feat(Data): added daily_glassnode DataCollection (#99)

README.md

@@ -1,5 +1,34 @@
-# Financial time series prediction models
+# Financial time series prediction
+
+And end-to-end pipeline to train predictive Machine Learning models on financial (non-stationary, regime changing) time series.
+
+## Why?
+
+Machine learning on financial time series require a fundamentally different approach than used in other ML domains. The data is non-stationary, where the patterns frequently change, and it's extremely important to not to leak out-of-sample data into the training set objective evaluation is important. 
+
+There are very few open-source end-to-end machine learning pipelines that can be effectively used to train and evaluate ML models on financial time series. Among them are[qlib](https://github.com/microsoft/qlib), [AlphaPy](https://github.com/ScottfreeLLC/AlphaPy).
+
+This repo is different to them in a couple of angles:
+- Feature extraction and selection is an important, pre-built step in the pipeline. Training models on 
+- Training and evaluation is done in a [walk-forward manner](https://en.wikipedia.org/wiki/Walk_forward_optimization). We argue that that one or two train/test split is not adoquate to evaluate an ML model's performance in a non-stationary, regime changing environment. The walk-forward methodology enables us to evaluate the model's performance on almost the whole time series.
+- The walk-forward training/evaluation methodology enables "online" (ever-changing) models, that adapt to the market environment. You can specify how frequently would you like to re-train the models.
+
+
+This project is inspired partially by [Marcos Lopez de Prado's Advances in Financial Machine Learning](https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086) and [The Alpha Scientist's blogposts](https://alphascientist.com/).
+
+
 
 ## Installation
 
-Use the conda environment file attached!:)
+Use the conda environment file attached!:)
+
+
+## Pipeline components
+
+- Feature extraction
+- Dimensionality reduction
+- Feature selection
+- Training Level-1 models
+- Training Level-2 (Ensemble) model
+- Evaluation of models
+- Cross-sectional portfolio construction [IN PROGRESS]