Merging Statistical Analysis With Artificial Intelligence: Tanya Rawat

Artificial Intelligence
Artificial Intelligence

Merging Statistical Analysis With Artificial Intelligence: Tanya Rawat

We interviewed Tanya Rawat, the Chief Investment Officer at Boolean Algorithmic Trading. With more than 14 years of experience in the investment sector, Tanya is a dedicated and true veteran of this industry. She is an expert in the fields of Finance and Economics and offers investment advice to family offices, HNWIs, UHNWIs across multi-asset investments. She combined  the concept of econometrics analysis with machine learning to develop the first statistical arbitrage strategy in the Middle Eastern region. Through this interview, let’s learn more about Tanya and her journey.

The Major Idea Behind This Journey

We asked, “What’s the idea behind your company?”

Tanya shared, “Boolean Algorithmic Trading (BAT) is a proprietary quantitative investment management firm applying a hedge fund strategy using artificial intelligence with a heuristic overlay. We are a data-driven firm that believes in the power of nano information and its ability to drive better decision-making to deploy and extract value from managed futures strategies. We synthesize quantitative methods with heuristics to optimize returns by identifying patterns and predicting the price movements by studying correlation across asset classes.”

Risk management is a subset of tail behaviour of returns – the nature of the symmetry and peakedness of returns and embodies an anti-Martingale system. Our proprietary algorithm vītabhaya (Sanskrit for fearless) is premised on this ethos of anti-Martingale binary decision-making. The investment strategy is a high-frequency managed futures strategy that embeds vītabhaya to comb through the market microstructure for futures instruments and generates alpha.” she added.

We wanted to know about the start of her journey, so we asked, “How did you begin your career journey? Do you have some advice for beginners?”

She replied, “It doesn’t matter how one’s journey starts, nor is it about how it ends. It’s about the journey itself and enjoying, learning, and growing along with your peers as you as peers set the benchmark as a collective for your field of expertise.

Follow your passion. After working for close to 14 years in the investment industry, I have learned to appreciate a simple fact; you have to get up daily and go to work and spend 10-15 hours a day with your colleagues. Therefore, make sure you enjoy what you do because you probably have to do it daily for the rest of your life.

Be hungry, be curious, ask questions and leave it the minute you are the most competent person in the room! And understand there is no set path to go about doing things. You can go from being an educator one minute to starting a coffee roastery the next. Don’t limit yourself by examples around you, be your own version 2.0.”

Goals And The Key CornerStones

What’s the goal of Your Company?” we asked.

The goal is to create and lay the foundation and groundwork of a purely quantitative investing framework for a refined algorithmic driven investment firm in an equally novel asset class viz. managed futures in the MENA region.

On the coattails of this comes knowledge accretion in the managed futures space and an understanding of how a high-frequency hedge fund strategy such as this adds value as a diversification play, given that this industry is relatively nascent in the region. Education in this space and knowledge sharing are the two fundamental cornerstones of my firm.”, Tanya shared.

Algorithms And Quant Investing

As she is an expert in the field, we were intrigued to learn her views on this concept. We asked, “Could you tell us something about Quant Investing and your views on using algorithms in Quant Investing?

Tanya shared, “Quantitative (or Quant for short) investing is a synonym for rules-based investing. Algorithms (or Algos for short) are the tools(quantitative analysts/researchers (or quants for short) develop to make the lives of traders easier by automating tasks. Quant strategies are those employed scientifically and mathematically. Like this: when a happens, do b or else do c. Algos, usually expressed using computer code, define those instructions, essentially doing everything from taking the effort of making the trade to allowing for the testing of multiple data points across years of data to extrapolate meaningful strategies. Trading with algos covers a few steps, from the strategy creation, moving into the strategy generation (both order creation and size), then the actual trade execution, and finishing out with position management.

However, Quant investing is sometimes seen as a black box with limited to no transparency or a magic pill expected to resolve and mitigate uncertainty associated with investing. This polarity is what BAT seeks to address.”

Machine learning and other basic quant investing techniques should be seen as something different than a panacea. The evolution in the quant industry and the third wave per-se in this field is not just about using one singular or a new technique. It’s about combining techniques across the artificial intelligence universe, viz., machine learning techniques such as K-cluster, random forest algorithms, et cetera, and understanding deep learning methods enough to train neural networks to extrapolate and forecast with improved efficiency and smaller margins of error.

There is no one size fits all.”, she further added.

Views On CTA Strategies

While we are at it, do you have any views on the rise of CTA strategies used by hedge funds?

Tanya replied, “Managed Futures strategies are a diverse subset of active hedge fund strategies that trade liquid, transparent, centrally-cleared exchange-traded products, and deep interbank foreign exchange markets. Managers in this sector are called Commodity Trading Advisors (CTAs). This name is archaic as; historically, most CTA activity was in commodities. While the predominant strategy remains trend-following, this approach has evolved significantly in sophistication in recent years, and the overall space has become increasingly diverse, given advancements in technology. The number and variety of short-term, high-frequency systems have risen sharply with the advances in trading technology and data analysis.

They have seen increased adoption and, thus, higher AUM allocation because of their ability to be successful in any economic environment. This bodes well for Managed Futures as they are consistent long-term track records despite economic downturns. Moreover, they often do so with limited volatility in such periods and smaller drawdowns than other asset classes (see chart below).

“In the last four decades, assets under management (AUMs) for the Managed Futures industry have grown 1000-fold. Current assets under management stand at over c. $300 billion.

Additionally, they offer uncorrelated or limited correlation to traditional asset classes. Managed Futures are an alternative asset class that has achieved strong performance in all kinds of market and economic conditions, exhibiting low correlation to traditional asset classes, such as equities, fixed income, real estate, crypto assets, and cash.” She further added.

Aspects That Keeps On Going During Challenges

“What keeps Your Company moving forward even during challenges?” We asked Tanya to learn how her company deals with challenges.

She replied, “Consistency, pure hard work and discipline trump anything else in life. Getting up in the morning and showing up is the simple magic formula.”

Crucial Facets Of Investment And Message To Viewers

What do you think is the most crucial part about investments?” we asked.

“It is fundamental but important to understand why you want to invest and what is your yearly return expectation and/or requirement. It’s as binary as understanding two crucial facets :

  1. a) What purpose is the investment expected to fulfil?

  2. b) Understanding your liquidity constraints/ requirements i.e. how long can one keep the monies invested in XYZ investments?” Tanya shared.

Would you like to say anything else to our viewers?

Tanya replied, “Investing isn’t a difficult exercise. It’s more about looking inwards and understanding your workings as an individual versus being outward looking and letting FOMO or JOMO (a word I came across recently) or crowd/herd mentality cloud your rational judgments. Sometimes simplicity is the key to it all. Doing nothing and being all cash also takes discipline.”

Quants and Artificial Intelligence

We were intrigued to learn about her idea on quant advantage, so we asked, “Does the presence of quants eventually remove or scrape away the quant advantage of generating alpha?

Tanya responded, “A couple of decades ago, the question was whether people could learn how to trade or whether people were born to trade. Did you have to have the magic touch, or could a quantitative scientific approach be taken to make a trader?

It was one of the first trading algorithms and cradle for the birth of quants in the investment space, and it was called the Turtle Trader experiment proving that anyone can learn to trade with a quantitative set of rules if followed with discipline.

The next step in the evolution cycle was coding those rules into computers, essentially teaching them how to trade. Now, firms are using machine learning to adapt to the market in ways humans can’t.

Thus, there’s no doubt quants and AI will have to continue to grow and learn and find new ways to generate alpha by combining different algorithmic techniques with multiple time frames, and that it will take more than just turning your data over to the AI-powered algorithm and then sitting back and watching the cash roll in. On the flip side, there’s just as much chance that the AI model comes up with a curve fitting model or otherwise inferring a relationship that doesn’t or shouldn’t be there i.e. is spurious.”

Connect with Tanya Rawat on LinkedIn.

Find Boolean Algorithmic Trading on LinkedIn or visit

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