Pinned Repositories
CNN-LSTM_Limit_Order_Book
This notebook contains an independently developed Keras/Tensorflow implementation of the CNN-LSTM model for Limit Order Book forecasting originally proposed by Zhang et al. (https://arxiv.org/pdf/1808.03668.pdf). The current implementation was adopted in the paper written by Briola et al. (https://arxiv.org/pdf/2007.07319.pdf).
entsoe-py
Python client for the ENTSO-E API (european network of transmission system operators for electricity)
Keras_Deep_Adaptive_Input_Normalization
This notebook contains the Keras/Tensorflow Layer implementation of the Deep Adaptive Input Normalization model for Time Series Forecasting proposed by Passalis et al. The authors of the above mentioned paper propose a PyTorch implementation (PyTorch implementation) of the model. A slightly reviewed version (software structure) is here reported. Results obtained by the two implementations are compared through an explicative example.
DataDrivenModeling
Tutorials for Data Driven Modeling
HomologicalCNN
LOBFrame
We release `LOBFrame', a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data.
Triangulated_Maximally_Filtered_Graph
This repository contains an ulta-fast Python implementation of the Triangulated Maximally filtered Graph (TMFG).
unwinding_complexity
skfolio
Python library for portfolio optimization built on top of scikit-learn
AntoBr96's Repositories
AntoBr96/CNN-LSTM_Limit_Order_Book
This notebook contains an independently developed Keras/Tensorflow implementation of the CNN-LSTM model for Limit Order Book forecasting originally proposed by Zhang et al. (https://arxiv.org/pdf/1808.03668.pdf). The current implementation was adopted in the paper written by Briola et al. (https://arxiv.org/pdf/2007.07319.pdf).
AntoBr96/Keras_Deep_Adaptive_Input_Normalization
This notebook contains the Keras/Tensorflow Layer implementation of the Deep Adaptive Input Normalization model for Time Series Forecasting proposed by Passalis et al. The authors of the above mentioned paper propose a PyTorch implementation (PyTorch implementation) of the model. A slightly reviewed version (software structure) is here reported. Results obtained by the two implementations are compared through an explicative example.
AntoBr96/entsoe-py
Python client for the ENTSO-E API (european network of transmission system operators for electricity)