CTYPTO/freqtrade
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2025-11-29 08:33:07 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #8790 from freqtrade/docs/link-to-articles

Browse Source 
Add links to more FreqAI learning content
This commit is contained in:
Matthias
2023-06-16 18:20:05 +02:00
committed by GitHub
parent 7939716a5e ffd7394adb
commit efaa959bfa
2 changed files with 10 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
docs/freqai-feature-engineering.md
View File

@@ -180,6 +180,9 @@ You can ask for each of the defined features to be included also for informative
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
!!! note "Learn more about creative feature engineering"
Check out our [medium article](https://emergentmethods.medium.com/freqai-from-price-to-prediction-6fadac18b665) geared toward helping users learn how to creatively engineer features.
### Gain finer control over `feature_engineering_*` functions with `metadata`

7
docs/freqai.md
View File

@@ -107,6 +107,13 @@ This is for performance reasons - FreqAI relies on making quick predictions/retr
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. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
## Additional learning materials
Here we compile some external materials that provide deeper looks into various components of FreqAI:
- [Real-time head-to-head: Adaptive modeling of financial market data using XGBoost and CatBoost](https://emergentmethods.medium.com/real-time-head-to-head-adaptive-modeling-of-financial-market-data-using-xgboost-and-catboost-995a115a7495)
- [FreqAI - from price to prediction](https://emergentmethods.medium.com/freqai-from-price-to-prediction-6fadac18b665)
## Credits
FreqAI is developed by a group of individuals who all contribute specific skillsets to the project.