384 lines
15 KiB
Python
384 lines
15 KiB
Python
# pragma pylint: disable=W0603
|
|
""" Edge positioning package """
|
|
import logging
|
|
from typing import Any, Dict
|
|
import arrow
|
|
|
|
import numpy as np
|
|
import utils_find_1st as utf1st
|
|
from pandas import DataFrame
|
|
|
|
import freqtrade.optimize as optimize
|
|
from freqtrade.optimize.backtesting import BacktestResult
|
|
from freqtrade.arguments import Arguments
|
|
from freqtrade.arguments import TimeRange
|
|
from freqtrade.strategy.interface import SellType
|
|
from freqtrade.strategy.resolver import IStrategy, StrategyResolver
|
|
from freqtrade.optimize.backtesting import Backtesting
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class Edge():
|
|
|
|
config: Dict = {}
|
|
_last_updated: int # Timestamp of pairs last updated time
|
|
_cached_pairs: list = [] # Keeps an array of
|
|
# [pair, stoploss, winrate, risk reward ratio, required risk reward, expectancy]
|
|
|
|
_total_capital: float
|
|
_allowed_risk: float
|
|
_since_number_of_days: int
|
|
_timerange: TimeRange
|
|
|
|
def __init__(self, config: Dict[str, Any], exchange=None) -> None:
|
|
self.config = config
|
|
self.exchange = exchange
|
|
self.strategy: IStrategy = StrategyResolver(self.config).strategy
|
|
self.ticker_interval = self.strategy.ticker_interval
|
|
self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
|
|
self.get_timeframe = Backtesting.get_timeframe
|
|
self.advise_sell = self.strategy.advise_sell
|
|
self.advise_buy = self.strategy.advise_buy
|
|
|
|
self.edge_config = self.config.get('edge', {})
|
|
self._cached_pairs: list = []
|
|
self._total_capital = self.edge_config.get('total_capital_in_stake_currency')
|
|
self._allowed_risk = self.edge_config.get('allowed_risk')
|
|
self._since_number_of_days = self.edge_config.get('calculate_since_number_of_days', 14)
|
|
self._last_updated = 0
|
|
|
|
self._timerange = Arguments.parse_timerange("%s-" % arrow.now().shift(
|
|
days=-1 * self._since_number_of_days).format('YYYYMMDD'))
|
|
|
|
self.fee = self.exchange.get_fee()
|
|
|
|
def calculate(self) -> bool:
|
|
pairs = self.config['exchange']['pair_whitelist']
|
|
heartbeat = self.edge_config.get('process_throttle_secs')
|
|
|
|
if (self._last_updated > 0) and (
|
|
self._last_updated + heartbeat > arrow.utcnow().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) ...')
|
|
|
|
data = optimize.load_data(
|
|
self.config['datadir'],
|
|
pairs=pairs,
|
|
ticker_interval=self.ticker_interval,
|
|
refresh_pairs=True,
|
|
exchange=self.exchange,
|
|
timerange=self._timerange
|
|
)
|
|
|
|
if not data:
|
|
logger.critical("No data found. Edge is stopped ...")
|
|
return False
|
|
|
|
preprocessed = self.tickerdata_to_dataframe(data)
|
|
|
|
# Print timeframe
|
|
min_date, max_date = self.get_timeframe(preprocessed)
|
|
logger.info(
|
|
'Measuring data from %s up to %s (%s days) ...',
|
|
min_date.isoformat(),
|
|
max_date.isoformat(),
|
|
(max_date - min_date).days
|
|
)
|
|
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
|
|
|
|
stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
|
|
stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
|
|
stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
|
|
stoploss_range = np.arange(stoploss_range_min, stoploss_range_max, stoploss_range_step)
|
|
|
|
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)
|
|
|
|
ticker_data = self.advise_sell(
|
|
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
|
|
|
|
trades += self._find_trades_for_stoploss_range(ticker_data, pair, stoploss_range)
|
|
|
|
# If no trade found then exit
|
|
if len(trades) == 0:
|
|
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().timestamp
|
|
|
|
# Not a nice hack but probably simplest solution:
|
|
# When backtest load data it loads the delta between disk and exchange
|
|
# The problem is that exchange consider that recent.
|
|
# it is but it is incomplete (c.f. _async_get_candle_history)
|
|
# So it causes get_signal to exit cause incomplete ticker_hist
|
|
# A patch to that would be update _pairs_last_refresh_time of exchange
|
|
# so it will download again all pairs
|
|
# Another solution is to add new data to klines instead of reassigning it:
|
|
# self.klines[pair].update(data) instead of self.klines[pair] = data in exchange package.
|
|
# But that means indexing timestamp and having a verification so that
|
|
# there is no empty range between two timestaps (recently added and last
|
|
# one)
|
|
self.exchange._pairs_last_refresh_time = {}
|
|
|
|
return True
|
|
|
|
def stake_amount(self, pair: str) -> float:
|
|
info = [x for x in self._cached_pairs if x[0] == pair][0]
|
|
stoploss = info[1]
|
|
allowed_capital_at_risk = round(self._total_capital * self._allowed_risk, 5)
|
|
position_size = abs(round((allowed_capital_at_risk / stoploss), 5))
|
|
return position_size
|
|
|
|
def stoploss(self, pair: str) -> float:
|
|
info = [x for x in self._cached_pairs if x[0] == pair][0]
|
|
return info[1]
|
|
|
|
def filter(self, pairs) -> list:
|
|
# Filtering pairs acccording to the expectancy
|
|
filtered_expectancy: list = []
|
|
filtered_expectancy = [
|
|
x[0] for x in self._cached_pairs if x[5] > float(
|
|
self.edge_config.get(
|
|
'minimum_expectancy', 0.2))]
|
|
|
|
# Only return pairs which are included in "pairs" argument list
|
|
final = [x for x in filtered_expectancy if x in pairs]
|
|
if final:
|
|
logger.info(
|
|
'Edge validated only %s',
|
|
final
|
|
)
|
|
else:
|
|
logger.info('Edge removed all pairs as no pair with minimum expectancy was found !')
|
|
|
|
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
|
|
"""
|
|
|
|
# stake and fees
|
|
# stake = 0.015
|
|
# 0.05% is 0.0005
|
|
# fee = 0.001
|
|
|
|
stake = self.config.get('stake_amount')
|
|
fee = self.fee
|
|
|
|
open_fee = fee / 2
|
|
close_fee = fee / 2
|
|
|
|
result['trade_duration'] = result['close_time'] - result['open_time']
|
|
|
|
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 * open_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'] * close_fee
|
|
result['sell_take'] = result['sell_sum'] - result['sell_fee']
|
|
|
|
# profit_percent
|
|
result['profit_percent'] = (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) -> list:
|
|
"""
|
|
This is a temporary version of edge positioning calculation.
|
|
The function will be eventually moved to a plugin called Edge in order
|
|
to calculate necessary WR, RRR and
|
|
other indictaors related to money management periodically (each X minutes)
|
|
and keep it in a storage.
|
|
The calulation 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', 15)
|
|
results = results.groupby('pair').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)
|
|
#
|
|
# Calculating standard deviation of profits
|
|
std = results[["profit_abs"]].std()
|
|
#
|
|
# Calculating average of profits
|
|
avg = results[["profit_abs"]].mean()
|
|
#
|
|
# Removing Pumps
|
|
if self.edge_config.get('remove_pumps', True):
|
|
results = results[results.profit_abs <= float(avg + 2 * std)]
|
|
##########################################################################
|
|
|
|
# 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]
|
|
#######################################################################
|
|
|
|
# Win Rate is the number of profitable trades
|
|
# Divided by number of trades
|
|
def winrate(x):
|
|
x = x[x > 0].count() / x.count()
|
|
return x
|
|
#############################
|
|
|
|
# Risk Reward Ratio
|
|
# 1 / ((loss money / losing trades) / (gained money / winning trades))
|
|
def risk_reward_ratio(x):
|
|
x = abs(1 / ((x[x < 0].sum() / x[x < 0].count()) / (x[x > 0].sum() / x[x > 0].count())))
|
|
return x
|
|
##############################
|
|
|
|
# Required Risk Reward
|
|
# (1/(winrate - 1)
|
|
def required_risk_reward(x):
|
|
x = (1 / (x[x > 0].count() / x.count()) - 1)
|
|
return x
|
|
##############################
|
|
|
|
# Expectancy
|
|
# Tells you the interest percentage you should hope
|
|
# E.x. if expectancy is 0.35, on $1 trade you should expect a target of $1.35
|
|
def expectancy(x):
|
|
average_win = float(x[x > 0].sum() / x[x > 0].count())
|
|
average_loss = float(abs(x[x < 0].sum() / x[x < 0].count()))
|
|
winrate = float(x[x > 0].count() / x.count())
|
|
x = ((1 + average_win / average_loss) * winrate) - 1
|
|
return x
|
|
##############################
|
|
|
|
final = results.groupby(['pair', 'stoploss'])['profit_abs'].\
|
|
agg([winrate, risk_reward_ratio, required_risk_reward, expectancy]).\
|
|
reset_index().sort_values(by=['expectancy', 'stoploss'], ascending=False)\
|
|
.groupby('pair').first().sort_values(by=['expectancy'], ascending=False)
|
|
|
|
# Returning an array of pairs in order of "expectancy"
|
|
return final.reset_index().values
|
|
|
|
def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
|
|
buy_column = ticker_data['buy'].values
|
|
sell_column = ticker_data['sell'].values
|
|
date_column = ticker_data['date'].values
|
|
ohlc_columns = ticker_data[['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,
|
|
start_point=0):
|
|
|
|
result: list = []
|
|
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 of array
|
|
if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
|
|
return []
|
|
|
|
stop_price_percentage = stoploss + 1
|
|
open_price = ohlc_columns[open_trade_index + 1, 0]
|
|
stop_price = (open_price * stop_price_percentage)
|
|
|
|
# Searching for the index where stoploss is hit
|
|
stop_index = utf1st.find_1st(
|
|
ohlc_columns[open_trade_index + 1:, 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:], 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'):
|
|
return []
|
|
|
|
if stop_index <= sell_index:
|
|
exit_index = open_trade_index + stop_index + 1
|
|
exit_type = SellType.STOP_LOSS
|
|
exit_price = stop_price
|
|
elif stop_index > sell_index:
|
|
exit_index = open_trade_index + sell_index + 1
|
|
exit_type = SellType.SELL_SIGNAL
|
|
exit_price = ohlc_columns[exit_index, 0]
|
|
|
|
trade = {'pair': pair,
|
|
'stoploss': stoploss,
|
|
'profit_percent': '',
|
|
'profit_abs': '',
|
|
'open_time': date_column[open_trade_index],
|
|
'close_time': date_column[exit_index],
|
|
'open_index': start_point + open_trade_index + 1,
|
|
'close_index': start_point + exit_index,
|
|
'trade_duration': '',
|
|
'open_rate': round(open_price, 15),
|
|
'close_rate': round(exit_price, 15),
|
|
'exit_type': exit_type
|
|
}
|
|
|
|
result.append(trade)
|
|
|
|
# Calling again the same function recursively but giving
|
|
# it a view of exit_index till the end of array
|
|
return result + self._detect_next_stop_or_sell_point(
|
|
buy_column[exit_index:],
|
|
sell_column[exit_index:],
|
|
date_column[exit_index:],
|
|
ohlc_columns[exit_index:],
|
|
stoploss,
|
|
pair,
|
|
(start_point + exit_index)
|
|
)
|