492 lines
20 KiB
Python
492 lines
20 KiB
Python
# pragma pylint: disable=W0603
|
|
""" Edge positioning package """
|
|
import logging
|
|
from collections import defaultdict
|
|
from copy import deepcopy
|
|
from typing import Any, Dict, List, NamedTuple
|
|
|
|
import arrow
|
|
import numpy as np
|
|
import utils_find_1st as utf1st
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.configuration import TimeRange
|
|
from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
|
|
from freqtrade.data.history import get_timerange, load_data, refresh_data
|
|
from freqtrade.enums import RunMode, SellType
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.exchange.exchange import timeframe_to_seconds
|
|
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
|
|
from freqtrade.strategy.interface import IStrategy
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class PairInfo(NamedTuple):
|
|
stoploss: float
|
|
winrate: float
|
|
risk_reward_ratio: float
|
|
required_risk_reward: float
|
|
expectancy: float
|
|
nb_trades: int
|
|
avg_trade_duration: float
|
|
|
|
|
|
class Edge:
|
|
"""
|
|
Calculates Win Rate, Risk Reward Ratio, Expectancy
|
|
against historical data for a give set of markets and a strategy
|
|
it then adjusts stoploss and position size accordingly
|
|
and force it into the strategy
|
|
Author: https://github.com/mishaker
|
|
"""
|
|
|
|
config: Dict = {}
|
|
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
|
|
|
|
def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
|
|
|
|
self.config = config
|
|
self.exchange = exchange
|
|
self.strategy: IStrategy = strategy
|
|
|
|
self.edge_config = self.config.get('edge', {})
|
|
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
|
|
self._final_pairs: list = []
|
|
|
|
# checking max_open_trades. it should be -1 as with Edge
|
|
# the number of trades is determined by position size
|
|
if self.config['max_open_trades'] != float('inf'):
|
|
logger.critical('max_open_trades should be -1 in config !')
|
|
|
|
if self.config['stake_amount'] != UNLIMITED_STAKE_AMOUNT:
|
|
raise OperationalException('Edge works only with unlimited stake amount')
|
|
|
|
self._capital_ratio: float = self.config['tradable_balance_ratio']
|
|
self._allowed_risk: float = self.edge_config.get('allowed_risk')
|
|
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
|
|
self._last_updated: int = 0 # Timestamp of pairs last updated time
|
|
self._refresh_pairs = True
|
|
|
|
self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
|
|
self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
|
|
self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
|
|
|
|
# calculating stoploss range
|
|
self._stoploss_range = np.arange(
|
|
self._stoploss_range_min,
|
|
self._stoploss_range_max,
|
|
self._stoploss_range_step
|
|
)
|
|
|
|
self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
|
|
days=-1 * self._since_number_of_days).format('YYYYMMDD'))
|
|
if config.get('fee'):
|
|
self.fee = config['fee']
|
|
else:
|
|
try:
|
|
self.fee = self.exchange.get_fee(symbol=expand_pairlist(
|
|
self.config['exchange']['pair_whitelist'], list(self.exchange.markets))[0])
|
|
except IndexError:
|
|
self.fee = None
|
|
|
|
def calculate(self, pairs: List[str]) -> bool:
|
|
if self.fee is None and pairs:
|
|
self.fee = self.exchange.get_fee(pairs[0])
|
|
|
|
heartbeat = self.edge_config.get('process_throttle_secs')
|
|
|
|
if (self._last_updated > 0) and (
|
|
self._last_updated + heartbeat > arrow.utcnow().int_timestamp):
|
|
return False
|
|
|
|
data: Dict[str, Any] = {}
|
|
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
|
|
logger.info('Using local backtesting data (using whitelist in given config) ...')
|
|
|
|
if self._refresh_pairs:
|
|
timerange_startup = deepcopy(self._timerange)
|
|
timerange_startup.subtract_start(timeframe_to_seconds(
|
|
self.strategy.timeframe) * self.strategy.startup_candle_count)
|
|
refresh_data(
|
|
datadir=self.config['datadir'],
|
|
pairs=pairs,
|
|
exchange=self.exchange,
|
|
timeframe=self.strategy.timeframe,
|
|
timerange=timerange_startup,
|
|
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
|
)
|
|
# Download informative pairs too
|
|
res = defaultdict(list)
|
|
for pair, timeframe, _ in self.strategy.gather_informative_pairs():
|
|
res[timeframe].append(pair)
|
|
for timeframe, inf_pairs in res.items():
|
|
timerange_startup = deepcopy(self._timerange)
|
|
timerange_startup.subtract_start(timeframe_to_seconds(
|
|
timeframe) * self.strategy.startup_candle_count)
|
|
refresh_data(
|
|
datadir=self.config['datadir'],
|
|
pairs=inf_pairs,
|
|
exchange=self.exchange,
|
|
timeframe=timeframe,
|
|
timerange=timerange_startup,
|
|
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
|
)
|
|
|
|
data = load_data(
|
|
datadir=self.config['datadir'],
|
|
pairs=pairs,
|
|
timeframe=self.strategy.timeframe,
|
|
timerange=self._timerange,
|
|
startup_candles=self.strategy.startup_candle_count,
|
|
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
|
)
|
|
|
|
if not data:
|
|
# Reinitializing cached pairs
|
|
self._cached_pairs = {}
|
|
logger.critical("No data found. Edge is stopped ...")
|
|
return False
|
|
# Fake run-mode to Edge
|
|
prior_rm = self.config['runmode']
|
|
self.config['runmode'] = RunMode.EDGE
|
|
preprocessed = self.strategy.advise_all_indicators(data)
|
|
self.config['runmode'] = prior_rm
|
|
|
|
# Print timeframe
|
|
min_date, max_date = get_timerange(preprocessed)
|
|
logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
|
|
f'({(max_date - min_date).days} days)..')
|
|
# TODO-lev: Should edge support shorts? needs to be investigated further...
|
|
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long']
|
|
|
|
trades: list = []
|
|
for pair, pair_data in preprocessed.items():
|
|
# Sorting dataframe by date and reset index
|
|
pair_data = pair_data.sort_values(by=['date'])
|
|
pair_data = pair_data.reset_index(drop=True)
|
|
|
|
df_analyzed = self.strategy.advise_exit(
|
|
dataframe=self.strategy.advise_entry(
|
|
dataframe=pair_data,
|
|
metadata={'pair': pair}
|
|
),
|
|
metadata={'pair': pair}
|
|
)[headers].copy()
|
|
|
|
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
|
|
|
|
# If no trade found then exit
|
|
if len(trades) == 0:
|
|
logger.info("No trades found.")
|
|
return False
|
|
|
|
# Fill missing, calculable columns, profit, duration , abs etc.
|
|
trades_df = self._fill_calculable_fields(DataFrame(trades))
|
|
self._cached_pairs = self._process_expectancy(trades_df)
|
|
self._last_updated = arrow.utcnow().int_timestamp
|
|
|
|
return True
|
|
|
|
def stake_amount(self, pair: str, free_capital: float,
|
|
total_capital: float, capital_in_trade: float) -> float:
|
|
stoploss = self.stoploss(pair)
|
|
available_capital = (total_capital + capital_in_trade) * self._capital_ratio
|
|
allowed_capital_at_risk = available_capital * self._allowed_risk
|
|
max_position_size = abs(allowed_capital_at_risk / stoploss)
|
|
# Position size must be below available capital.
|
|
position_size = min(min(max_position_size, free_capital), available_capital)
|
|
if pair in self._cached_pairs:
|
|
logger.info(
|
|
'winrate: %s, expectancy: %s, position size: %s, pair: %s,'
|
|
' capital in trade: %s, free capital: %s, total capital: %s,'
|
|
' stoploss: %s, available capital: %s.',
|
|
self._cached_pairs[pair].winrate,
|
|
self._cached_pairs[pair].expectancy,
|
|
position_size, pair,
|
|
capital_in_trade, free_capital, total_capital,
|
|
stoploss, available_capital
|
|
)
|
|
return round(position_size, 15)
|
|
|
|
def stoploss(self, pair: str) -> float:
|
|
if pair in self._cached_pairs:
|
|
return self._cached_pairs[pair].stoploss
|
|
else:
|
|
logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
|
|
'strategy stoploss is returned instead.')
|
|
return self.strategy.stoploss
|
|
|
|
def adjust(self, pairs: List[str]) -> list:
|
|
"""
|
|
Filters out and sorts "pairs" according to Edge calculated pairs
|
|
"""
|
|
final = []
|
|
for pair, info in self._cached_pairs.items():
|
|
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
|
|
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
|
|
pair in pairs:
|
|
final.append(pair)
|
|
|
|
if self._final_pairs != final:
|
|
self._final_pairs = final
|
|
if self._final_pairs:
|
|
logger.info(
|
|
'Minimum expectancy and minimum winrate are met only for %s,'
|
|
' so other pairs are filtered out.',
|
|
self._final_pairs
|
|
)
|
|
else:
|
|
logger.info(
|
|
'Edge removed all pairs as no pair with minimum expectancy '
|
|
'and minimum winrate was found !'
|
|
)
|
|
|
|
return self._final_pairs
|
|
|
|
def accepted_pairs(self) -> List[Dict[str, Any]]:
|
|
"""
|
|
return a list of accepted pairs along with their winrate, expectancy and stoploss
|
|
"""
|
|
final = []
|
|
for pair, info in self._cached_pairs.items():
|
|
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
|
|
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)):
|
|
final.append({
|
|
'Pair': pair,
|
|
'Winrate': info.winrate,
|
|
'Expectancy': info.expectancy,
|
|
'Stoploss': info.stoploss,
|
|
})
|
|
return final
|
|
|
|
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
|
|
"""
|
|
The result frame contains a number of columns that are calculable
|
|
from other columns. These are left blank till all rows are added,
|
|
to be populated in single vector calls.
|
|
|
|
Columns to be populated are:
|
|
- Profit
|
|
- trade duration
|
|
- profit abs
|
|
:param result Dataframe
|
|
:return: result Dataframe
|
|
"""
|
|
# We set stake amount to an arbitrary amount, as it doesn't change the calculation.
|
|
# All returned values are relative, they are defined as ratios.
|
|
stake = 0.015
|
|
|
|
result['trade_duration'] = result['close_date'] - result['open_date']
|
|
|
|
result['trade_duration'] = result['trade_duration'].map(
|
|
lambda x: int(x.total_seconds() / 60))
|
|
|
|
# Spends, Takes, Profit, Absolute Profit
|
|
|
|
# Buy Price
|
|
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
|
|
result['buy_fee'] = stake * self.fee
|
|
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
|
|
|
|
# Sell price
|
|
result['sell_sum'] = result['buy_vol'] * result['close_rate']
|
|
result['sell_fee'] = result['sell_sum'] * self.fee
|
|
result['sell_take'] = result['sell_sum'] - result['sell_fee']
|
|
|
|
# profit_ratio
|
|
result['profit_ratio'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
|
|
|
|
# Absolute profit
|
|
result['profit_abs'] = result['sell_take'] - result['buy_spend']
|
|
|
|
return result
|
|
|
|
def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
|
|
"""
|
|
This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
|
|
The calculation will be done per pair and per strategy.
|
|
"""
|
|
# Removing pairs having less than min_trades_number
|
|
min_trades_number = self.edge_config.get('min_trade_number', 10)
|
|
results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
|
|
###################################
|
|
|
|
# Removing outliers (Only Pumps) from the dataset
|
|
# The method to detect outliers is to calculate standard deviation
|
|
# Then every value more than (standard deviation + 2*average) is out (pump)
|
|
#
|
|
# Removing Pumps
|
|
if self.edge_config.get('remove_pumps', False):
|
|
results = results[results['profit_abs'] < 2 * results['profit_abs'].std()
|
|
+ results['profit_abs'].mean()]
|
|
##########################################################################
|
|
|
|
# Removing trades having a duration more than X minutes (set in config)
|
|
max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
|
|
results = results[results.trade_duration < max_trade_duration]
|
|
#######################################################################
|
|
|
|
if results.empty:
|
|
return {}
|
|
|
|
groupby_aggregator = {
|
|
'profit_abs': [
|
|
('nb_trades', 'count'), # number of all trades
|
|
('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
|
|
('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
|
|
('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
|
|
],
|
|
'trade_duration': [('avg_trade_duration', 'mean')]
|
|
}
|
|
|
|
# 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)
|
|
|
|
# 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'])
|
|
|
|
# Win rate = number of profitable trades / number of 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']
|
|
|
|
# required_risk_reward = (1 / 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'])
|
|
|
|
# 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()
|
|
|
|
final = {}
|
|
for x in df.itertuples():
|
|
final[x.pair] = PairInfo(
|
|
x.stoploss,
|
|
x.winrate,
|
|
x.risk_reward_ratio,
|
|
x.required_risk_reward,
|
|
x.expectancy,
|
|
x.nb_trades,
|
|
x.avg_trade_duration
|
|
)
|
|
|
|
# Returning a list of pairs in order of "expectancy"
|
|
return final
|
|
|
|
def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
|
|
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:
|
|
result += self._detect_next_stop_or_sell_point(
|
|
buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
|
|
)
|
|
|
|
return result
|
|
|
|
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
|
|
ohlc_columns, stoploss, pair):
|
|
"""
|
|
Iterate through ohlc_columns in order to find the next trade
|
|
Next trade opens from the first buy signal noticed to
|
|
The sell or stoploss signal after it.
|
|
It then cuts OHLC, buy_column, sell_column and date_column.
|
|
Cut from (the exit trade index) + 1.
|
|
|
|
Author: https://github.com/mishaker
|
|
"""
|
|
|
|
result: list = []
|
|
start_point = 0
|
|
|
|
while True:
|
|
open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
|
|
|
|
# Return empty if we don't find trade entry (i.e. buy==1) or
|
|
# we find a buy but at the end of array
|
|
if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
|
|
break
|
|
else:
|
|
# When a buy signal is seen,
|
|
# trade opens in reality on the next candle
|
|
open_trade_index += 1
|
|
|
|
open_price = ohlc_columns[open_trade_index, 0]
|
|
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)
|
|
|
|
# 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')
|
|
|
|
# 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')
|
|
|
|
# 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'):
|
|
break
|
|
|
|
if stop_index <= sell_index:
|
|
exit_index = open_trade_index + stop_index
|
|
exit_type = SellType.STOP_LOSS
|
|
exit_price = stop_price
|
|
elif stop_index > sell_index:
|
|
# If exit is SELL then we exit at the next candle
|
|
exit_index = open_trade_index + sell_index + 1
|
|
|
|
# Check if we have the next candle
|
|
if len(ohlc_columns) - 1 < exit_index:
|
|
break
|
|
|
|
exit_type = SellType.SELL_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
|
|
}
|
|
|
|
result.append(trade)
|
|
|
|
# Giving a view of exit_index till the end of array
|
|
buy_column = buy_column[exit_index:]
|
|
sell_column = sell_column[exit_index:]
|
|
date_column = date_column[exit_index:]
|
|
ohlc_columns = ohlc_columns[exit_index:]
|
|
start_point += exit_index
|
|
|
|
return result
|