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add model expiration feature, fix bug in DI return values

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This commit is contained in:
robcaulk 2022-06-17 14:55:40 +02:00
parent 0b0688a91e
commit f631ae911b
5 changed files with 69 additions and 18 deletions
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18
docs/freqai.md
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@ -452,6 +452,24 @@ config:
which will automatically purge all models older than the two most recently trained ones.
## Defining model expirations
During dry/live, FreqAI trains each pair sequentially (on separate threads/GPU from the main
Freqtrade bot). This means there is always an age discrepancy between models. If a user is training
on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old.
This may be undesirable if the characteristic time scale (read trade duration target) for a strategy
is much less than 4 hours. The user can decide to only make trade entries if the model is less than
a certain number of hours in age by setting the `expiration_hours` in the config file:
```json
"freqai": {
"expiration_hours": 0.5,
}
```
In the present example, the user will only allow predictions on models that are less than 1/2 hours
old.
<!-- ## Dynamic target expectation
The labels used for model training have a unique statistical distribution for each separate model training.

20
freqtrade/freqai/data_drawer.py
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@ -30,6 +30,7 @@ class FreqaiDataDrawer:
def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
self.config = config
self.freqai_info = config.get('freqai', {})
# dictionary holding all pair metadata necessary to load in from disk
self.pair_dict: Dict[str, Any] = {}
# dictionary holding all actively inferenced models in memory given a model filename
@ -168,7 +169,8 @@ class FreqaiDataDrawer:
self.model_return_values[pair]['do_preds'] = dh.full_do_predict
self.model_return_values[pair]['target_mean'] = dh.full_target_mean
self.model_return_values[pair]['target_std'] = dh.full_target_std
self.model_return_values[pair]['DI_values'] = dh.full_DI_values
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.model_return_values[pair]['DI_values'] = dh.full_DI_values
# if not self.follow_mode:
# self.save_model_return_values_to_disk()
@ -189,8 +191,9 @@ class FreqaiDataDrawer:
self.model_return_values[pair]['predictions'] = np.append(
self.model_return_values[pair]['predictions'][i:], predictions[-1])
self.model_return_values[pair]['DI_values'] = np.append(
self.model_return_values[pair]['DI_values'][i:], dh.DI_values[-1])
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.model_return_values[pair]['DI_values'] = np.append(
self.model_return_values[pair]['DI_values'][i:], dh.DI_values[-1])
self.model_return_values[pair]['do_preds'] = np.append(
self.model_return_values[pair]['do_preds'][i:], do_preds[-1])
self.model_return_values[pair]['target_mean'] = np.append(
@ -202,8 +205,9 @@ class FreqaiDataDrawer:
prepend = np.zeros(abs(length_difference) - 1)
self.model_return_values[pair]['predictions'] = np.insert(
self.model_return_values[pair]['predictions'], 0, prepend)
self.model_return_values[pair]['DI_values'] = np.insert(
self.model_return_values[pair]['DI_values'], 0, prepend)
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.model_return_values[pair]['DI_values'] = np.insert(
self.model_return_values[pair]['DI_values'], 0, prepend)
self.model_return_values[pair]['do_preds'] = np.insert(
self.model_return_values[pair]['do_preds'], 0, prepend)
self.model_return_values[pair]['target_mean'] = np.insert(
@ -215,7 +219,8 @@ class FreqaiDataDrawer:
dh.full_do_predict = copy.deepcopy(self.model_return_values[pair]['do_preds'])
dh.full_target_mean = copy.deepcopy(self.model_return_values[pair]['target_mean'])
dh.full_target_std = copy.deepcopy(self.model_return_values[pair]['target_std'])
dh.full_DI_values = copy.deepcopy(self.model_return_values[pair]['DI_values'])
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
dh.full_DI_values = copy.deepcopy(self.model_return_values[pair]['DI_values'])
# if not self.follow_mode:
# self.save_model_return_values_to_disk()
@ -227,7 +232,8 @@ class FreqaiDataDrawer:
dh.full_do_predict = np.zeros(len_df)
dh.full_target_mean = np.zeros(len_df)
dh.full_target_std = np.zeros(len_df)
dh.full_DI_values = np.zeros(len_df)
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
dh.full_DI_values = np.zeros(len_df)
def purge_old_models(self) -> None:

12
freqtrade/freqai/data_kitchen.py
View File

@ -673,7 +673,7 @@ class FreqaiDataKitchen:
self.full_predictions = np.append(self.full_predictions, predictions)
self.full_do_predict = np.append(self.full_do_predict, do_predict)
if self.freqai_config.get('feature_parameters', {}).get('DI-threshold', 0) > 0:
if self.freqai_config.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.full_DI_values = np.append(self.full_DI_values, self.DI_values)
self.full_target_mean = np.append(self.full_target_mean, target_mean)
self.full_target_std = np.append(self.full_target_std, target_std)
@ -689,7 +689,7 @@ class FreqaiDataKitchen:
filler = np.zeros(len_dataframe - len(self.full_predictions)) # startup_candle_count
self.full_predictions = np.append(filler, self.full_predictions)
self.full_do_predict = np.append(filler, self.full_do_predict)
if self.freqai_config.get('feature_parameters', {}).get('DI-threshold', 0) > 0:
if self.freqai_config.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.full_DI_values = np.append(filler, self.full_DI_values)
self.full_target_mean = np.append(filler, self.full_target_mean)
self.full_target_std = np.append(filler, self.full_target_std)
@ -725,6 +725,12 @@ class FreqaiDataKitchen:
return full_timerange
def check_if_model_expired(self, trained_timestamp: int) -> bool:
time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
elapsed_time = (time - trained_timestamp) / 3600 # hours
max_time = self.freqai_config.get('expiration_hours', 0)
return elapsed_time > max_time
def check_if_new_training_required(self, trained_timestamp: int) -> Tuple[bool,
TimeRange, TimeRange]:
@ -873,6 +879,8 @@ class FreqaiDataKitchen:
# check if newest candle is already appended
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
if len(df_dp.index) == 0:
continue
if (
str(history_data[pair][tf].iloc[-1]['date']) ==
str(df_dp.iloc[-1:]['date'].iloc[-1])

35
freqtrade/freqai/freqai_interface.py
View File

@ -8,6 +8,7 @@ from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
@ -65,6 +66,7 @@ class IFreqaiModel(ABC):
self.identifier = self.freqai_info.get('identifier', 'no_id_provided')
self.scanning = False
self.ready_to_scan = False
self.first = True
def assert_config(self, config: Dict[str, Any]) -> None:
@ -252,7 +254,7 @@ class IFreqaiModel(ABC):
# # trained_timestamp=trained_timestamp,
# # model_filename=model_filename)
(self.retrain,
(_,
new_trained_timerange,
data_load_timerange) = dh.check_if_new_training_required(trained_timestamp)
dh.set_paths(metadata['pair'], new_trained_timerange.stopts)
@ -288,6 +290,7 @@ class IFreqaiModel(ABC):
# load the model and associated data into the data kitchen
self.model = dh.load_data(coin=metadata['pair'])
if not self.model:
logger.warning('No model ready, returning null values to strategy.')
self.data_drawer.return_null_values_to_strategy(dataframe, dh)
@ -296,22 +299,38 @@ class IFreqaiModel(ABC):
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dataframe, dh)
self.build_strategy_return_arrays(dataframe, dh, metadata['pair'], trained_timestamp)
return dh
def build_strategy_return_arrays(self, dataframe: DataFrame,
dh: FreqaiDataKitchen, pair: str,
trained_timestamp: int) -> None:
# hold the historical predictions in memory so we are sending back
# correct array to strategy FIXME currently broken, but only affecting
# Frequi reporting. Signals remain unaffeted.
if metadata['pair'] not in self.data_drawer.model_return_values:
if pair not in self.data_drawer.model_return_values:
preds, do_preds = self.predict(dataframe, dh)
dh.append_predictions(preds, do_preds, len(dataframe))
dh.fill_predictions(len(dataframe))
self.data_drawer.set_initial_return_values(metadata['pair'], dh)
self.data_drawer.set_initial_return_values(pair, dh)
return
elif self.dh.check_if_model_expired(trained_timestamp):
preds, do_preds, dh.DI_values = np.zeros(2), np.ones(2) * 2, np.zeros(2)
logger.warning('Model expired, returning null values to strategy. Strategy '
'construction should take care to consider this event with '
'prediction == 0 and do_predict == 2')
else:
preds, do_preds = self.predict(dataframe.iloc[-2:], dh)
self.data_drawer.append_model_predictions(metadata['pair'], preds, do_preds,
dh.data["target_mean"],
dh.data["target_std"], dh,
len(dataframe))
return dh
self.data_drawer.append_model_predictions(pair, preds, do_preds,
dh.data["target_mean"],
dh.data["target_std"],
dh,
len(dataframe))
return
def check_if_feature_list_matches_strategy(self, dataframe: DataFrame,
dh: FreqaiDataKitchen) -> None:

2
freqtrade/freqai/prediction_models/CatboostPredictionModel.py
View File

@ -24,7 +24,7 @@ class CatboostPredictionModel(IFreqaiModel):
dataframe["do_predict"] = dh.full_do_predict
dataframe["target_mean"] = dh.full_target_mean
dataframe["target_std"] = dh.full_target_std
if self.freqai_info.get('feature_parameters', {}).get('DI-threshold', 0) > 0:
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
dataframe["DI"] = dh.full_DI_values
return dataframe