Algorithmic trading, also called automated trading, makes use of a computer program that works on a defined set of instructions to place a deal in the market. The trade is generally capable of generating profits frequently, swiftly, and efficiently, which is quite impossible when a human trader or broker is involved.
The set of defined instructions are based on timing, quantity, price, or any other mathematical model. Apart from considering and figuring out the profit opportunities for the user, algorithmic-trading renders trading more systematic, and markets are more liquid by ruling out the effect of human emotions on trading activities.
Algorithmic trading gives a more systematic approach to active trading than other methods based on the trader’s intuition or instinct.
Algorithmic Trading Strategies
Algorithmic trading strategies require an identification opportunity of the market forces that turn out to be profitable in earnings or cost reduction. Here are some commonly used Algo-trading strategies:
- Trend-following strategies: The most common strategies follow trends in moving averages, price level movements, channel breakouts, and related technical indicators. These are the most uncomplicated and straightforward plans to implement through algorithmic trading because they don’t involve any price-based predictions or forecasts. Using 50-200 day moving averages is a common trend-following strategy in the market.
- Arbitrage Opportunities: This involves buying a dual-listed stock at a low price in a particular stock market and sell it off at a higher price simultaneously in another market. This offers a price differentiation and risk-free trading. Implementing an algorithm to identify these price differentials and efficiently placing orders facilitate profits.
- Trading Range (Mean Reversion): This trading strategies is based on the concept that an asset's low and high prices are a temporary phenomenon that reverts to their average value periodically. Identifying and defining a price range to implement an algorithm allows automatic trading when market range reaches defined quotes.
- Volume-weighted Average Price (VWAP): This strategy breaks up a large chunk and releases dynamically smaller chunks to the market using stock-specific historical volume profiles. The goal is to execute the order close to the volume-weighted average price.
- Time Weighted Average Price (TWAP): This automated trading strategy breaks up large orders and releases them in chunks using regularly divided time slots between a start and an end time period. The aim is to execute the order close to the average price between the start and end times, thus minimizing impact.
- Implementation Shortfall: This quantitative trading strategy aims to minimize the execution cost of an order by trading off in a real-time market, thereby saving the cost and benefiting from the opportunity cost of delayed execution. The strategy accordingly increases the targeted participation rate when the stock price in the market moves favorably and decreases when the stock price moves adversely.