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removing whitespaces and long lines

This commit is contained in:
misagh 2018-09-26 16:50:17 +02:00
parent 75ba6578a3
commit 0594deafc6
1 changed files with 39 additions and 28 deletions
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67
freqtrade/edge/__init__.py
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@ -5,7 +5,6 @@ from typing import Any, Dict
import arrow
from pandas import DataFrame
import pandas as pd
import freqtrade.optimize as optimize
from freqtrade.optimize.backtesting import BacktestResult
@ -40,7 +39,7 @@ class Edge():
self.edge_config = self.config.get('edge', {})
self._last_updated = None
self._cached_pairs : list = []
self._cached_pairs: list = []
self._total_capital = self.edge_config['total_capital_in_stake_currency']
self._allowed_risk = self.edge_config['allowed_risk']
@ -62,14 +61,15 @@ class Edge():
pairs = self.config['exchange']['pair_whitelist']
heartbeat = self.config['edge']['process_throttle_secs']
if ((self._last_updated is not None) and (self._last_updated + heartbeat > arrow.utcnow().timestamp)):
if (self._last_updated is not None) 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 stake_amount: %s ...', self.config['stake_amount'])
logger.info('Using local backtesting data (using whitelist in given config) ...')
#TODO: add "timerange" to Edge config
# TODO: add "timerange" to Edge config
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
@ -103,7 +103,6 @@ class Edge():
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)
########################### Call out BSlap Loop instead of Original BT code
trades: list = []
for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index
@ -114,7 +113,6 @@ class Edge():
self.populate_buy_trend(pair_data))[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, stoploss_range)
# Switch List of Trade Dicts (trades) to Dataframe
# Fill missing, calculable columns, profit, duration , abs etc.
@ -126,7 +124,6 @@ class Edge():
trades_df = []
trades_df = DataFrame.from_records(trades_df, columns=BacktestResult._fields)
self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().timestamp
return True
@ -146,7 +143,7 @@ class Edge():
if len(self._cached_pairs) == 0:
self.calculate()
edge_sorted_pairs = [x[0] for x in self._cached_pairs]
return [x for _, x in sorted(zip(edge_sorted_pairs,pairs), key=lambda pair: pair[0])]
return [x for _, x in sorted(zip(edge_sorted_pairs, pairs), key=lambda pair: pair[0])]
def _fill_calculable_fields(self, result: DataFrame):
"""
@ -173,9 +170,11 @@ class Edge():
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
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
@ -188,8 +187,9 @@ class Edge():
result['sell_take'] = result['sell_sum'] - result['sell_fee']
# profit_percent
result['profit_percent'] = (result['sell_take'] - result['buy_spend']) \
/ result['buy_spend']
result['profit_percent'] = \
(result['sell_take'] - result['buy_spend']) / result['buy_spend']
# Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend']
@ -198,8 +198,10 @@ class Edge():
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 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.
"""
@ -238,21 +240,17 @@ class Edge():
# 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())))
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)
x = (1 / (x[x > 0].count() / x.count()) - 1)
return x
##############################
def delta(x):
x = (abs(1/ ((x[x < 0].sum() / x[x < 0].count()) / (x[x > 0].sum() / x[x > 0].count())))) - (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
@ -265,7 +263,7 @@ class Edge():
##############################
final = results.groupby(['pair', 'stoploss'])['profit_abs'].\
agg([winrate, risk_reward_ratio, required_risk_reward, expectancy, delta]).\
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)
@ -277,17 +275,29 @@ class Edge():
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_stop_and_sell_points(buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair)
result += self._detect_stop_and_sell_points(
buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
)
return result
def _detect_stop_and_sell_points(self, buy_column, sell_column, date_column, ohlc_columns, stoploss, pair, start_point=0):
def _detect_stop_and_sell_points(
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)
#open_trade_index = np.argmax(buy_column == 1)
# open_trade_index = np.argmax(buy_column == 1)
# return empty if we don't find trade entry (i.e. buy==1)
if open_trade_index == -1:
@ -298,13 +308,14 @@ class Edge():
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)
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')
#stop_index = np.argmax((ohlc_columns[open_trade_index + 1:, 2] < stop_price) == True)
# stop_index = np.argmax((ohlc_columns[open_trade_index + 1:, 2] < stop_price) == True)
# Searching for the index where sell is hit
sell_index = utf1st.find_1st(sell_column[open_trade_index + 1:], 1, utf1st.cmp_equal)
@ -313,7 +324,7 @@ class Edge():
if sell_index == -1:
sell_index = float('inf')
#sell_index = np.argmax(sell_column[open_trade_index + 1:] == 1)
# sell_index = np.argmax(sell_column[open_trade_index + 1:] == 1)
# 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