stable/freqtrade/optimize/optimize_reports.py

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from datetime import timedelta
from typing import Dict
from pandas import DataFrame
from tabulate import tabulate
def generate_text_table(data: Dict[str, Dict], stake_currency: str, max_open_trades: int,
results: DataFrame, skip_nan: bool = False) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
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:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades
:param results: Dataframe containing the backtest results
:param skip_nan: Print "left open" open trades
:return: pretty printed table with tabulate as string
"""
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',
'Wins',
'Losses'
]
for pair in data:
result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
continue
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
result.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
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
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:param data: Dict of <pair: dataframe> containing data that was used during backtesting.
:param results: Dataframe containing the backtest results
:return: pretty printed table with tabulate as string
"""
tabular_data = []
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():
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result = results.loc[results['sell_reason'] == reason]
profit = len(result[result['profit_abs'] >= 0])
loss = len(result[result['profit_abs'] < 0])
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profit_mean = round(result['profit_percent'].mean() * 100.0, 2)
profit_sum = round(result["profit_percent"].sum() * 100.0, 2)
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profit_tot = result['profit_abs'].sum()
profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
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")
def generate_text_table_strategy(stake_currency: str, max_open_trades: str,
all_results: Dict) -> str:
"""
Generate summary table per strategy
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:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: pretty printed table with tabulate as string
"""
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
f'tot profit {stake_currency}', 'tot profit %', 'avg duration',
'profit', 'loss']
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore
def generate_edge_table(results: dict) -> str:
floatfmt = ('s', '.10g', '.2f', '.2f', '.2f', '.2f', 'd', '.d')
tabular_data = []
headers = ['pair', 'stoploss', 'win rate', 'risk reward ratio',
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'required risk reward', 'expectancy', 'total number of trades',
'average duration (min)']
for result in results.items():
if result[1].nb_trades > 0:
tabular_data.append([
result[0],
result[1].stoploss,
result[1].winrate,
result[1].risk_reward_ratio,
result[1].required_risk_reward,
result[1].expectancy,
result[1].nb_trades,
round(result[1].avg_trade_duration)
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(tabular_data, headers=headers,
floatfmt=floatfmt, tablefmt="pipe") # type: ignore