What Is Algorithmic Trading?
Algorithmic trading is when you use a computer program that follows specific instructions to make trades faster than a human ever could.
Instructions for these trades are based on timing, price, quantity, or a mathematical model. Algorithmic trading makes the market run smoother and trading more systematic by taking out human emotions.
How It Works
For example, a trader might set up these rules:
- Buy 50 shares of a stock when its 50-day average is above the 200-day average.
- Sell shares when the 50-day average drops below the 200-day average.
A computer can watch the stock price and averages and then make these buys and sells as soon as the conditions are met, without the trader having to watch the prices or do the trades by hand.
Pros and Cons of Algorithmic Trading
- You get the best prices for your trades.
- Orders are placed instantly and accurately.
- You pay less in fees.
- It checks multiple market conditions at once.
- No mistakes from manual entries or emotional decisions.
- You can test strategies using historical data to see if they would work (backtesting).
- You need fast execution. Delays can mean missed chances or losses.
- Unexpected market events can lead to losses.
- It depends on technology, which can sometimes fail.
- Big trades can affect market prices, which can cause losses.
- There are complex rules and fees to follow.
- Setting up these systems can be expensive.
- You can’t customize much since the rules are predefined.
- It doesn’t consider the human aspect, which sometimes can be a downside.
When Is It Used?
- High-frequency trading (HFT) is a type of algo-trading that’s all about making a lot of orders very fast.
- Long-term investors like pension funds use it to buy large amounts without changing market prices.
- Short-term traders use it for quick and automated trading, which helps provide enough liquidity in the market.
- Systematic traders use it because it’s more efficient to program their trading rule
Strategies for Algorithmic Trading
- Trend-Following Strategies: These are easy to implement with algorithms because they follow clear trends like moving averages and don’t need predictions.
- https://tradelocker.com/glossary/short-selling/Arbitrage Opportunities: If a stock is listed at different prices in different markets, you can buy low in one and sell high in another to make a profit.
- Index Fund Rebalancing: Algorithmic traders can make profits from the expected trades that happen just before index funds rebalance.
- Mathematical Model-Based Strategies: These use models like delta-neutral strategies, which involve options and underlying securities.
- Mean Reversion Strategies: This is about the concept that prices will revert to their average value over time.
- Volume-Weighted Average Price (VWAP) and Time Weighted Average Price (TWAP): These strategies break up big orders to minimize market impact.
- Percentage of Volume (POV): This algorithm keeps sending orders based on how much is being traded in the market.
- Implementation Shortfall: This strategy aims to minimize the cost of an order by trading in real-time.
What Tech do You Need for Algo Trading?
You need to:
- Know how to program or hire someone who does.
- Have network connectivity and access to trading platforms.
- Get market data feeds.
- Be able to backtest your system with historical data.
Let’s say you’re trading Royal Dutch Shell stock listed on both the Amsterdam Stock Exchange and London Stock Exchange. An algorithm can spot price differences in real-time and make trades that take advantage of these differences for a profit.
But remember, algorithmic trading isn’t foolproof. Other traders might be using similar strategies, which can affect prices and make strategies less effective. Plus, there are risks like system failures or network issues.
Algorithmic trading is about using computer software to trade based on programmed instructions. It allows for automated high-frequency trading and comes with a variety of strategies for different trading styles. Despite its prevalence, remember that all investing involves risks.