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Limex-com / ziplime
Forked from quantopian/ziplineZipLime - Reinventing the Classic Backtesting Experience of Zipline
Discover our Python package designed for algorithmic trading. It brings ICT's smart money concepts to Python, offering a range of indicators for your algorithmic trading strategies.
[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"
AI-powered CLI tool: Transform trading research papers into QuantConnect algorithms
MambaStock: Selective state space model for stock prediction
Portfolio analytics for quants, written in Python
Course on graph machin learning.
Some pleasing customizable 3d plots in Jupyter.
A complete set of volatility estimators based on Euan Sinclair's Volatility Trading
Unofficial implementation of Palette: Image-to-Image Diffusion Models by Pytorch
SOTA Semantic Segmentation Models in PyTorch
Materials for DGL hands-on tutorial in WWW 2020
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Reconstruction of the original paper on neural style transfer (Gatys et al.). I've additionally included reconstruction scripts which allow you to reconstruct only the content or the style of the i…
Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Vector Quantized VAEs - PyTorch Implementation
OBSOLETE: now part of https://github.com/plotly/dash
AI Tool for querying natural language on tabular data.
Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021
This project is to develop tools for investment decision-making and make investment analysis using data science techniques.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading