Algorithm Trading

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Algorithmic trading, a technological advancement to the securities market, is catching up quick among Indian traders and investors. Market regulators, SEBI have recently established a strong framework for algorithmic trading so that is standard, transparent and ethical and gets accepted by various traders and investors. To stay up with dynamic times, it has become essential for skilled traders and arbitrageurs to increase speed of execution by using new technology tools.

This technique of trading first entered stock markets in mid-1980s, and nowadays it constitutes nearly seventy per cent of total trading volumes in developed markets.

Algo trade is nothing but orders placed on the exchange platform by computers through a programme designed by the user. It was introduced in India in 2009 and is already quite the craze among institutional investors and accounts for 35-40 per cent of turnover on the Indian exchanges. Algo trades will involve completely different degrees of manual intervention. In zero-touch algos, programs determine the trading chance and execute it with no manual intervention. Here, the trades are also initiated by pre-set technical levels or quantitative indicators or arbitrage opportunities within the market, based on the client’s preference. However ordinarily used algos in India use Application Programming Interfaces (API) that enable investors to pick out their strategy, programme their needs and then execute it through the broker.

Difference between Algorithmic Trading, Automated Trading, Quantitative Trading, and High-Frequency Trading

Algorithmic Trading – Algorithmic trading means turning a trading plan into an algorithmic trading strategy via an algorithm/logic. The strategy can then be back-tested with historical data to check if it gives positive returns and less scope of error when implemented in real markets. The algorithmic trading strategy can be executed either manually or can be automated.

Quantitative Trading – It involves using advanced mathematical and statistical models for formulating and executing a strategy.

Automated Trading – It means complete automation i.e. the order generation, submission, and execution process.

HFT (High-Frequency) Trading – Trading strategies can be categorized as low-frequency, medium-frequency and high-frequency strategies depending on the holding time of the trade. High-frequency strategies are strategies which get executed in an automated way in short time in a sub-second time scale. It holds the trade positions for a very short span and try to make small profits per trade and execute millions of trades every day.

Since there are various algo trading approaches for trading and investing, few are discussed below:

Momentum investing

It is one of the most basic and common algorithmic trading systems followed by investors. This approach waits for the market trend to move significantly in one direction and with high volume. With this great momentum the investor might invest in the five best performing shares in an index based on a 12- month performance of the stock. A more difficult version of this could be when momentum is blended over time. The investor will then have to interpret both relative and absolute momentum. Furthermore, using this approach investors are able to rebalance their portfolio weekly, monthly, quarterly, or even yearly.

Mean revision

There is a tendency of many asset prices to revert to the mean after sometime i.e. after being oversold or overbought. Mean reversion strategy uses this tendency to make the algorithm. Investors who follow this strategy make an assumption that the price of the share will revert back to its long-period average price. So they purchase the assets when it is trading at the lower end of a trading range and decide to sell when the assets price approach the centre of the trading range or a moving average.

Factor-based investing

It is a strategy used by investors to select stocks on attributes that are related to higher returns, based on past data. Few factors that are included – market capitalization, momentum, earnings momentum, beta, dividends, debt/equity ratio and free cash flow. Financial investors will combine these factors using a static weighing system, or a dynamic allocation and select their securities.

ETF rotation strategies

Some investors with certain amount of risk capacity choose to use exchange traded fund to optimize the return for that amount of risk. The strategy can be to rotate into ETFs with strong momentum and maximize return. While selecting ETFs, investor must consider the correlation between the two. By selecting the uncorrelated ETFs, investors can reduce and control its risks. Investors use these strategies to explore the patterns and trends and get the advantage of the low fees charged by ETFs.

Smart beta

It is a strategy used by investors in an attempt to reduce the gap between active and passive investing. The objective of the strategy is to minimize risk or maximize diversification at a lower cost as compared to a traditional active investing. This strategy emphasizes on capturing various investment factors or market inefficiencies by making rules and formulating a strategy. Market capitalization based on the index can be used as a fundamental metric for the rule. Many investors use smart beta systems for portfolio risk management and diversification. The smart beta strategy applies to various asset classes other than equities such as fixed income, multi-asset classes, and commodities.

Trend following

This is one of the oldest strategies used by investors. It involves algorithms monitoring the market for technical indicators to initiate the trade. Generally, these trades use technical analysis (use moving averages, Fibonacci series), market patterns (like W pattern, U pattern etc.) and indicators (like relative strength index, stochastic index etc.) to make decisions. The purpose of this strategy is to buy assets when prices breaks the resistance levels and sell short assets which fall below support levels. This strategy is popular among investors because of its easy to use and understand.

Sentiment analysis

This trading strategy is determined by crowd sentiments, as investors purchase stocks to predict the crowd’s reactions due to recent and relevant news. The objective of this strategy is to analyse the unstructured data from newspaper articles, social posts, reports, blog posts, videos. Many advisors and investors utilize this strategy to capture short-term change in price and obtain quick benefits.

Statistical arbitrage strategy

Arbitrage is buying a listed stock from one market at lower price and selling the same in other market at slightly higher price. Implementing an algorithm to identify the price differentials and allow profitable opportunities in an efficient manner is a Statistical arbitrage system. It consists of a set of quantitatively driven trading strategies. These strategies exploit the relative price movements across thousands of financial instruments by analysing the differences and patterns of the prices.

Seasonality strategies

Investors must create strategies depending on the time of the year. Many investors are aware that markets generally have better returns during the warm, summer months and at the end of the year. In order to avoid capital loss, financial investors and advisors may choose to sell their positions with losses towards the end of December to get tax benefits.

Some of these strategies focus on creating long-term returns, while some on short-term returns. Many algorithmic trading strategies, like the ones above, are great for investors and advisors who are looking to optimize their portfolio risk-return trade-off.

SEBI has been quite frugal with the regulations in this domain. SEBI guidelines on algorithmic trading have assisted in adoption of the technique and the Indian market have not seen many flash crashes as compared to similar instances in the developed markets.

“Algo trading can be beneficial for small-time investors, as it increases liquidity in the market and thereby simplifies the entry and exit process. Increasing depth of algo trading would be good for capital markets as it will remove price inefficiencies in traded securities,” says Ajay Kejriwal, President, Choice Broking.

References:

 https://blog.quantopian.com/common-types-of-trading-algorithms/ https://www.investopedia.com/articles/activetrading/101014/basics-algorithmic-trading- concepts-and-examples.asp

https://learn.alphadroid.com/blog/algorithmic-trading-strategies/common-types-of- algorithmic-trading-strategies/ https://economictimes.indiatimes.com/markets/stocks/news/slow-steady-algo-trading-takes- over-decent-share-on-dalal-street/articleshow/66214063.cms https://www.thehindubusinessline.com/opinion/columns/slate/allyou-wanted-to-know-about- algo-trading/article23417138.ece

https://www.quantinsti.com/blog/learnalgorithmic-trading

Khushbu Mehta

MMS Finance

2017-19

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