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@ -441,7 +441,7 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
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def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
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trades: pd.DataFrame, timeframe: str) -> go.Figure:
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trades: pd.DataFrame, timeframe: str, stake_currency: str) -> go.Figure:
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# Combine close-values for all pairs, rename columns to "pair"
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df_comb = combine_dataframes_with_mean(data, "close")
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@ -466,8 +466,8 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
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subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
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fig['layout'].update(title="Freqtrade Profit plot")
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fig['layout']['yaxis1'].update(title='Price')
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fig['layout']['yaxis2'].update(title='Profit')
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fig['layout']['yaxis3'].update(title='Profit')
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fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
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fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
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fig['layout']['xaxis']['rangeslider'].update(visible=False)
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fig.add_trace(avgclose, 1, 1)
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@ -581,6 +581,6 @@ def plot_profit(config: Dict[str, Any]) -> None:
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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['ohlcv'],
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trades, config.get('timeframe', '5m'))
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trades, config.get('timeframe', '5m'), config.get('stake_currency'))
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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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