adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Allow user to go live and start from pretrained models (after a completed backtest) by simply reusing the identifier config parameter while dry/live.

Browse Source
This commit is contained in:
robcaulk
2022-05-25 14:40:32 +02:00
parent 7486d9d9e2
commit b79d4e8876
5 changed files with 33 additions and 39 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
docs/freqai.md
View File

@@ -158,7 +158,7 @@ a specific pair or timeframe, they should use the following structure inside `po
if pair == metadata['pair'] and tf == self.timeframe:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
```
(Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`)
@@ -270,27 +270,22 @@ 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`. 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. FreqAI in live mode permits
the user to use fractional days (i.e. 0.1) in the `backtest_period`, which enables more frequent
retraining.
If the user wishes to start dry/live from a saved model, the following configuration
parameters need to be set:
If the user wishes to start dry/live from a backtested saved model, the user only needs to reuse
the same `identifier` parameter
```json
"freqai": {
"identifier": "example",
"live_trained_timerange": "20220330-20220429",
"live_full_backtestrange": "20220302-20220501"
}
```
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
during backtesting/training. These are available to the user inside `user_data/models/*/sub-train-*`.
`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
inside the `user_data/models/` directory. In this case, although Freqai will initiate with a
pre-trained model, if a full `backtest_period` has elapsed since the end of the user set
`live_trained_timerange`, it will self retrain.
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 `backtest_period` has elapsed since the end of the loaded model, FreqAI will self retrain.
## Data anylsis techniques