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@ -11,18 +11,20 @@ Among the the features included:
* Sweep model training and backtesting to simulate consistent model retraining through time.
* Remove outliers automatically from training and prediction sets using a Dissimilarity Index and Support Vector Machines.
* Reduce the dimensionality of the data with Principal Component Analysis.
* Store models to disk to make reloading from a crash fast and easy (and purge obsolete files automatically for sustained dry/live runs.)
* Store models to disk to make reloading from a crash fast and easy (and purge obsolete files automatically for sustained dry/live runs).
* Normalize the data automatically in a smart and statistically safe way.
* Automated data download and data handling.
* Clean the incoming data and of NaNs in a safe way and before training and prediction.
* Clean the incoming data of NaNs in a safe way before training and prediction.
* Retrain live automatically so that the model self-adapts to the market in an unsupervised manner.
## General approach
The user provides FreqAI with a set of custom indicators (created inside the strategy the same way
a typical Freqtrade strategy is created) as well as a target value (typically some price change into the future). FreqAI trains a model to predict the target value based on the input of custom indicators.
FreqAI will train and save a new model for each pair in the config whitelist.
Users employ FreqAI to backtest a strategy (emulate reality with retraining a model as new data is introduced) and run the model live to generate buy and sell signals. In dry/live, FreqAI works in a background thread to keep all models as updated as possible with consistent retraining.
a typical Freqtrade strategy is created) as well as a target value (typically some price change into the future).
FreqAI trains a model to predict the target value based on the input of custom indicators.
FreqAI will train and save a new model for each pair in the config whitelist.
Users employ FreqAI to backtest a strategy (emulate reality with retraining a model as new data is introduced) and run the model live to generate entry and exit signals.
In dry/live, FreqAI works in a background thread to keep all models as updated as possible with consistent retraining.
## Background and vocabulary
@ -32,16 +34,14 @@ builds the features from anything they can construct in the strategy.
**Labels** are the target values with which the weights inside a model are trained
toward. Each set of features is associated with a single label, which is also
defined within the strategy by the user. These labels look forward into the
defined within the strategy by the user. These labels intentionally look into the
future, and are not available to the model during dryrun/live/backtesting.
**Training** refers to the process of feeding individual feature sets into the
model with associated labels with the goal of matching input feature sets to
associated labels.
model with associated labels with the goal of matching input feature sets to associated labels.
**Train data** is a subset of the historic data which is fed to the model during
training to adjust weights. This data directly influences weight connections
in the model.
training to adjust weights. This data directly influences weight connections in the model.
**Test data** is a subset of the historic data which is used to evaluate the
intermediate performance of the model during training. This data does not
@ -51,15 +51,17 @@ directly influence nodal weights within the model.
Use `pip` to install the prerequisites with:
`pip install -r requirements-freqai.txt`
``` bash
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`,
`config_examples/config_freqai_futures.example.json`, respectively. Assuming the user has downloaded
the necessary data, Freqai can be executed from these templates with:
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`,
`config_examples/config_freqai_futures.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
@ -68,6 +70,7 @@ freqtrade backtesting --config config_examples/config_freqai.example.json --stra
## Configuring the bot
### Parameter table
The table below will list all configuration parameters available for `FreqAI`.
Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways.
@ -75,14 +78,14 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| Parameter | Description |
|------------|-------------|
| `freqai` | **Required.** The dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** dictionary.
| `identifier` | **Required.** A unique name for the current model. This can be reused to reload pretrained models/data. <br> **Datatype:** string.
| `identifier` | **Required.** A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** string.
| `train_period_days` | **Required.** Number of days to use for the training data (width of the sliding window). <br> **Datatype:** positive integer.
| `backtest_period_days` | **Required.** Number of days to inference into the trained model before sliding the window and retraining. This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
| `live_retrain_hours` | Frequency of retraining during dry/live runs. Default set to 0, which means it will retrain as often as possible. **Datatype:** Float > 0.
| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. False by default. <br> **Datatype:** boolean.
| `live_trained_timestamp` | Useful if user wants to start from models trained during a *backtest*. The timestamp can be located in the `user_data/models` backtesting folder. This is not a commonly used parameter, leave undefined for most applications. <br> **Datatype:** positive integer.
| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models. `False` by default. <br> **Datatype:** boolean.
| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period. <br> **Datatype:** positive integer.
| `fit_live_predictions_candles` | Computes target (label) statistics from prediction data, instead of from the training data set. Number of candles is the number of historical candles it uses to generate the statistics. <br> **Datatype:** positive integer.
| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to False. <br> **Datatype:** boolean.
| `purge_old_models` | Tell FreqAI to delete obsolete models. Otherwise, all historic models will remain on disk. Defaults to `False`. <br> **Datatype:** boolean.
| `expiration_hours` | Ask FreqAI to avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 which means models never expire. <br> **Datatype:** positive integer.
| | **Feature Parameters**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples shown [here](#building-the-feature-set) <br> **Datatype:** dictionary.
@ -98,25 +101,27 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `stratify_training_data` | This value is used to indicate the stratification of the data. e.g. 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. <br> **Datatype:** positive integer.
| `indicator_max_period_candles` | The maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
| `indicator_periods_candles` | A list of integers used to duplicate all indicators according to a set of periods and add them to the feature set. <br> **Datatype:** list of positive integers.
| | **Data split parameters**
| `data_split_parameters` | include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) <br> **Datatype:** dictionary.
| | **Data split parameters**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) <br> **Datatype:** dictionary.
| `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**
| `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.
| `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.
### Return values for use in strategy
Here are the values you can expect to receive inside the dataframe returned by FreqAI:
| Parameter | Description |
### Important FreqAI dataframe key patterns
Here are the values the user can expect to include/use inside the typical strategy dataframe (`df[]`):
| DataFrame Key | Description |
|------------|-------------|
| `&-s*` | user defined labels in the user made strategy. Anything prepended with `&` is treated as a training target inside FreqAI. These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `&-s_close`. FreqAI makes the predictions and gives them back to the user under the same key (`&-s_close`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** depends on the output of the model.
| `&-s*_std/mean` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)<br> **Datatype:** floats.
| `do_predict` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilartiy Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined treshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`. <br> **Datatype:** integer between -1 and 2.
| `DI_values` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space. <br> **Datatype:** float.
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target inside FreqAI (typically following the naming convention `&-s*`). These same dataframe columns names are fed back to the user as the predictions. For example, the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['&-s_close']`. FreqAI makes the predictions and gives them back to the user under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** depends on the output of the model.
| `df['&*_std/mean']` | The standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand rarity of prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training (or historically with `fit_live_predictions_candles`)<br> **Datatype:** float.
| `df['do_predict']` | An indication of an outlier, this return value is integer between -1 and 2 which lets the user understand if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the [Dissimilarity Index](#removing-outliers-with-the-dissimilarity-index) is above the user defined threshold, it will subtract 1 from `do_predict`. If `use_SVM_to_remove_outliers()` is active, then the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract one from `do_predict`. A particular case is when `do_predict == 2`, it means that the model has expired due to `expired_hours`. <br> **Datatype:** integer between -1 and 2.
| `df['DI_values']` | The raw Dissimilarity Index values to give the user a sense of confidence in the prediction. Lower DI means the data point is closer to the trained parameter space. <br> **Datatype:** float.
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature inside FreqAI. For example, the user can include the rsi in the training feature set (similar to `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#building-the-feature-set). Note: since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table.) these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, you can prepend it with `%%`. <br> **Datatype:** depends on the output of the model.
### Example config file
@ -159,13 +164,13 @@ config setup includes:
### Building the feature set
Features are added by the user inside the `populate_any_indicators()` method of the strategy
by prepending indicators with `%` and labels are added by prependng `&`. There are some important
components/structures that the user *must* include when building their feature set. As shown below,
`with self.model.bridge.lock:` must be used to ensure thread safety - especially when using third
party libraries for indicator construction such as TA-lib. Another structure to consider is the
location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
by prepending indicators with `%` and labels are added by prepending `&`.
There are some important components/structures that the user *must* include when building their feature set.
As shown below, `with self.freqai.lock:` must be used to ensure thread safety - especially when using third
party libraries for indicator construction such as TA-lib.
Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
This is where the user will add single features and labels to their feature set to avoid duplication from
various configuration paramters which multiply the feature set such as `include_timeframes`.
various configuration parameters which multiply the feature set such as `include_timeframes`.
```python
def populate_any_indicators(
@ -186,7 +191,7 @@ various configuration paramters which multiply the feature set such as `include_
:coin: the name of the coin which will modify the feature names.
"""
with self.model.bridge.lock:
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -252,12 +257,13 @@ various configuration paramters which multiply the feature set such as `include_
return df
```
The user of the present example does not want to pass the `bb_lowerband` as a feature to the model,
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 `%`._
model for training/prediction and has therefore 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
will fail in live/dry mode. 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`:
@ -299,41 +305,43 @@ set will include all the features from `populate_any_indicators` on all the `inc
`ETH/USD`, `LINK/USD`, and `BNB/USD`.
`include_shifted_candles` is another user controlled parameter which indicates the number of previous
candles to include in the present feature set. In other words, `innclude_shifted_candles: 2`, tells
Freqai to include the the past 2 candles for each of the features included
in the dataset.
candles to include in the present feature set. In other words, `include_shifted_candles: 2`, tells
Freqai to include the the past 2 candles for each of the features included in the dataset.
In total, the number of features the present user has created is:_
In total, the number of features the present user has created is:
legnth of `include_timeframes` * no. features in `populate_any_indicators()` * legnth of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`_
3 * 3 * 3 * 2 * 2 = 108._
length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
_3 * 3 * 3 * 2 * 2 = 108_.
### Deciding the sliding training window and backtesting duration
Users define the backtesting timerange with the typical `--timerange` parameter in the user
configuration file. `train_period_days` is the duration of the sliding training window, while
`backtest_period_days` is the sliding backtesting window, both in number of days (backtest_period_days can be
`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
a float to indicate sub daily retraining in live/dry mode). In the present example,
the user is asking Freqai to use a training period of 30 days and backtest the subsequent 7 days.
This means that if the user sets `--timerange 20210501-20210701`,
This means that if the user sets `--timerange 20210501-20210701`,
Freqai will train 8 separate models (because the full range comprises 8 weeks),
and then backtest the subsequent week associated with each of the 8 training
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
month of data._
month of data.
In live, the required training data is automatically computed and downloaded. However, in backtesting
the user must manually enter the required number of `startup_candles` in the config. This value
is used to increase the available data to FreqAI and should be sufficient to enable all indicators
to be NaN free at the beginning of the first training timerange. This boils down to identifying the
is used to increase the available data to FreqAI and should be sufficient to enable all indicators
to be NaN free at the beginning of the first training timerange. This boils down to identifying the
highest timeframe (`4h` in present example) and the longest indicator period (25 in present example)
and adding this to the `train_period_days`. The units need to be in the base candle time frame:_
and adding this to the `train_period_days`. The units need to be in the base candle time frame:
`startup_candles` = ( 4 hours * 25 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 1488.
!!! Note
In dry/live, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live.
!!! Note
Although fractional `backtest_period_days` is allowed, the user should be ware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. This is why it is physically impossible to truly backtest FreqAI adaptive training. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
## Running Freqai
### Training and backtesting
@ -344,16 +352,16 @@ The freqai training/backtesting module can be executed with the following comman
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai_futures.example.json --freqaimodel LightGBMPredictionModel --timerange 20210501-20210701
```
If this command has never been executed with the existing config file, then it will train a new model
for each pair, for each backtesting window within the bigger `--timerange`._
If this command has never been executed with the existing config file, then it will train a new model
for each pair, for each backtesting window within the bigger `--timerange`.
---
**NOTE**
Once the training is completed, the user can execute this again with the same config file and
FreqAI will find the trained models and load them instead of spending time training. This is useful
if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user
*wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`.
This way, the user can return to using any model they wish by simply changing the `identifier`.
!!! Note "Model reuse"
Once the training is completed, the user can execute this again with the same config file and
FreqAI will find the trained models and load them instead of spending time training. This is useful
if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. IF the user
*wants* to retrain a new model with the same config file, then he/she should simply change the `identifier`.
This way, the user can return to using any model they wish by simply changing the `identifier`.
---
@ -362,7 +370,6 @@ This way, the user can return to using any model they wish by simply changing th
The Freqai strategy requires the user to include the following lines of code in the strategy:
```python
from freqtrade.freqai.strategy_bridge import CustomModel
def informative_pairs(self):
whitelist_pairs = self.dp.current_whitelist()
@ -377,9 +384,6 @@ The Freqai strategy requires the user to include the following lines of code in
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
@ -392,7 +396,7 @@ The Freqai strategy requires the user to include the following lines of code in
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
dataframe = self.model.bridge.start(dataframe, metadata, self)
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
```
@ -402,8 +406,9 @@ the feature set with a proper naming convention for the IFreqaiModel to use late
### Building an IFreqaiModel
FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostPredictionModel.py`) and `LightGBM` regression. However, users can customize and create
their own prediction models using the `IFreqaiModel` class. Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
FreqAI has multiple example prediction model based libraries such as `Catboost` regression (`freqai/prediction_models/CatboostPredictionModel.py`) and `LightGBM` regression.
However, users can customize and create their own prediction models using the `IFreqaiModel` class.
Users are encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures.
### Running the model live
@ -414,12 +419,7 @@ 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
given the user configuration settings. Following training, it will use that model to predict for the
duration of `backtest_period_days`. After a full `backtest_period_days` has elapsed, Freqai will auto retrain
a new model, and begin making predictions with the updated model. FreqAI backtesting and live both
permit the user to use fractional days (i.e. 0.1) in the `backtest_period_days`, which enables more frequent
retraining. But the user should be careful that using a fractional `backtest_period_days` with a large
`--timerange` in backtesting will result in a huge amount of required trainings/models.
given the user configuration settings. Following training, it will use that model to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the pairs to try and keep all models equally "young." FreqAI will always use the newest trained model to make predictions on incoming live data. If users do not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before retraining a new model. Additionally, users can set `expired_hours` to tell FreqAI to avoid making predictions on models aged over this number of hours.
If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
the same `identifier` parameter
@ -431,28 +431,27 @@ the same `identifier` parameter
}
```
In this case, although Freqai will initiate with a
In this case, although Freqai will initiate with a
pre-trained model, it will still check to see how much time has elapsed since the model was trained,
and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will self retrain.
It is common to want constant retraining, in whichcase, user should set `live_retrain_hours` to 0.
## Data anylsis techniques
## Data analysis techniques
### Controlling the model learning process
Model training parameters are unqiue to the library employed by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries.
Model training parameters are unique to the ML library used by the user. FreqAI allows users to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration files show some of the example parameters associated with `Catboost` and `LightGBM`, but users can add any parameters available in those libraries.
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function. Meanwhile, FreqAI includes some additional parameters such `weight_factor` which allows the user to weight more recent data more strongly
than past data via an exponential function:
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points.
![weight-factor](assets/weights_factor.png)
`train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally autocorrelated data.
`train_test_split()` has a parameters called `shuffle`, which users also have access to in FreqAI, that allows them to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data.
Finally, `label_period_candles` defines the offset used for the `labels`. In the present example,
the user is asking for `labels` that are 24 candles in the future.
@ -489,7 +488,7 @@ to low levels of certainty. Activating the Dissimilarity Index can be achieved w
```json
"freqai": {
"feature_parameters" : {
"DI_threshold": 1
"DI_threshold": 1
}
}
```
@ -504,7 +503,7 @@ Users can reduce the dimensionality of their features by activating the `princip
```json
"freqai": {
"feature_parameters" : {
"principal_component_analysis": true
"principal_component_analysis": true
}
}
```
@ -525,8 +524,7 @@ The user can tell Freqai to remove outlier data points from the training/test da
```
Freqai will train an SVM on the training data (or components if the user activated
`principal_component_analysis`) and remove any data point that it deems to be sit beyond the
feature space.
`principal_component_analysis`) and remove any data point that it deems to be sitting beyond the feature space.
### Stratifying the data
@ -541,10 +539,10 @@ The user can stratify the training/testing data using:
```
which will split the data chronologically so that every Xth 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.
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.
### Setting up a follower
## Setting up a follower
The user can define:
@ -555,15 +553,15 @@ The user can define:
}
```
to indicate to the bot that it should not train models, but instead should look for models trained
by a leader with the same `identifier`. In this example, the user has a leader bot with the
`identifier: "example"` already running or launching simultaneously as the present follower.
to indicate to the bot that it should not train models, but instead should look for models trained
by a leader with the same `identifier`. In this example, the user has a leader bot with the
`identifier: "example"` already running or launching simultaneously as the present follower.
The follower will load models created by the leader and inference them to obtain predictions.
### Purging old model data
## Purging old model data
FreqAI stores new model files each time it retrains. These files become obsolete as new models
are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running
FreqAI stores new model files each time it retrains. These files become obsolete as new models
are trained and FreqAI adapts to the new market conditions. Users planning to leave FreqAI running
for extended periods of time with high frequency retraining should set `purge_old_models` in their
config:
@ -591,17 +589,17 @@ a certain number of hours in age by setting the `expiration_hours` in the config
```
In the present example, the user will only allow predictions on models that are less than 1/2 hours
old.
old.
## Choosing the calculation of the `target_roi`
As shown in `templates/FreqaiExampleStrategy.py`, the `target_roi` is based on two metrics computed
by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used
*during the most recent training*. This allows the model to know what magnitude of a target to be
expecting since it is directly stemming from the training data. By default, FreqAI computes this based
on trainig data and it assumes the labels are Gaussian distributed. These are big assumptions
that the user should consider when creating their labels. If the user wants to consider the population
of *historical predictions* for creating the dynamic target instead of the trained labels, the user
by FreqAI: `label_mean` and `label_std`. These are the statistics associated with the labels used
*during the most recent training*.
This allows the model to know what magnitude of a target to be expecting since it is directly stemming from the training data.
By default, FreqAI computes this based on training data and it assumes the labels are Gaussian distributed.
These are big assumptions that the user should consider when creating their labels. If the user wants to consider the population
of *historical predictions* for creating the dynamic target instead of the trained labels, the user
can do so by setting `fit_live_prediction_candles` to the number of historical prediction candles
the user wishes to use to generate target statistics.
@ -620,7 +618,7 @@ this historical data to be reloaded if the user stops and restarts with the same
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._
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:
@ -646,7 +644,7 @@ below this value. An example usage in the strategy may look something like:
dataframe["do_predict"],
dataframe["target_upper_quantile"],
dataframe["target_lower_quantile"],
) = self.model.bridge.start(dataframe, metadata, self)
) = self.freqai.start(dataframe, metadata, self)
return dataframe
@ -663,25 +661,40 @@ below this value. An example usage in the strategy may look something like:
``` -->
## Additional information
### Common pitfalls
FreqAI cannot be combined with `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due
new candles automatically for future retrains. But this means that if new pairs arrive later in the dry run due
to a volume pairlist, it will not have the data ready. FreqAI does work, however, with the `ShufflePairlist`.
### Feature normalization
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).
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
`user_data_dir/models/` contains all the data associated with the trainings and
backtests. This file structure is heavily controlled and read by the `FreqaiDataKitchen()`
and should thus not be modified.
`user_data_dir/models/` contains all the data associated with the trainings and backtests.
This file structure is heavily controlled and read by the `FreqaiDataKitchen()`
and should therefore not be modified.
## Credits
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
project.
Conception and software development:
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @thorntwig
Code review, software architecture brainstorming:
@xmatthias
Beta testing and bug reporting:
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
Juha Nykänen @suikula, Wagner Costa @wagnercosta

69
freqtrade/freqai/data_drawer.py
View File

@ -1,7 +1,6 @@
import collections
import json
import logging
import pickle
import re
import shutil
import threading
@ -10,12 +9,10 @@ from typing import Any, Dict, Tuple
import numpy as np
import pandas as pd
from joblib.externals import cloudpickle
from pandas import DataFrame
# from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@ -25,6 +22,23 @@ class FreqaiDataDrawer:
/loading to/from disk.
This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is
reinstantiated for each coin.
Record of contribution:
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
project.
Conception and software development:
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @thorntwig
Code review, software architecture brainstorming:
@xmatthias
Beta testing and bug reporting:
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
Juha Nykänen @suikula, Wagner Costa @wagnercosta
"""
def __init__(self, full_path: Path, config: dict, follow_mode: bool = False):
@ -41,6 +55,12 @@ class FreqaiDataDrawer:
self.historic_predictions: Dict[str, Any] = {}
self.follower_dict: Dict[str, Any] = {}
self.full_path = full_path
self.follower_name = self.config.get("bot_name", "follower1")
self.follower_dict_path = Path(
self.full_path / f"follower_dictionary-{self.follower_name}.json"
)
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
self.follow_mode = follow_mode
if follow_mode:
self.create_follower_dict()
@ -56,9 +76,9 @@ class FreqaiDataDrawer:
:returns:
exists: bool = whether or not the drawer was located
"""
exists = Path(self.full_path / str("pair_dictionary.json")).resolve().exists()
exists = self.pair_dictionary_path.is_file()
if exists:
with open(self.full_path / str("pair_dictionary.json"), "r") as fp:
with open(self.pair_dictionary_path, "r") as fp:
self.pair_dict = json.load(fp)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
@ -76,13 +96,15 @@ class FreqaiDataDrawer:
:returns:
exists: bool = whether or not the drawer was located
"""
exists = Path(self.full_path / str("historic_predictions.pkl")).resolve().exists()
exists = self.historic_predictions_path.is_file()
if exists:
with open(self.full_path / str("historic_predictions.pkl"), "rb") as fp:
self.historic_predictions = pickle.load(fp)
logger.info(f"Found existing historic predictions at {self.full_path}, but beware "
"that statistics may be inaccurate if the bot has been offline for "
"an extended period of time.")
with open(self.historic_predictions_path, "rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.info(
f"Found existing historic predictions at {self.full_path}, but beware "
"that statistics may be inaccurate if the bot has been offline for "
"an extended period of time."
)
elif not self.follow_mode:
logger.info("Could not find existing historic_predictions, starting from scratch")
else:
@ -97,38 +119,31 @@ class FreqaiDataDrawer:
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with open(self.full_path / str("historic_predictions.pkl"), "wb") as fp:
pickle.dump(self.historic_predictions, fp, protocol=pickle.HIGHEST_PROTOCOL)
with open(self.historic_predictions_path, "wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
def save_drawer_to_disk(self):
"""
Save data drawer full of all pair model metadata in present model folder.
"""
with open(self.full_path / str("pair_dictionary.json"), "w") as fp:
with open(self.pair_dictionary_path, "w") as fp:
json.dump(self.pair_dict, fp, default=self.np_encoder)
def save_follower_dict_to_disk(self):
"""
Save follower dictionary to disk (used by strategy for persistent prediction targets)
"""
follower_name = self.config.get("bot_name", "follower1")
with open(
self.full_path / str("follower_dictionary-" + follower_name + ".json"), "w"
) as fp:
with open(self.follower_dict_path, "w") as fp:
json.dump(self.follower_dict, fp, default=self.np_encoder)
def create_follower_dict(self):
"""
Create or dictionary for each follower to maintain unique persistent prediction targets
"""
follower_name = self.config.get("bot_name", "follower1")
whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
exists = (
Path(self.full_path / str("follower_dictionary-" + follower_name + ".json"))
.resolve()
.exists()
)
exists = self.follower_dict_path.is_file()
if exists:
logger.info("Found an existing follower dictionary")
@ -136,9 +151,7 @@ class FreqaiDataDrawer:
for pair in whitelist_pairs:
self.follower_dict[pair] = {}
with open(
self.full_path / str("follower_dictionary-" + follower_name + ".json"), "w"
) as fp:
with open(self.follow_path, "w") as fp:
json.dump(self.follower_dict, fp, default=self.np_encoder)
def np_encoder(self, object):

116
freqtrade/freqai/data_kitchen.py
View File

@ -2,7 +2,6 @@ import copy
import datetime
import json
import logging
import pickle as pk
import shutil
from pathlib import Path
from typing import Any, Dict, List, Tuple
@ -11,6 +10,7 @@ import numpy as np
import numpy.typing as npt
import pandas as pd
from joblib import dump, load # , Parallel, delayed # used for auto distribution assignment
from joblib.externals import cloudpickle
from pandas import DataFrame
from sklearn import linear_model
from sklearn.metrics.pairwise import pairwise_distances
@ -35,7 +35,23 @@ class FreqaiDataKitchen:
"""
Class designed to analyze data for a single pair. Employed by the IFreqaiModel class.
Functionalities include holding, saving, loading, and analyzing the data.
author: Robert Caulk, rob.caulk@gmail.com
Record of contribution:
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
project.
Conception and software development:
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @thorntwig
Code review, software architecture brainstorming:
@xmatthias
Beta testing and bug reporting:
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
Juha Nykänen @suikula, Wagner Costa @wagnercosta
"""
def __init__(
@ -60,6 +76,9 @@ class FreqaiDataKitchen:
self.keras = self.freqai_config.get("keras", False)
self.set_all_pairs()
if not self.live:
if not self.config["timerange"]:
raise OperationalException(
'Please pass --timerange if you intend to use FreqAI for backtesting.')
self.full_timerange = self.create_fulltimerange(
self.config["timerange"], self.freqai_config.get("train_period_days")
)
@ -130,7 +149,7 @@ class FreqaiDataKitchen:
)
if self.freqai_config.get("feature_parameters", {}).get("principal_component_analysis"):
pk.dump(
cloudpickle.dump(
self.pca, open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "wb")
)
@ -192,7 +211,7 @@ class FreqaiDataKitchen:
)
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
self.pca = pk.load(
self.pca = cloudpickle.load(
open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "rb")
)
@ -358,28 +377,30 @@ class FreqaiDataKitchen:
2 * (data_dictionary["test_features"] - train_min) / (train_max - train_min) - 1
)
train_labels_max = data_dictionary["train_labels"].max()
train_labels_min = data_dictionary["train_labels"].min()
data_dictionary["train_labels"] = (
2
* (data_dictionary["train_labels"] - train_labels_min)
/ (train_labels_max - train_labels_min)
- 1
)
data_dictionary["test_labels"] = (
2
* (data_dictionary["test_labels"] - train_labels_min)
/ (train_labels_max - train_labels_min)
- 1
)
for item in train_max.keys():
self.data[item + "_max"] = train_max[item]
self.data[item + "_min"] = train_min[item]
self.data["labels_max"] = train_labels_max.to_dict()
self.data["labels_min"] = train_labels_min.to_dict()
for item in data_dictionary["train_labels"].keys():
if data_dictionary["train_labels"][item].dtype == str:
continue
train_labels_max = data_dictionary["train_labels"][item].max()
train_labels_min = data_dictionary["train_labels"][item].min()
data_dictionary["train_labels"][item] = (
2
* (data_dictionary["train_labels"][item] - train_labels_min)
/ (train_labels_max - train_labels_min)
- 1
)
data_dictionary["test_labels"][item] = (
2
* (data_dictionary["test_labels"][item] - train_labels_min)
/ (train_labels_max - train_labels_min)
- 1
)
self.data[f"{item}_max"] = train_labels_max # .to_dict()
self.data[f"{item}_min"] = train_labels_min # .to_dict()
return data_dictionary
def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
@ -393,13 +414,32 @@ class FreqaiDataKitchen:
for item in df.keys():
df[item] = (
2
* (df[item] - self.data[item + "_min"])
/ (self.data[item + "_max"] - self.data[item + "_min"])
* (df[item] - self.data[f"{item}_min"])
/ (self.data[f"{item}_max"] - self.data[f"{item}_min"])
- 1
)
return df
def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
"""
Normalize a set of data using the mean and standard deviation from
the associated training data.
:params:
:df: Dataframe of predictions to be denormalized
"""
for label in self.label_list:
if df[label].dtype == str:
continue
df[label] = (
(df[label] + 1)
* (self.data[f"{label}_max"] - self.data[f"{label}_min"])
/ 2
) + self.data[f"{label}_min"]
return df
def split_timerange(
self, tr: str, train_split: int = 28, bt_split: int = 7
) -> Tuple[list, list]:
@ -433,7 +473,7 @@ class FreqaiDataKitchen:
tr_training_list_timerange = []
tr_backtesting_list_timerange = []
first = True
# within_config_timerange = True
while True:
if not first:
timerange_train.startts = timerange_train.startts + bt_period
@ -475,7 +515,7 @@ class FreqaiDataKitchen:
:df: Dataframe containing all candles to run the entire backtest. Here
it is sliced down to just the present training period.
"""
# timerange = TimeRange.parse_timerange(tr)
start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc)
stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc)
df = df.loc[df["date"] >= start, :]
@ -1132,32 +1172,6 @@ class FreqaiDataKitchen:
# Functions containing useful data manpulation examples. but not actively in use.
# def build_feature_list(self, config: dict, metadata: dict) -> list:
# """
# SUPERCEDED BY self.find_features()
# Build the list of features that will be used to filter
# the full dataframe. Feature list is construced from the
# user configuration file.
# :params:
# :config: Canonical freqtrade config file containing all
# user defined input in config['freqai] dictionary.
# """
# features = []
# for tf in config["freqai"]["timeframes"]:
# for ft in config["freqai"]["base_features"]:
# for n in range(config["freqai"]["feature_parameters"]["shift"] + 1):
# shift = ""
# if n > 0:
# shift = "_shift-" + str(n)
# features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf)
# for p in config["freqai"]["corr_pairlist"]:
# if metadata['pair'] in p:
# continue # avoid duplicate features
# features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
# # logger.info("number of features %s", len(features))
# return features
# Possibly phasing these outlier removal methods below out in favor of
# use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance).
# But these have good data manipulation examples, so keep them commented here for now.

28
freqtrade/freqai/freqai_interface.py
View File

@ -38,7 +38,23 @@ class IFreqaiModel(ABC):
"""
Class containing all tools for training and prediction in the strategy.
Base*PredictionModels inherit from this class.
Author: Robert Caulk, rob.caulk@gmail.com
Record of contribution:
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
project.
Conception and software development:
Robert Caulk @robcaulk
Theoretical brainstorming:
Elin Törnquist @thorntwig
Code review, software architecture brainstorming:
@xmatthias
Beta testing and bug reporting:
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
Juha Nykänen @suikula, Wagner Costa @wagnercosta
"""
def __init__(self, config: Dict[str, Any]) -> None:
@ -485,12 +501,8 @@ class IFreqaiModel(ABC):
) -> None:
trained_predictions = model.predict(df)
pred_df = DataFrame(trained_predictions, columns=dk.label_list)
for label in dk.label_list:
pred_df[label] = (
(pred_df[label] + 1)
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
/ 2
) + dk.data["labels_min"][label]
pred_df = dk.denormalize_labels_from_metadata(pred_df)
self.dd.historic_predictions[pair] = pd.DataFrame()
self.dd.historic_predictions[pair] = copy.deepcopy(pred_df)
@ -511,7 +523,7 @@ class IFreqaiModel(ABC):
"""
@abstractmethod
def fit(self) -> Any:
def fit(self, data_dictionary: Dict[str, Any]) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data

7
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@ -107,11 +107,6 @@ class BaseRegressionModel(IFreqaiModel):
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)
for label in dk.label_list:
pred_df[label] = (
(pred_df[label] + 1)
* (dk.data["labels_max"][label] - dk.data["labels_min"][label])
/ 2
) + dk.data["labels_min"][label]
pred_df = dk.denormalize_labels_from_metadata(pred_df)
return (pred_df, dk.do_predict)

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

@ -38,8 +38,6 @@ class CatboostPredictionModel(BaseRegressionModel):
model = CatBoostRegressor(
allow_writing_files=False,
verbose=100,
early_stopping_rounds=400,
**self.model_training_parameters,
)
model.fit(X=train_data, eval_set=test_data)

3
freqtrade/freqai/prediction_models/CatboostPredictionMultiModel.py
View File

@ -27,9 +27,6 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
cbr = CatBoostRegressor(
allow_writing_files=False,
gpu_ram_part=0.5,
verbose=100,
early_stopping_rounds=400,
**self.model_training_parameters,
)

12
freqtrade/freqai/strategy_bridge.py
View File

@ -1,12 +0,0 @@
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
class CustomModel:
"""
A bridge between the user defined IFreqaiModel class
and the strategy.
"""
def __init__(self, config):
self.bridge = FreqaiModelResolver.load_freqaimodel(config)

2
freqtrade/resolvers/freqaimodel_resolver.py
View File

@ -44,7 +44,7 @@ class FreqaiModelResolver(IResolver):
)
if freqaimodel_name in disallowed_models:
raise OperationalException(
f"{freqaimodel_name} is a baseclass and cannot be used directly. User must choose "
f"{freqaimodel_name} is a baseclass and cannot be used directly. Please choose "
"an existing child class or inherit from this baseclass.\n"
)
freqaimodel = FreqaiModelResolver.load_object(

12
freqtrade/strategy/interface.py
View File

@ -145,11 +145,20 @@ class IStrategy(ABC, HyperStrategyMixin):
informative_data.candle_type = config['candle_type_def']
self._ft_informative.append((informative_data, cls_method))
def load_freqAI_model(self) -> None:
if self.config.get('freqai', None):
# Import here to avoid importing this if freqAI is disabled
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
def ft_bot_start(self, **kwargs) -> None:
"""
Strategy init - runs after dataprovider has been added.
Must call bot_start()
"""
self.load_freqAI_model()
strategy_safe_wrapper(self.bot_start)()
self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)
@ -555,7 +564,8 @@ class IStrategy(ABC, HyperStrategyMixin):
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User can add
additional features here, but must follow the naming convention.
Defined in IStrategy because Freqai needs to know it exists.
This method is *only* used in FreqaiDataKitchen class and therefore
it is only called if FreqAI is active.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives

34
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -7,10 +7,8 @@ from pandas import DataFrame
from technical import qtpylib
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
@ -20,8 +18,7 @@ class FreqaiExampleStrategy(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.model = CustomModel(self.config)
self.model.bridge.start(dataframe, metadata)
self.freqai.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
@ -67,9 +64,6 @@ class FreqaiExampleStrategy(IStrategy):
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
):
@ -88,7 +82,7 @@ class FreqaiExampleStrategy(IStrategy):
:coin: the name of the coin which will modify the feature names.
"""
with self.model.bridge.lock:
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -180,7 +174,7 @@ class FreqaiExampleStrategy(IStrategy):
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
dataframe = self.model.bridge.start(dataframe, metadata, self)
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
@ -234,9 +228,9 @@ class FreqaiExampleStrategy(IStrategy):
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.model.bridge.dd.pair_dict
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.model.bridge.dd.follower_dict
pair_dict = self.freqai.dd.follower_dict
entry_tag = trade.enter_tag
@ -244,12 +238,12 @@ class FreqaiExampleStrategy(IStrategy):
"prediction" + entry_tag not in pair_dict[pair]
or pair_dict[pair]["prediction" + entry_tag] > 0
):
with self.model.bridge.lock:
with self.freqai.lock:
pair_dict[pair]["prediction" + entry_tag] = abs(trade_candle["&-s_close"])
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
self.freqai.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
self.freqai.dd.save_follower_dict_to_disk()
roi_price = pair_dict[pair]["prediction" + entry_tag]
roi_time = self.max_roi_time_long.value
@ -284,16 +278,16 @@ class FreqaiExampleStrategy(IStrategy):
entry_tag = trade.enter_tag
follow_mode = self.config.get("freqai", {}).get("follow_mode", False)
if not follow_mode:
pair_dict = self.model.bridge.dd.pair_dict
pair_dict = self.freqai.dd.pair_dict
else:
pair_dict = self.model.bridge.dd.follower_dict
pair_dict = self.freqai.dd.follower_dict
with self.model.bridge.lock:
with self.freqai.lock:
pair_dict[pair]["prediction" + entry_tag] = 0
if not follow_mode:
self.model.bridge.dd.save_drawer_to_disk()
self.freqai.dd.save_drawer_to_disk()
else:
self.model.bridge.dd.save_follower_dict_to_disk()
self.freqai.dd.save_follower_dict_to_disk()
return True

15
tests/freqai/conftest.py
View File

@ -2,6 +2,8 @@ from copy import deepcopy
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@ -10,13 +12,14 @@ from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
from tests.conftest import get_patched_exchange
# @pytest.fixture(scope="function")
def freqai_conf(default_conf):
@pytest.fixture(scope="function")
def freqai_conf(default_conf, tmpdir):
freqaiconf = deepcopy(default_conf)
freqaiconf.update(
{
"datadir": Path(default_conf["datadir"]),
"strategy": "freqai_test_strat",
"user_data_dir": Path(tmpdir),
"strategy-path": "freqtrade/tests/strategy/strats",
"freqaimodel": "LightGBMPredictionModel",
"freqaimodel_path": "freqai/prediction_models",
@ -61,7 +64,7 @@ def get_patched_data_kitchen(mocker, freqaiconf):
def get_patched_freqai_strategy(mocker, freqaiconf):
strategy = StrategyResolver.load_strategy(freqaiconf)
strategy.bot_start()
strategy.ft_bot_start()
return strategy
@ -76,7 +79,7 @@ def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
freqai = strategy.model.bridge
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
@ -91,7 +94,7 @@ def get_freqai_analyzed_dataframe(mocker, freqaiconf):
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.model.bridge
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
@ -107,7 +110,7 @@ def get_ready_to_train(mocker, freqaiconf):
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.model.bridge
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")

87
tests/freqai/test_freqai_datakitchen.py
View File

@ -1,6 +1,3 @@
# from unittest.mock import MagicMock
# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
import copy
import datetime
import shutil
from pathlib import Path
@ -13,7 +10,7 @@ from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from tests.conftest import get_patched_exchange
from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_patched_freqai_strategy
from tests.freqai.conftest import get_patched_data_kitchen, get_patched_freqai_strategy
@pytest.mark.parametrize(
@ -24,15 +21,15 @@ from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_pat
],
)
def test_create_fulltimerange(
timerange, train_period_days, expected_result, default_conf, mocker, caplog
timerange, train_period_days, expected_result, freqai_conf, mocker, caplog
):
dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
dk = get_patched_data_kitchen(mocker, freqai_conf)
assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
shutil.rmtree(Path(dk.full_path))
def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf):
dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
def test_create_fulltimerange_incorrect_backtest_period(mocker, freqai_conf):
dk = get_patched_data_kitchen(mocker, freqai_conf)
with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
dk.create_fulltimerange("20220101-20220201", 0.5)
with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
@ -49,11 +46,10 @@ def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf):
],
)
def test_split_timerange(
mocker, default_conf, timerange, train_period_days, backtest_period_days, expected_result
mocker, freqai_conf, timerange, train_period_days, backtest_period_days, expected_result
):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
freqaiconf.update({"timerange": "20220101-20220401"})
dk = get_patched_data_kitchen(mocker, freqaiconf)
freqai_conf.update({"timerange": "20220101-20220401"})
dk = get_patched_data_kitchen(mocker, freqai_conf)
tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days)
assert len(tr_list) == len(bt_list) == expected_result
@ -64,14 +60,13 @@ def test_split_timerange(
shutil.rmtree(Path(dk.full_path))
def test_update_historic_data(mocker, default_conf):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
freqai = strategy.model.bridge
def test_update_historic_data(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
@ -93,69 +88,65 @@ def test_update_historic_data(mocker, default_conf):
(datetime.datetime.now(tz=datetime.timezone.utc).timestamp(), False),
],
)
def test_check_if_model_expired(mocker, default_conf, timestamp, expected):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
dk = get_patched_data_kitchen(mocker, freqaiconf)
def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected):
dk = get_patched_data_kitchen(mocker, freqai_conf)
assert dk.check_if_model_expired(timestamp) == expected
shutil.rmtree(Path(dk.full_path))
def test_load_all_pairs_histories(mocker, default_conf):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
freqai = strategy.model.bridge
def test_load_all_pairs_histories(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
assert len(freqai.dd.historic_data.keys()) == len(
freqaiconf.get("exchange", {}).get("pair_whitelist")
freqai_conf.get("exchange", {}).get("pair_whitelist")
)
assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
)
shutil.rmtree(Path(freqai.dk.full_path))
def test_get_base_and_corr_dataframes(mocker, default_conf):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
freqai = strategy.model.bridge
def test_get_base_and_corr_dataframes(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
num_tfs = len(
freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
)
assert len(base_df.keys()) == num_tfs
assert len(corr_df.keys()) == len(
freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
)
assert len(corr_df["ADA/BTC"].keys()) == num_tfs
shutil.rmtree(Path(freqai.dk.full_path))
def test_use_strategy_to_populate_indicators(mocker, default_conf):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.model.bridge
def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180114")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180111-20180114")

138
tests/freqai/test_freqai_interface.py
View File

@ -1,29 +1,29 @@
# from unittest.mock import MagicMock
# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
import copy
# import platform
import platform
import shutil
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from tests.conftest import get_patched_exchange, log_has_re
from tests.freqai.conftest import freqai_conf, get_patched_freqai_strategy
from tests.freqai.conftest import get_patched_freqai_strategy
def test_train_model_in_series_LightGBM(mocker, default_conf):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
freqaiconf.update({"timerange": "20180110-20180130"})
def test_train_model_in_series_LightGBM(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.model.bridge
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
@ -58,64 +58,47 @@ def test_train_model_in_series_LightGBM(mocker, default_conf):
shutil.rmtree(Path(freqai.dk.full_path))
# FIXME: hits segfault
# @pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM..")
# def test_train_model_in_series_Catboost(mocker, default_conf):
# freqaiconf = freqai_conf(copy.deepcopy(default_conf))
# freqaiconf.update({"timerange": "20180110-20180130"})
# freqaiconf.update({"freqaimodel": "CatboostPredictionModel"})
# strategy = get_patched_freqai_strategy(mocker, freqaiconf)
# exchange = get_patched_exchange(mocker, freqaiconf)
# strategy.dp = DataProvider(freqaiconf, exchange)
# strategy.freqai_info = freqaiconf.get("freqai", {})
# freqai = strategy.model.bridge
# freqai.live = True
# freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
# timerange = TimeRange.parse_timerange("20180110-20180130")
# freqai.dk.load_all_pair_histories(timerange)
@pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM for Catboost ...")
def test_train_model_in_series_Catboost(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostPredictionModel"})
del freqai_conf['freqai']['model_training_parameters']['verbosity']
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
# freqai.dd.pair_dict = MagicMock()
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
# data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
# new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.dd.pair_dict = MagicMock()
# freqai.train_model_in_series(new_timerange, "ADA/BTC",
# strategy, freqai.dk, data_load_timerange)
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
# assert (
# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_model.joblib"))
# .resolve()
# .exists()
# )
# assert (
# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_metadata.json"))
# .resolve()
# .exists()
# )
# assert (
# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_trained_df.pkl"))
# .resolve()
# .exists()
# )
# assert (
# Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_svm_model.joblib"))
# .resolve()
# .exists()
# )
freqai.train_model_in_series(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
# shutil.rmtree(Path(freqai.dk.full_path))
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting(mocker, default_conf):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
freqaiconf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.model.bridge
def test_start_backtesting(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
@ -132,16 +115,15 @@ def test_start_backtesting(mocker, default_conf):
shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting_from_existing_folder(mocker, default_conf, caplog):
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
freqaiconf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.model.bridge
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai_conf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
@ -157,14 +139,14 @@ def test_start_backtesting_from_existing_folder(mocker, default_conf, caplog):
# without deleting the exiting folder structure, re-run
freqaiconf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
exchange = get_patched_exchange(mocker, freqaiconf)
strategy.dp = DataProvider(freqaiconf, exchange)
strategy.freqai_info = freqaiconf.get("freqai", {})
freqai = strategy.model.bridge
freqai_conf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dk.load_all_pair_histories(timerange)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")

14
tests/strategy/strats/freqai_test_strat.py
View File

@ -5,9 +5,7 @@ import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.freqai.strategy_bridge import CustomModel
from freqtrade.strategy import DecimalParameter, IntParameter, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
@ -17,8 +15,7 @@ class freqai_test_strat(IStrategy):
"""
Example strategy showing how the user connects their own
IFreqaiModel to the strategy. Namely, the user uses:
self.model = CustomModel(self.config)
self.model.bridge.start(dataframe, metadata)
self.freqai.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
@ -64,9 +61,6 @@ class freqai_test_strat(IStrategy):
informative_pairs.append((pair, tf))
return informative_pairs
def bot_start(self):
self.model = CustomModel(self.config)
def populate_any_indicators(
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
):
@ -85,7 +79,7 @@ class freqai_test_strat(IStrategy):
:coin: the name of the coin which will modify the feature names.
"""
with self.model.bridge.lock:
with self.freqai.lock:
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
@ -146,7 +140,7 @@ class freqai_test_strat(IStrategy):
# 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 = self.model.bridge.start(dataframe, metadata, self)
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25