fix logger, debug some flake8 appeasements
This commit is contained in:
robcaulk
parent
29c2d1d189
commit
764f9449b4
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@ -8,88 +8,133 @@ from typing import List, Literal, Tuple
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from freqtrade.enums import CandleType
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DEFAULT_CONFIG = 'config.json'
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DEFAULT_EXCHANGE = 'bittrex'
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DEFAULT_CONFIG = "config.json"
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DEFAULT_EXCHANGE = "bittrex"
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PROCESS_THROTTLE_SECS = 5 # sec
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HYPEROPT_EPOCH = 100 # epochs
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RETRY_TIMEOUT = 30 # sec
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TIMEOUT_UNITS = ['minutes', 'seconds']
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EXPORT_OPTIONS = ['none', 'trades', 'signals']
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DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
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DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
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UNLIMITED_STAKE_AMOUNT = 'unlimited'
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TIMEOUT_UNITS = ["minutes", "seconds"]
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EXPORT_OPTIONS = ["none", "trades", "signals"]
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DEFAULT_DB_PROD_URL = "sqlite:///tradesv3.sqlite"
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DEFAULT_DB_DRYRUN_URL = "sqlite:///tradesv3.dryrun.sqlite"
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UNLIMITED_STAKE_AMOUNT = "unlimited"
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DEFAULT_AMOUNT_RESERVE_PERCENT = 0.05
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REQUIRED_ORDERTIF = ['entry', 'exit']
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REQUIRED_ORDERTYPES = ['entry', 'exit', 'stoploss', 'stoploss_on_exchange']
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PRICING_SIDES = ['ask', 'bid', 'same', 'other']
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ORDERTYPE_POSSIBILITIES = ['limit', 'market']
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ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
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HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
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'SharpeHyperOptLoss', 'SharpeHyperOptLossDaily',
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'SortinoHyperOptLoss', 'SortinoHyperOptLossDaily',
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'CalmarHyperOptLoss',
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'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
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'ProfitDrawDownHyperOptLoss']
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AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
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'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
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'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
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'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
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AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
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AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
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BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
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BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
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BACKTEST_CACHE_DEFAULT = 'day'
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REQUIRED_ORDERTIF = ["entry", "exit"]
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REQUIRED_ORDERTYPES = ["entry", "exit", "stoploss", "stoploss_on_exchange"]
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PRICING_SIDES = ["ask", "bid", "same", "other"]
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ORDERTYPE_POSSIBILITIES = ["limit", "market"]
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ORDERTIF_POSSIBILITIES = ["gtc", "fok", "ioc"]
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HYPEROPT_LOSS_BUILTIN = [
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"ShortTradeDurHyperOptLoss",
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"OnlyProfitHyperOptLoss",
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"SharpeHyperOptLoss",
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"SharpeHyperOptLossDaily",
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"SortinoHyperOptLoss",
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"SortinoHyperOptLossDaily",
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"CalmarHyperOptLoss",
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"MaxDrawDownHyperOptLoss",
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"MaxDrawDownRelativeHyperOptLoss",
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"ProfitDrawDownHyperOptLoss",
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]
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AVAILABLE_PAIRLISTS = [
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"StaticPairList",
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"VolumePairList",
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"AgeFilter",
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"OffsetFilter",
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"PerformanceFilter",
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"PrecisionFilter",
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"PriceFilter",
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"RangeStabilityFilter",
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"ShuffleFilter",
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"SpreadFilter",
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"VolatilityFilter",
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]
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AVAILABLE_PROTECTIONS = ["CooldownPeriod", "LowProfitPairs", "MaxDrawdown", "StoplossGuard"]
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AVAILABLE_DATAHANDLERS = ["json", "jsongz", "hdf5"]
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BACKTEST_BREAKDOWNS = ["day", "week", "month"]
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BACKTEST_CACHE_AGE = ["none", "day", "week", "month"]
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BACKTEST_CACHE_DEFAULT = "day"
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DRY_RUN_WALLET = 1000
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DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
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DATETIME_PRINT_FORMAT = "%Y-%m-%d %H:%M:%S"
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MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
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DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
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DEFAULT_DATAFRAME_COLUMNS = ["date", "open", "high", "low", "close", "volume"]
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# Don't modify sequence of DEFAULT_TRADES_COLUMNS
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# it has wide consequences for stored trades files
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DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
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TRADING_MODES = ['spot', 'margin', 'futures']
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MARGIN_MODES = ['cross', 'isolated', '']
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DEFAULT_TRADES_COLUMNS = ["timestamp", "id", "type", "side", "price", "amount", "cost"]
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TRADING_MODES = ["spot", "margin", "futures"]
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MARGIN_MODES = ["cross", "isolated", ""]
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LAST_BT_RESULT_FN = '.last_result.json'
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FTHYPT_FILEVERSION = 'fthypt_fileversion'
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LAST_BT_RESULT_FN = ".last_result.json"
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FTHYPT_FILEVERSION = "fthypt_fileversion"
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USERPATH_HYPEROPTS = 'hyperopts'
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USERPATH_STRATEGIES = 'strategies'
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USERPATH_NOTEBOOKS = 'notebooks'
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USERPATH_FREQAIMODELS = 'freqaimodels'
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USERPATH_HYPEROPTS = "hyperopts"
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USERPATH_STRATEGIES = "strategies"
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USERPATH_NOTEBOOKS = "notebooks"
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USERPATH_FREQAIMODELS = "freqaimodels"
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TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
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WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
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TELEGRAM_SETTING_OPTIONS = ["on", "off", "silent"]
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WEBHOOK_FORMAT_OPTIONS = ["form", "json", "raw"]
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ENV_VAR_PREFIX = 'FREQTRADE__'
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ENV_VAR_PREFIX = "FREQTRADE__"
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NON_OPEN_EXCHANGE_STATES = ('cancelled', 'canceled', 'closed', 'expired')
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NON_OPEN_EXCHANGE_STATES = ("cancelled", "canceled", "closed", "expired")
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# Define decimals per coin for outputs
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# Only used for outputs.
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DECIMAL_PER_COIN_FALLBACK = 3 # Should be low to avoid listing all possible FIAT's
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DECIMALS_PER_COIN = {
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'BTC': 8,
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'ETH': 5,
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"BTC": 8,
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"ETH": 5,
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}
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DUST_PER_COIN = {
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'BTC': 0.0001,
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'ETH': 0.01
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}
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DUST_PER_COIN = {"BTC": 0.0001, "ETH": 0.01}
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# Source files with destination directories within user-directory
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USER_DATA_FILES = {
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'sample_strategy.py': USERPATH_STRATEGIES,
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'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
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'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
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"sample_strategy.py": USERPATH_STRATEGIES,
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"sample_hyperopt_loss.py": USERPATH_HYPEROPTS,
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"strategy_analysis_example.ipynb": USERPATH_NOTEBOOKS,
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}
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SUPPORTED_FIAT = [
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"AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
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"EUR", "GBP", "HKD", "HUF", "IDR", "ILS", "INR", "JPY",
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"KRW", "MXN", "MYR", "NOK", "NZD", "PHP", "PKR", "PLN",
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"RUB", "UAH", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR",
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"USD", "BTC", "ETH", "XRP", "LTC", "BCH"
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"AUD",
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"BRL",
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"CAD",
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"CHF",
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"CLP",
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"CNY",
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"CZK",
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"DKK",
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"EUR",
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"GBP",
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"HKD",
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"HUF",
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"IDR",
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"ILS",
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"INR",
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"JPY",
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"KRW",
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"MXN",
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"MYR",
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"NOK",
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"NZD",
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"PHP",
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"PKR",
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"PLN",
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"RUB",
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"UAH",
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"SEK",
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"SGD",
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"THB",
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"TRY",
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"TWD",
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"ZAR",
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"USD",
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"BTC",
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"ETH",
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"XRP",
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"LTC",
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"BCH",
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]
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MINIMAL_CONFIG = {
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@ -100,380 +145,416 @@ MINIMAL_CONFIG = {
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"key": "",
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"secret": "",
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"pair_whitelist": [],
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"ccxt_async_config": {
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}
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}
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"ccxt_async_config": {},
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},
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}
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# Required json-schema for user specified config
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CONF_SCHEMA = {
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'type': 'object',
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'properties': {
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'max_open_trades': {'type': ['integer', 'number'], 'minimum': -1},
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'new_pairs_days': {'type': 'integer', 'default': 30},
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'timeframe': {'type': 'string'},
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'stake_currency': {'type': 'string'},
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'stake_amount': {
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'type': ['number', 'string'],
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'minimum': 0.0001,
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'pattern': UNLIMITED_STAKE_AMOUNT
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"type": "object",
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"properties": {
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"max_open_trades": {"type": ["integer", "number"], "minimum": -1},
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"new_pairs_days": {"type": "integer", "default": 30},
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"timeframe": {"type": "string"},
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"stake_currency": {"type": "string"},
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"stake_amount": {
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"type": ["number", "string"],
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"minimum": 0.0001,
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"pattern": UNLIMITED_STAKE_AMOUNT,
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},
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'tradable_balance_ratio': {
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'type': 'number',
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'minimum': 0.0,
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'maximum': 1,
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'default': 0.99
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"tradable_balance_ratio": {"type": "number", "minimum": 0.0, "maximum": 1, "default": 0.99},
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"available_capital": {
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"type": "number",
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"minimum": 0,
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},
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'available_capital': {
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'type': 'number',
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'minimum': 0,
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"amend_last_stake_amount": {"type": "boolean", "default": False},
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"last_stake_amount_min_ratio": {
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"type": "number",
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"minimum": 0.0,
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"maximum": 1.0,
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"default": 0.5,
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},
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'amend_last_stake_amount': {'type': 'boolean', 'default': False},
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'last_stake_amount_min_ratio': {
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'type': 'number', 'minimum': 0.0, 'maximum': 1.0, 'default': 0.5
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"fiat_display_currency": {"type": "string", "enum": SUPPORTED_FIAT},
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"dry_run": {"type": "boolean"},
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"dry_run_wallet": {"type": "number", "default": DRY_RUN_WALLET},
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"cancel_open_orders_on_exit": {"type": "boolean", "default": False},
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"process_only_new_candles": {"type": "boolean"},
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"minimal_roi": {
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"type": "object",
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"patternProperties": {"^[0-9.]+$": {"type": "number"}},
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"minProperties": 1,
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},
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'fiat_display_currency': {'type': 'string', 'enum': SUPPORTED_FIAT},
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'dry_run': {'type': 'boolean'},
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'dry_run_wallet': {'type': 'number', 'default': DRY_RUN_WALLET},
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'cancel_open_orders_on_exit': {'type': 'boolean', 'default': False},
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'process_only_new_candles': {'type': 'boolean'},
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'minimal_roi': {
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'type': 'object',
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'patternProperties': {
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'^[0-9.]+$': {'type': 'number'}
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"amount_reserve_percent": {"type": "number", "minimum": 0.0, "maximum": 0.5},
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"stoploss": {"type": "number", "maximum": 0, "exclusiveMaximum": True, "minimum": -1},
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"trailing_stop": {"type": "boolean"},
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"trailing_stop_positive": {"type": "number", "minimum": 0, "maximum": 1},
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"trailing_stop_positive_offset": {"type": "number", "minimum": 0, "maximum": 1},
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"trailing_only_offset_is_reached": {"type": "boolean"},
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"use_exit_signal": {"type": "boolean"},
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"exit_profit_only": {"type": "boolean"},
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"exit_profit_offset": {"type": "number"},
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"ignore_roi_if_entry_signal": {"type": "boolean"},
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"ignore_buying_expired_candle_after": {"type": "number"},
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"trading_mode": {"type": "string", "enum": TRADING_MODES},
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"margin_mode": {"type": "string", "enum": MARGIN_MODES},
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"liquidation_buffer": {"type": "number", "minimum": 0.0, "maximum": 0.99},
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"backtest_breakdown": {
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"type": "array",
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"items": {"type": "string", "enum": BACKTEST_BREAKDOWNS},
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},
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"bot_name": {"type": "string"},
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"unfilledtimeout": {
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"type": "object",
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"properties": {
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"entry": {"type": "number", "minimum": 1},
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"exit": {"type": "number", "minimum": 1},
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"exit_timeout_count": {"type": "number", "minimum": 0, "default": 0},
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"unit": {"type": "string", "enum": TIMEOUT_UNITS, "default": "minutes"},
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},
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'minProperties': 1
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},
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'amount_reserve_percent': {'type': 'number', 'minimum': 0.0, 'maximum': 0.5},
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'stoploss': {'type': 'number', 'maximum': 0, 'exclusiveMaximum': True, 'minimum': -1},
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'trailing_stop': {'type': 'boolean'},
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'trailing_stop_positive': {'type': 'number', 'minimum': 0, 'maximum': 1},
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'trailing_stop_positive_offset': {'type': 'number', 'minimum': 0, 'maximum': 1},
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'trailing_only_offset_is_reached': {'type': 'boolean'},
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'use_exit_signal': {'type': 'boolean'},
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'exit_profit_only': {'type': 'boolean'},
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'exit_profit_offset': {'type': 'number'},
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'ignore_roi_if_entry_signal': {'type': 'boolean'},
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'ignore_buying_expired_candle_after': {'type': 'number'},
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'trading_mode': {'type': 'string', 'enum': TRADING_MODES},
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'margin_mode': {'type': 'string', 'enum': MARGIN_MODES},
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'liquidation_buffer': {'type': 'number', 'minimum': 0.0, 'maximum': 0.99},
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'backtest_breakdown': {
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'type': 'array',
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'items': {'type': 'string', 'enum': BACKTEST_BREAKDOWNS}
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},
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'bot_name': {'type': 'string'},
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'unfilledtimeout': {
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'type': 'object',
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'properties': {
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'entry': {'type': 'number', 'minimum': 1},
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'exit': {'type': 'number', 'minimum': 1},
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'exit_timeout_count': {'type': 'number', 'minimum': 0, 'default': 0},
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'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
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}
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},
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'entry_pricing': {
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'type': 'object',
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'properties': {
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'price_last_balance': {
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'type': 'number',
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'minimum': 0,
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'maximum': 1,
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'exclusiveMaximum': False,
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"entry_pricing": {
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"type": "object",
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"properties": {
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"price_last_balance": {
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"type": "number",
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"minimum": 0,
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"maximum": 1,
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"exclusiveMaximum": False,
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},
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'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'},
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'use_order_book': {'type': 'boolean'},
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'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, },
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'check_depth_of_market': {
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'type': 'object',
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'properties': {
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'enabled': {'type': 'boolean'},
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'bids_to_ask_delta': {'type': 'number', 'minimum': 0},
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}
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"price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"},
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"use_order_book": {"type": "boolean"},
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"order_book_top": {
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"type": "integer",
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"minimum": 1,
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"maximum": 50,
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},
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"check_depth_of_market": {
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"type": "object",
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"properties": {
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"enabled": {"type": "boolean"},
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"bids_to_ask_delta": {"type": "number", "minimum": 0},
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},
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},
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},
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'required': ['price_side']
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"required": ["price_side"],
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},
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'exit_pricing': {
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'type': 'object',
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'properties': {
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'price_side': {'type': 'string', 'enum': PRICING_SIDES, 'default': 'same'},
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'price_last_balance': {
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'type': 'number',
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'minimum': 0,
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'maximum': 1,
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'exclusiveMaximum': False,
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"exit_pricing": {
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"type": "object",
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"properties": {
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"price_side": {"type": "string", "enum": PRICING_SIDES, "default": "same"},
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"price_last_balance": {
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"type": "number",
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"minimum": 0,
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"maximum": 1,
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"exclusiveMaximum": False,
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},
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'use_order_book': {'type': 'boolean'},
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'order_book_top': {'type': 'integer', 'minimum': 1, 'maximum': 50, },
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},
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'required': ['price_side']
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},
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'custom_price_max_distance_ratio': {
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'type': 'number', 'minimum': 0.0
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},
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'order_types': {
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'type': 'object',
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'properties': {
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'entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
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'exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
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'force_exit': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
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'force_entry': {'type': 'string', 'enum': ORDERTYPE_POSSIBILITIES},
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'emergency_exit': {
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'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 = {
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
Loading…
Reference in New Issue