415 lines
16 KiB
Python
415 lines
16 KiB
Python
# pragma pylint: disable=W0603
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""" Edge positioning package """
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import logging
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from typing import Any, Dict, NamedTuple
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import arrow
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import numpy as np
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import utils_find_1st as utf1st
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from pandas import DataFrame
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import freqtrade.optimize as optimize
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from freqtrade.arguments import Arguments
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from freqtrade.arguments import TimeRange
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from freqtrade.strategy.interface import SellType
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logger = logging.getLogger(__name__)
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class PairInfo(NamedTuple):
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stoploss: float
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winrate: float
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risk_reward_ratio: float
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required_risk_reward: float
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expectancy: float
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nb_trades: int
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avg_trade_duration: float
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class Edge():
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"""
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Calculates Win Rate, Risk Reward Ratio, Expectancy
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against historical data for a give set of markets and a strategy
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it then adjusts stoploss and position size accordingly
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and force it into the strategy
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Author: https://github.com/mishaker
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"""
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config: Dict = {}
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_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
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def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
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self.config = config
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self.exchange = exchange
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self.strategy = strategy
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self.ticker_interval = self.strategy.ticker_interval
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self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
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self.get_timeframe = optimize.get_timeframe
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self.advise_sell = self.strategy.advise_sell
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self.advise_buy = self.strategy.advise_buy
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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._total_capital: float = self.config['stake_amount']
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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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# 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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)
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self._timerange: TimeRange = Arguments.parse_timerange("%s-" % arrow.now().shift(
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days=-1 * self._since_number_of_days).format('YYYYMMDD'))
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self.fee = self.exchange.get_fee()
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def calculate(self) -> bool:
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pairs = self.config['exchange']['pair_whitelist']
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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 > arrow.utcnow().timestamp):
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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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data = optimize.load_data(
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self.config['datadir'],
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pairs=pairs,
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ticker_interval=self.ticker_interval,
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refresh_pairs=self._refresh_pairs,
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exchange=self.exchange,
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timerange=self._timerange
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)
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if not data:
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# Reinitializing cached pairs
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self._cached_pairs = {}
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logger.critical("No data found. Edge is stopped ...")
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return False
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preprocessed = self.tickerdata_to_dataframe(data)
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# Print timeframe
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min_date, max_date = self.get_timeframe(preprocessed)
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logger.info(
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'Measuring data from %s up to %s (%s days) ...',
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min_date.isoformat(),
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max_date.isoformat(),
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(max_date - min_date).days
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)
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headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
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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.reset_index(drop=True)
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ticker_data = self.advise_sell(
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self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
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trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
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# If no trade found then exit
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if len(trades) == 0:
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return False
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# Fill missing, calculable columns, profit, duration , abs etc.
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trades_df = self._fill_calculable_fields(DataFrame(trades))
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self._cached_pairs = self._process_expectancy(trades_df)
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self._last_updated = arrow.utcnow().timestamp
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# Not a nice hack but probably simplest solution:
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# When backtest load data it loads the delta between disk and exchange
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# The problem is that exchange consider that recent.
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# it is but it is incomplete (c.f. _async_get_candle_history)
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# So it causes get_signal to exit cause incomplete ticker_hist
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# A patch to that would be update _pairs_last_refresh_time of exchange
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# so it will download again all pairs
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# Another solution is to add new data to klines instead of reassigning it:
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# self.klines[pair].update(data) instead of self.klines[pair] = data in exchange package.
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# But that means indexing timestamp and having a verification so that
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# there is no empty range between two timestaps (recently added and last
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# one)
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self.exchange._pairs_last_refresh_time = {}
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return True
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def stake_amount(self, pair: str) -> float:
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stoploss = self._cached_pairs[pair].stoploss
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allowed_capital_at_risk = round(self._total_capital * self._allowed_risk, 5)
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position_size = abs(round((allowed_capital_at_risk / stoploss), 5))
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return position_size
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def stoploss(self, pair: str) -> float:
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return self._cached_pairs[pair].stoploss
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def adjust(self, pairs) -> list:
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"""
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Filters out and sorts "pairs" according to Edge calculated pairs
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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)) and \
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pair in pairs:
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final.append(pair)
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if final:
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logger.info('Edge validated only %s', final)
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else:
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logger.info('Edge removed all pairs as no pair with minimum expectancy was found !')
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return final
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def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
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"""
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The result frame contains a number of columns that are calculable
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from other columns. These are left blank till all rows are added,
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to be populated in single vector calls.
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Columns to be populated are:
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- Profit
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- trade duration
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- profit abs
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:param result Dataframe
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:return: result Dataframe
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"""
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# stake and fees
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# stake = 0.015
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# 0.05% is 0.0005
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# fee = 0.001
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stake = self.config.get('stake_amount')
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fee = self.fee
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open_fee = fee / 2
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close_fee = fee / 2
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result['trade_duration'] = result['close_time'] - result['open_time']
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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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# 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 * open_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'] * close_fee
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result['sell_take'] = result['sell_sum'] - result['sell_fee']
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# profit_percent
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result['profit_percent'] = (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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return result
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def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
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"""
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This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
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The calulation 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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###################################
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# Removing outliers (Only Pumps) from the dataset
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# The method to detect outliers is to calculate standard deviation
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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.groupby(['pair', 'stoploss']).apply(
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lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
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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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results = results[results.trade_duration < max_trade_duration]
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#######################################################################
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if results.empty:
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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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],
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'trade_duration': [('avg_trade_duration', 'mean')]
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}
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# Group by (pair and stoploss) by applying above aggregator
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df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
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groupby_aggregator).reset_index(col_level=1)
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# Dropping level 0 as we don't need it
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df.columns = df.columns.droplevel(0)
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# Calculating number of losing trades, average win and average loss
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df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
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df['average_win'] = df['profit_sum'] / df['nb_win_trades']
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df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
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# Win rate = number of profitable trades / number of trades
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df['winrate'] = df['nb_win_trades'] / df['nb_trades']
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# risk_reward_ratio = average win / average loss
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df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
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# required_risk_reward = (1 / winrate) - 1
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df['required_risk_reward'] = (1 / df['winrate']) - 1
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# expectancy = (risk_reward_ratio * winrate) - (lossrate)
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df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
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# sort by expectancy and stoploss
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df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
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'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
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final = {}
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for x in df.itertuples():
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final[x.pair] = PairInfo(
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x.stoploss,
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x.winrate,
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x.risk_reward_ratio,
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x.required_risk_reward,
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x.expectancy,
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x.nb_trades,
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x.avg_trade_duration
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)
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# Returning a list of pairs in order of "expectancy"
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return final
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def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
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buy_column = ticker_data['buy'].values
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sell_column = ticker_data['sell'].values
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date_column = ticker_data['date'].values
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ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
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result: list = []
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for stoploss in stoploss_range:
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result += self._detect_next_stop_or_sell_point(
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buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
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)
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return result
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def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
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ohlc_columns, stoploss, pair, start_point=0):
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"""
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Iterate through ohlc_columns recursively in order to find the next trade
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Next trade opens from the first buy signal noticed to
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The sell or stoploss signal after it.
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It then calls itself cutting OHLC, buy_column, sell_colum and date_column
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Cut from (the exit trade index) + 1
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Author: https://github.com/mishaker
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"""
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result: list = []
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open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
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# return empty if we don't find trade entry (i.e. buy==1) or
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# we find a buy but at the of array
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if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
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return []
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else:
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open_trade_index += 1 # when a buy signal is seen,
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# trade opens in reality on the next candle
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stop_price_percentage = stoploss + 1
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open_price = ohlc_columns[open_trade_index, 0]
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stop_price = (open_price * stop_price_percentage)
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# Searching for the index where stoploss is hit
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stop_index = utf1st.find_1st(
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ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
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# If we don't find it then we assume stop_index will be far in future (infinite number)
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if stop_index == -1:
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stop_index = float('inf')
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# Searching for the index where sell is hit
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sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
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# If we don't find it then we assume sell_index will be far in future (infinite number)
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if sell_index == -1:
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sell_index = float('inf')
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# Check if we don't find any stop or sell point (in that case trade remains open)
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# It is not interesting for Edge to consider it so we simply ignore the trade
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# And stop iterating there is no more entry
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if stop_index == sell_index == float('inf'):
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return []
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if stop_index <= sell_index:
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exit_index = open_trade_index + stop_index
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exit_type = SellType.STOP_LOSS
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exit_price = stop_price
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elif stop_index > sell_index:
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# if exit is SELL then we exit at the next candle
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exit_index = open_trade_index + sell_index + 1
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# check if we have the next candle
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if len(ohlc_columns) - 1 < exit_index:
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return []
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exit_type = SellType.SELL_SIGNAL
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exit_price = ohlc_columns[exit_index, 0]
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trade = {'pair': pair,
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'stoploss': stoploss,
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'profit_percent': '',
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'profit_abs': '',
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'open_time': date_column[open_trade_index],
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'close_time': date_column[exit_index],
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'open_index': start_point + open_trade_index,
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'close_index': start_point + exit_index,
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'trade_duration': '',
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'open_rate': round(open_price, 15),
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'close_rate': round(exit_price, 15),
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'exit_type': exit_type
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}
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result.append(trade)
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# Calling again the same function recursively but giving
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# it a view of exit_index till the end of array
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return result + self._detect_next_stop_or_sell_point(
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buy_column[exit_index:],
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sell_column[exit_index:],
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date_column[exit_index:],
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ohlc_columns[exit_index:],
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stoploss,
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pair,
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(start_point + exit_index)
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)
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