stable/freqtrade/edge/__init__.py
2018-10-05 17:06:17 +02:00

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)
)