adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

more consistent backtesting tables and labels

Browse Source
This commit is contained in:
Yazeed Al Oyoun
2020-01-31 04:39:18 +01:00
parent 2396f35586
commit e2b3907df5
3 changed files with 68 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
freqtrade/optimize/optimize_reports.py
View File

@@ -19,9 +19,17 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
'profit', 'loss']
headers = [
'Pair',
'Buy Count',
'Avg Profit %',
'Cum Profit %',
f'Tot Profit {stake_currency}',
'Tot Profit %',
'Avg Duration',
'Wins',
'Losses'
]
for pair in data:
result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
@@ -58,7 +66,9 @@ def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_tra
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
def generate_text_table_sell_reason(data: Dict[str, Dict], results: DataFrame) -> str:
def generate_text_table_sell_reason(
data: Dict[str, Dict], stake_currency: str, max_open_trades: int, results: DataFrame
) -> str:
"""
Generate small table outlining Backtest results
:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
@@ -66,13 +76,36 @@ def generate_text_table_sell_reason(data: Dict[str, Dict], results: DataFrame) -
:return: pretty printed table with tabulate as string
"""
tabular_data = []
headers = ['Sell Reason', 'Count', 'Profit', 'Loss', 'Profit %']
headers = [
"Sell Reason",
"Sell Count",
"Wins",
"Losses",
"Avg Profit %",
"Cum Profit %",
f"Tot Profit {stake_currency}",
"Tot Profit %",
]
for reason, count in results['sell_reason'].value_counts().iteritems():
result = results.loc[results['sell_reason'] == reason]
profit = len(result[result['profit_abs'] >= 0])
loss = len(result[result['profit_abs'] < 0])
profit_mean = round(result['profit_percent'].mean() * 100.0, 2)
tabular_data.append([reason.value, count, profit, loss, profit_mean])
profit_sum = round(result["profit_percent"].sum() * 100.0, 2)
profit_tot = result["profit_abs"].sum()
profit_percent_tot = result["profit_percent"].sum() * 100.0 / max_open_trades
tabular_data.append(
[
reason.value,
count,
profit,
loss,
profit_mean,
profit_sum,
profit_tot,
profit_percent_tot,
]
)
return tabulate(tabular_data, headers=headers, tablefmt="pipe")