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fix bug for target_mean/std array merging in backtesting

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This commit is contained in:
robcaulk
2022-05-26 21:07:50 +02:00
parent ff531c416f
commit 6193205012
6 changed files with 186 additions and 110 deletions
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docs/freqai.md
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@@ -221,33 +221,43 @@ This way, the user can return to using any model they wish by simply changing th
### Building a freqai strategy
The Freqai strategy requires the user to include the following lines of code in `populate_ any _indicators()`
The Freqai strategy requires the user to include the following lines of code in the strategy:
```python
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.freqai.strategy_bridge import CustomModel
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the configuration file parameters are stored here
self.freqai_info = self.config['freqai']
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
corr_pairs = self.config["freqai"]["corr_pairlist"]
informative_pairs = []
for tf in self.config["freqai"]["timeframes"]:
for pair in whitelist_pairs:
informative_pairs.append((pair, tf))
for pair in corr_pairs:
if pair in whitelist_pairs:
continue # avoid duplication
informative_pairs.append((pair, tf))
return informative_pairs
# the model is instantiated here
self.model = CustomModel(self.config)
def bot_start(self):
self.model = CustomModel(self.config)
print('Populating indicators...')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config['freqai']
# the following loops are necessary for building the features
# indicated by the user in the configuration file.
for tf in self.freqai_info['timeframes']:
for i in self.freqai_info['corr_pairlist']:
dataframe = self.populate_any_indicators(i,
dataframe.copy(), tf, coin=i.split("/")[0]+'-')
# the following loops are necessary for building the features
# indicated by the user in the configuration file.
for tf in self.freqai_info['timeframes']:
for i in self.freqai_info['corr_pairlist']:
dataframe = self.populate_any_indicators(i,
dataframe.copy(), tf, coin=i.split("/")[0]+'-')
# the model will return 4 values, its prediction, an indication of whether or not the prediction
# should be accepted, the target mean/std values from the labels used during each training period.
(dataframe['prediction'], dataframe['do_predict'],
dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata)
# the model will return 4 values, its prediction, an indication of whether or not the prediction
# should be accepted, the target mean/std values from the labels used during each training period.
(dataframe['prediction'], dataframe['do_predict'],
dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata)
return dataframe
return dataframe
```
The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds
@@ -314,7 +324,7 @@ data point and all other training data points:
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
where $d_{ab}$ is the distance between the standardized points $a$ and $b$. $p$
where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$
is the number of features i.e. the length of the vector $X$. The
characteristic distance, $\overline{d}$ for a set of training data points is simply the mean
of the average distances:
@@ -392,13 +402,63 @@ The user can stratify the training/testing data using:
which will split the data chronolocially so that every X data points is a testing data point. In the
present example, the user is asking for every third data point in the dataframe to be used for
testing, the other points are used for training.
testing, the other points are used for training.
<!-- ## Dynamic target expectation
The labels used for model training have a unique statistical distribution for each separate model training.
We can use this information to know if our current prediction is in the realm of what the model was trained on,
and if so, what is the statistical probability of the current prediction. With this information, we can
make more informed prediction._
FreqAI builds this label distribution and provides a quantile to the strategy, which can be optionally used as a
dynamic threshold. The `target_quantile: X` means that X% of the labels are below this value. So setting:
```json
"freqai": {
"feature_parameters" : {
"target_quantile": 0.9
}
}
```
Means the user will get back in the strategy the label threshold at which 90% of the labels were
below this value. An example usage in the strategy may look something like:
```python
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# ... #
(
dataframe["prediction"],
dataframe["do_predict"],
dataframe["target_upper_quantile"],
dataframe["target_lower_quantile"],
) = self.model.bridge.start(dataframe, metadata, self)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
buy_conditions = [
(dataframe["prediction"] > dataframe["target_upper_quantile"]) & (dataframe["do_predict"] == 1)
]
if buy_conditions:
dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), "buy"] = 1
return dataframe
``` -->
## Additional information
### Feature standardization
### Feature normalization
The feature set created by the user is automatically standardized to the training
The feature set created by the user is automatically normalized to the training
data only. This includes all test data and unseen prediction data (dry/live/backtest).
### File structure