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robcaulk 2022-05-05 15:49:19 +02:00
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@ -49,9 +49,8 @@ config setup includes:
```json
"freqai": {
"timeframes" : ["5m","15m","4h"],
"full_timerange" : "20211220-20220220",
"train_period" : "month",
"backtest_period" : "week",
"train_period" : 30,
"backtest_period" : 7,
"identifier" : "unique-id",
"base_features": [
"rsi",
@ -63,18 +62,18 @@ config setup includes:
"LINK/USD",
"BNB/USD"
],
"train_params" : {
"feature_parameters" : {
"period": 24,
"shift": 2,
"drop_features": false,
"DI_threshold": 1,
"weight_factor": 0,
},
"SPLIT_PARAMS" : {
"data_split_parameters" : {
"test_size": 0.25,
"random_state": 42
},
"CLASSIFIER_PARAMS" : {
"model_training_parameters" : {
"n_estimators": 100,
"random_state": 42,
"learning_rate": 0.02,
@ -110,11 +109,11 @@ no. `timeframes` * no. `base_features` * no. `corr_pairlist` * no. `shift`_
### Deciding the sliding training window and backtesting duration
`full_timerange` lets the user set the full backtesting range to train and
backtest through. Meanwhile `train_period` is the sliding training window and
`backtest_period` is the sliding backtesting window. In the present example,
the user is asking Freqai to train and backtest the range of `20211220-20220220` (`month`).
The user wishes to backtest each `week` with a newly trained model. This means that
Users define the backtesting timerange with the typical `--timerange` parameter in the user
configuration file. `train_period` is the duration of the sliding training window, while
`backtest_period` is the sliding backtesting window, both in number of days. 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`,
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
@ -128,7 +127,7 @@ month of data.
The freqai training/backtesting module can be executed with the following command:
```bash
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel
freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel ExamplePredictionModel --timerange 20210501-20210701
```
where the user needs to have a FreqaiExampleStrategy that fits to the requirements outlined
@ -178,19 +177,7 @@ and `make_labels()` to let them customize various aspects of their training proc
### Running the model live
After the user has designed a desirable featureset, Freqai can be run in dry/live
using the typical trade command:
```bash
freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --training_timerange '20211220-20220120'
```
Where the user has now specified exactly which of the models from the sliding window
that they wish to run live using `--training_timerange` (typically this would be the most
recent model trained). As of right now, freqai will
not automatically retain itself, so the user needs to manually retrain and then
reload the config file with a new `--training_timerange` in order to update the
model.
TODO: Freqai is not automated for live yet.
## Data anylsis techniques
@ -208,12 +195,6 @@ $$ W_i = \exp(\frac{-i}{\alpha*n}) $$
where $W_i$ is the weight of data point $i$ in a total set of $n$ data points._
`drop_features` tells Freqai to train the model on the user defined features,
followed by a feature importance evaluation where it drops the top and bottom
performing features (there is evidence to suggest the top features may not be
helpful in equity/crypto trading since the ultimate objective is to predict low
frequency patterns, source: numerai)._
Finally, `period` defines the offset used for the `labels`. In the present example,
the user is asking for `labels` that are 24 candles in the future.