# Buggy code
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
    :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",
        "profit",
        "loss",
    ]
    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


# Traceback (Exception)
2020-02-20 22:47:28,025 - freqtrade - ERROR - Fatal exception!
Traceback (most recent call last):
File "/opt/tradebot/freqtrade/.env/lib/python3.7/site-packages/talib/__init__.py", line 20, in wrapper
for arg in chain(args, kwargs.values())
StopIteration

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/freqtrade/freqtrade/main.py", line 36, in main
return_code = args['func'](args)
File "/freqtrade/freqtrade/commands/optimize_commands.py", line 76, in start_hyperopt
hyperopt.start()
File "/freqtrade/freqtrade/optimize/hyperopt.py", line 488, in start
preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
File "/freqtrade/freqtrade/strategy/interface.py", line 448, in tickerdata_to_dataframe
for pair, pair_data in tickerdata.items()}
File "/freqtrade/freqtrade/strategy/interface.py", line 448, in <dictcomp>
for pair, pair_data in tickerdata.items()}
File "/freqtrade/freqtrade/strategy/interface.py", line 464, in advise_indicators
return self.populate_indicators(dataframe, metadata)
File "/freqtrade/user_data/strategies/MyDev.py", line 205, in populate_indicators
dataframe['mfi'] = ta.MFI(dataframe)
File "talib/_abstract.pxi", line 352, in talib._ta_lib.Function.__call__
File "talib/_abstract.pxi", line 383, in talib._ta_lib.Function.__call_function
File "/opt/tradebot/freqtrade/.env/lib/python3.7/site-packages/talib/__init__.py", line 24, in wrapper
return func(*args, **kwargs)
File "talib/_func.pxi", line 8454, in talib._ta_lib.MFI
Exception: volume is not double

# Fixed code
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
    :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",
        "Buys",
        "Avg Profit %",
        "Cum Profit %",
        f"Tot Profit {stake_currency}",
        "Tot Profit %",
        "Avg Duration",
        "Wins",
        "Draws",
        "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]),
                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]),
            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