Edge calculation refactored: removing redundant calculations
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misagh
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8a316aba35
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@ -215,15 +215,11 @@ 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
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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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This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
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The calulation will be done per pair and per strategy.
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"""
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# Removing pairs having less than min_trades_number
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min_trades_number = self.edge_config.get('min_trade_number', 15)
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min_trades_number = self.edge_config.get('min_trade_number', 10)
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results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
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###################################
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@ -242,61 +238,53 @@ class Edge():
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results = results[results.trade_duration < max_trade_duration]
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#######################################################################
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# Win Rate is the number of profitable trades
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# Divided by number of trades
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def winrate(x):
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x = x[x > 0].count() / x.count()
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return x
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#############################
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# Risk Reward Ratio
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# 1 / ((loss money / losing trades) / (gained money / winning trades))
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def risk_reward_ratio(x):
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x = abs(1 / ((x[x < 0].sum() / x[x < 0].count()) / (x[x > 0].sum() / x[x > 0].count())))
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return x
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##############################
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# Required Risk Reward
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# (1/(winrate - 1)
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def required_risk_reward(x):
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x = (1 / (x[x > 0].count() / x.count()) - 1)
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return x
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##############################
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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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def expectancy(x):
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average_win = float(x[x > 0].sum() / x[x > 0].count())
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average_loss = float(abs(x[x < 0].sum() / x[x < 0].count()))
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winrate = float(x[x > 0].count() / x.count())
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x = ((1 + average_win / average_loss) * winrate) - 1
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return x
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##############################
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if results.empty:
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return []
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groupby_aggregator = {
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'profit_abs': [
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winrate,
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risk_reward_ratio,
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required_risk_reward,
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expectancy,
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'count'],
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'trade_duration': ['mean']}
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('nb_trades', 'count'), # number of all trades
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('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
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('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
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('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
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],
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'trade_duration': [('avg_trade_duration', 'mean')]
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}
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final = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
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# Group by (pair and stoploss) the applying above aggregator
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df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
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groupby_aggregator).reset_index(col_level=1)
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final.columns = final.columns.droplevel(0)
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final = final.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
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# Dropping level 0 as we don't need it
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df.columns = df.columns.droplevel(0)
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# Calculating number of losing trades, average win and average loss
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df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
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df['average_win'] = df['profit_sum'] / df['nb_win_trades']
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df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
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# Win rate = number of profitable trades / number of trades
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df['winrate'] = df['nb_win_trades'] / df['nb_trades']
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# risk_reward_ratio = 1 / (average loss / average win)
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df['risk_reward_ratio'] = 1 / (df['average_loss'] / df['average_win'])
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# required_risk_reward = (1 / winrate) - 1
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df['required_risk_reward'] = (1 / df['winrate']) - 1
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# expectancy = ((1 + average_win/average_loss) * winrate) - 1
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df['expectancy'] = ((1 + df['average_win'] / df['average_loss']) * df['winrate']) - 1
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# sort by expectancy and stoploss
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df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
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'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
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final.rename(columns={'mean': 'avg_duration(min)'}, inplace=True)
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# dropping unecessary columns
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df.drop(columns=['nb_loss_trades', 'nb_win_trades', 'average_win', 'average_loss',
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'profit_sum', 'loss_sum', 'avg_trade_duration', 'nb_trades'], inplace=True)
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# Returning an array of pairs in order of "expectancy"
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return final.values
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return df.values
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def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
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buy_column = ticker_data['buy'].values
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