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use profit_ratio in calculate_cum_profit

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Matthias 2021-01-29 19:06:46 +01:00
parent ad9efd3ac5
commit ea0ffbae73
3 changed files with 9 additions and 9 deletions
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12
freqtrade/data/btanalysis.py
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@ -337,7 +337,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
""" """
Adds a column `col_name` with the cumulative profit for the given trades array. Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index :param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_date and profit_percent) :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param col_name: Column name that will be assigned the results :param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations :param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit. :return: Returns df with one additional column, col_name, containing the cumulative profit.
@ -349,8 +349,8 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
timeframe_minutes = timeframe_to_minutes(timeframe) timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles # Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date' _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
)[['profit_percent']].sum() )[['profit_ratio']].sum()
df.loc[:, col_name] = _trades_sum['profit_percent'].cumsum() df.loc[:, col_name] = _trades_sum['profit_ratio'].cumsum()
# Set first value to 0 # Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0 df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous # FFill to get continuous
@ -359,13 +359,13 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date', def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_percent' value_col: str = 'profit_ratio'
) -> Tuple[float, pd.Timestamp, pd.Timestamp]: ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
""" """
Calculate max drawdown and the corresponding close dates Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_percent) :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date') :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_percent') :param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
:return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
:raise: ValueError if trade-dataframe was found empty. :raise: ValueError if trade-dataframe was found empty.
""" """

2
tests/data/test_btanalysis.py
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@ -142,7 +142,7 @@ def test_extract_trades_of_period(testdatadir):
trades = DataFrame( trades = DataFrame(
{'pair': [pair, pair, pair, pair], {'pair': [pair, pair, pair, pair],
'profit_percent': [0.0, 0.1, -0.2, -0.5], 'profit_ratio': [0.0, 0.1, -0.2, -0.5],
'profit_abs': [0.0, 1, -2, -5], 'profit_abs': [0.0, 1, -2, -5],
'open_date': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime, 'open_date': to_datetime([Arrow(2017, 11, 13, 15, 40, 0).datetime,
Arrow(2017, 11, 14, 9, 41, 0).datetime, Arrow(2017, 11, 14, 9, 41, 0).datetime,

4
tests/test_plotting.py
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@ -179,7 +179,7 @@ def test_plot_trades(testdatadir, caplog):
trade_sell = find_trace_in_fig_data(figure.data, 'Sell - Profit') trade_sell = find_trace_in_fig_data(figure.data, 'Sell - Profit')
assert isinstance(trade_sell, go.Scatter) assert isinstance(trade_sell, go.Scatter)
assert trade_sell.yaxis == 'y' assert trade_sell.yaxis == 'y'
assert len(trades.loc[trades['profit_percent'] > 0]) == len(trade_sell.x) assert len(trades.loc[trades['profit_ratio'] > 0]) == len(trade_sell.x)
assert trade_sell.marker.color == 'green' assert trade_sell.marker.color == 'green'
assert trade_sell.marker.symbol == 'square-open' assert trade_sell.marker.symbol == 'square-open'
assert trade_sell.text[0] == '4.0%, roi, 15 min' assert trade_sell.text[0] == '4.0%, roi, 15 min'
@ -187,7 +187,7 @@ def test_plot_trades(testdatadir, caplog):
trade_sell_loss = find_trace_in_fig_data(figure.data, 'Sell - Loss') trade_sell_loss = find_trace_in_fig_data(figure.data, 'Sell - Loss')
assert isinstance(trade_sell_loss, go.Scatter) assert isinstance(trade_sell_loss, go.Scatter)
assert trade_sell_loss.yaxis == 'y' assert trade_sell_loss.yaxis == 'y'
assert len(trades.loc[trades['profit_percent'] <= 0]) == len(trade_sell_loss.x) assert len(trades.loc[trades['profit_ratio'] <= 0]) == len(trade_sell_loss.x)
assert trade_sell_loss.marker.color == 'red' assert trade_sell_loss.marker.color == 'red'
assert trade_sell_loss.marker.symbol == 'square-open' assert trade_sell_loss.marker.symbol == 'square-open'
assert trade_sell_loss.text[5] == '-10.4%, stop_loss, 720 min' assert trade_sell_loss.text[5] == '-10.4%, stop_loss, 720 min'