Keys to Success in Algorithmic Trading | Podcast | Dr. E.P. Chan

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Dr. Ernest P. Chan is the Managing Member of QTS Capital Management, LLC. He has worked for various investment banks (Morgan Stanley, Credit Suisse, Maple) and hedge funds (Mapleridge, Millennium Partners, MANE) since 1997.

Quantitative trading or trading, in general, is considered one of the most difficult professions to break into or to succeed in, and let me quote somebody I will tell you who wrote recently that quantitative trading became more challenging with every passing year.

None other than Dr.D.E.Shaw was quoted saying that in a book by Professor Andrew Lo. Now if as you know D.E.Shaw was one of the real early pioneers of quantitative trading and has built a multi-billion dollar extremely profitable hedge fund in New York.
He has at his hands’ hundreds of PhDs and millions of dollars of supercomputing power at his disposal and yet he said that even for him quantitative trading has become more challenging with every passing year.

Now let’s hold this thought for a moment in a mind.
Why is it still worth doing if it is so difficult.

Well, I would say that this field is not any more difficult to be successful than to become a successful actor or a successful singer, or model or a fiction writer or a visual artist.
Of these fields are extremely difficult to be highly successful and algorithmic trading is no different.
However, that doesn’t mean that nobody should try to become an actor nobody should try to become a fiction writer and so for.

However, one should be prepared for failure that is who anybody who enters into any one of these professions including quantitative trading should be prepared for.

Now therefore my first advice is if you are not already into the financial industry make sure you don’t quit your day job when you graduate and started your first trading strategy.

One of the things I am gonna talk about today is what I have done wrong in the past and hopefully, you would be able to avoid these pitfalls.
One of the things that I have done wrong and extension of being true optimistic about the past performance of a strategy is that I would leverage too high.

The second thing that I have done wrong is related to the first one which is that strategy performance typically is not trending that is the mistake many even sophisticated individuals make I certainly made myself.

The third thing that I have done wrong in my career in terms of trading is that I didn’t invest much of my profit into data equipment and personnel my times were good. A lot of traders did that especially independent traders they think wow we had a great year so I should take out all my profits put into the bank but that’s not the best way to run a business.

Well, what I have done right is that I have started with simple strategies that have intuitive justification.

I didn’t start when I was trading with my money the strategies build based on recurrent neural networks, deep learning and so for. No, I started with strategies that I can understand and that even some of the people have already talk about but I have been able to improve on them by adding some thousand whistles.

I advised you to start from the same.
So with that, I don’t want to bore you further and again congratulate you for your success in completing the course and the best of luck for your trading career.

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Forex Algorithmic Trading Chan, Keys to Success in Algorithmic Trading | Podcast | Dr. E.P. Chan.

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What is Algo trading instance?

artificial intelligence trading utilizes computer system programs to trade at broadband as well as volume based on a number of predetermined requirements, such as stock prices and details market conditions. As an example, an investor could use algo trading to execute orders swiftly when a particular supply gets to or drops below a specific cost.

Recommended Book for Automated Trading

Professional Automated Trading: Theory and Practice

Book by Eugene A. Durenard

An insider’s view of how to develop and operate an automated proprietary trading network Reflecting author Eugene Durenard’s extensive experience in this field, Professional Automated Trading offers valuable insights you won’t find anywhere else. read more…

Originally published: 2013
Author: Eugene A. Durenard

An Example of artificial intelligence Trading

Royal Dutch Covering (RDS) is noted on the Amsterdam Stock Market (AEX) and London Stock Market (LSE).1 We begin by developing an algorithm to identify arbitrage possibilities. Right here are a couple of interesting monitorings:

AEX sells euros while LSE trades in British pound sterling.

Because of the one-hour time distinction, AEX opens an hour earlier than LSE complied with by both exchanges trading at the same time for the following couple of hours and afterwards trading just in LSE throughout the last hr as AEX shuts.

Can we explore the possibility of arbitrage trading on the Royal Dutch Covering stock listed on these 2 markets in 2 different currencies?

Demands

A computer system program that can read current market prices.
Rate feeds from both LSE and AEX.
A forex (fx) rate feed for GBP-EUR.

• Order-placing capability that can path the order to the right exchange.
  Backtesting capacity on historic cost feeds.
• The computer system program should perform the following:.
• Read the inbound price feed of RDS supply from both exchanges.
• Using the available foreign exchange rates, transform the rate of one currency to the various other.
• If there is a huge adequate price inconsistency (discounting the broker agent prices) leading to a rewarding chance, after that the program needs to place the buy order on the lower-priced exchange and market the order on the higher-priced exchange.
• If the orders are performed as wanted, the arbitrage revenue will adhere to.

Simple as well as simple! Nonetheless, the method of artificial intelligence trading is not that easy to keep and perform. Bear in mind, if one financier can position an algo-generated profession, so can other market individuals. As a result, costs fluctuate in milli- as well as also microseconds. In the above example, what happens if a buy trade is carried out yet the sell trade does not since the sell rates alter by the time the order hits the market? The investor will be left with an employment opportunity making the arbitrage approach worthless.

There are extra dangers as well as difficulties such as system failing dangers, network connectivity errors, time-lags in between trade orders and implementation as well as, essential of all, incomplete algorithms. The even more facility an algorithm, the much more rigorous backtesting is needed before it is used.

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