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alleviate FutureWarning in sklearn about ensuring svm model features are passed with identical order

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robcaulk
2022-05-24 14:46:16 +02:00
parent 255d35976e
commit 31ae2b3060
4 changed files with 61 additions and 11 deletions
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docs/freqai.md
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@@ -105,11 +105,11 @@ config setup includes:
### Building the feature set
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 `%`:
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=""):
def populate_any_indicators(self, metadata, 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)
@@ -120,11 +120,46 @@ inside the `populate_any_indicators()` method of the strategy by prepending indi
informative['%-' + coin + "bb_width"] = (
informative[coin + "bb_upperband"] - informative[coin + "bb_lowerband"]
) / informative[coin + "bb_middleband"]
# The following code automatically adds features according to the `shift` parameter passed
# in the config. Do not remove
indicators = [col for col in informative if col.startswith('%')]
for n in range(self.freqai_info["feature_parameters"]["shift"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
# The following code safely merges into the base timeframe.
# Do not remove.
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]]
df = df.drop(columns=skip_columns)
```
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 `%`._
Note: features **must** be defined in `populate_any_indicators()`. Making features in `populate_indicators()`
will fail in live/dry. If the user wishes to add generalized features that are not associated with
a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`:
```python
def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin=""):
# Add generalized indicators here (because in live, it will call only this function to populate
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
# these generalized indicators to the basepair/timeframe
if pair == metadata['pair'] and tf == self.timeframe:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
(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()`