Python Algo Trading: Market Neutral Hedge Fund Strategy – Edu fyre – Anthony Ng

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Hands on Python guide to developing your own market neutral long short hedge fund strategy with sentiment analysis

Python Algo Trading: Market Neutral Hedge Fund Strategy by Edu fyre – Anthony Ng,
Salepage link: At HERE. Archive: https://archive.is/wip/7IlTX

Hands on Python guide to developing your own market neutral long short hedge fund strategy with sentiment analysis.

This course provides you with the tools that top hedge funds used. These institutional tools include but are not limited to market data, fundamental data, sentiment analysis data, and more. 

In this course, you will learn to design, develop, and test long short hedge funds strategy incorporating sentiment analysis, one of the hottest research area in finance, in a systematic and scientific manner. You will be able to assess your strategy and evaluate the strength and weaknesses, improve and finally at your own wish go-live.

You will get a detailed tour of the investment process of the quantitative investment world, one used by Professional Quant on a daily basis. The course will cover all the key areas such as sourcing data and making data-driven investment, defining trading universe suitable for your trading strategy, search and discovery of the elusive alpha, combining multiple alpha, follow by portfolio construction, and trading.

The hedge fund market has seen massive adoption of data-drive, quantitative approach. In an article published in Wired Technology in Apr 2017, it states:

“There was $38 billion of institutional investment into algorithmically driven hedge funds in the first quarter of 2016 alone. In October 2016, one of the biggest players, Renaissance Technologies, which has $60 billion under management, announced that it received $7 billion in investment the previous year.”

“The time will come when no human investment manager will be able to beat the computer,” David Siegel, the co-founder of quantitative fund Two Sigma, which manages $35 billion, told an investment conference in 2015.

The time to take action is now. Look forward to seeing you inside!

This course is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory. Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available at the time of publication. No guarantees have been made as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.

Your Instructor(s)

Anthony Ng

Anthony has spent the last 7 years lecturing, consulting and conducting workshops in Singapore covering topics such as algorithmic trading, financial data analytics, banking, finance, investment and portfolio management.Since 2016, he has been assisting Quantopian to conduct Algorithmic Trading Workshops in Singapore and has recently presented in QuantCon Singapore 2017.

Passionate about finance, data science, and Python, he enjoyed researching, teaching and sharing on these topics. He studied Masters of Science in Financial Engineering at NUS Singapore and also hold an MBA, BCom from Otago University

Course Curriculum

Introduction

• Introduction (3:59)

Introduction to the Platform

• Platform Introduction (1:16)
• Platform Ecosystem (18:45)

Introduction to Python

• Code Comments (3:04)
• Variables (7:50)
• Basic Math (4:49)
• Collections (20:51)
• Strings (5:57)
• Logical Operators (12:29)
• Loop Structures (8:20)
• Functions (10:21)

NumPy

• Basic NumPy Arrays (14:44)
• Calculating Expected Return (6:56)
• Linear Algebra (12:05)

Pandas

• Motivational Example (2:33)
• Series (21:47)
• DataFrame (14:02)

Pipeline

• Introduction (9:44)
•  Creating a Pipeline (5:05)
• Factors (13:32)
• Combining Factors (4:25)
• Filters (11:05)
• Combining Filters (3:47)
•  Masking (7:46)
• Classifiers (7:52)
• Datasets (6:02)
• Custom Factors (12:54)
• Putting It Together (8:23)
• Moving to the IDE (12:25)

Professional Quant Workflow

• Professional Quant Workflow (1:45)

Trading with Sentiment Analysis – Psychsignal

• Introduction – Psychsignal (10:45)
• Importing the Libraries and Exploring the API (21:13)
• Defining Our Factor (13:50)
• Analyze the Predictive Power of our Factor (13:04)
• Backtesting Factor (16:34)
• Analyze Backtest Result (7:06)

Trading with Sentiment Analysis 2 – Sentdex

• Introduction – Sentdex (6:22)
• Importing the Libraries and Exploring the API (10:03)
• Define Factor (5:50)
• Analyze the Predictive Power of Our Factor (8:01)
• Backtest Factor (10:48)
• Analyze Backtest Result (11:48)

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