Algorithmic trading encompasses a range of strategies and approaches that leverage computational power to execute trades. Some prominent types of algorithms utilised in algo trading include:

• Momentum-Based Algorithms: These algorithms aim to capitalise on the continuation of existing market trends. They identify assets experiencing significant price movements and seek to exploit the momentum for profitable trades.
• Mean Reversion Algorithms: In contrast to momentum-based strategies, mean reversion algorithms operate on the principle that asset prices tend to revert to their historical average. They trade on the belief that deviations from the mean will eventually correct themselves.
• Statistical Arbitrage Algorithms: These strategies identify mispricing or statistical imbalances among related financial instruments. They seek to profit from price divergences or convergences by simultaneously buying and selling correlated assets.
• Market-Making Algorithms: Market-making algorithms aim to provide liquidity to markets by placing simultaneous buy and sell orders. They profit from the bid-ask spread and typically operate in high-frequency trading environments.
• Volume-Weighted Average Price (VWAP) Algorithms: These algorithms execute trades based on the volume of assets traded over a specified period. They aim to execute orders at average prices relative to trading volume, reducing market impact.
• Time-Weighted Average Price (TWAP) Algorithms: TWAP algorithms aim to execute trades evenly over a predefined period, regardless of volume fluctuations. This approach helps minimise market impact by spreading orders uniformly over time.

Each algorithmic strategy serves distinct purposes, and traders should select that which is most suitable for them based on market conditions, their trading objectives, and risk tolerance levels.