SEBI Releases Discussion Paper on Algorithmic Trading & Co-Location

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SEBI releases discussion paper on Algorithmic Trading & Co-LocationImage Source: Reuters India

SEBI issued a discussion paper today with inputs from all stakeholders such as investors, infrastructure institutions and intermediary to understand how Algorithmic Trading has led to fairness, concerns and changes in market quality in recent years.

It states that more than 80% of the orders placed on most of the exchange traded products are generated by algorithms and such orders contribute to approximately 40% of the trades on the exchanges.

The study quotes the available literature that indicates pros and cons of Algorithmic Trading.

Pros of Algorithmic Trading

  • improved market quality
  • tightening of spreads
  • better liquidity

Cons of Algorithmic Trading

  • adverse selection costs for non-algorithmic traders and
  • increased probability of ‘flash crashes’ vis-à-vis the situation in the pre-algo / pre-colocation era

The document highlights different regulatory possibilities which SEBI is currently analysing pertaining to Resting time for orders, continuous matching systems, delays in order processing, randomization of orders and maximum order message to trade ratio.

Read the entire article here. It invites public comments along with reasoning and data on the proposals and discussion.

From 2008, when SEBI allowed direct market access in India, it had played the crucial role of maintaining regulations and performing audit checks very well!

To be able to manage risk well while allowing the market to grow intelligently, a thorough knowledge about algorithmic trading, networking, and technology involved is required. It is very crucial that an optimum combination of checks and leverages are allowed for the market to remain flexible, adjust itself while avoiding losses and crashes from human or machine errors. We hope such efforts from SEBI and other regulators worldwide would result into more mature and safe markets globally.

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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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Artificial Intelligence Transforming the Trading Industry

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Artificial Intelligence Transforming the Trading Industry

Financial firms, day-by-day are turning to machines to do the job of humans. In August 2015, a wealth management firm Charles Schwab launched a service called Schwab Intelligent portfolios. Unique as this service is, it’s not a human being that decides where to invest but an algorithm does. An algorithm is a set of lines of code programmed into a computer which can take decisions and make changes in an existing system. (more…)

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Financial Information eXchange (FIX) Trading Protocol

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Financial Information eXchange Trading Protocol

By Dhanjit Das

FIX (Financial Information eXchange) protocol is the defacto standard for message communication for almost two decades of electronic trading. In 1992, Salamon brothers and Fidelity Investments initiated FIX protocol to be used in equity trading. Today, it is used by a variety of market participants, firms, and vendors. With the advent of electronic trading, various exchanges and firms devised their own messaging formats, and so FIX was seen and developed as an intermediary messaging format, a common underlying means of standardised communications. (more…)

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Market Structure and Regulatory Changes in HFT

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Market Structure and Regulatory Changes in High-Frequency Trading

Around the world a number of laws have been implemented to discourage activities which may be detrimental to financial markets. There has been an active debate going on some of these changes to ascertain whether these changes can be detrimental to the market itself. Some experts have been arguing that some of the regulations targeted at HFT activities would not be beneficial to the market. They state that on one hand we have high-frequency traders acting as market makers who have order-flow driven information and speed advantages and on the other hand we have traders who are not sensitive to the latency as such and often arrive randomly as a Poisson process. Empirical results in general suggest that these regulations targeted towards HFT do not necessarily improve market quality as they fail to offer sufficient evidence pertaining to sudden market failures such as the Flash Crash.

(more…)

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Latency War: Why is Low Latency Important?

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Latency War

It’s the latency stupid! David Cheriton once said that if you have a network link with low bandwidth then it’s an easy matter of putting several in parallel to make a combined link with higher bandwidth, but if you have a network link with bad latency then no amount of money can turn any number of them into a link with good latency.

Let us have a look at an example to break down the technical jargon of latency. Boeing 747 carries 500 passengers whereas Boeing 737 carries 150. Would you say 747 is 3 times faster than 737? The Boeing 747 is 3 times bigger than the 737, not faster since both travel at 500 miles per hour. Latency plays a vital role in algorithmic trading where speed is the key entity in executing a trade. A brief comparison between traditional system architecture and automated system architecture. (more…)

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Artificial Intelligence and Machine Learning in Trading

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Artificial Intelligence and Machine Learning in Trading

Artificial intelligence is the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. According to Wikipedia definition “Artificial intelligence is the intelligence of machines, where intelligent agent (system) perceives its environment and takes action which maximizes its chances of success.

Adoption of Machine Learning

Machine learning is a subset of AI dedicated to classifying and finding patterns and extrapolates it to new data. We see lot of machine learning applications implementation. Netflix uses machine learning based algorithm to select the top movies to be recommended. Amazon shopping portal uses machine learning technique to recommend the shopping items based on the recent search and other recognizable patterns. (more…)

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QuantInsti Authors Algorithmic Trading Module for NSE’s NCFM Certification

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QuantInsti Authors Algorithmic Trading Module for NSE’s NCFM Certification

The National Stock Exchange (NSE) is one of the largest stock exchanges covering multiple cities across the country. Leading institutions took the initiative and set up NSE in order to provide a modern fully automated screen-based trading system with a national reach. NSE is known to bring about unparalleled transparency, speed along with efficiency, market integrity and safety. In terms of market microstructure, market practices and trading volumes, NSE has played a catalytic role in reforming the Indian securities. (more…)

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Changing Landscapes of Algorithmic Trading

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Landscapes of Algorithmic Trading

by Anupriya Gupta

Algorithmic Trading across different exchanges and countries

This webinar is based on the lines of discussions which were a part of 4th Annual Conference on ‘Behavioural Models and Sentiment Analysis Applied to Finance’, in London, on 16-20 June 2014.

In this session, the speaker Mr. Rajib Ranjan Borah, co-Founder QuantInsti & iRageCapital Advisory, compares algorithmic trading in different geographic across the globe. He shares his insights and experience of algorithmic trading across the major exchanges in Asia Pacific (APAC), Europe & Middle East (EMEA) and the Americas. The presentation has data of volumes of equity and options traded in more than 30 exchanges monthly and annually. (more…)

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Algorithmic Trading in India: History, Regulations and Future

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Execution of trades on stock exchanges based on pre – defined criteria and without any human intervention using computer programs and software is called algorithmic trading or algo trading. While being a subset of algorithmic trading, high-frequency trading involves buying and selling thousands of shared in fractions of seconds.

Bombay Stock Exchange Building

by Anupriya Gupta

In the US and other developed markets, High Frequency Trading and Algorithmic trading accounts estimated 70% of equities market share. This form of high-speed trading rose 12 percent on the Bombay Stock Exchange, to account for almost 30 percent of total trades. Its share is higher in the National Stock Exchange, with nearly 46 percent of trades happening on the platform, according to latest reports. (more…)

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