Mahesh, a passionate investor and trader, realized he was spending countless hours analyzing charts, executing trades, and dealing with inevitable human errors. Despite his efforts, mistakes and inefficiencies were costing him both time and money.
Mahesh started searching for a solution—something faster, more efficient, and error-free. That’s when he discovered algo trading.
But how is this helping him? And there’s always another side of the coin. So, in this blog we will discuss both Positives and negatives of this.
But before, what is Algo trading?
Algorithmic trading, or algo trading, is the process of executing trades using automated systems that follow predefined strategies Completely eliminating human intervention. These advanced algorithms analyze market conditions, detect trading opportunities, and place orders with unmatched speed and precision.
By leveraging technology, algo trading enhances efficiency, minimizes transaction costs, and reduces human error. While profitability is often a key goal, the primary focus is on optimizing execution, managing market risks, and improving overall trading performance.
According to a report by NIFM, more than 50% of total client-side orders on NSE and BSE are executed through algorithmic trading, while proprietary trading firms contribute over 40% of total order volumes. Additionally, over 80% of algo orders are placed through co-location servers at both exchanges, a figure that aligns with global market trends.
As financial markets continue to evolve, algorithmic trading is no longer just an advantage. It’s a fundamental shift that is transforming the way trades are executed, making automation an essential tool for modern traders and institutions alike.
The algorithms are used in various stages of the trading cycle, and they are classified into pre-trade analytics, execution stage and post-trade analytics.
But what do you mean by Pre-trade, execution stage and post-trade analytics?
Pre-trade analytics involves several steps like order routing, risk management assessment and compliance checking, along with actual trade negotiation, affirmation and confirmation.
Execution stages refer to the systematic analysis of trade execution processes to optimize trading performance.
Post-trade analytics is the process of examining and evaluating the details of an executed trade.
It includes various processes like execution price, slippage and also market impact and other metrics.
The algorithms which are used in the algo trading are also of different types:
- Execution algorithms or Agency trading algorithms
- Proprietary trading algorithms
- HFT algorithms
There’s some interesting update from SEBI, On 13th December 2024, SEBI released a consultation paper on algorithmic trading. This actually focusses on the retail participation in algo trading.
In 2008, algorithmic trading was formally introduced in India by SEBI through the direct market access facility. First, only institutional investors were allowed to do this and later in 2016 several companies launched APIs which allowed individual investors to programmatically place buy and sell orders.
Now retail investors can also do algo trading as per the SEBI guidelines.
But what are the advantages and disadvantages of Algo trading?
Advantages:
- One of the key advantages of algorithmic trading is its ability to process and react to market-moving news faster than any human trader.
For instance, if a company releases an important update, an algorithm can instantly analyze its impact, tracking whether the stock price moves by 1% or more within five minutes. It can then execute trades, accordingly, ensuring swift action before manual traders even process the news.
- Real-Time Risk Monitoring & Automated Hedging
Beyond trading decisions, algorithms also play a crucial role in risk management. Advanced real-time analytics continuously recalculate risk metrics such as Value-at-Risk (VaR).
If a position exceeds the predefined VaR threshold, the algorithm can automatically hedge against potential losses, reducing exposure and optimizing portfolio stability.
By combining lightning-fast news analysis with proactive risk management, algorithmic trading not only enhances market efficiency but also empowers traders with greater control and precision in navigating volatile markets.
Disadvantages:
- Technical sufficiency and resources required: One of the biggest disadvantages of algo trading is the technical sufficiency and resources required for algo trading. Algo trading requires knowing how to program in specific program languages, which can take quite a while to learn. This facility may not be accessible to retail investors and small traders.
- Lack of control: As the algo trading automates the trades, if the programme runs in a way that it doesn’t want it to, it creates huge losses. So, the programme needs to be tested before it actually operates.
There are various scenarios where the algos run out of context and create massive losses.
One such example is this:
On April 21, 2012, the Indian stock market witnessed a sudden and sharp decline in Nifty April futures, dropping from 5,300 to 5,000 levels within minutes. During this brief period, nearly 35,000 Nifty futures were traded, triggering panic across the market. The underlying Nifty 50 index also experienced a rapid fall, plunging from 5,313 to 5,245 in a matter of seconds before partially recovering.
By the end of the trading session:
- Nifty April Futures settled at 5,304.8, down 0.96%.
- Benchmark Nifty 50 closed at 5,290.85, down 0.78%.
What Caused the Flash Crash?
According to market speculation, the crash was caused by a faulty algorithm deployed by a leading foreign institutional investor (FII). The algorithmic system, which was designed to execute trades at high speeds, malfunctioned and began placing an excessive number of sell orders in a short span, overwhelming market liquidity.
This event is a classic example of how algorithmic trading, when not properly monitored or tested, can lead to extreme market volatility.
There are different opinions of people on this particular scenario. Sankarsh Chanda, CEO of Savart has expressed his views on retail participation into algo trading.
Sankarsh Chanda, Founder & CEO of Savart points out that retail participation in algorithmic trading carries inherent risks, as noted in SEBI’s studies and market feedback, which often highlight a pattern of losses for retail investors. While recent measures have made algo trading somewhat safer, he believes its speculative nature remains unchanged.
Instead, Sankarsh suggests a better approach—a large public-private fund dedicated to educating investors. By promoting research-backed, long-term investing, he believes this initiative can empower investors to make more informed and sustainable decisions in the market.
So, what’s your view on Algo Trading. Let us know in the comments.