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