As 2016 nears its finish line, here we are with the list of recommended reading on our blog with the top-rated blog posts, as voted by you! Enjoy the last few days doing what you love most! Read on.
This one is straight out of a lecture in the curriculum of QuantInsti’s Executive Programme in Algorithmic Trading (EPAT™). It compares the traditional trading structure with algorithmic trading architecture and highlights the complexities in the latter. The post explains the three core components of the trading server: Complex Event Processing Engine (the brain), Order Management System (the limbs) and the Data Storage component. Life Cycle of the entire system is also explained so that the readers under what happens when a data package is received from the exchange, where trading decisions happen, how risk is monitored and how are orders managed.
Backtesting platforms for quants
There are many platforms out there and for beginners, it is often confusing to pick the most relevant for them. The posts list out the USPs of available platforms so that you can make an informed choice before you start using a platform for backtesting. It is important to make this decision carefully as you would require to spend enough time on one platform to get comfortable with it!
In this highly insightful article, QuantInsti’s EPAT™ graduate, Jacques Joubert shares his project work on Statistical Arbitrage in R programming language. For readers who are more comfortable in Excel, they can download a pair trading model in Excel here
to get started. He talks briefly about the history of Statistical Arbitrage before moving on to the strategy and its markdown in R programming language.
What are the different Algo Trading Strategies? What are the strategy paradigms and modelling ideas associated with each strategy? How do we build an Algo trading strategy? These are some of the key questions answered in this in-depth article. QuantInsti’s article on Algorithic Trading Strategies covers the following:
- Momentum based strategies
- Statistical Arbitrage
- Market Making
- Machine Learning Based
Python as programming language for DIY traders
Python has sufficed as one of the most popular programming languages for algorithmic traders. In this set of articles, we have talked about Zipline, building technical indicators and the benefits of learning Python for trading. The articles came into light during the webinar on Automated trading using Python
conducted by Dr. Yves Hilpisch. This year, we also had Dr. Hui Liu conducting a webinar on implementing Python in Interactive Broker’s C++ based API. Both Dr. Yves and Dr. Hui, who are two of the renowned names in the field of automated trading, have joined QuantInsti’s impressive line-up of outstanding faculty for EPAT™.
Learn Machine Learning for Trading
Machine Learning and Artificial Intelligence are the most sought-after streams of technology in this era. As trading has become automated, Machine Learning’s importance has only become critical for maintaining competency in the market. From fetching historical information to placing orders to buy or sell in the market, machine learning is an integral part of Automated trading and we have covered it in detail on our blog.
As Algorithmic trading picks up pace in India, more and more conventional traders and beginners are wanting to know about this lucrative field. However, owing to shortage of resources in the market, QuantInsti decided to churn out a very primitive article for amateurs who want to step out in the world of algorithmic trading. Explained in basic language, this article covers all the things one needs to know before starting algorithmic trading.
We would love to hear from you – why you liked any or all. If you would like to read something specific in 2017, all suggestions are welcome!