Photo courtesy Tima Miroshnichenko.
Algo trading also called automated trading is an algorithm-based stock trading. The software uses high-level algorithms to process and execute the trades. These trades are made by a computer on the basis of defined rules and the trading instructions are there in the form of algorithms with regard to price, time, and volume.
Trading is a profession full of stress when you have large amounts of money involved, and requires patience and flexibility. Many times the traders lose their calm and get affected by emotions while making a decision. This results in errors and loss of money. Algo trading does not have involvement of emotions; is precise, and free from human errors.
The programs that can be used to build Algo trading software are MQL, Matlab, Java, C++, and Python. The algorithms can be built with no human intervention called zero-touch algorithms, while there are others that require minimum human intervention called Application Programming Interfaces (API). The algorithms are run by the system to obtain output and as a result, buy and sell signals are generated that help in placing orders. Traders don’t have to sit in front of their computer screens the whole day and when the preset conditions are met the buy or sell signals are sent to the trader. Human inefficiencies are reduced and the trader is only involved in the strategy creation steps.
Is Algo trading safe?
Algo trading is safe when a person is aware of the trading strategies, markets, and the entire system. The person will not do trading based on emotions or fear of missing out and the buying and selling at the wrong price is reduced. Algo trading is highly profitable and can provide a great reward when a person is aware of the strategies and applies them correctly. Backtesting has to be done to check if the Algos created are working as expected. An example of simple Algo trading is-
- Buy 500 shares of a stock when the 50 days moving average goes above the 200-day moving average.
- Sell 500 shares of a stock when its 50 days moving average goes below the 200-day moving average.
The computer program will monitor these conditions and automatically place buy and sell orders when the above conditions are met.
Mutual funds, hedge funds, banks, and insurance companies carry out algorithmic trading for executing high-volume trades that are difficult for humans to execute, while individuals can use algorithmic trading for making more number of trades in a limited time without getting affected by emotions.
One of the popular types of algorithmic trading is High-Frequency Trading (HFT) which utilizes volumes to generate profits. Many orders are placed in multiple markets in different conditions by programmed instructions.
Algo trading in India-
Algo trading was introduced in Indian financial markets by SEBI in 2008 and by 2012 the regulator had put in place guidelines for trading in the equity markets. It initiated price checks and quality limit checks and specified a monthly report on Algo trading by the stock exchanges. At present, the exchanges approve Algos submitted by the brokers. The unregulated Algos deployed by retail investors using APIs can pose a risk to the market and could be used for market manipulation, so they should be treated as Algos subject to control by the brokers.
In India, Algo trades account for 43% of the stock market turnover, while in the USA it is 90% of the turnover and the global average is 75%.
Some of the best Algo trading platforms in India are-
Algo trading requires having a suitable trading strategy in place as the success of a trade depends on the strategy. The strategy is then turned into an algorithm, automated, and sent to the exchange for approval. Once approved it is ready to be implemented. The requirement of Algo trading is advanced software that can be built by the traders or they can even acquire the prebuilt ones. Now the trader only has to wait for trading signals to execute the trade.
Algo trading strategies-
The commonly used strategies in Algo trading are-
1) Arbitrage opportunities-
Arbitrage opportunity can be used to buy a lower-priced stock in one market and sell it in another market where the stock price is higher. Using an algorithm to identify the price differences and placing orders results in a profitable trade.
2) Trend Following-
The trend-following strategy is one of the most commonly used and involves moving averages, channel breakouts, price movements, and other technical indicators. These strategies are simple to implement and are initiated when the desired trend occurs which is then implemented through the algorithm.
3) Index Fund Rebalancing-
The index funds carry out rebalancing of their portfolios in order to align their holdings in line with their benchmark indices. This often leads to some profitable opportunities for trades initiated through the algorithmic trading system with timely execution. The opportunity size depends on the number of stocks that are rebalanced.
4) Mean reversion-
The mean reversion strategy is based on the opinion that the high and low price of an asset is a temporary phenomenon and the prices will always revert to the mean value. A price range is identified and an algorithm is designed to place a trade when the price of an asset breaks in and out of its range.
5) Percentage of Volume-
In the percentage of volume strategy the algorithm keeps sending orders according to the volume traded in the market. The orders are sent at a user-defined percentage of market volumes and the participation rate increases or decrease with the stock reaching defined levels.
6) Volume Weighted Average Price-
This strategy breaks a large order and releases smaller parts of the order in the market using the historical volume profile of a particular stock. The aim is to place the order close to the volume-weighted average price.
7) Time Weighted Average Price-
The time-weighted average price strategy also breaks big orders into small parts and the investors use the time slots between the start and the end time to execute the strategy with the help of algorithmic trading. The aim of this strategy is to place orders near the average price between the start and end time.
Pros and Cons of Algo Trading-
1) Increased Speed-
With the help of Algo, trading orders are sent to the market at high speed often in milliseconds or microseconds. The algorithms analyze the parameters and execute the trade quickly and this becomes important for the traders helping them to catch the price movements.
2) Decrease in cost-
Large volumes of orders are executed instantly with the help of Algo trading. As a result, multiple trades are processed and the cost comes down significantly.
3) Increase in accuracy-
In the Algo trades, the human interference gets reduces which reduces the chance of mistakes. Humans can get affected by emotions and make mistakes but with Algo trading, precision is increased as the computers don’t get affected by these factors. The strategies are already formulated and the orders get executed automatically. The habit of overtrading also gets checked.
4) Backtesting is possible-
There is an abundance of data availability in the stock markets and Algo trading uses the data and provides future expectations from past performance. This process is called backtesting which is the exposure of a strategy to historical financial data.
Backtesting provides for a filtration mechanism where the strategies that don’t meet the performance criteria are eliminated. The performance of a strategy can be enhanced by modifying the values of the parameters.
It is possible to execute multiple trades and multiple strategies with Algo trading. Opportunities are found across various markets and the orders can be sent simultaneously. This results in diversification which is very difficult to perform by humans.
6) Discipline in trading-
The Algo trades are carried out according to the strategies. Humans, even if they have planned may not adhere to them due to the volatility of the market. Algo trading does not get affected by human factors such as fear and greed and discipline is maintained.
1) Loss of human control-
Algo trading is completely automated. If a person feels that the strategy will not work in a particular situation they can’t make any changes. When a trade goes in a particular direction that they don’t want to, they can’t do anything.
The problem is its complete dependence on technology. If the internet connection is interrupted the order will not be executed. The trader can miss out on a good trading opportunity. A person can become completely dependent on technology and may feel helpless when not able to perform a trade.
2) Requirement of capital-
Though Algo trading helps in reducing transaction costs there is are added expenditures. The initial setup costs include the hardware and the software required for algorithmic trading. If you are going to try high-frequency trading you may need a leased line. There is a software requirement for creating and testing trading strategies. There are the added data costs which are due to the real-time and historical data requirement which is fed into software to develop algorithms. Besides this, there will be the requirement of VPS (virtual private server).
3) Failure of Strategy-
People may plan out various strategies on paper but these may not be successful during real-time trading. The strategy may have been built on historical data but there is a possibility that it may fail during live trading.
There also may be some good strategies that can’t be automated. This reduces the chances of making more money with these successful strategies.
Algo trading is subjected to a lot of regulations. Different countries have their own regulations and there are many restrictions on Algo trading that have to be dealt with.
5) Life of algorithms-
The lifespan of the algorithms is very short. They may work for a time period and after that, they stop working in a changing marketplace environment; so they may have to be refreshed. It is a regular process to create, monitor, reinvent and improve the algorithms.
Algo trading is good as it eliminates human emotions like fear and greed. The drawback is that it involves costs to build strategies and algorithms. But the world over the use is increasing due to automation and people have to learn to get the best results from Algo trading.
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