removing whitespaces and long lines
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misagh
parent
75ba6578a3
commit
0594deafc6
@ -5,7 +5,6 @@ from typing import Any, Dict
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import arrow
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from pandas import DataFrame
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import pandas as pd
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import freqtrade.optimize as optimize
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from freqtrade.optimize.backtesting import BacktestResult
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@ -62,7 +61,8 @@ class Edge():
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pairs = self.config['exchange']['pair_whitelist']
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heartbeat = self.config['edge']['process_throttle_secs']
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if ((self._last_updated is not None) and (self._last_updated + heartbeat > arrow.utcnow().timestamp)):
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if (self._last_updated is not None) and \
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(self._last_updated + heartbeat > arrow.utcnow().timestamp):
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return False
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data: Dict[str, Any] = {}
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@ -103,7 +103,6 @@ class Edge():
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stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
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stoploss_range = np.arange(stoploss_range_min, stoploss_range_max, stoploss_range_step)
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########################### Call out BSlap Loop instead of Original BT code
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trades: list = []
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for pair, pair_data in preprocessed.items():
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# Sorting dataframe by date and reset index
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@ -115,7 +114,6 @@ class Edge():
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trades += self._find_trades_for_stoploss_range(ticker_data, pair, stoploss_range)
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# Switch List of Trade Dicts (trades) to Dataframe
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# Fill missing, calculable columns, profit, duration , abs etc.
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trades_df = DataFrame(trades)
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@ -126,7 +124,6 @@ class Edge():
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trades_df = []
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trades_df = DataFrame.from_records(trades_df, columns=BacktestResult._fields)
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self._cached_pairs = self._process_expectancy(trades_df)
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self._last_updated = arrow.utcnow().timestamp
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return True
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@ -173,9 +170,11 @@ class Edge():
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close_fee = fee / 2
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result['trade_duration'] = result['close_time'] - result['open_time']
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result['trade_duration'] = result['trade_duration'].map(lambda x: int(x.total_seconds() / 60))
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## Spends, Takes, Profit, Absolute Profit
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result['trade_duration'] = \
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result['trade_duration'].map(lambda x: int(x.total_seconds() / 60))
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# Spends, Takes, Profit, Absolute Profit
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# Buy Price
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result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
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@ -188,8 +187,9 @@ class Edge():
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result['sell_take'] = result['sell_sum'] - result['sell_fee']
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# profit_percent
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result['profit_percent'] = (result['sell_take'] - result['buy_spend']) \
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/ result['buy_spend']
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result['profit_percent'] = \
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(result['sell_take'] - result['buy_spend']) / result['buy_spend']
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# Absolute profit
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result['profit_abs'] = result['sell_take'] - result['buy_spend']
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@ -198,8 +198,10 @@ class Edge():
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def _process_expectancy(self, results: DataFrame) -> list:
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"""
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This is a temporary version of edge positioning calculation.
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The function will be eventually moved to a plugin called Edge in order to calculate necessary WR, RRR and
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other indictaors related to money management periodically (each X minutes) and keep it in a storage.
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The function will be eventually moved to a plugin called Edge in order
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to calculate necessary WR, RRR and
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other indictaors related to money management periodically (each X minutes)
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and keep it in a storage.
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The calulation will be done per pair and per strategy.
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"""
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@ -249,10 +251,6 @@ class Edge():
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return x
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##############################
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def delta(x):
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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)
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return x
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# Expectancy
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# Tells you the interest percentage you should hope
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# E.x. if expectancy is 0.35, on $1 trade you should expect a target of $1.35
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@ -265,7 +263,7 @@ class Edge():
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##############################
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final = results.groupby(['pair', 'stoploss'])['profit_abs'].\
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agg([winrate, risk_reward_ratio, required_risk_reward, expectancy, delta]).\
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agg([winrate, risk_reward_ratio, required_risk_reward, expectancy]).\
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reset_index().sort_values(by=['expectancy', 'stoploss'], ascending=False)\
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.groupby('pair').first().sort_values(by=['expectancy'], ascending=False)
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@ -280,11 +278,23 @@ class Edge():
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result: list = []
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for stoploss in stoploss_range:
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result += self._detect_stop_and_sell_points(buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair)
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result += self._detect_stop_and_sell_points(
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buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
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)
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return result
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def _detect_stop_and_sell_points(self, buy_column, sell_column, date_column, ohlc_columns, stoploss, pair, start_point=0):
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def _detect_stop_and_sell_points(
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self,
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buy_column,
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sell_column,
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date_column,
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ohlc_columns,
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stoploss,
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pair,
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start_point=0
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):
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result: list = []
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open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
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# open_trade_index = np.argmax(buy_column == 1)
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@ -298,7 +308,8 @@ class Edge():
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stop_price = (open_price * stop_price_percentage)
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# Searching for the index where stoploss is hit
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stop_index = utf1st.find_1st(ohlc_columns[open_trade_index + 1:, 2], stop_price, utf1st.cmp_smaller)
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stop_index = \
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utf1st.find_1st(ohlc_columns[open_trade_index + 1:, 2], stop_price, utf1st.cmp_smaller)
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# If we don't find it then we assume stop_index will be far in future (infinite number)
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if stop_index == -1:
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