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docs/freqai.md
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@ -4,8 +4,9 @@
Freqai is still experimental, and should be used at the user's own discretion. Freqai is still experimental, and should be used at the user's own discretion.
Freqai is a module designed to automate a variety of tasks associated with Freqai is a module designed to automate a variety of tasks associated with
training a regressor to predict signals based on input features. Among the training a regressor to predict signals based on input features.
the features includes:
Among the the features included:
* Easy large feature set construction based on simple user input * Easy large feature set construction based on simple user input
* Sweep model training and backtesting to simulate consistent model retraining through time * Sweep model training and backtesting to simulate consistent model retraining through time
@ -16,6 +17,7 @@ the features includes:
* Cleaning of NaNs from the data set before training and prediction. * Cleaning of NaNs from the data set before training and prediction.
TODO: TODO:
* live is not automated, still some architectural work to be done * live is not automated, still some architectural work to be done
## Background and vocabulary ## Background and vocabulary
@ -43,7 +45,7 @@ directly influence nodal weights within the model.
## Install prerequisites ## Install prerequisites
Use `pip` to install the prerequisities with: Use `pip` to install the prerequisites with:
`pip install -r requirements-freqai.txt` `pip install -r requirements-freqai.txt`
@ -62,7 +64,9 @@ FreqaiExampleStrategy --freqaimodel CatboostPredictionModel --strategy-path freq
``` ```
## Configuring the bot ## Configuring the bot
### Example config file ### Example config file
The user interface is isolated to the typical config file. A typical Freqai The user interface is isolated to the typical config file. A typical Freqai
config setup includes: config setup includes:
@ -152,8 +156,8 @@ data set timerange months. Users can think of this as a "sliding window" which
emulates Freqai retraining itself once per week in live using the previous emulates Freqai retraining itself once per week in live using the previous
month of data. month of data.
## Running Freqai ## Running Freqai
### Training and backtesting ### Training and backtesting
The freqai training/backtesting module can be executed with the following command: The freqai training/backtesting module can be executed with the following command:
@ -196,6 +200,7 @@ The Freqai strategy requires the user to include the following lines of code in
return dataframe return dataframe
``` ```
The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds The user should also include `populate_any_indicators()` from `templates/FreqaiExampleStrategy.py` which builds
the feature set with a proper naming convention for the IFreqaiModel to use later. the feature set with a proper naming convention for the IFreqaiModel to use later.
@ -216,7 +221,7 @@ freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.
By default, Freqai will not find find any existing models and will start by training a new one By default, Freqai will not find find any existing models and will start by training a new one
given the user configuration settings. Following training, it will use that model to predict for the given the user configuration settings. Following training, it will use that model to predict for the
duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain duration of `backtest_period`. After a full `backtest_period` has elapsed, Freqai will auto retrain
a new model, and begin making predictions with the updated model. a new model, and begin making predictions with the updated model.
If the user wishes to start dry/live from a saved model, the following configuration If the user wishes to start dry/live from a saved model, the following configuration
parameters need to be set: parameters need to be set:
@ -232,13 +237,14 @@ parameters need to be set:
Where the `identifier` is the same identifier which was set during the backtesting/training. Meanwhile, Where the `identifier` is the same identifier which was set during the backtesting/training. Meanwhile,
the `live_trained_timerange` is the sub-trained timerange (the training window) which was set the `live_trained_timerange` is the sub-trained timerange (the training window) which was set
during backtesting/training. These are available to the user inside `user_data/models/*/sub-train-*`. during backtesting/training. These are available to the user inside `user_data/models/*/sub-train-*`.
`live_full_backtestrange` was the full data range assocaited with the backtest/training (the full time `live_full_backtestrange` was the full data range associated with the backtest/training (the full time
window that the training window and backtesting windows slide through). These values can be located window that the training window and backtesting windows slide through). These values can be located
inside the `user_data/models/` directory. In this case, although Freqai will initiate with a inside the `user_data/models/` directory. In this case, although Freqai will initiate with a
pretrained model, if a full `backtest_period` has elapsed since the end of the user set pre-trained model, if a full `backtest_period` has elapsed since the end of the user set
`live_trained_timerange`, it will self retrain. `live_trained_timerange`, it will self retrain.
## Data anylsis techniques ## Data anylsis techniques
### Controlling the model learning process ### Controlling the model learning process
The user can define model settings for the data split `data_split_parameters` and learning parameters The user can define model settings for the data split `data_split_parameters` and learning parameters
@ -258,7 +264,7 @@ the user is asking for `labels` that are 24 candles in the future.
### Removing outliers with the Dissimilarity Index ### Removing outliers with the Dissimilarity Index
The Dissimilarity Index (DI) aims to quantiy the uncertainty associated with each The Dissimilarity Index (DI) aims to quantity the uncertainty associated with each
prediction by the model. To do so, Freqai measures the distance between each training prediction by the model. To do so, Freqai measures the distance between each training
data point and all other training data points: data point and all other training data points:
@ -310,11 +316,11 @@ Users can reduce the dimensionality of their features by activating the `princip
``` ```
Which will perform PCA on the features and reduce the dimensionality of the data so that the explained Which will perform PCA on the features and reduce the dimensionality of the data so that the explained
variance of the data set is >= 0.999. variance of the data set is >= 0.999.
### Removing outliers based on feature statistical distributions ### Removing outliers based on feature statistical distributions
The user can tell Freqai to remove outlier data points from the trainig/test data sets by setting: The user can tell Freqai to remove outlier data points from the training/test data sets by setting:
```json ```json
"freqai": { "freqai": {
@ -326,9 +332,10 @@ The user can tell Freqai to remove outlier data points from the trainig/test dat
Freqai will check the statistical distributions of each feature (or component if the user activated Freqai will check the statistical distributions of each feature (or component if the user activated
`principal_component_analysis`) and remove any data point that sits more than 3 standard deviations away `principal_component_analysis`) and remove any data point that sits more than 3 standard deviations away
from the mean. from the mean.
## Additional information ## Additional information
### Feature standardization ### Feature standardization
The feature set created by the user is automatically standardized to the training The feature set created by the user is automatically standardized to the training
@ -337,5 +344,5 @@ data only. This includes all test data and unseen prediction data (dry/live/back
### File structure ### File structure
`user_data_dir/models/` contains all the data associated with the trainings and `user_data_dir/models/` contains all the data associated with the trainings and
backtestings. This file structure is heavily controlled and read by the `FreqaiDataKitchen()` backtests. This file structure is heavily controlled and read by the `FreqaiDataKitchen()`
and should thus not be modified. and should thus not be modified.

6
freqtrade/freqai/freqai_interface.py
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@ -43,7 +43,7 @@ class IFreqaiModel(ABC):
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame: def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
""" """
Entry point to the FreqaiModel, it will train a new model if Entry point to the FreqaiModel, it will train a new model if
necesssary before making the prediction. necessary before making the prediction.
The backtesting and training paradigm is a sliding training window The backtesting and training paradigm is a sliding training window
with a following backtest window. Both windows slide according to the with a following backtest window. Both windows slide according to the
length of the backtest window. This function is not intended to be length of the backtest window. This function is not intended to be
@ -54,7 +54,7 @@ class IFreqaiModel(ABC):
:dataframe: Full dataframe coming from strategy - it contains entire :dataframe: Full dataframe coming from strategy - it contains entire
backtesting timerange + additional historical data necessary to train backtesting timerange + additional historical data necessary to train
the model. the model.
:metadata: pair metadataa coming from strategy. :metadata: pair metadata coming from strategy.
""" """
live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE) live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
@ -71,7 +71,7 @@ class IFreqaiModel(ABC):
logger.info("going to train %s timeranges", len(self.dh.training_timeranges)) logger.info("going to train %s timeranges", len(self.dh.training_timeranges))
# Loop enforcing the sliding window training/backtesting paragigm # Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month # tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly # tr_backtest is the backtesting time range e.g. the week directly
# following tr_train. Both of these windows slide through the # following tr_train. Both of these windows slide through the

2
freqtrade/templates/ExamplePredictionModel.py
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@ -42,7 +42,7 @@ class ExamplePredictionModel(IFreqaiModel):
def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]: def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
""" """
Filter the training data and train a model to it. Train makes heavy use of the datahkitchen Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data. for storing, saving, loading, and analyzing the data.
:params: :params:
:unfiltered_dataframe: Full dataframe for the current training period :unfiltered_dataframe: Full dataframe for the current training period

2
mkdocs.yml
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@ -35,8 +35,8 @@ nav:
- Edge Positioning: edge.md - Edge Positioning: edge.md
- Advanced Strategy: strategy-advanced.md - Advanced Strategy: strategy-advanced.md
- Advanced Hyperopt: advanced-hyperopt.md - Advanced Hyperopt: advanced-hyperopt.md
- Sandbox Testing: sandbox-testing.md
- Freqai: freqai.md - Freqai: freqai.md
- Sandbox Testing: sandbox-testing.md
- FAQ: faq.md - FAQ: faq.md
- SQL Cheat-sheet: sql_cheatsheet.md - SQL Cheat-sheet: sql_cheatsheet.md
- Strategy migration: strategy_migration.md - Strategy migration: strategy_migration.md