378 lines
15 KiB
Python
378 lines
15 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
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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.optimize.backtesting import BacktestResult
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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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from freqtrade.strategy.resolver import IStrategy, StrategyResolver
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from freqtrade.optimize.backtesting import Backtesting
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logger = logging.getLogger(__name__)
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class Edge():
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config: Dict = {}
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_last_updated: int # Timestamp of pairs last updated time
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_cached_pairs: list = [] # Keeps an array of
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# [pair, winrate, risk reward ratio, required risk reward, expectancy]
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_total_capital: float
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_allowed_risk: float
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_since_number_of_days: int
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_timerange: TimeRange
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def __init__(self, config: Dict[str, Any], exchange=None) -> None:
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self.config = config
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self.exchange = exchange
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self.strategy: IStrategy = StrategyResolver(self.config).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 = Backtesting.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: list = []
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self._total_capital = self.edge_config.get('total_capital_in_stake_currency')
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self._allowed_risk = self.edge_config.get('allowed_risk')
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self._since_number_of_days = self.edge_config.get('calculate_since_number_of_days', 14)
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self._last_updated = 0
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self._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=True,
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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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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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stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
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stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
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stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
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stoploss_range = np.arange(stoploss_range_min, stoploss_range_max, stoploss_range_step)
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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, stoploss_range)
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# Switch List of Trade Dicts (trades) to Dataframe
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# Fill missing, calculable columns, profit, duration , abs etc.
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trades_df = DataFrame(trades)
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if len(trades_df) > 0: # Only post process a frame if it has a record
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trades_df = self._fill_calculable_fields(trades_df)
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else:
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trades_df = DataFrame.from_records(trades_df, columns=BacktestResult._fields)
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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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info = [x for x in self._cached_pairs if x[0] == pair][0]
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stoploss = info[1]
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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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info = [x for x in self._cached_pairs if x[0] == pair][0]
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return info[1]
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def filter(self, pairs) -> list:
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# Filtering pairs acccording to the expectancy
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filtered_expectancy: list = []
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filtered_expectancy = [
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x[0] for x in self._cached_pairs if x[5] > float(
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self.edge_config.get(
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'minimum_expectancy', 0.2))]
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# Only return pairs which are included in "pairs" argument list
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final = [x for x in filtered_expectancy if x in pairs]
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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) -> list:
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"""
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This is a temporary version of edge positioning calculation.
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The function will be eventually moved to a plugin called Edge in order
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to calculate necessary WR, RRR and
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other indictaors related to money management periodically (each X minutes)
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and keep it in a storage.
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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', 15)
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results = results.groupby('pair').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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# Calculating standard deviation of profits
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std = results[["profit_abs"]].std()
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#
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# Calculating average of profits
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avg = results[["profit_abs"]].mean()
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#
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# Removing Pumps
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if self.edge_config.get('remove_pumps', True):
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results = results[results.profit_abs < float(avg + 2 * std)]
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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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# Win Rate is the number of profitable trades
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# Divided by number of trades
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def winrate(x):
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x = x[x > 0].count() / x.count()
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return x
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#############################
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# Risk Reward Ratio
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# 1 / ((loss money / losing trades) / (gained money / winning trades))
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def risk_reward_ratio(x):
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x = abs(1 / ((x[x < 0].sum() / x[x < 0].count()) / (x[x > 0].sum() / x[x > 0].count())))
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return x
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##############################
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# Required Risk Reward
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# (1/(winrate - 1)
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def required_risk_reward(x):
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x = (1 / (x[x > 0].count() / x.count()) - 1)
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return x
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##############################
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# Expectancy
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# Tells you the interest percentage you should hope
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# E.x. if expectancy is 0.35, on $1 trade you should expect a target of $1.35
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def expectancy(x):
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average_win = float(x[x > 0].sum() / x[x > 0].count())
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average_loss = float(abs(x[x < 0].sum() / x[x < 0].count()))
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winrate = float(x[x > 0].count() / x.count())
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x = ((1 + average_win / average_loss) * winrate) - 1
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return x
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##############################
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final = results.groupby(['pair', 'stoploss'])['profit_abs'].\
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agg([winrate, risk_reward_ratio, required_risk_reward, expectancy]).\
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reset_index().sort_values(by=['expectancy', 'stoploss'], ascending=False)\
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.groupby('pair').first().sort_values(by=['expectancy'], ascending=False)
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# Returning an array of pairs in order of "expectancy"
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return final.reset_index().values
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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_stop_and_sell_points(
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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_stop_and_sell_points(
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self,
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buy_column,
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sell_column,
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date_column,
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ohlc_columns,
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stoploss,
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pair,
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start_point=0):
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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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stop_price_percentage = stoploss + 1
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open_price = ohlc_columns[open_trade_index + 1, 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 + 1:, 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:], 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 + 1
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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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exit_index = open_trade_index + sell_index + 1
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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 + 1,
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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_stop_and_sell_points(
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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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