• Introduction
• Description of repository structure
  • Gre-Systems-Model
  • Ideas_papaers
  • ML-for-TA
  • OrderBook
  • Resources
• Files structure

This GitHub repository is dedicated to advanced concepts and cutting-edge techniques in trading strategies and financial engineering. Collection features a vast array of materials including research papers, code examples, and expert insights from experienced traders and analysts.

Whether you're a seasoned professional or a newcomer to the field, this repository offers a wealth of knowledge and resources to help you stay ahead of the curve. Explore carefully curated folders and discover the latest trends and innovative approaches to trading.

This repository will cover a wide range of strategies and approaches, including quantitative trading, algorithmic trading, statistical arbitrage, and more. Each folder will include detailed explanations and practical examples, allowing you to dive deep into the topics that interest you the most.

We are committed to providing high-quality content that is accessible and engaging for all levels of experience.

This folder contains resources and research papers related to the application of Grey System Theory (GST) in advanced trading and financial markets. Grey System Theory is an interdisciplinary research methodology that deals with problems characterized by insufficient information, incomplete data, and uncertain relationships between variables. The materials in this folder showcase how GST can be applied to predict and analyze various aspects of financial markets, such as cryptocurrency and gold price forecasting.

The Ideas_papers folder contains a curated collection of research papers focused on advanced trading strategies and financial engineering techniques. Topics covered include deep learning, prediction of stock returns, and volatility models, among others.

This folder is dedicated to the implementation of a hybrid system that combines technical analysis and machine learning algorithms to predict stock market behavior. The Technical Analysis folder contains a collection of resources, including code examples and additional tools, to facilitate the implementation of this innovative approach. Additionally, this repository provides a comprehensive overview of the results obtained through the implementation of this hybrid system.

The OrderBook folder contains a Python-based order book simulator and associated code, which allows users to practice and explore different order book strategies. This folder includes detailed documentation, tests, and an app for visualizing order book data.

The Resources folder offers a comprehensive collection of links to resources like: books, scientific journals, websites, YouTube channels, and other resources focused on trading strategies and financial engineering. This folder is designed to serve as a one-stop-shop for industry professionals and enthusiasts seeking to broaden their knowledge and stay up-to-date with the latest trends and developments.

├── Grey-Systems-Model
│   └── research-ideas.md
├── Ideas_papaers_PDF
│   ├── A deep neural network perspective on pricing and calibration in (rough) volatility models.pdf
│   ├── An Ensemble of LSTM Neural Networks for High-Frequency Stock Market Classification.pdf
│   ├── Deep Hedging under Rough Volatility.pdf
│   ├── Deep Learning in Finance Prediction of Stock Returns with Long Short-Term Memory Networks.pdf
│   ├── Deeply Learning Derivatives.pdf
│   ├── Prediction of Stock Market using Stochastic.pdf
│   └── Short‑term stock market price trend prediction using a comprehensive deep learning system.pdf
├── LICENSE
├── ML-for-TA
│   ├── README.MD
│   └── research-articles.md
├── OrderBook
│   ├── Dockerfile
│   ├── README.md
│   ├── __init__.py
│   ├── alternative_match_orders.py
│   ├── app
│   │   ├── __init__.py
│   │   ├── app.py
│   │   ├── basic_logs.log
│   │   ├── data.json
│   │   ├── history.json
│   │   ├── order_book.py
│   │   ├── process_records.zsh
│   │   ├── requirements.txt
│   │   ├── templates
│   │   │   └── index.html
│   │   └── utils.py
│   ├── cons.md
│   ├── general_description.md
│   ├── test_data.json
│   └── tests
│       ├── __init__.py
│       ├── test_generic.py
│       └── test_orderbook.py
├── README.md
├── Resources
│   ├── books.md
│   ├── data-sources.md
│   ├── github-repos.md
│   ├── pure-mathematics.md
│   ├── quant-economics.md
│   ├── research-articles.md
│   ├── scientific-journals.md
│   ├── websites.md
│   └── youtube-channels.md
└── hf-list.md