Candid Conversations with an Algo Trader (Part 2)

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Candid Conversation with Algorithmic Trader part 2

If you don’t know who you are, the stock market is an expensive place to find out – George Goodman 

In the previous post, I had a conversation with a few experts in the field of Algorithmic Trading to gain some insights into this seemingly “black-box”. That conversation not only helped me dispel some of my doubts regarding Algo Trading, but it also strengthened my desire to jump headfirst into a world dominated by complex mathematical models, managing positions in stock market, and coding woes.

So, here I stand, ready to take the plunge. I am told that I don’t need a university degree in finance or computer science to become an Automated Trader. Of course, to succeed in the field, I do understand that a formalized education will be highly beneficial, but right now, I am just scratching the surface here. Although, my engineering background does give me certain leeway into the understanding of financial mathematical models and programming.

Yet, the road seems bumpy. I still have a truck load of questions going through my head. How much knowledge of the markets is good enough to start trading using algos? Where can I verify my trading strategies? How can I make my strategies profitable? What are the “Must Dos” and “Rule-of-the-thumbs” of the field that I need to follow?

I guess I need to sit down with an expert all over again, over a cup of steaming hot latte, to clear the air.

So, I have read some literature on Algorithmic Trading and eventually want to set up my own trading desk. What path do you recommend?

If your idea is eventually to have your own setup, you would need to build expertise in quant and/or programming. To start with, I would recommend you to master at least one of the programming language commonly used by Quants and Algo traders (Matlab/Python/R), because that is an essential ingredient. Of course, the skills that you will develop in any one of the languages will be useful for others as well. The best way is to get your hands dirty as soon as possible. Start learning about the markets. Build your knowledge on charting. Read about different kind of strategies used by traders in the market. Perhaps, the important thing is to understand the basics of market inefficiency and to capitalise on it by building a strategy around it. Once the strategy is in place, backtest it on historical data to remove the inaccurate assumptions you might have had. If all goes well, you should be able to trade with this strategy in live markets.

 What do you mean by Market inefficiency?

 A situation where live prices of stocks or securities do not accurately reflect their true value. In an inefficient market, some securities are overpriced while the others underpriced. This gives a certain risk or in some cases, opportunity to make money for the traders.  Ever heard of the term “Statistical Arbitrage”?

Yes. Isn’t it a fancy word for pair trading? I would like to know more about it.

That is only partially true. Pair trading is one of the many kinds of statistical arbitrage. It is also a type of market inefficiency. Being quite popular amongst Algo and High-Frequency Traders, It is actually an evolved version of pair-trading strategies, in which stocks are put into pairs by fundamental or market-based similarities. When one stock in a pair outperforms the other, the poorer performing stock is bought long with the expectation that it climbs its outperforming partner, and the other is sold short. This hedges risk from whole-market movements.

That does seem logical. So what do you opine as the most important things to keep in mind while designing a trading strategy?

There is no direct answer to this question, as it’s a mix and match of several things. You need to understand how the market operates and determine the entry and exit points accordingly. As far as my experience goes, recognising when to exit the market and identifying stop loss is more difficult than the entry position. Every strategy is unique, and therefore in need of its own customised positions.

Once your strategy is ready, it is good idea to backtest it against historical data. However, there is an element of caution to be exercised when backtesting. E.g. a trader wants to test a strategy based on the notion that Internet IPOs outperform the overall market. If you were to test this strategy during the dotcom boom years in the late 90s, the strategy would outperform the market significantly. However, trying the same strategy after the bubble burst would result in dismal returns.

Therefore it is important to prepare a contingency plan, as no strategy will work in all prevailing market conditions. Drawdown is unavoidable, and it should be incorporated in the trading strategy. Remember, a good strategy is not a slave of technology. You can hire a programmer to implement your trading strategy, but not vice versa.

Quantopian, NinjaTrader are free to use platforms in case you want to put your algorithms to test. There are a lot of other free backtesting tools that can be looked into, if you want more information.  

 I like your point about the exits, as I understand that any trading strategy is not devoid of risk. How are the risks handled in an Algo Trading system?

You should understand that risk management and its reduction is one of the most critical areas of Algorithmic Trading. The market is full of oddities and once you realise that you are on the verge of losing money, your algorithm should check out of the deal. That is the reason that exit points of an Algorithm are more important than the entry points.

Algorithmic risk can be categorised into several things: Consistency, Quality, Scalability being a few of them. Consistency means that the data which is being fed to the system is not old or outdated.  Market data packets have time-stamp embedded in them. The advanced exchanges in the world are adopting concepts like time-syncs to atomic clocks. Your algorithmic trading system should be able to track these time-stamps to ensure the data you are getting is indeed fresh.

For any normal trading activity, you have to take care of market risk, financial risk, regulatory risk, liquidity risk and credit/counter-party risk. In addition, algorithmic traders also need to prepare fo the statutory risks. There are two places in an Algorithmic Trading system where the risk has to be handled: within the application and before generating the order in the Order Management System. India, having one of the most stringent regulatory environment, a lot of these algorithmic trading related risks have to be mandatorily checked in the system before an order flows out.

Wow! That’s a whole lot of risk managements that we are talking about here. But I would want to take a step back and ask you about the Order Management System

Order Management System or OMS as it is called, is the pathway through which the orders are routed to their correct destinations. Orders need to contain the security identifier, order size, price limits, order type and conditions, type of algorithms used, to name a few. This information is usually entered via a winform by the end user but for fully automated systems no winform is required, however in both cases it is recommended to have each order object stored in a relational database for record keeping.

Once the order is captured by the system, it needs to be routed to the required destination. As most systems receiving the order have their own proprietary protocol, order routing requires each order to be encoded in the correct format.

There are several checks in place to make sure that order information is correctly sent and received. The venue will run various checksums and order length so that the FIX engine can confirm that the order received matches the expected order transmitted. The order manager also performs risk checks before sending out the order.

Well, well. I guess I will stop my queries for now. I have got some really great insights from your talks. Probably I should just start with some programming tutorials to get me started.

That’s a good idea. Here are a few resources where you can look for. Aggregated Reading list for Algorithmic Trading

Step Ahead

If you an aspiring quant looking for a career in Algorithmic/HFT trading, QuantInsti is hosting a career development webinar on Wednesday, January 25, 2017 at 6:00 PM IST | 6.30 AM CST.  This webinar offers a unique chance for attendees to interact with a team of Quants & HFT developers on a one-to-one level and ask career-related queries. Share your career related questions and we will try our best to take them up in the webinar! Click here to Register.

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A Candid Discussion with an Algorithmic Trader

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Machine

The role of Algorithm in a person’s life is too substantial to be ignored. From a simple coffee-making machine to the music system in his car, from elevators to search engine like Google, all are governed by a set of logical instructions – Algorithms or Algos, which enable them to respond to a person’s specific requirement.

With the advent of internet, Algorithmic potential has been unleashed in its truest form. Defining trends, identifying preferences through social media and targeting relevant groups with tailor-made services have been possible through sophisticated Algorithms put in place.

Of course, with all the technological advancement, stock markets have been on the forefront of adapting to the riveting world of Algorithms. Algorithmic Trading has gradually become the most preferred way to trade on the bourses, with an estimated 80% of the total volumes of the Wall Street.  Institutional investors, hedge funds and large financial brokerage houses have switched to Algo-trading to stay competitive, cost-effective and cater to their clientele.

So, what exactly is Algorithmic Trading or “Black Box Trading”? Does being a successful Algo Trader carries prerequisites like expert programming skills? What is the investment required for Algo trading desk to be set up? These were some of the questions that I pondered upon, while deciding a career switch in favour of Algorithmic trading.

(more…)

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Free Resources to Learn Machine Learning for Trading

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Free Resources to Learn Machine Learning for Trading

by Anupriya Gupta

While being a vibrant subfield of computer science, machine learning is used for drawing models and methods from statistics, algorithms, computational complexity, control theory and artificial intelligence. It focuses on efficient algorithms for inferring good predictive models from large data sets and is natural candidate for problems arising in HFT – both trade execution & alpha generation. (more…)

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What They Don’t Teach You at MBA?

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Business world is an intense and competitive field. An MBA degree can help develop skills that can be used in a number of situations. In a dramatic shift versus a decade ago, technology jobs are just sought as roles in finance. MBA’s are proving that they can make a difference as leaders in many different industries.

Woo Hoo MBA Done

In the below list we try to help these MBA’s with courses and programmes to do after getting their MBA degree. Any list does involve a certain bit of subjectivity and thus it will never be perfect. Despite all this one must attempt to create a list. After all, a list helps us in the simplest way to know why we decide to take a certain action. (more…)

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What do Hedge Funds Look for When Employing?

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What do Hedge Funds Look for When Employing?

By: Brandon Msimanga

One might consider Hedge Funds and Hedge Fund management to be risky business which begs to differ why anyone would find a job at a Hedge Fund to be an appealing prospect. However when it comes to working at Hedge Funds the high risk does come with an unusually high compensation in the form of a very attractive monthly pay cheque. (more…)

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Algorithmic Trading for Technocrats and Engineers

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Algorithmic Trading for Technocrats and Engineers

by Anupriya Gupta

Being a pioneer in the field of algorithmic trading education, we often get queries like why should I do this certification? How will it benefit me? Is trading right for me? Many who come up with such queries are students from various fields of study and particularly from engineering and mathematical backgrounds. We will try to address some of these frequently asked questions. (more…)

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How Do You Become A High Frequency Trader?

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While the broad contours remain the same, this post is written from Indian market perspective.

HFT is an extremely technical discipline and it attracts the very best candidates from varied areas of science and engineering – mathematics, physics, computer science and electronic engineering. While in the US, they usually look at Ph.D. in CS or physics/maths or an MFE degree, it’s not so much in India. In India, an engineering degree in CS/Maths or MBA in finance from a reputed college along with your zeal for problem-solving and coding can give you a fighting chance to land up quant analyst or a quant developer job in an HFT firm. While the degree makes the resume presentable, it’s not the barrier. If you have done lots of work and have something to show for in your resume, the industry recognizes it. But be aware that getting a quant analyst or a quant developer job will take a significant investment in terms of study and effort.

High Frequency Trader

by Anupriya Gupta

(more…)

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Financial Engineering Courses Evolution in India

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Evolution of Financial Engineering Courses

Changing market structure – and the emergence of Financial Engineering

With the fast changing global scenario and increasing competition, large financial banks and brokerage houses are faced with the need to develop customized solutions and complex models to tackle niche and difficult client problems. These led to the creation of a new discipline – Financial Engineering; and thereby demand for a new breed of professionals. Being a nascent and highly paid field, more and more people are trying to acquire requisite knowledge and skills and move to this profession.

Growth of financial engineering courses across all segments

In the wake of the above, there has been a surge of financial engineering courses in India being offered by various colleges and institutions at different levels. These finance courses target students/professionals at different points: intermediate, graduation, and even after post graduation and provide degree/ certificate in financial engineering. In addition, some of them are also offered as skill development programs for working professionals. (more…)

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QuantInsti Invited to Conduct a Session at IIT KGP

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IIT Kharagpur

Mr. Gaurav Raizada spoke on Algorithmic trading on 28th January, 2013 at IIT KGP.

The session started with a brief introduction to Algorithmic Trading including an overview of its exponential growth in India in the last 3-4 years. The speaker also discussed a few basic trading strategies leading the discussion on development of a simple algorithmic trading strategy by step-wise development of complex order types.

The talk focused on Asynchronous nature of Market Data Events and Concept of order book in trading. Overall, it provided an insight into the mathematical and programming aspects of Algorithmic Trading.

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