
self.model = self.dh.load_data()
preds, do_preds = self.predict(dataframe_backtest)
@@ -167,7 +162,7 @@ class IFreqaiModel(ABC):
path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib"))
file_exists = path_to_modelfile.is_file()
if file_exists:
logger.info("Found model at", self.dh.model_path / self.dh.model_filename)
logger.info("Found model at %s", self.dh.model_path / self.dh.model_filename)
else:
logger.info("Could not find model at", self.dh.model_path / self.dh.model_filename)
logger.info("Could not find model at %s", self.dh.model_path / self.dh.model_filename)
return file_exists

8
freqtrade/optimize/backtesting.py
View File

@@ -204,12 +204,12 @@ class Backtesting:
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
if self.config['freqaimodel']:
self.required_startup += int((self.config['freqai']['train_period']*86400) /
timeframe_to_seconds(self.config['timeframe']))
if self.config['freqai']['train_period'] > 0:
self.required_startup += int((self.config['freqai']['train_period'] * 86400) /
timeframe_to_seconds(self.config['timeframe']))
logger.info("Increasing startup_candle_count for freqai to %s", self.required_startup)
self.config['startup_candle_count'] = self.required_startup
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,

5
freqtrade/templates/ExamplePredictionModel.py
View File

@@ -36,7 +36,7 @@ class ExamplePredictionModel(IFreqaiModel):
self.dh.data["s_mean"] = dataframe["s"].mean()
self.dh.data["s_std"] = dataframe["s"].std()
logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
# logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
return dataframe["s"]
@@ -77,11 +77,10 @@ class ExamplePredictionModel(IFreqaiModel):
if self.feature_parameters["DI_threshold"]:
self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
logger.info("length of train data", len(data_dictionary["train_features"]))
logger.info("length of train data %s", len(data_dictionary["train_features"]))
model = self.fit(data_dictionary)
logger.info("Finished training")
logger.info(f'--------------------done training {metadata["pair"]}--------------------')
return model