Inspired by - ZuzooVn - John Jwasham

How I (Tu Anh) plan to become a Financial Engineer

• What do I have for you?
• Why use it?
• Data processing and curation
• Investment Strategies/Alpha/Feature Analysis
• Portfolio Management/Diversified
• Execution
• Back test
• Risk-Management
• Mathematics Finance
• About Video Resources
• Book List
• Industry Leader
• Top ranking School in Quantitative Finance
• Kaggle Competion and Note Book

This is my long-term study plan for going from Equity Analyst to Financial/Machine Learning Engineer (self-taught, no CS degree) through real-life problem and research material from books, industry leaders, online course and top-ranked course from US (ref below)

My main goal was to find an approach to studying Financial Engineer that is mainly hands-on and abstracts most of the Math for the beginner. This approach is unconventional because it’s the top-down and results-first approach designed for Financial Engineers.

In investing, there are two way for you to get your alpha , you have more information or you processing the data you have faster. I choose the latte and I want all of you to be a part of my journey.

Please, feel free to make any contributions and feedback.

I'm following this plan to advance in my career Quantitative Research. I've been study and working in Finance Industry since 2013. I have a Finance/Investment Banking degree, not a Financial Engineering degree. I have an itty-bitty amount of basic knowledge about: Calculus, Linear Algebra, Discrete Mathematics, Probability & Statistics from university.

• How do I learn quantitative finance?
• Can I learn and get a job in Machine Learning without studying CS Master and PhD?
  • "You can, but it is far more difficult than when I got into the field."_ Drac Smith
• How do I get a job in Machine Learning as a software programmer who self-studies Machine Learning, but never has a chance to use it at work?
• What skills are needed for machine learning jobs?
• "First, you need to have a decent CS/Math background. ML is an advanced topic so most textbooks assume that you have that background. Second, machine learning is a very general topic with many sub-specialties requiring unique skills. You may want to browse the curriculum of an MS program in Machine Learning to see the course, curriculum and textbook."_ Uri
• "Probability, distributed computing, and Statistics."_ Hydrangea

I think the best way for practice-focused methodology is something like 'practice — learning — practice', that means where students first come with some existing projects with problems and solutions (practice) to get familiar with traditional methods in the area and perhaps also with their methodology. After practicing with some elementary experiences, they can go into the books and study the underlying theory, which serves to guide their future advanced practice and will enhance their toolbox of solving practical problems. Studying theory also further improves their understanding on the elementary experiences, and will help them acquire advanced experiences more quickly.

It's a life study plan. It's going to take me my whole life since quantitative is evolving everyday.

Data Source - OHLC/Volume

• yfinance - Yahoo! Finance market data downloader (+faster Pandas Datareader)
• findatapy - Python library to download market data via Bloomberg, Quandl, Yahoo etc.
• googlefinance - Python module to get real-time stock data from Google Finance API.
• pandas-datareader - Python module to get data from various sources (Google Finance, Yahoo Finance, FRED, OECD, Fama/French, World Bank, Eurostat...) into Pandas datastructures such as DataFrame, Panel with a caching mechanism.
• pandas-finance - High level API for access to and analysis of financial data.
• exchange - Get current exchange rate.
• coinmarketcap - Python API for coinmarketcap.
• investpy - Financial Data Extraction from Investing.com with Python! https://investpy.readthedocs.io/
• FinanceDataReader - Open Source Financial data reader for U.S, Korean, Japanese, Chinese, Vietnamese Stocks
• VNquant - API for Vietnamese Stocks

• Technical Strategies
  • Qlib
  • Finrl
  • Ta-lib
• Fundamental Strategis(To be updated)
  • Canlism, Buffett, Value, Growth, Piotroski, Altman, Beneish

• MVO, Black Litter Man, HRP
• Riskfolio-Lib

• Backtrader
• Backtest

• MIT mathematics
• Free test
• The algorithm
• C-plus-plus
• Awesome-Math(Must Read)

• Data Science: Probability
• Data Science: Probability - Part 2
• Statistical Inference and Modeling for High-throughput Experiments
• Statistics and R
• Statistical Thinking for Data Science and Analytics
• Free test
• G2

Self_collection

• scikit-learn-videos
• Kaggle Blog

• Tech Ceo profile
• Jim Simons - World's Smartest Billionaire
• Victor DeMiguel - Phd LonDon Business Schools
• Marcos Lopez de Prado
• Nassim Nicholas Taleb
• Stefan Jenson\
• Paul Wilmot

• Top ranking School in Quantitative Finance

• Baruch (rank 1)

  • Probability Theory for Financial Applications
  • Numerical Linear Algebra for Financial Engineering

• Carnegie Me (rank 2)

  • Master of Science in Computational Finance
  • Artifical Intelligence

• Princeton (rank 3)

  • Operation Research and Financial Engineering
  • Computer Science

• Columbia (rank 4)

  • Master of Science In Financial Engineering

• Nanyang Technological University

  • MSc in Financial Technology

• CQF

Kaggle is a great place to start for student, research who first starting with Quantitative and Machine Learning.

• Jane Street Prediction

  • EDA, Feautre Importance
  • Time, volatility
  • Neural Network
  • Bottle neck encoder, MLP, Keras
  • Big Data sets reading
  • HFT

• Winston Market Challenge

  • XGBoost
  • Solution Sharing

• The Big Data Combine By Battle Fin

  • Solution Sharing

• Two Sigma: Using News to Predict Stock Movements

  • Simple Note Book
  • Sharing thoughts after winning
  • Private Solution
  • Two Sigma News Official Getting Started Kernel
  • fulltimeseriesvalid LSTM, LGBM, GOSS, DART
  • EDA, feature engineering and everything
  • Winner Interview
  • Twitter sentiment Extaction-Analysis,EDA and Model