Sensex@75K! Risk management in algorithmic trading: 8 ways to minimise risk

Since depends hugely on quantitative models, it also presents certain challenges. Besides sophisticated computer algorithms and lightning-fast speed, a robust risk management system is also needed.

In this blog, we will explore volatilities associated with algo trading and how to mitigate them.

Risk Management – The Foundation of Stable Trading Amidst the Fluctuating Market Scenario

Risk management is a crucial tool for stabilising your portfolio during market volatility. Sudden spikes or declines are part and parcel of the stock market.

In April 2024, the and Nifty peaked at their highest levels to date. The benchmark Sensex reached a record mark of around 75,124 in April. Besides, the broader NSE Nifty progressed to achieve the mark of 22,775.70.

Such scenarios depict the uncertainty of the stock market, with rapid peaks being an inevitable yet temporary phase.

If your portfolio is optimised for risk management, you can easily survive market instability.

Why Risk Management Matters – Exploring the Geopolitical Influence on the Stock Market

Factors such as surges in commodity prices, banks’ interest rates, supply chain disruptions, and inflation arise from geopolitical tensions.

It directly affects the companies operating in international trade with countries suffering from geopolitical issues. However, its indirect influence is as significant as the direct one.

This is because geopolitical tensions hamper the market interconnectivity globally. Therefore, if anything goes wrong in one region, it shakes the confidence of investors worldwide, causing volatility in the stock markets.

The crude oil prices have surged over 20% since mid-December 2023, costing up to $90 per barrel. It results from wars in the Middle East and Ukraine, leading to supply interruptions.

For all such sudden cases of market volatility, your investment portfolio should be prepared through robust risk management.

Decoding the Challenges that Accompany Algo Trading

Trade execution within seconds — the most significant advantage of algo trading, is also fraught with specific challenges. They emanate mainly from sudden market fluctuations, complexities, and the peerless speed of algo trading systems. Before implementing a dependable risk management system, it is crucial to understand these challenges.

1. Technical Glitches:

Since algorithmic trading depends entirely on intricate software and multifaceted technology, it is prone to technical faults. Hardware malfunctioning, system bugs, and software shut-downs can interrupt trading processes, resulting in unforeseen losses.

2. Inaccurate Data:

Up-to-date and exact market data forms the core of algorithmic trading. Any anomalies or inconsistencies in the data can cause the algorithm to fulfil flawed trades.

3. Excessive Fine-Tuning:

It takes place when algorithms are over-refined or over-optimised to attain incredible performance with historical data. Consequently, the algorithms face difficulties in adapting to market dynamics in real time. This process is also called "curve fitting,” and it causes dissatisfactory performance and unpredicted losses in live stock markets.

4. Poor Network Latency:

Every microsecond matters in algo trading, which makes network latency a critical aspect to rely on. If there is a delay or disruption in data transmission between the stock exchange and the algorithms of the trader, it can break the trade. Poor network latency leads to wasted opportunities and deficient trade executions.

5. Instable Market Conditions:

Despite their capability to handle dynamic market conditions, algo trading can crash due to extreme fluctuations. A sudden, rapid spike in price fluctuations can induce erratic behaviour of algorithms or stop-loss orders.

Strategies and Practices for Effective Risk Management in Algo Trading

Functional risk management equates to successful algo trading. Its role cannot be overstated in safeguarding capital and harnessing the best opportunities in the market. To mitigate the risks stemming from algo trading, here is what you need to follow:

1. Integrating Stop-Loss Order System:

Employing stop-loss orders instructs the algorithm to auto-exit trades when pre-specific loss beginnings are peaked. It averts sentimental decision-making and restricts the losses to tolerable limits.

2. Stress Evaluation:

Conduct a stress evaluation to check the performance of algorithms under fluctuating market conditions. Such examinations simulate extreme, unfavourable scenarios to help you detect vulnerabilities in your system and strategies. Once you find the loopholes, fine-tune algorithms accordingly to reinforce their efficiency.

3. Pilot Projects:

Once the algorithms are equipped to perform with optimal success rate, run on experiential projects. It will help verify its risk mitigation competence while bringing out any inefficiencies to the light.

4. Diversification is the Key:

Do not pump all your capital resources into a single asset class or an algorithm. Instead, disseminate your investments across different strategies, instruments, and algorithms.

5. Establish Loss Limits:

Clearly define the maximum risk limit for your entire portfolio and every algorithm. Establish tolerable and cumulative loss parameters for every single trade and complete portfolio, respectively. When the algorithms adhere to these imposed limits, they reduce the risks of bigger losses by keeping trades within those pre-specified limitations only.

6. Avoid Overleveraging:

Despite offering investors several advantages, algo trading does not promise hefty gains in each trade. That is why you need to think from a peripheral vision. You should determine the suitable position size for every individual trade based on your risk-taking capacity. Do not overleverage, as it can intensify losses. Position sizing is an effective way to allocate capital thoughtfully to keep your portfolio projected against unbearable losses.

7. Automated Alerts:

No matter how advanced the algorithm trading evolves, continual, real-time monitoring is necessary. You can integrate automated notifications or alerts to stay aware of any irregularities or disruptions in the trading strategy. Timely identification of anomalies facilitates quick intervention, which averts downtime and loss-inducing trading decisions.

8. Risk-Based Model Categorisation:

It is important to note that not all algorithm models pose similar risks. Hence, there should be more scrutiny and monitoring of the models with higher risk. You can assign risk-based tiers based on the ambiguity of model outputs, performance speed, and system complexity.

The Final Words

As algo trading progresses, risk mitigation is the current need of the hour. From posing risk limits to constant monitoring and running pilot projects, assessing the system's preciseness and efficiency is crucial. Besides the measures mentioned above, you should stay aware of the latest stock market trends and adapt to the tech innovations.

(The author is Managing Director of Findoc)

(Disclaimer: Recommendations, suggestions, views, and opinions given by experts are their own. These do not represent the views of the Economic Times)