Fix create_cum_profit to work with trades that don't open on candle
opens
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@@ -264,12 +264,12 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
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def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
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trades: pd.DataFrame) -> go.Figure:
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trades: pd.DataFrame, timeframe: str) -> go.Figure:
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# Combine close-values for all pairs, rename columns to "pair"
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df_comb = combine_tickers_with_mean(tickers, "close")
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# Add combined cumulative profit
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df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
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df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
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# Plot the pairs average close prices, and total profit growth
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avgclose = go.Scatter(
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@@ -293,7 +293,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
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for pair in pairs:
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profit_col = f'cum_profit_{pair}'
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df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
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df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col, timeframe)
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fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
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@@ -382,9 +382,9 @@ def plot_profit(config: Dict[str, Any]) -> None:
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)
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# Filter trades to relevant pairs
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trades = trades[trades['pair'].isin(plot_elements["pairs"])]
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# Create an average close price of all the pairs that were involved.
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# this could be useful to gauge the overall market trend
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fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"], trades)
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fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
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trades, config.get('ticker_interval', '5m'))
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store_plot_file(fig, filename='freqtrade-profit-plot.html',
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directory=config['user_data_dir'] / "plot", auto_open=True)
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