With the rise in digitization driving the need for smart computer programs, algorithms are a part of everyone's daily lives. These algos leverage previous patterns, data trends, and instructions for a user-specific application.
Recently, algorithms have been used to enhance their profit-making potential & minimize human errors. This is done under the umbrella term of 'Algo Trading.'
Developed markets like the US contribute approximately 70- 80% of the equity market turnover. Algo trading in India has also increased by more than 50% of total turnover from 9% in 2010.
Algorithmic trading involves automated transactions to understand when investors enter and exit trades. The lack of human intervention saves time and increases the chances of traders generating more significant swings in market prices.
On that note, let's understand the concepts, strategies, advantages, and consequences of algo trading.
Algo trading involves a well-designed mix of mathematical models, software codes, and formulas to enter and exit trades. The predetermined criteria follow instructions that combine to make the algorithm. This executes trades on the traders' behalf, thereby saving time from manual scans.
Before making transactional decisions, these trading commands factor in volume, price, and timing. Large firms often deploy such automated mechanisms to make thousands of trades in a short period.
By analyzing every quote and trade in the market, identification of liquidity opportunities, and the ability to turn information into intelligent trading decisions, algorithms have become revolutionary game-changers in the stocks, futures and options, and securities arena.
The strategies while coding instructions impact the trading patterns by a wide margin. Here are some of the strategies in algo trading to help traders/investors identify the best algo trading strategy for them are:
Arbitrage is when you buy stocks of the same entity from a market with a lower price and sell them in other exchanges that host slightly higher prices for that same entity. Traders often scour through real-time data to identify stocks. These stocks are trading at different prices in different markets. Traders deploy algo trading systems to profit from the difference.
It is common to exploit such market inefficiencies, resulting in price differentials for a brief window. Designing and implementing an algorithm to spot tiny differences in the asset's listed price in different markets enables profitable opportunities.
This is one of the most simple algo trading strategies, which is popular among traders/investors in India. Algo traders usually follow trends like moving averages, channel breakouts, and price movements to curate codes for the algorithmic trading software. They leverage these indicators that make for the simplest executable strategies that do not deal with any kind of predictive forecasting.
The accurate trend identification capability of the algo trading system helps execute the order for the trader/investor at the opportune moment. The codes also consider the support, resistance, volume, and other indicators before transacting.
There always occurs rebalancing of indexes regularly, which is essentially adding or subtracting securities or modifying the weights of the existent index constituents. This is major because a fund must buy and sell securities to remain in balance with its index. This aligns its holdings with their corresponding benchmark indices. This means matching the underlying asset's current market price.
Quantitatively, the difference of around 20 to 80 basis points is pounced upon by algo trading systems to book deals for increased returns. This essentially means that algorithmic traders looking to book profits capitalise on expected trades that offer 20 to 80 basis points profits which depend on the number of stocks in the index fund right before rebalancing.
Tested and proven mathematical models like the delta-neutral trading strategy enable trading on options and the underlying security. The delta-neutral strategy consists of multiple positions with offsetting positive and negative deltas. This adds up to making the overall delta of the assets zero.
Deltas are usually the ratio comparing the change in the price of an asset to the respective fluctuation in the price of its derivative. It involves trading on the same underlying asset's stock and derivative. This is why algo trading software is used for the identification of such classes and execution based on price changes.
Expert traders often suggest deploying mathematical models as one of the best algo trading tips for risk management in a volatile market.
By definition, VWAP is an intraday trading benchmark that stands for the average price that a security has traded at throughout the day, considering both the volume and price.
Investors often look to execute orders nearer to the volume-weighted average price. Algorithmic trading enables them to break large order volumes into smaller pieces to reach the closing price goals.
Using the stock-specific historical volume profiles leads to increased returns via the right timing. In practice, traders use VWAP as a tool to confirm trends and build trading systems/rules around them. Usually, stocks with prices below VWAP are deemed undervalued, and those above it as overvalued. This effectively means that if prices below VWAP move above it, traders long the stock, and vice versa.
By definition, TWAP is pulled by averaging the entire day's price trend (including open, high, low, and close points). Following this, every day's average price is taken to calculate the average of the entire duration's price.
Drawing parallels to VWAP, this strategy aims to break big order volumes into smaller chunks. In this strategy, traders incorporate divided time slots between the start and end time to deploy algo trading systems.
The ultimate aim is to minimize the market impact by executing an order close to the average price between the start and end times. In practice, high-volume traders use TWAP to execute their orders in smaller chunks when the market price is closer to TWAP, making the execution smooth.
Mean Reversion relies heavily on the concept that no matter the lows and highs, the asset price is bound to revert to its mean value or average rate. So algo traders define the asset's price range and ensure that the asset transaction occurs if it pops in or out of the specified range.
Algorithmic trading substantially cuts down costs for large-scale brokerage firms or institutional investors. Since it enables faster and smoother execution of orders, traders can swiftly book profits from short-lived price fluctuations.
This is one of the major reasons algorithms are incorporated into scalping trading systems to facilitate rapid securities transactions. To sum it up, algo trading minimizes human error, executes large volume orders quickly, identifies price changes across markets, and reduces additional transaction costs.
Therefore, to answer the question 'Is algo trading profitable?' Yes! Algorithmic trading is profitable, provided that one gets a few things right.
Though there are many algo trading benefits, there are also a few downfalls that traders need to be aware of. The liquidity introduced via rapid selling/buying orders disappears instantaneously, leaving traders devoid of the chance to profit from price fluctuations.
The high-speed execution of trades negatively impacts live trades and settlements. This limits the power of trading platforms and financial markets. Algo trades also introduce unwanted volatility into the markets. Also, choosing the right algo trading app or software is difficult for traders as unlimited options are available in the market. Picking the right one is crucial as they trust their hard-earned money in their trading software.
Algo trading is the best avenue for traders looking to minimize errors related to human intervention and build profits. Algo trades demand data analysis, coded instructions, and an understanding of the financial market. Investors must learn algo trading before doing algorithmic trading with real money.
To become proficient in algorithmic trading, you need to look at quantitative analysis or quantitative modelling, as it is significantly used in algorithmic trading. You'll require trading expertise or prior financial market experience because you'll be investing in the stock market. The trading sense will help you build a more intuitive algorithm that seamlessly tracks patterns and provides insights.
Finally, because algorithmic trading frequently uses technology and computers, you'll probably need experience with coding or programming. Hands-on experience with software development, data structures, and algorithms is a major plus on the road to building cutting-edge algorithmic trading bots.
From a language perspective, C++ is a popular programming language among algorithmic traders because it is very effective at processing large amounts of data. However, a more intuitive and manageable language like Python can also be considered a better choice for financial professionals who want to start programming than C or C++.
Although algorithmic trading is permitted, some individuals disagree with how it could affect the markets. Although these worries may be valid, no regulations or legislation prohibit retail traders from using trading algorithms.
However, the ongoing investment in technology in computers and other fields suggests that algo trading is widely accepted in the western world. Essentially, it is a step in the evolution of trade resulting from technological advancement.
In other words, there is no justification for categorising algorithmic trading as criminal. To put it another way, the legality of algorithmic trading depends on the countries and the sort of trader or investor. Only institutional traders may use algorithmic trading lawfully in some nations.
For instance, institutional investors and traders in India are the only ones allowed to use algorithmic trading. Retail traders and investors are not permitted to use it. SEBI, the Securities and Exchange Board of India, has not approved algorithmic trading for retail traders and investors. However, algo trading is treated the same as any other trading in the US and Western countries.
The last step in algorithmic trading is to put the algorithm into practice using a computer programme after backtesting. This includes testing the algorithm on historical periods of past stock-market performance to determine if it would have been profitable. The difficult part is integrating the determined strategy into a computerized system that can access a trading account and accept orders.
You'll also need network connectivity to access trading platforms, live market data streams for placing buy/sell orders, historical data for backtesting, and technical infrastructure. Computer programming skills are not required.