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simplifying calculations to be more readable

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misagh 2018-11-06 19:05:42 +01:00
parent 5754e51960
commit 5c38b92a75
1 changed files with 6 additions and 6 deletions
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12
freqtrade/edge/__init__.py
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@ -226,7 +226,7 @@ class Edge():
# Then every value more than (standard deviation + 2*average) is out (pump) # Then every value more than (standard deviation + 2*average) is out (pump)
# #
# Removing Pumps # Removing Pumps
if self.edge_config.get('remove_pumps', True): if self.edge_config.get('remove_pumps', False):
results = results.groupby(['pair', 'stoploss']).apply( results = results.groupby(['pair', 'stoploss']).apply(
lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()]) lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
########################################################################## ##########################################################################
@ -249,7 +249,7 @@ class Edge():
'trade_duration': [('avg_trade_duration', 'mean')] 'trade_duration': [('avg_trade_duration', 'mean')]
} }
# Group by (pair and stoploss) the applying above aggregator # Group by (pair and stoploss) by applying above aggregator
df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg( df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
groupby_aggregator).reset_index(col_level=1) groupby_aggregator).reset_index(col_level=1)
@ -264,14 +264,14 @@ class Edge():
# Win rate = number of profitable trades / number of trades # Win rate = number of profitable trades / number of trades
df['winrate'] = df['nb_win_trades'] / df['nb_trades'] df['winrate'] = df['nb_win_trades'] / df['nb_trades']
# risk_reward_ratio = 1 / (average loss / average win) # risk_reward_ratio = average win / average loss
df['risk_reward_ratio'] = 1 / (df['average_loss'] / df['average_win']) df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
# required_risk_reward = (1 / winrate) - 1 # required_risk_reward = (1 / winrate) - 1
df['required_risk_reward'] = (1 / df['winrate']) - 1 df['required_risk_reward'] = (1 / df['winrate']) - 1
# expectancy = ((1 + average_win/average_loss) * winrate) - 1 # expectancy = (risk_reward_ratio * winrate) - (lossrate)
df['expectancy'] = ((1 + df['average_win'] / df['average_loss']) * df['winrate']) - 1 df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
# sort by expectancy and stoploss # sort by expectancy and stoploss
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby( df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(