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@ -781,6 +781,8 @@ Printing more than a few rows is also possible (simply use `print(dataframe)` i
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## Common mistakes when developing strategies
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## Common mistakes when developing strategies
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### Peeking into the future while backtesting
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Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
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Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
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This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
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This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
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@ -791,6 +793,33 @@ The following lists some common patterns which should be avoided to prevent frus
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- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
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- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
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- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
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- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
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### Performance warning
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When executing a strategy, one can sometimes be greeted by the following in the logs
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> PerformanceWarning: DataFrame is highly fragmented.
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This is a warning from [`pandas`](https://github.com/pandas-dev/pandas) and as the warning continues to say:
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use `pd.concat(axis=1)`. For example
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```python
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for i in range(100):
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dataframe[i] = ta.indicator(dataframe, param=i)
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```
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should be rewritten to
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```python
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frames = [dataframe]
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for i in range(100):
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frames.append({
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str(i): ta.indicator(dataframe, param=i)
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})
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# Append columns to existing dataframe
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merged_frame = pd.concat(frames, axis=1)
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```
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## Further strategy ideas
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## Further strategy ideas
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To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
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To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
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