mirror of
https://github.com/freqtrade/freqtrade.git
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Ruff format edge
This commit is contained in:
Matthias
parent
794e30fedb
commit
5eb4ad2208
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@ -1,5 +1,6 @@
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# pragma pylint: disable=W0603
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""" Edge positioning package """
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"""Edge positioning package"""
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import logging
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from collections import defaultdict
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from copy import deepcopy
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@ -46,48 +47,49 @@ class Edge:
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_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
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def __init__(self, config: Config, exchange, strategy) -> None:
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self.config = config
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self.exchange = exchange
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self.strategy: IStrategy = strategy
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self.edge_config = self.config.get('edge', {})
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self.edge_config = self.config.get("edge", {})
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self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
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self._final_pairs: list = []
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# checking max_open_trades. it should be -1 as with Edge
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# the number of trades is determined by position size
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if self.config['max_open_trades'] != float('inf'):
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logger.critical('max_open_trades should be -1 in config !')
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if self.config["max_open_trades"] != float("inf"):
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logger.critical("max_open_trades should be -1 in config !")
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if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
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raise OperationalException('Edge works only with unlimited stake amount')
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if self.config["stake_amount"] != UNLIMITED_STAKE_AMOUNT:
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raise OperationalException("Edge works only with unlimited stake amount")
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self._capital_ratio: float = self.config['tradable_balance_ratio']
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self._allowed_risk: float = self.edge_config.get('allowed_risk')
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self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
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self._capital_ratio: float = self.config["tradable_balance_ratio"]
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self._allowed_risk: float = self.edge_config.get("allowed_risk")
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self._since_number_of_days: int = self.edge_config.get("calculate_since_number_of_days", 14)
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self._last_updated: int = 0 # Timestamp of pairs last updated time
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self._refresh_pairs = True
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self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
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self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
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self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
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self._stoploss_range_min = float(self.edge_config.get("stoploss_range_min", -0.01))
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self._stoploss_range_max = float(self.edge_config.get("stoploss_range_max", -0.05))
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self._stoploss_range_step = float(self.edge_config.get("stoploss_range_step", -0.001))
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# calculating stoploss range
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self._stoploss_range = np.arange(
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self._stoploss_range_min,
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self._stoploss_range_max,
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self._stoploss_range_step
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self._stoploss_range_min, self._stoploss_range_max, self._stoploss_range_step
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)
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self._timerange: TimeRange = TimeRange.parse_timerange(
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f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-")
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if config.get('fee'):
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self.fee = config['fee']
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f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-"
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)
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if config.get("fee"):
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self.fee = config["fee"]
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else:
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try:
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self.fee = self.exchange.get_fee(symbol=expand_pairlist(
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self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
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self.fee = self.exchange.get_fee(
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symbol=expand_pairlist(
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self.config["exchange"]["pair_whitelist"], list(self.exchange.markets)
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)[0]
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)
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except IndexError:
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self.fee = None
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@ -95,28 +97,30 @@ class Edge:
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if self.fee is None and pairs:
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self.fee = self.exchange.get_fee(pairs[0])
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heartbeat = self.edge_config.get('process_throttle_secs')
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heartbeat = self.edge_config.get("process_throttle_secs")
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if (self._last_updated > 0) and (
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self._last_updated + heartbeat > int(dt_now().timestamp())):
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self._last_updated + heartbeat > int(dt_now().timestamp())
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):
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return False
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data: Dict[str, Any] = {}
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logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
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logger.info('Using local backtesting data (using whitelist in given config) ...')
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logger.info("Using stake_currency: %s ...", self.config["stake_currency"])
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logger.info("Using local backtesting data (using whitelist in given config) ...")
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if self._refresh_pairs:
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timerange_startup = deepcopy(self._timerange)
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timerange_startup.subtract_start(timeframe_to_seconds(
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self.strategy.timeframe) * self.strategy.startup_candle_count)
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timerange_startup.subtract_start(
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timeframe_to_seconds(self.strategy.timeframe) * self.strategy.startup_candle_count
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)
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refresh_data(
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datadir=self.config['datadir'],
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datadir=self.config["datadir"],
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pairs=pairs,
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exchange=self.exchange,
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timeframe=self.strategy.timeframe,
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timerange=timerange_startup,
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data_format=self.config['dataformat_ohlcv'],
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candle_type=self.config.get('candle_type_def', CandleType.SPOT),
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data_format=self.config["dataformat_ohlcv"],
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candle_type=self.config.get("candle_type_def", CandleType.SPOT),
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)
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# Download informative pairs too
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res = defaultdict(list)
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@ -124,26 +128,27 @@ class Edge:
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res[timeframe].append(pair)
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for timeframe, inf_pairs in res.items():
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timerange_startup = deepcopy(self._timerange)
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timerange_startup.subtract_start(timeframe_to_seconds(
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timeframe) * self.strategy.startup_candle_count)
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timerange_startup.subtract_start(
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timeframe_to_seconds(timeframe) * self.strategy.startup_candle_count
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)
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refresh_data(
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datadir=self.config['datadir'],
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datadir=self.config["datadir"],
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pairs=inf_pairs,
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exchange=self.exchange,
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timeframe=timeframe,
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timerange=timerange_startup,
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data_format=self.config['dataformat_ohlcv'],
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candle_type=self.config.get('candle_type_def', CandleType.SPOT),
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data_format=self.config["dataformat_ohlcv"],
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candle_type=self.config.get("candle_type_def", CandleType.SPOT),
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)
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data = load_data(
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datadir=self.config['datadir'],
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datadir=self.config["datadir"],
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pairs=pairs,
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timeframe=self.strategy.timeframe,
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timerange=self._timerange,
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startup_candles=self.strategy.startup_candle_count,
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data_format=self.config['dataformat_ohlcv'],
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candle_type=self.config.get('candle_type_def', CandleType.SPOT),
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data_format=self.config["dataformat_ohlcv"],
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candle_type=self.config.get("candle_type_def", CandleType.SPOT),
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)
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if not data:
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@ -152,27 +157,29 @@ class Edge:
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logger.critical("No data found. Edge is stopped ...")
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return False
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# Fake run-mode to Edge
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prior_rm = self.config['runmode']
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self.config['runmode'] = RunMode.EDGE
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prior_rm = self.config["runmode"]
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self.config["runmode"] = RunMode.EDGE
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preprocessed = self.strategy.advise_all_indicators(data)
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self.config['runmode'] = prior_rm
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self.config["runmode"] = prior_rm
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# Print timeframe
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min_date, max_date = get_timerange(preprocessed)
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logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
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f'({(max_date - min_date).days} days)..')
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logger.info(
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f"Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} "
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f"up to {max_date.strftime(DATETIME_PRINT_FORMAT)} "
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f"({(max_date - min_date).days} days).."
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)
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# TODO: Should edge support shorts? needs to be investigated further
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# * (add enter_short exit_short)
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headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long']
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headers = ["date", "open", "high", "low", "close", "enter_long", "exit_long"]
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trades: list = []
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for pair, pair_data in preprocessed.items():
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# Sorting dataframe by date and reset index
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pair_data = pair_data.sort_values(by=['date'])
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pair_data = pair_data.sort_values(by=["date"])
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pair_data = pair_data.reset_index(drop=True)
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df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})[headers].copy()
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df_analyzed = self.strategy.ft_advise_signals(pair_data, {"pair": pair})[headers].copy()
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trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
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@ -188,8 +195,9 @@ class Edge:
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return True
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def stake_amount(self, pair: str, free_capital: float,
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total_capital: float, capital_in_trade: float) -> float:
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def stake_amount(
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self, pair: str, free_capital: float, total_capital: float, capital_in_trade: float
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) -> float:
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stoploss = self.get_stoploss(pair)
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available_capital = (total_capital + capital_in_trade) * self._capital_ratio
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allowed_capital_at_risk = available_capital * self._allowed_risk
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@ -198,14 +206,18 @@ class Edge:
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position_size = min(min(max_position_size, free_capital), available_capital)
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if pair in self._cached_pairs:
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logger.info(
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'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
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' capital in trade: %s, free capital: %s, total capital: %s,'
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' stoploss: %s, available capital: %s.',
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"winrate: %s, expectancy: %s, position size: %s, pair: %s,"
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" capital in trade: %s, free capital: %s, total capital: %s,"
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" stoploss: %s, available capital: %s.",
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self._cached_pairs[pair].winrate,
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self._cached_pairs[pair].expectancy,
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position_size, pair,
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capital_in_trade, free_capital, total_capital,
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stoploss, available_capital
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position_size,
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pair,
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capital_in_trade,
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free_capital,
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total_capital,
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stoploss,
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available_capital,
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)
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return round(position_size, 15)
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@ -213,8 +225,10 @@ class Edge:
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if pair in self._cached_pairs:
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return self._cached_pairs[pair].stoploss
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else:
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logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
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'strategy stoploss is returned instead.')
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logger.warning(
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f"Tried to access stoploss of non-existing pair {pair}, "
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"strategy stoploss is returned instead."
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)
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return self.strategy.stoploss
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def adjust(self, pairs: List[str]) -> list:
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@ -224,8 +238,8 @@ class Edge:
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final = []
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for pair, info in self._cached_pairs.items():
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if (
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info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2))
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and info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))
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info.expectancy > float(self.edge_config.get("minimum_expectancy", 0.2))
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and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60))
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and pair in pairs
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):
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final.append(pair)
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@ -234,14 +248,14 @@ class Edge:
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self._final_pairs = final
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if self._final_pairs:
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logger.info(
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'Minimum expectancy and minimum winrate are met only for %s,'
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' so other pairs are filtered out.',
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self._final_pairs
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"Minimum expectancy and minimum winrate are met only for %s,"
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" so other pairs are filtered out.",
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self._final_pairs,
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)
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else:
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logger.info(
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'Edge removed all pairs as no pair with minimum expectancy '
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'and minimum winrate was found !'
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"Edge removed all pairs as no pair with minimum expectancy "
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"and minimum winrate was found !"
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)
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return self._final_pairs
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@ -252,14 +266,17 @@ class Edge:
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"""
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final = []
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for pair, info in self._cached_pairs.items():
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if (info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and
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info.winrate > float(self.edge_config.get('minimum_winrate', 0.60))):
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final.append({
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'Pair': pair,
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'Winrate': info.winrate,
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'Expectancy': info.expectancy,
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'Stoploss': info.stoploss,
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})
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if info.expectancy > float(
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self.edge_config.get("minimum_expectancy", 0.2)
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) and info.winrate > float(self.edge_config.get("minimum_winrate", 0.60)):
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final.append(
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{
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"Pair": pair,
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"Winrate": info.winrate,
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"Expectancy": info.expectancy,
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"Stoploss": info.stoploss,
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}
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)
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return final
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def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
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@ -279,28 +296,29 @@ class Edge:
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# All returned values are relative, they are defined as ratios.
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stake = 0.015
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result['trade_duration'] = result['close_date'] - result['open_date']
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result["trade_duration"] = result["close_date"] - result["open_date"]
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result['trade_duration'] = result['trade_duration'].map(
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lambda x: int(x.total_seconds() / 60))
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result["trade_duration"] = result["trade_duration"].map(
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lambda x: int(x.total_seconds() / 60)
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)
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# Spends, Takes, Profit, Absolute Profit
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# Buy Price
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result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
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result['buy_fee'] = stake * self.fee
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result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
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result["buy_vol"] = stake / result["open_rate"] # How many target are we buying
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result["buy_fee"] = stake * self.fee
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result["buy_spend"] = stake + result["buy_fee"] # How much we're spending
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# Sell price
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result['sell_sum'] = result['buy_vol'] * result['close_rate']
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result['sell_fee'] = result['sell_sum'] * self.fee
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result['sell_take'] = result['sell_sum'] - result['sell_fee']
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result["sell_sum"] = result["buy_vol"] * result["close_rate"]
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result["sell_fee"] = result["sell_sum"] * self.fee
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result["sell_take"] = result["sell_sum"] - result["sell_fee"]
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# profit_ratio
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result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
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result["profit_ratio"] = (result["sell_take"] - result["buy_spend"]) / result["buy_spend"]
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# Absolute profit
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result['profit_abs'] = result['sell_take'] - result['buy_spend']
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result["profit_abs"] = result["sell_take"] - result["buy_spend"]
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return result
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@ -310,8 +328,8 @@ class Edge:
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The calculation will be done per pair and per strategy.
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"""
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# Removing pairs having less than min_trades_number
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min_trades_number = self.edge_config.get('min_trade_number', 10)
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results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
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min_trades_number = self.edge_config.get("min_trade_number", 10)
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results = results.groupby(["pair", "stoploss"]).filter(lambda x: len(x) > min_trades_number)
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###################################
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# Removing outliers (Only Pumps) from the dataset
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@ -319,13 +337,15 @@ class Edge:
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# Then every value more than (standard deviation + 2*average) is out (pump)
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#
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# Removing Pumps
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if self.edge_config.get('remove_pumps', False):
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results = results[results['profit_abs'] < 2 * results['profit_abs'].std()
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+ results['profit_abs'].mean()]
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if self.edge_config.get("remove_pumps", False):
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results = results[
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results["profit_abs"]
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< 2 * results["profit_abs"].std() + results["profit_abs"].mean()
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]
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##########################################################################
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# Removing trades having a duration more than X minutes (set in config)
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max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
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max_trade_duration = self.edge_config.get("max_trade_duration_minute", 1440)
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results = results[results.trade_duration < max_trade_duration]
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#######################################################################
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@ -333,44 +353,54 @@ class Edge:
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return {}
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groupby_aggregator = {
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'profit_abs': [
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('nb_trades', 'count'), # number of all trades
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('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
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('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
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('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
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"profit_abs": [
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("nb_trades", "count"), # number of all trades
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("profit_sum", lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
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("loss_sum", lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
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("nb_win_trades", lambda x: x[x > 0].count()), # number of winning trades
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],
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'trade_duration': [('avg_trade_duration', 'mean')]
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"trade_duration": [("avg_trade_duration", "mean")],
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}
|
||||
|
||||
# Group by (pair and stoploss) by applying above aggregator
|
||||
df = results.groupby(['pair', 'stoploss'])[['profit_abs', 'trade_duration']].agg(
|
||||
groupby_aggregator).reset_index(col_level=1)
|
||||
df = (
|
||||
results.groupby(["pair", "stoploss"])[["profit_abs", "trade_duration"]]
|
||||
.agg(groupby_aggregator)
|
||||
.reset_index(col_level=1)
|
||||
)
|
||||
|
||||
# Dropping level 0 as we don't need it
|
||||
df.columns = df.columns.droplevel(0)
|
||||
|
||||
# Calculating number of losing trades, average win and average loss
|
||||
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
|
||||
df['average_win'] = np.where(df['nb_win_trades'] == 0, 0.0,
|
||||
df['profit_sum'] / df['nb_win_trades'])
|
||||
df['average_loss'] = np.where(df['nb_loss_trades'] == 0, 0.0,
|
||||
df['loss_sum'] / df['nb_loss_trades'])
|
||||
df["nb_loss_trades"] = df["nb_trades"] - df["nb_win_trades"]
|
||||
df["average_win"] = np.where(
|
||||
df["nb_win_trades"] == 0, 0.0, df["profit_sum"] / df["nb_win_trades"]
|
||||
)
|
||||
df["average_loss"] = np.where(
|
||||
df["nb_loss_trades"] == 0, 0.0, df["loss_sum"] / df["nb_loss_trades"]
|
||||
)
|
||||
|
||||
# Win rate = number of profitable trades / number of trades
|
||||
df['winrate'] = df['nb_win_trades'] / df['nb_trades']
|
||||
df["winrate"] = df["nb_win_trades"] / df["nb_trades"]
|
||||
|
||||
# risk_reward_ratio = average win / average loss
|
||||
df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
|
||||
df["risk_reward_ratio"] = df["average_win"] / df["average_loss"]
|
||||
|
||||
# required_risk_reward = (1 / winrate) - 1
|
||||
df['required_risk_reward'] = (1 / df['winrate']) - 1
|
||||
df["required_risk_reward"] = (1 / df["winrate"]) - 1
|
||||
|
||||
# expectancy = (risk_reward_ratio * winrate) - (lossrate)
|
||||
df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
|
||||
df["expectancy"] = (df["risk_reward_ratio"] * df["winrate"]) - (1 - df["winrate"])
|
||||
|
||||
# sort by expectancy and stoploss
|
||||
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
|
||||
'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
|
||||
df = (
|
||||
df.sort_values(by=["expectancy", "stoploss"], ascending=False)
|
||||
.groupby("pair")
|
||||
.first()
|
||||
.sort_values(by=["expectancy"], ascending=False)
|
||||
.reset_index()
|
||||
)
|
||||
|
||||
final = {}
|
||||
for x in df.itertuples():
|
||||
|
|
@ -381,17 +411,17 @@ class Edge:
|
|||
x.required_risk_reward,
|
||||
x.expectancy,
|
||||
x.nb_trades,
|
||||
x.avg_trade_duration
|
||||
x.avg_trade_duration,
|
||||
)
|
||||
|
||||
# Returning a list of pairs in order of "expectancy"
|
||||
return final
|
||||
|
||||
def _find_trades_for_stoploss_range(self, df, pair: str, stoploss_range) -> list:
|
||||
buy_column = df['enter_long'].values
|
||||
sell_column = df['exit_long'].values
|
||||
date_column = df['date'].values
|
||||
ohlc_columns = df[['open', 'high', 'low', 'close']].values
|
||||
buy_column = df["enter_long"].values
|
||||
sell_column = df["exit_long"].values
|
||||
date_column = df["date"].values
|
||||
ohlc_columns = df[["open", "high", "low", "close"]].values
|
||||
|
||||
result: list = []
|
||||
for stoploss in stoploss_range:
|
||||
|
|
@ -401,8 +431,9 @@ class Edge:
|
|||
|
||||
return result
|
||||
|
||||
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
|
||||
ohlc_columns, stoploss, pair: str):
|
||||
def _detect_next_stop_or_sell_point(
|
||||
self, buy_column, sell_column, date_column, ohlc_columns, stoploss, pair: str
|
||||
):
|
||||
"""
|
||||
Iterate through ohlc_columns in order to find the next trade
|
||||
Next trade opens from the first buy signal noticed to
|
||||
|
|
@ -429,27 +460,28 @@ class Edge:
|
|||
open_trade_index += 1
|
||||
|
||||
open_price = ohlc_columns[open_trade_index, 0]
|
||||
stop_price = (open_price * (stoploss + 1))
|
||||
stop_price = open_price * (stoploss + 1)
|
||||
|
||||
# Searching for the index where stoploss is hit
|
||||
stop_index = utf1st.find_1st(
|
||||
ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
|
||||
ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller
|
||||
)
|
||||
|
||||
# If we don't find it then we assume stop_index will be far in future (infinite number)
|
||||
if stop_index == -1:
|
||||
stop_index = float('inf')
|
||||
stop_index = float("inf")
|
||||
|
||||
# Searching for the index where sell is hit
|
||||
sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
|
||||
|
||||
# If we don't find it then we assume sell_index will be far in future (infinite number)
|
||||
if sell_index == -1:
|
||||
sell_index = float('inf')
|
||||
sell_index = float("inf")
|
||||
|
||||
# Check if we don't find any stop or sell point (in that case trade remains open)
|
||||
# It is not interesting for Edge to consider it so we simply ignore the trade
|
||||
# And stop iterating there is no more entry
|
||||
if stop_index == sell_index == float('inf'):
|
||||
if stop_index == sell_index == float("inf"):
|
||||
break
|
||||
|
||||
if stop_index <= sell_index:
|
||||
|
|
@ -467,17 +499,18 @@ class Edge:
|
|||
exit_type = ExitType.EXIT_SIGNAL
|
||||
exit_price = ohlc_columns[exit_index, 0]
|
||||
|
||||
trade = {'pair': pair,
|
||||
'stoploss': stoploss,
|
||||
'profit_ratio': '',
|
||||
'profit_abs': '',
|
||||
'open_date': date_column[open_trade_index],
|
||||
'close_date': date_column[exit_index],
|
||||
'trade_duration': '',
|
||||
'open_rate': round(open_price, 15),
|
||||
'close_rate': round(exit_price, 15),
|
||||
'exit_type': exit_type
|
||||
}
|
||||
trade = {
|
||||
"pair": pair,
|
||||
"stoploss": stoploss,
|
||||
"profit_ratio": "",
|
||||
"profit_abs": "",
|
||||
"open_date": date_column[open_trade_index],
|
||||
"close_date": date_column[exit_index],
|
||||
"trade_duration": "",
|
||||
"open_rate": round(open_price, 15),
|
||||
"close_rate": round(exit_price, 15),
|
||||
"exit_type": exit_type,
|
||||
}
|
||||
|
||||
result.append(trade)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user