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stable/freqtrade/edge/edge_positioning.py

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# 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.exceptions import OperationalException
from freqtrade.exchange.exchange import timeframe_to_seconds
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.state import RunMode
from freqtrade.strategy.interface import IStrategy, SellType
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 p, t in self.strategy.informative_pairs():
res[t].append(p)
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.ohlcvdata_to_dataframe(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)..')
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
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_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'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 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', 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['buy'].values
sell_column = df['sell'].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
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