How Are Trading Software Algorithms Impacting Stock Market?

It’s difficult not to imagine the stock exchange as a crowded floor with hundreds of traders shouting their orders, based on movies like The Wolf of Wall Street. However, since the 1970s, when orders became computerized, the world has been steadily shifting toward electronic trading. Algorithmic trading is Placing a buy or sell order in a model that automatically activates an order based on the goals stated by an algorithm’s parameters. Algorithmic trading, in which a computer conducts trades based on pre-programmed instructions, has been around for a long time and is now a significant factor in the stock market’s regular ups and downs. Nowadays, one cannot instruct a person to position a trade by calling a broker.

Algorithms have become increasingly common in the trading world, with many large clients requesting them. These mathematical algorithms analyze every stock market quote and exchange, spot liquidity opportunities, and transform the data into smart trading decisions. Computer-directed trading, also known as algorithmic trading, reduces transaction costs and enables investment managers to control their trading processes. Firms with the scale to bear the costs and reap the gains continue to benefit from algorithm innovation.

A brief history of Algorithmic Trading

Automated Trading accounts for 70% of US equities in 2013. According to analysts, algorithmic trading accounts for a third of total volume on Indian cash shares and nearly half of total volume in the derivatives market. The history of trading goes back to four centuries to 1602. In the first decade of the seventeenth century, the secondary market for VOC (Dutch East India Company or Vereenigde Oost-Indische Compagnie) shares began. In 1602 the Dutch East India Company started transforming Amsterdam from a regional market town to a major financial center. Within days of the release of easily transferable shares, buyers began trading them. Soon, the general public was engaged in many dynamic trades, such as forwards, futures, options, and bear attacks, and by 1680, the Amsterdam market’s strategies were as advanced as those we use today.

The starting of high-frequency trading(HFT)

The phenomenon of “quick information” transmission traces back to the 17th century. And this is what high-frequency trading is all about. Increasing the pace at which information moves is the aim of high-frequency trading. A high-frequency trader (HFT) uses cutting-edge technological advances to obtain information faster than the competition and then conducts his trading order more quickly than the competition. In the nineteenth century, Julius Reuter, the founder of Thomson Reuters, used telegraph cables and a fleet of carrier pigeons to operate a news distribution system.

How did the stock market growth in the 20th century?

The collapse of the railways from about 1900 to 1929 and then the financial sector’s growth from 1982 to 2007 were possibly the two most significant structural shifts in the US stock market. The stock market’s history is the story of a changing economy. The launch of the New York Stock Exchange’s “designated order turnaround” system (DOT, and later SuperDOT), which redirected orders electronically to the correct trading post, which manually executed them, was a watershed moment in the computerization of the order flow in financial markets. Michael Bloomberg founded Innovative Market Systems in 1983.

Michael Bloomberg, a general partner at Salomon Brothers, received a partnership settlement of $10 million in 1981. Bloomberg founded Innovative Market Systems after designing in-house computerized financial systems for Salomon (IMS). Merrill Lynch pumped $30 million into IMS to help fund the construction of the Bloomberg terminal computer system, and IMS was selling computers to all Merrill Lynch clients by 1984. In humanity’s history, and, by extension, the stock market, social and technological upheaval is a recurrent trend.

The inception of algorithm trading

In the late 1980s and 1990s, financial markets with thoroughly electronic execution and related electronic communication networks emerged. Decimalization(a price quoting scheme that uses decimals instead of fractions to reflect security prices) may have aided algorithmic trading in the United States by altering market microstructure by allowing smaller gaps between bid and offer rates, reducing market-makers trading advantage and increasing market liquidity.

The Securities and Exchange Commission (SEC) of the United States allowed electronic exchanges to operate until 1998, paving the way for computerized High-Frequency Trading. HFT was 1000 times quicker than a person at executing trades. Since then, high-frequency trading (HFT) has grown in popularity.

The rise of high-frequency trading (HFT)

HFT trades had an execution time of several seconds by 2001. By 2010, the time had narrowed to milliseconds, microseconds, and then nanoseconds. High-frequency trading accounted for less than 10% of stock orders in the early 2000s, but this has gradually increased. According to the NYSE, high-frequency trading volume increased by 164% between 2005 and 2009.

Innovations in algorithm trading

The industry is expected to boom with the onset of these tech-savvy gen-Z traders, with more than 60% of traders dependent on algorithmic trading by 2020. The main advantage of algorithmic trading is that it can perform many trades in a matter of seconds with minimal human interference. According to a report, a human’s average reaction time is around 215 MS or 14th of a second.

Types of Algorithms being used by traders

Any algorithmic trading approach necessitates identifying a lucrative opportunity in terms of increased profits or cost reduction. The following are some of the most popular Algo-trading strategies:

1. Buying the double stock at a lower price in one market and selling it at a higher price in another market provides a risk-free benefit of an arbitrage opportunity. As price differentials occur from time to time, the same operation can be repeated for stocks vs futures instruments. Financially viable opportunities can be found using an algorithm to classify certain price differentials and place orders efficiently.
2. The idea behind a mean reversion strategy is that an asset’s high and low values are a transient phenomenon that reverts to its mean value (average value) regularly. Identifying and identifying a price range and implementing an algorithm based on it allows trades to be executed automatically when an asset’s price moves inside or outside of its given range.
3. Moving averages, channel breakouts, price level fluctuations, and other technical indicators are used in the most popular algorithmic trading strategies. Since these strategies do not need any projections or price forecasts, they are the easiest and simplest to execute by algorithmic trading. Without going into the complexities of predictive analysis, trades are initiated based on favorable patterns, which are simple and straightforward to enforce through algorithms.
4. The execution shortfall strategy seeks to reduce an order’s execution cost by selling off the real-time market, saving money on demand, and taking advantage of the opportunity cost of delayed execution. When the stock price moves positively, the strategy will increase the desired participation rate and decrease it when the stock price moves in a negative direction.
5. Index funds have set rebalancing cycles to put their assets up to date with their respective benchmark indices. This creates lucrative opportunities for algorithmic traders, who benefit from planned trades that deliver 20 to 80 basis points gains just before index fund rebalancing, depending on the number of stocks in the index fund. For timely execution and the best rates, such trades are initiated using algorithmic trading systems.
6. Using equally split time slots between a start and end time, the time-weighted average price strategy breaks up a large order and releases dynamically calculated smaller chunks of the order to the consumer. The aim is to conduct the order as close to the average price between the start and end times to minimize the market effect.
7. Percentage of volume (POV) algorithm continues sending partial orders until the trade order is filled, based on the given participation ratio and the amount traded in the markets. When the stock price hits user-defined thresholds, the associated “steps plan” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate.

Common Algorithm Trading software

Traders are increasingly interested in Algo trading these days. Many companies in India have developed applications for Algo trading as a result of its benefits. Despite the abundance of options, we must choose the best software for our needs. As we all know, goods on the market do not always meet the required requirements. We must correctly assess them and choose the best option. Below we list some of the algorithm trading software for Algo trading.

1.  AlgoTraders has been extremely popular since its inception in 2009. It is an Algo trading software that is based on open-source technology. AlgoTraders 4.0 is the most recent edition. AlgoTraders makes use of the Esper Engine, which allows it to run at a high pace. It has an available processing speed of 5 lakh events per second. The support team will assist you after you have installed AlgoTrader by providing documentation and online training. The robust backup support is also a significant benefit. It is a low-cost algorithmic trading software framework.
2. Only Zerodha subscribers are eligible for Streak. To trade with the Streak Algo, you must be a Zerodha subscriber. Streak connects to the Kite trading platform from Zerodha. Kite comes in two flavors: we and app. Pi is another program from Zerodha. All of them fit well with Streak. You must use your Zerodha credentials to log into Streak.
3. This is another Zerodha-affiliate Algo trading site. It’s an old Algo website. AlgoZ was established in 2013 to help with Algo trading. It also aids in automated backtesting. AlgoZ has a lot of appealing features, but not all of them are free. It is most likely the first Algo trading app for Indian traders.
4. It’s another well-known name in the Algo trading basket program. Omnesys Nest is a high-end algorithmic trading platform. It supports basket trading, order slicing, and option hedge strategies with 2l and 3l spread. Omnesys supports trading on a variety of markets, including the NSE, CDSL, and MCX. Omnesys Nest is an Algo trading site with a lot of flexibility.
5. Financial Technologies created this app, which is also very common. This platform allows for seamless integration of third-party algorithms. It’s an Algo trading site with several segments. The risk management and order management capabilities of ODIN are well-known.

Does Algorithmic Trading Worsen Stock Market Volatility

Most trading leaves a computerized paper trail. Thus, one thinks it would be simple to address this question by looking at the activities of high-frequency traders. HFT firms, are reticent to reveal their trading practices. Thus, the vast quantities of data involved make it difficult to piece together a coherent picture. Critics of high-frequency trading point to the May 6, 2010 flash crash as an example.

The major indices mysteriously dropped 5-6% in minutes before quickly rebounding. Individual company shares execute at more than 60% values below their value just moments before. As the VIX index, a measure of expected market volatility rises, algorithmic trading can become active. Furthermore, Algo trading can begin when the chances of future market losses increase. When the chances of a recession rise abruptly, pre-programmed sell orders kick in. An inverted yield curve, for example, has been a precursor of a recession.

Bitcoin unexpectedly increased by 20% in April 2019. Some suspected that Algo trading was to blame for the abrupt change in the world’s most famous cryptocurrency. Traders are increasingly using Algo trading for traditional currencies, in addition to stocks. The majority of national governments, including our own, are stimulating their economies. This maintains consumer demand, reduce market uncertainty, and aid the private sector in creating employment.

This is beneficial in terms of reducing uncertainty, but it also entails borrowing. Large amounts of borrowing happen now, particularly because interest rates are near zero. Even at the current borrowing level, only a small portion of annual tax receipts are needed to service the debt. A high level of government debt is manageable while borrowing rates are low. One thing is sure: uncertainty is a common part of capital markets. Thus, we must learn to live with it, algorithmic trading or not.

Conclusion

Using trading software algorithms guarantees big gains with a high frequency. They do, however, have both pros and cons, as do almost all things. Before moving on to the investment section, it’s always a good idea to learn about the algorithm’s positives and negatives.