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removing whitespaces and long lines

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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 import arrow
from pandas import DataFrame from pandas import DataFrame
import pandas as pd
import freqtrade.optimize as optimize import freqtrade.optimize as optimize
from freqtrade.optimize.backtesting import BacktestResult from freqtrade.optimize.backtesting import BacktestResult
@ -40,7 +39,7 @@ class Edge():
self.edge_config = self.config.get('edge', {}) self.edge_config = self.config.get('edge', {})
self._last_updated = None self._last_updated = None
self._cached_pairs : list = [] self._cached_pairs: list = []
self._total_capital = self.edge_config['total_capital_in_stake_currency'] self._total_capital = self.edge_config['total_capital_in_stake_currency']
self._allowed_risk = self.edge_config['allowed_risk'] self._allowed_risk = self.edge_config['allowed_risk']
@ -62,14 +61,15 @@ class Edge():
pairs = self.config['exchange']['pair_whitelist'] pairs = self.config['exchange']['pair_whitelist']
heartbeat = self.config['edge']['process_throttle_secs'] 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 return False
data: Dict[str, Any] = {} data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency']) logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount']) logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
logger.info('Using local backtesting data (using whitelist in given config) ...') 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 = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange'))) '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_step = float(self.edge_config.get('stoploss_range_step', -0.001))
stoploss_range = np.arange(stoploss_range_min, stoploss_range_max, stoploss_range_step) stoploss_range = np.arange(stoploss_range_min, stoploss_range_max, stoploss_range_step)
########################### Call out BSlap Loop instead of Original BT code
trades: list = [] trades: list = []
for pair, pair_data in preprocessed.items(): for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index # Sorting dataframe by date and reset index
@ -114,7 +113,6 @@ class Edge():
self.populate_buy_trend(pair_data))[headers].copy() self.populate_buy_trend(pair_data))[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, stoploss_range) trades += self._find_trades_for_stoploss_range(ticker_data, pair, stoploss_range)
# Switch List of Trade Dicts (trades) to Dataframe # Switch List of Trade Dicts (trades) to Dataframe
# Fill missing, calculable columns, profit, duration , abs etc. # Fill missing, calculable columns, profit, duration , abs etc.
@ -126,7 +124,6 @@ class Edge():
trades_df = [] trades_df = []
trades_df = DataFrame.from_records(trades_df, columns=BacktestResult._fields) trades_df = DataFrame.from_records(trades_df, columns=BacktestResult._fields)
self._cached_pairs = self._process_expectancy(trades_df) self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().timestamp self._last_updated = arrow.utcnow().timestamp
return True return True
@ -146,7 +143,7 @@ class Edge():
if len(self._cached_pairs) == 0: if len(self._cached_pairs) == 0:
self.calculate() self.calculate()
edge_sorted_pairs = [x[0] for x in self._cached_pairs] 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): def _fill_calculable_fields(self, result: DataFrame):
""" """
@ -173,9 +170,11 @@ class Edge():
close_fee = fee / 2 close_fee = fee / 2
result['trade_duration'] = result['close_time'] - result['open_time'] 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 # Buy Price
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying 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'] result['sell_take'] = result['sell_sum'] - result['sell_fee']
# profit_percent # profit_percent
result['profit_percent'] = (result['sell_take'] - result['buy_spend']) \ result['profit_percent'] = \
/ result['buy_spend'] (result['sell_take'] - result['buy_spend']) / result['buy_spend']
# Absolute profit # Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend'] result['profit_abs'] = result['sell_take'] - result['buy_spend']
@ -198,8 +198,10 @@ class Edge():
def _process_expectancy(self, results: DataFrame) -> list: def _process_expectancy(self, results: DataFrame) -> list:
""" """
This is a temporary version of edge positioning calculation. 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 The function will be eventually moved to a plugin called Edge in order
other indictaors related to money management periodically (each X minutes) and keep it in a storage. 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. The calulation will be done per pair and per strategy.
""" """
@ -238,21 +240,17 @@ class Edge():
# Risk Reward Ratio # Risk Reward Ratio
# 1 / ((loss money / losing trades) / (gained money / winning trades)) # 1 / ((loss money / losing trades) / (gained money / winning trades))
def risk_reward_ratio(x): 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 return x
############################## ##############################
# Required Risk Reward # Required Risk Reward
# (1/(winrate - 1) # (1/(winrate - 1)
def required_risk_reward(x): 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 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 # Expectancy
# Tells you the interest percentage you should hope # 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 # 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'].\ 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)\ reset_index().sort_values(by=['expectancy', 'stoploss'], ascending=False)\
.groupby('pair').first().sort_values(by=['expectancy'], ascending=False) .groupby('pair').first().sort_values(by=['expectancy'], ascending=False)
@ -277,17 +275,29 @@ class Edge():
sell_column = ticker_data['sell'].values sell_column = ticker_data['sell'].values
date_column = ticker_data['date'].values date_column = ticker_data['date'].values
ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
result: list = [] result: list = []
for stoploss in stoploss_range: 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 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 = [] result: list = []
open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal) 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) # return empty if we don't find trade entry (i.e. buy==1)
if open_trade_index == -1: if open_trade_index == -1:
@ -298,13 +308,14 @@ class Edge():
stop_price = (open_price * stop_price_percentage) stop_price = (open_price * stop_price_percentage)
# Searching for the index where stoploss is hit # 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 we don't find it then we assume stop_index will be far in future (infinite number)
if stop_index == -1: if stop_index == -1:
stop_index = float('inf') 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 # Searching for the index where sell is hit
sell_index = utf1st.find_1st(sell_column[open_trade_index + 1:], 1, utf1st.cmp_equal) 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: if sell_index == -1:
sell_index = float('inf') 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) # 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 # It is not interesting for Edge to consider it so we simply ignore the trade