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Improve strategy dataframe documentation

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Matthias 2021-11-13 11:47:28 +01:00
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docs/strategy-customization.md
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@ -12,12 +12,15 @@ Also, several other strategies are available in the [strategy repository](https:
You will however most likely have your own idea for a strategy.
This document intends to help you convert your strategy idea into your own strategy.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
!!! Note
This is just a template file, which will most likely not be profitable out of the box.
??? Hint "Different template levels"
`freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
### Anatomy of a strategy
A strategy file contains all the information needed to build a good strategy:
@ -54,6 +57,46 @@ file as reference.**
needs to take care to avoid having the strategy utilize data from the future.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
### Dataframe
Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
Pandas is a great library developed for processing large amounts of data.
Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
``` output
> dataframe.head()
date open high low close volume
0 2021-11-09 23:25:00+00:00 67279.67 67321.84 67255.01 67300.97 44.62253
1 2021-11-09 23:30:00+00:00 67300.97 67301.34 67183.03 67187.01 61.38076
2 2021-11-09 23:35:00+00:00 67187.02 67187.02 67031.93 67123.81 113.42728
3 2021-11-09 23:40:00+00:00 67123.80 67222.40 67080.33 67160.48 78.96008
4 2021-11-09 23:45:00+00:00 67160.48 67160.48 66901.26 66943.37 111.39292
```
Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
As a dataframe is a table, simple python comparisons like the following will not work
``` python
if dataframe['rsi'] > 30:
dataframe['buy'] = 1
```
The above section will fail with `The truth value of a Series is ambiguous. [...]`.
This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
``` python
dataframe.loc[
(dataframe['rsi'] > 30)
, 'buy'] = 1
```
With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
### Customize Indicators
Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.