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auto populate features based on a prepended % in the strategy (remove feature assignment from config). Update doc/constants/example strategy to reflect change

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robcaulk
2022-05-17 18:15:03 +02:00
parent 8664e8f9a3
commit d1d451c27e
6 changed files with 80 additions and 69 deletions
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docs/freqai.md
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@@ -72,11 +72,6 @@ config setup includes:
"train_period" : 30,
"backtest_period" : 7,
"identifier" : "unique-id",
"base_features": [
"rsi",
"mfi",
"roc",
],
"corr_pairlist": [
"ETH/USD",
"LINK/USD",
@@ -102,11 +97,31 @@ config setup includes:
### Building the feature set
Most of these parameters are controlling the feature data set. The `base_features`
indicates the basic indicators the user wishes to include in the feature set.
The `timeframes` are the timeframes of each base_feature that the user wishes to
include in the feature set. In the present case, the user is asking for the
`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, etc. to be included
Most of these parameters are controlling the feature data set. Features are added by the user
inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`:
```python
def populate_any_indicators(self, pair, df, tf, informative=None, coin=""):
informative['%-''%-' + coin + "rsi"] = ta.RSI(informative, timeperiod=14)
informative['%-' + coin + "mfi"] = ta.MFI(informative, timeperiod=25)
informative['%-' + coin + "adx"] = ta.ADX(informative, window=20)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
informative[coin + "bb_lowerband"] = bollinger["lower"]
informative[coin + "bb_middleband"] = bollinger["mid"]
informative[coin + "bb_upperband"] = bollinger["upper"]
informative['%-' + coin + "bb_width"] = (
informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
) / informative[coin + "bb_middleband"]
```
The user of the present example does not want to pass the `bb_lowerband` as a feature to the model,
and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the
model for training/prediction and has therfore prepended it with `%`._
(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`)
The `timeframes` from the example config above are the timeframes of each `populate_any_indicator()`
included metric for inclusion in the feature set. In the present case, the user is asking for the
`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included
in the feature set.
In addition, the user can ask for each of these features to be included from