Top 5 Algo Trading Strategies to Automate Your Trade

Imagine this – you have set your trading rules, stepped out to grab a coffee with your friend and finish your daily chores. By the time you get back home, your trading system has already executed some trades on your behalf, and you’ve just made a profit! Welcome to the world of algorithmic trading, short for algo trading, where your computer becomes your clone but only smarter, faster and without bias.

In simple words, algo trading uses computer programs to automatically place trades on your behalf based on some pre-set rules, instead of relying on gut feeling or emotion. Algo trading is fast (it executes multiple trades in a fraction of a second), precise, and takes off any kind of bias or indecision from trading. And it is transforming how traders trade in the stock market.

Behind every successful algo trader lies a powerful strategy — a set of rules that tell this computer program when to buy, when to sell, and how to manage risk.  But here’s the real question: what strategies actually power these algorithms? Let’s explore five popular algo trading strategies that traders in India are using to stay ahead of the curve.

As the name suggests, mean reversion strategy is based on the idea that stock prices tend to return to their average or ‘mean’ value over time. A stock’s price cannot go too far away from its average price. If there is any sudden spike or drop in a stock’s price, it will come back to its average.  Traders build algorithms that detect when a stock or index deviates significantly from its mean and then take trades expecting it to move back.

For example, let’s say Reliance Industries’ stock usually trades around ₹2,500 but suddenly drops to ₹2,400 due to short-term panic. A mean reversion algorithm may detect this oversold level and trigger a buy order, betting that the price will bounce back toward ₹2,500.

Common indicators used in mean reversion include:

• Bollinger Bands
• Moving Averages (SMA, EMA)

• Relative Strength Index (RSI)

   

The mean reversion strategy is one of the oldest and most popular approaches in algorithmic trading.  Algo traders love this strategy because it performs well in sideways or range-bound markets — where prices frequently oscillate around a median value.

Pro tip: Always backtest your mean reversion strategy, as sudden market trends or news events can break this logic in trending markets. 

Have you ever seen the same phone sold at different prices at two different stores? The latest iPhone 17 is sold online at Rs 1 lac but at the store at Rs 1.2 lacs. Theoretically, you can buy it cheaper online and sell it for a higher price to the other store, making Rs 20,000 profit right away. That’s exactly what arbitrade is and traders in the stock market do it all the time. In the stock market, stocks often trade at a price gap between different exchanges.

Arbitrage strategies exploit small price gaps of the same asset across different platforms or markets. Algorithms are perfect for identifying price gaps in securities because they can scan multiple exchanges in milliseconds and execute trades instantly.

Here’s a quick example: 

Suppose Infosys is trading at ₹1,500 on NSE and ₹1,503 on BSE. The algo immediately buys on NSE and sells on BSE, locking in a ₹3 profit per share — risk-free and lightning-fast.

There are different types of arbitrage strategies, including:

• Index arbitrage: Buying and selling index futures versus constituent stocks.
• Currency arbitrage: Exploiting exchange rate mismatches.
• Cross-border arbitrage: Between international markets.

In India, institutional traders have long used this method, but now, with advanced APIs and co-location services, even retail algo traders can tap into micro-arbitrage opportunities.

Note: Arbitrage margins are small and depend heavily on speed, execution cost, and infrastructure — so you’ll need a fast and reliable setup to make it worthwhile. 

If you’ve heard the saying “The trend is your friend,” that’s exactly what trend-following algorithms are built around.

These strategies don’t predict where the market will go — they follow the direction of the trend once it’s established. The goal is to ride the wave as long as the trend remains intact.

Typical trend-following indicators include:

• Moving Average Crossovers (like 50-day and 200-day MAs)
• MACD (Moving Average Convergence Divergence)
• ADX (Average Directional Index)

Here’s how it works: 

An algo might buy Nifty futures when the 20-day moving average crosses above the 50-day moving average, signaling an uptrend. It might exit (or short) when the crossover reverses.

This strategy works well in strongly trending markets, like when indices or sectors show directional momentum — for example, during bull runs or post-event reactions.

Pro tip: Add stop-loss and trailing stop mechanisms in your code to protect profits and avoid whipsaws when trends reverse suddenly. 

Pivot point trading is another popular algorithmic approach used for identifying potential support and resistance levels in the market.

Pivot points are calculated using the previous day’s high, low, and closing prices to determine where today’s price might face buying or selling pressure.

The formula for the main pivot point (P) is:

• P = (High + Low + Close) / 3

From this, other levels — R1, R2 (resistance) and S1, S2 (support) — are derived.

So how does an algo use this? 
Let’s say the market opens above the pivot point — the algorithm interprets this as a bullish signal and triggers buy positions near support levels, aiming to exit near resistance. Similarly, if the market opens below the pivot, it can trigger sell positions.

This strategy is especially effective for intraday trading, as it helps identify short-term opportunities with clear entry and exit levels.

Many algo traders combine pivot points with candlestick patterns or volume filters to increase accuracy. 

VWAP stands for Volume Weighted Average Price — and it’s one of the most-used tools by institutional traders and algos alike.

VWAP gives the average price a stock has traded throughout the day, based on both price and volume. It acts as a benchmark for trade execution — showing whether a stock is trading above (bullish) or below (bearish) its average.

An algo using the VWAP strategy might:

• Buy when the price crosses above VWAP, signaling momentum and institutional buying.
• Sell when it dips below VWAP, indicating weakness.

 This strategy helps achieve better execution prices, especially for large-volume trades where timing matters.

For retail traders, VWAP-based algos can be a great tool to filter false breakouts and improve intraday entries.

Bonus Tip: Combine VWAP with RSI or MACD to confirm momentum before placing trades — it improves accuracy significantly. 

There is no single “best algo trading strategy”. The best strategy for you will depend on your trading style, your risk tolerance and also market conditions.

If, for instance, you prefer making steady range-restricted trades, you would want to consider either a mean reversion or a pivot point strategy. If you are the type of trader who likes momentum, then something like trend-following strategies or VWAP-based strategies may be preferable. If you’re looking for low-risk, quick-profit setups, arbitrage strategies can certainly fit your needs.

Besides, different strategies could work in different scenarios. There’s no one size fit approach when it comes to algorithmic trading, considering how dynamic markets are. The rThe real strength in all algo trading is not just in one specific trading strategy, but the opportunity to combine these various strategies, optimizing them through backtesting, and employing data for your decisions rather than emotions. 

Algorithmic trading  is revolutionising the financial markets by harnessing the power of data and automation. Automation gives traders the power to trade with precision and speed by dodging emotions and biases that often lead to mistakes, and consequently loss.

Whether it’s mean reversion bringing order after chaos or arbitrage exploiting tiny price mismatches, there are several algo trading strategies that you can choose to suit your personal trading preference and risk tolerance. Regardless of whether you are a beginner looking for automated tools or an advanced trader constructing your own strategies, there are solutions for you to kickstart your algo trading journey.