Algorithmic trading implies the execution of trades on stock exchanges without much human intervention (except for tweaking the algorithms according to the required trade positions) using computer programs and software.<\/p>\n\n\n\n
While it has its detractors, the general consensus is that algorithmic trading is an inevitable evolution of the trading process. In India, at present, around 50% plus of total orders \u207d\u00b9\u207e<\/a> at both NSE and BSE account for trades placed algorithmically.<\/p>\n\n\n\n Let us find out more about the popular algorithmic trading in India. with this blog we will cover:<\/p>\n\n\n\n In simple words, algorithmic trading<\/a> is a process of converting a trading strategy into computer code<\/a> which buys and sells (places the trades) for stocks in an automated, fast and accurate way.<\/p>\n\n\n\n Generally, the automated way of trading is faster and more accurate, and hence, it is preferred nowadays and is increasing its reach in emerging markets rapidly.<\/p>\n\n\n\n Technically, there are several mathematical algorithms at play for making trading decisions on the basis of current market data, which then send and execute the order(s) in the financial markets.<\/p>\n\n\n\n This method makes the trading free of all emotional human impact (like fear, greed, etc.) since decisions to carry out each trade are made by computers in a systematic manner.<\/p>\n\n\n\n For instance, you can design a simple algorithm that buys shares of Apple (AAPL) if the current market price of the share is less than the 200 days\u2019 average price. Conversely, you can also ensure that it sells Apple (AAPL) shares if the current market price is more than the 200 days\u2019 average price.<\/p>\n\n\n\n On April 3rd 2008, the Securities & Exchange Board of India (SEBI), introduced algorithmic trading by allowing a Direct Market Access facility to institutional clients.<\/p>\n\n\n\n In short, DMA allows brokers to provide their infrastructure to clients and gives them access to the exchange trading system without any intervention on their part. Initially, it was provided only to institutional clients and not retail traders.<\/p>\n\n\n\n Nevertheless, the facility brought down costs for the institutional investor as well as helped in better execution by cutting down the time spent in routing the order to the broker and issuing the necessary instructions.<\/p>\n\n\n\n On April 29th 2008, this facility had already become popular with some of the top global players signing up for the DMA facility. FI\u2019s & FII\u2019s like UBS, Morgan Stanley, JP Morgan and DSP Merrill Lynch were the entities awaiting approval.<\/p>\n\n\n\n Edelweiss Capital, India Infoline and Motilal Oswal Securities were among others who had submitted their request to the stock exchanges. It is worthwhile to note that Foreign Institutional Investors (FIIs) were allowed to use the DMA facility through investment managers nominated by them, from February 24th 2009.<\/p>\n\n\n\n By July 31st 2008, leading brokerages along with stock exchanges were preparing the ground for operationalising Direct Market Access (DMA). Brokerages such as Citi, Merrill Lynch, Morgan Stanley, JP Morgan, Goldman Sachs, CLSA and Deutsche Equities had started holding test runs of their DMA software, in an attempt to synchronise it with the systems at the stock exchange.<\/p>\n\n\n\n Algorithmic trading includes practices such as smart order routing, high-frequency trading (HFT) and co-location under it. You will see these practices discussed below in detail.<\/p>\n\n\n\n Order routing is a process by which an order goes from the end user to an exchange.<\/p>\n\n\n\n An order may go directly to the exchange from the customer, or it may go first to a broker, who then routes the order to the exchange.<\/p>\n\n\n\n Smart order routing is an automated process used in algorithmic trading that follows a set of rules for executing an order. Smart order routing attempts to achieve the best execution of trades while minimizing market impact.<\/p>\n\n\n\n High-frequency trading <\/a>(HFT) is a subset of algorithmic trading. Here, opportunities are sought and taken advantage of on very small timescales from nanoseconds up to milliseconds.<\/p>\n\n\n\n Some high-frequency strategies adopt a market maker type role, attempting to keep a relatively neutral position and proving liquidity (most of the time) while taking advantage of any price discrepancies.<\/p>\n\n\n\n Other strategies invoke methods from time series analysis, machine learning and artificial intelligence to predict movements and isolate trends among the masses of data. Specifics of the strategy aside, for HFT, monitoring the overall inventory risk and incorporating this information into pricing\/trading decisions is always vital.<\/p>\n\n\n\n Co-location is a data centre facility in the exchange premises where the exchange\u2019s servers are on the same network. It is used to rent space to trading firms to locate their servers and other computing hardware.<\/p>\n\n\n\n The co-location facility provides the power, bandwidth, IP address and cooling systems. Also, co-location helps in reducing the latency by minimizing the travel time between your server and the exchange\u2019s matching engine.<\/p>\n\n\n\n Let us now discuss how you can begin algorithmic trading in India<\/a>, along with some prerequisite resources to learn algorithmic trading<\/a>.<\/p>\n\n\n\n Let us begin with the prerequisites first.<\/p>\n\n\n\n Analytical skills<\/strong><\/p>\n\n\n\n Having an analytical bent of mind is a very important quality for any quant trader<\/a>\/developer, and is valued in an interview.<\/p>\n\n\n\n For example, a candidate may be given a huge data set and asked to find patterns from the data. Candidates get evaluated on how they approach any given problem and their ability to justify their solutions objectively.<\/p>\n\n\n\n Mathematical skills<\/strong><\/p>\n\n\n\n As the core of algorithmic trading revolves around algorithms, data, and programming, having reasonable programming skills and a basic understanding of statistics and calculus is important for any job seeker in algo\/HFT trading.<\/p>\n\n\n\n For example, if a candidate is applying to a firm that deploys low latency strategies, then an expert level of programming would be expected from such a candidate.<\/p>\n\n\n\n Programming skills<\/strong><\/p>\n\n\n\n Knowledge of a programming language (Python for Trading<\/a>) is an added advantage as it enables you to function independently. Traders are inclined toward learning the long-term effects and benefits of coding, especially Python.<\/p>\n\n\n\n Python<\/a> is good for conceptualizing, and backtesting strategies<\/a>, and has many libraries for validation and visualization of results. It can also be used by firms for strategies that are not dependent on low latency.<\/p>\n\n\n\n The strategy development process<\/strong><\/p>\n\n\n\n While devising any strategy, it is important to understand the risks and rewards associated with that strategy in order to determine whether it has an edge in the markets. This is done during the backtesting<\/a> of a strategy.<\/p>\n\n\n\n The frequency of trading, instruments traded, and leverage are all needed to be taken into consideration before going live with the strategy in the markets.<\/p>\n\n\n\n A single strategy doesn\u2019t guarantee profits year after year. One has to formulate and overhaul strategies regularly basis using advanced mathematical models & statistics to remain profitable in the markets.<\/p>\n\n\n\n To understand various algorithmic trading strategies, you can learn about the algorithmic trading strategies<\/a>, paradigms and modelling ideas.<\/p>\n\n\n\n Understanding the Financial Markets<\/strong><\/p>\n\n\n\n Quantitative trading involves dealing with large financial datasets<\/a>, trading in different instruments like stocks, derivatives, forex etc. Hence, even if you are coming from a non-finance technology background, as a developer in a quant firm, you need to have a fair understanding of the financial markets<\/a>.<\/p>\n\n\n\n Trading firms usually make their new recruits spend time on different desks (e.g. quant desk, trading, risk management desk) to gain an understanding of the markets.<\/p>\n\n\n\n Besides these, it is necessary that one is equipped with domain knowledge. To know more about the skill sets required, check out this infographic about the top skills for nailing a Quant or Trader interview<\/a>.<\/p>\n\n\n\n If one is thorough with the abovementioned prerequisites, you simply need to be prepared for the quant interview<\/a> if you need a job in an algorithmic trading<\/a> or HFT firm.<\/p>\n\n\n\n Besides the above mentioned, let us also see some general skill sets required to become an algorithmic trader<\/a>, which go as follows:<\/p>\n\n\n\n\n
A brief about algorithmic trading<\/h2>\n\n\n\n
\n\n\n\nHistory of algorithmic trading in India<\/h2>\n\n\n\n
\n\n\n\nRole of smart order routing, High-Frequency Trading and co-location in algorithmic trading<\/h2>\n\n\n\n
Smart order routing<\/h3>\n\n\n\n
High-Frequency Trading (HFT)<\/h3>\n\n\n\n
Co-location<\/h3>\n\n\n\n
\n\n\n\nHow to start algorithmic trading in India?<\/h2>\n\n\n\n
Prerequisites for doing algorithmic trading in India<\/h3>\n\n\n\n