Deep Learning Specialization on Coursera


Publication Date: March, 2020.
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Are you tired of the lack of transparency and reproducibility in Wall Street? Are you frustrated by the highly-complex no-hands-on approaches from the traditional outdated Quant references?

The book aims to be an Open Source gentle introduction of the most important aspects of financial data analysis, algo trading, portfolio selection, econophysics and machine learning in finance with an emphasis in reproducibility and openness not to be found in most other typical Wall Street references.

The Book is open and we welcome co-authors and collaborators, so visit our Github project and contribute!

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Table of Contents


Part I: Free Data for Markets

Chapter 1 Free Data for Markets
Chapter 2 Stylized Facts
Chapter 3 Correlation & Causation


Part II: Algo Trading

Chapter 4 Investment Process
Chapter 5 Backtesting
Chapter 6 Trading Strategies
Chapter 7 Factor Investing
Chapter 8 Limit Order


Part III: Portfolio Optimization

Chapter 9 Modern Portfolio Theory
Chapter 10 Convex Optimization
Chapter 11 Risk Parity Portfolios

Part IV: Machine Learning

Chapter 12 Reinforcement Learning
Chapter 13 Deep Learning
Chapter 14 AutoML
Chapter 15 Hierarchical Risk Parity

Part V: Econophysics

Chapter 16 Entropy, Efficiency and Nonlinear Coupling
Chapter 17 Transfer Entropy and Statistical Causality
Chapter 18 Financial Networks
Chapter 19 Fractals and Scaling Laws

Part VI: Alternative Data

Chapter 20 The Market, The Players and The Rules
Chapter 21 Case Studies

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The Book is open, so visit our Github project and contribute! The Book is licensed under Attribution-NonCommercial-ShareAlike 4.0 International.