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Fix create_cum_profit to work with trades that don't open on candle

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Matthias
2019-10-28 14:24:12 +01:00
parent 17fce00a5e
commit e82460bde6
4 changed files with 17 additions and 11 deletions
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12
freqtrade/data/btanalysis.py
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@@ -150,15 +150,21 @@ def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "c
return df_comb
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
timeframe: str) -> pd.DataFrame:
"""
Adds a column `col_name` with the cumulative profit for the given trades array.
:param df: DataFrame with date index
:param trades: DataFrame containing trades (requires columns close_time and profitperc)
:param col_name: Column name that will be assigned the results
:param timeframe: Timeframe used during the operations
:return: Returns df with one additional column, col_name, containing the cumulative profit.
"""
# Use groupby/sum().cumsum() to avoid errors when multiple trades sold at the same candle.
df[col_name] = trades.groupby('close_time')['profitperc'].sum().cumsum()
from freqtrade.exchange import timeframe_to_minutes
ticker_minutes = timeframe_to_minutes(timeframe)
# Resample to ticker_interval to make sure trades match candles
_trades_sum = trades.resample(f'{ticker_minutes}min', on='close_time')[['profitperc']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0
# FFill to get continuous

10
freqtrade/plot/plotting.py
View File

@@ -264,12 +264,12 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
trades: pd.DataFrame) -> go.Figure:
trades: pd.DataFrame, timeframe: str) -> go.Figure:
# Combine close-values for all pairs, rename columns to "pair"
df_comb = combine_tickers_with_mean(tickers, "close")
# Add combined cumulative profit
df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
# Plot the pairs average close prices, and total profit growth
avgclose = go.Scatter(
@@ -293,7 +293,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
for pair in pairs:
profit_col = f'cum_profit_{pair}'
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col, timeframe)
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
@@ -382,9 +382,9 @@ def plot_profit(config: Dict[str, Any]) -> None:
)
# Filter trades to relevant pairs
trades = trades[trades['pair'].isin(plot_elements["pairs"])]
# Create an average close price of all the pairs that were involved.
# this could be useful to gauge the overall market trend
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"], trades)
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
trades, config.get('ticker_interval', '5m'))
store_plot_file(fig, filename='freqtrade-profit-plot.html',
directory=config['user_data_dir'] / "plot", auto_open=True)