Making a trade with the use of a computer program that follows a set of established instructions, or an algorithm, is known as Algo-Trading. This way of dealing in stocks can generate profits at a rate and frequency that would be difficult for an individual to match. Timing, pricing, quantity, or any mathematical model define the sets of instructions.
Algo-Trading not only allows traders to profit but also makes markets more liquid and trading more regular by removing the impact of human emotions on online stock trading.
Algo-Trading in Practice
Some examples of Algo-trading are:
- When the 50-day moving average of a company crosses the 200-day moving average, buy 50 shares.
- When a stock’s 50-day moving average falls below its 200-day moving average, it is time to sell.
Using these two simple commands, a computer software program will automatically monitor the stock price and generate buy-and-sell orders when the preset conditions are met. The trader no longer needs to manually enter orders or examine current prices and data.
The online trading opportunity is determined automatically and reliably by the algorithmic trading system.
List of Algo-Trading Strategies
1. Trend Following Strategies
Moving averages, channel breakouts, price level variations, and other technical indicators are commonly used in algorithmic trading methods. Because they do not include any predictions or price forecasts, these procedures are the easiest and simplest to implement when employing algorithmic trading. Trades are made based on the recurrence of favourable patterns that are basic and straightforward to implement using algorithms without going into the complexity of predictive analysis.
2. Arbitrage Opportunities
A risk-free profit or arbitrage opportunity can be made by purchasing a dual-listed stock at a lower price in one market and selling it at a higher price in another market. Because price differentials occur from time to time, the same technique can be used to compare stocks and futures products. By utilising an algorithm to find such price differentials and placing orders promptly, profitable opportunities can be uncovered.
3. Index Fund Rebalancing
To bring their holdings up to par with their respective benchmark indexes, index funds have set rebalancing intervals. This presents appealing possibilities for algorithmic traders, who profit from expected trades that give 20 to 80 basis points profits just before index fund rebalancing, depending on the number of stocks in the index fund. Such transactions are initiated using algorithmic trading algorithms for quick execution and the best prices.
4. Trading Range
A mean reversion strategy assumes that an asset’s high and low prices are one-time events that revert to its mean (average) value on a regular basis. When the price of an asset moves within or outside of its stated range, transactions can be executed automatically by identifying and specifying a price range and developing an algorithm based on it.
Algorithmic trading uses process and rules-based algorithms to use methods for executing trades. Since the early 1980s, it has increased in prominence and is now utilised by institutional investors and online trading businesses for many objectives.
Algorithmic trading has benefits like faster execution and lower costs. It can also accentuate the market’s bearish inclinations by triggering flash crashes and immediate liquidity loss.
Here are some more insightful articles on stock market trading which will walk you through the basics in buying & selling shares, building of strategies & much more.
Happy Trading 📈