Common Algorithmic Trading Strategies

The financial markets move fast — and trading opportunities often appear and vanish within seconds. Trying to catch these moments manually can be difficult, if not impossible. This is where algorithmic trading strategies come into play. By using computer programmes that follow pre-set instructions, you can automate your trades and respond to price changes almost instantly. These strategies don’t just improve your speed — they also reduce trading errors and allow you to manage your trades more efficiently. If you're new to algo trading, here are some of the most popular types of algorithmic trading strategies to get started with. 

Common Algorithmic Trading Strategies to Use 

1. Trend-Following Strategy 

In algorithmic trading, trend-following strategies are designed to detect and ride ongoing market trends. These algorithms process large volumes of historical and live market data to identify patterns that indicate a trend in price movement. Once a trend is recognised—whether upward or downward—the algorithm executes trades in the same direction. 

Example: Suppose Reliance Industries stock is showing a steady upward trend for several days. An algorithm can be programmed to detect this trend using technical indicators like Moving Averages. The moment the price crosses a certain level — say, the 50-day moving average — the algorithm places a buy order. 

1. Momentum Trading 

This approach involves trading stocks that are already moving sharply in one direction with strong volume. This algo trading approach uses tools like RSI and MACD to detect when a stock is building or losing momentum. The goal is to stay with the trend until indicators suggest a possible reversal. 

Example: During the budget announcement, shares of infrastructure companies like L&T or IRB Infra see a sudden spike in both price and trading volume. An algorithmic strategy detects this spike and quickly buys into these stocks to benefit from the momentum. This strategy captures short bursts of price movement by entering trades when the momentum is high and exiting before it fades. 

1. Mean Reversion Strategy 

This strategy is built on the concept that asset prices tend to move back toward their historical average over time. When a stock price rises or falls too far from its usual range, the algorithm assumes it will bounce back — and places trades accordingly. Bollinger Bands and moving averages help the algorithm determine these average prices. 

Example: HDFC Bank stock typically trades between ₹1,500 and ₹1,700. Suddenly, due to a temporary market panic, the stock drops to ₹1,450. A mean reversion algorithm detects this drop and places a buy order expecting the price to revert to its usual average. 

1. Arbitrage Trading 

This algorithmic trading strategy uses automated programs to take advantage of price differences for the same asset across various markets or exchanges. This strategy aims to earn profits by taking advantage of price differences for the same stock across two markets, with minimal risk.  

It uses algorithms to scan multiple exchanges and execute trades swiftly before the price difference disappears. 

Example: If Infosys is trading at ₹1,420 on NSE and ₹1,423 on BSE, the algorithm can quickly buy from NSE and sell on BSE, securing a ₹3 gain per share. While such price differences may go unnoticed by humans, trading bots can detect even minor gaps and capitalise on them efficiently, especially when trading in large volumes. 

1. Index Fund Rebalancing 

Index fund investing is a passive strategy that mirrors the stocks and weights of a benchmark index. These funds are regularly rebalanced to stay in line with the index, which can lead to large stock buy or sell actions and cause price changes. 

Algorithms can be designed to spot upcoming rebalancing events and predict which stocks may be affected. This allows traders to act in advance and potentially profit from the price movements triggered by the rebalancing. 

Example: When the Nifty 50 index is rebalanced to add a new company — say, Adani Enterprises — large index funds are forced to buy the stock to match the index. An algorithm can detect this announcement early and buy the stock ahead of the index funds. 

1. Black Swan Event Trading 

This refers to strategies designed to profit from rare and unexpected events that cause extreme market volatility.Black swan events are rare, unpredictable incidents like the 2008 crash or the 2020 pandemic.  

They can cause massive market movements. Algorithms that track unusual price activity — especially in derivatives — can help spot early signs and place strategic trades during these volatile times. 

Example: When COVID-19 was declared a pandemic, stocks across sectors plunged. But soon after, pharma stocks like Dr Reddy’s and Cipla surged. A black swan catcher algorithm can be designed to detect such rare events and trade in sectors expected to benefit post-crisis. 

1. Risk-On/Risk-Off Trading 

Markets move through phases where traders are either willing to take risks or are more cautious. This strategy adjusts your trades based on market sentiment — buying risky assets in a ‘risk-on’ phase and switching to safer ones in a ‘risk-off’ phase. Algorithms track global cues and economic indicators to decide your next move. 

Example: During global uncertainty, investors move away from mid-cap stocks like Zomato or Paytm and invest in safer blue-chip stocks like TCS or HUL. A risk-off algorithm detects this shift and reallocates the portfolio to reflect the new sentiment. 

1. Inverse Volatility Strategy 

This involves adjusting your investments based on market volatility. When volatility is low, the algorithm increases your exposure to stocks. When it rises, it shifts towards safer options. It relies on tools like the volatility index (VIX) and historical patterns to guide trades. 

Example: The India VIX (Volatility Index) drops sharply, indicating a stable market. An inverse volatility algorithm increases exposure to equities like SBI or Tata Motors when the market is calm and reduces exposure during volatile phases. 

1. News-Based Trading 

Algorithms can also scan financial news in real time using Natural Language Processing (NLP). Based on the impact of a news story — like a company merger or economic data release — they can quickly place buy or sell orders before the broader market reacts. 

Example: A government policy favouring renewable energy is announced. The algorithm scans news feeds and buys stocks like Adani Green and Tata Power instantly before the broader market reacts. 

What Is High-Frequency Trading? 

High-frequency trading (HFT) is a special form of algorithmic trading strategy where algorithms place a large number of trades within milliseconds. These systems use powerful computers and ultra-fast networks to capture tiny price differences that last only for a split second. While it requires advanced infrastructure, HFT is one of the most aggressive types of algorithmic trading strategies and is mostly used by big institutions. 

Which Strategies Work Best for Algo Trading? 

There’s no one-size-fits-all answer. The best algorithmic trading strategies depend on your goals, trading style and market conditions.  

For example: 

• Trend-following works well in trending markets.

• Arbitrage strategies are great in markets with pricing inefficiencies.

• Momentum trading can perform well during earnings seasons or high news activity. 

Conclusion 

Using the right types of algorithmic trading strategies can help you trade smarter, faster and more efficiently. Even if you’re new to trading, these strategies are beginner-friendly and can be adapted to your risk appetite. Just remember to use proper risk controls like stop-losses and avoid overtrading. As you gain more experience, you can refine your strategies, explore high-frequency trading, and even test your ideas with free APIs. Algorithmic trading is not just for experts anymore — it’s for anyone looking to make better decisions in today’s fast-moving markets.