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improve flexibility of user defined prediction dataframe

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This commit is contained in:
Robert Caulk 2022-08-06 13:51:19 +02:00
parent fdc82f8302
commit c172ce1011
6 changed files with 42 additions and 31 deletions
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8
docs/freqai.md
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@ -62,13 +62,13 @@ pip install -r requirements-freqai.txt
## Running from the example files
An example strategy, an example prediction model, and example config can all be found in
`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMPredictionModel.py`,
`freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`,
`config_examples/config_freqai.example.json`, respectively.
Assuming the user has downloaded the necessary data, Freqai can be executed from these templates with:
```bash
freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMPredictionModel --strategy-path freqtrade/templates --timerange 20220101-20220201
freqtrade backtesting --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --timerange 20220101-20220201
```
## Configuring the bot
@ -111,7 +111,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `test_size` | Fraction of data that should be used for testing instead of training. <br> **Datatype:** positive float below 1.
| `shuffle` | Shuffle the training data points during training. Typically for time-series forecasting, this is set to False. **Datatype:** boolean.
| | **Model training parameters**
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMPredictionModel`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model. <br> **Datatype:** dictionary.
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected library. For example, if the user uses `LightGBMRegressor`, then this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html). If the user selects a different model, then this dictionary can contain any parameter from that different model. <br> **Datatype:** dictionary.
| `n_estimators` | A common parameter among regressors which sets the number of boosted trees to fit <br> **Datatype:** integer.
| `learning_rate` | A common parameter among regressors which sets the boosting learning rate. <br> **Datatype:** float.
| `n_jobs`, `thread_count`, `task_type` | Different libraries use different parameter names to control the number of threads used for parallel processing or whether or not it is a `task_type` of `gpu` or `cpu`. <br> **Datatype:** float.
@ -356,7 +356,7 @@ and adding this to the `train_period_days`. The units need to be in the base can
The freqai training/backtesting module can be executed with the following command:
```bash
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMPredictionModel --timerange 20210501-20210701
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
```
If this command has never been executed with the existing config file, then it will train a new model

37
freqtrade/freqai/data_drawer.py
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@ -245,7 +245,7 @@ class FreqaiDataDrawer:
logger.info(f'Setting initial FreqUI plots from historical data for {pair}.')
else:
for label in dk.label_list:
for label in pred_df.columns:
mrv_df[label] = pred_df[label]
if mrv_df[label].dtype == object:
continue
@ -278,15 +278,16 @@ class FreqaiDataDrawer:
# strat seems to feed us variable sized dataframes - and since we are trying to build our
# own return array in the same shape, we need to figure out how the size has changed
# and adapt our stored/returned info accordingly.
length_difference = len(self.model_return_values[pair]) - len_df
i = 0
if length_difference == 0:
i = 1
elif length_difference > 0:
i = length_difference + 1
# length_difference = len(self.model_return_values[pair]) - len_df
# i = 0
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
# if length_difference == 0:
# i = 1
# elif length_difference > 0:
# i = length_difference + 1
df = self.model_return_values[pair] = self.model_return_values[pair].shift(-1)
if pair in self.historic_predictions:
hp_df = self.historic_predictions[pair]
@ -296,7 +297,8 @@ class FreqaiDataDrawer:
hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0)
self.historic_predictions[pair] = hp_df[:-1]
for label in dk.label_list:
# incase user adds additional "predictions" e.g. predict_proba output:
for label in predictions.columns:
df[label].iloc[-1] = predictions[label].iloc[-1]
if df[label].dtype == object:
continue
@ -318,11 +320,11 @@ class FreqaiDataDrawer:
for key in df.keys():
self.historic_predictions[pair][key].iloc[-1] = df[key].iloc[-1]
if length_difference < 0:
prepend_df = pd.DataFrame(
np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
)
df = pd.concat([prepend_df, df], axis=0)
# if length_difference < 0:
# prepend_df = pd.DataFrame(
# np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
# )
# df = pd.concat([prepend_df, df], axis=0)
def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: DataFrame) -> DataFrame:
@ -343,7 +345,12 @@ class FreqaiDataDrawer:
dk.find_features(dataframe)
for label in dk.label_list:
if self.freqai_info.get('predict_proba', []):
full_labels = dk.label_list + self.freqai_info['predict_proba']
else:
full_labels = dk.label_list
for label in full_labels:
dataframe[label] = 0
dataframe[f"{label}_mean"] = 0
dataframe[f"{label}_std"] = 0

10
freqtrade/freqai/data_kitchen.py
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@ -342,7 +342,7 @@ class FreqaiDataKitchen:
:df: Dataframe of predictions to be denormalized
"""
for label in self.label_list:
for label in df.columns:
if df[label].dtype == object:
continue
df[label] = (
@ -716,14 +716,16 @@ class FreqaiDataKitchen:
weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
return weights
def append_predictions(self, predictions, do_predict, len_dataframe):
def append_predictions(self, predictions: DataFrame, do_predict: npt.ArrayLike) -> None:
"""
Append backtest prediction from current backtest period to all previous periods
"""
append_df = DataFrame()
for label in self.label_list:
for label in predictions.columns:
append_df[label] = predictions[label]
if append_df[label].dtype == object:
continue
append_df[f"{label}_mean"] = self.data["labels_mean"][label]
append_df[f"{label}_std"] = self.data["labels_std"][label]
@ -1009,7 +1011,7 @@ class FreqaiDataKitchen:
import scipy as spy
self.data["labels_mean"], self.data["labels_std"] = {}, {}
for label in self.label_list:
for label in self.data_dictionary["train_labels"].columns:
if self.data_dictionary["train_labels"][label].dtype == object:
continue
f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])

12
freqtrade/freqai/freqai_interface.py
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@ -221,7 +221,7 @@ class IFreqaiModel(ABC):
pred_df, do_preds = self.predict(dataframe_backtest, dk)
dk.append_predictions(pred_df, do_preds, len(dataframe_backtest))
dk.append_predictions(pred_df, do_preds)
dk.fill_predictions(dataframe)
@ -543,15 +543,17 @@ class IFreqaiModel(ABC):
self.dd.historic_predictions[pair] = pred_df
hist_preds_df = self.dd.historic_predictions[pair]
for label in hist_preds_df.columns:
if hist_preds_df[label].dtype == object:
continue
hist_preds_df[f'{label}_mean'] = 0
hist_preds_df[f'{label}_std'] = 0
hist_preds_df['do_predict'] = 0
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
hist_preds_df['DI_values'] = 0
for label in dk.data['labels_mean']:
hist_preds_df[f'{label}_mean'] = 0
hist_preds_df[f'{label}_std'] = 0
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0

2
tests/freqai/conftest.py
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@ -47,7 +47,7 @@ def freqai_conf(default_conf, tmpdir):
"indicator_periods_candles": [10],
},
"data_split_parameters": {"test_size": 0.33, "random_state": 1},
"model_training_parameters": {"n_estimators": 100, "verbosity": 0},
"model_training_parameters": {"n_estimators": 100},
},
"config_files": [Path('config_examples', 'config_freqai.example.json')]
}

4
tests/freqai/test_freqai_interface.py
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@ -74,8 +74,8 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
def test_train_model_in_series_Catboost(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostRegressor"})
freqai_conf.get('freqai', {}).update(
{'model_training_parameters': {"n_estimators": 100, "verbose": 0}})
# freqai_conf.get('freqai', {}).update(
# {'model_training_parameters': {"n_estimators": 100, "verbose": 0}})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)