Add samples for plotting to jupyter documentation
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@@ -68,8 +68,75 @@ df.tail()
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```python
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# Report results
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print(f"Generated {df['buy'].sum()} buy signals")
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data = df.set_index('date', drop=True)
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data = df.set_index('date', drop=False)
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data.tail()
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```
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## Load existing objects into a Jupyter notebook
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The following cells assume that you have already generated data using the cli.
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They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
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### Load backtest results to pandas dataframe
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Analyze a trades dataframe (also used below for plotting)
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```python
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from freqtrade.data.btanalysis import load_backtest_data
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# Load backtest results
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trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
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# Show value-counts per pair
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trades.groupby("pair")["sell_reason"].value_counts()
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```
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### Load live trading results into a pandas dataframe
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In case you did already some trading and want to analyze your performance
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```python
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from freqtrade.data.btanalysis import load_trades_from_db
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# Fetch trades from database
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trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
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# Display results
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trades.groupby("pair")["sell_reason"].value_counts()
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```
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## Plot results
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Freqtrade offers interactive plotting capabilities based on plotly.
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```python
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from freqtrade.plot.plotting import generate_candlestick_graph
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# Limit graph period to keep plotly quick and reactive
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data_red = data['2019-06-01':'2019-06-10']
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# Generate candlestick graph
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graph = generate_candlestick_graph(pair=pair,
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data=data_red,
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trades=trades,
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indicators1=['sma20', 'ema50', 'ema55'],
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indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
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)
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```
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```python
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# Show graph inline
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# graph.show()
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# Render graph in a seperate window
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graph.show(renderer="browser")
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```
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Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
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