Algorithmic trading, including low latency trading, emerged as a solution to the challenges of manual trading. The complexity of financial markets makes it difficult to make trading decisions based on human emotions and intuition. Algorithmic trading uses automated computer programs to execute trades based on pre-defined criteria. This helps take the psychology out of trading and enables traders to respond to market conditions and fluctuations in real-time. An Introduction to Algorithmic Trading will help demystify the topic to improve your trading performance. In this article, we will cover the basics of algorithmic trading to help you gain a clear understanding of how it works and what its components are. This will allow you to implement automated strategies and enhance your trading success confidently.
One way to support your algorithmic trading journey is with trading VPS. A VPS hosts your algorithmic trading programs on a remote server, ensuring they can run 24/7 and execute trades on your behalf without interruption.
Introduction to Algorithmic Trading
Algorithmic trading strategies involve making trading decisions based on pre-set rules programmed into a computer. A trader or investor writes code that executes trades on behalf of the trader or investor when certain conditions are met.
How Does Algorithmic Trading Work?
Trading algorithms are meticulously designed to follow specific trading strategies based on predefined rules and criteria. They include a few components and basic infrastructure to function. Here are a few:
Market Data Feed
Algorithms rely on real-time market data, often provided through market data feeds. These feeds offer information on stock prices, volumes, and other relevant market indicators.
Trading Platform
A robust trading platform (or form of trading software or trading bot) is essential for executing algorithmic strategies. It provides the necessary infrastructure for order placement and execution.
Algorithmic Models
These models contain the trading logic and rules that guide decision-making. They can range from simple strategies like moving averages to complex machine learning (ML) algorithms.
Risk Management
Effective risk management tools and mechanisms are vital to controlling potential losses in algo trading. Algorithms often include risk parameters to limit exposure.
Connectivity
Low-latency connectivity to exchanges or trading venues is critical. The faster an algorithm can access market data and execute orders, the more competitive it can be.
Backtesting and Simulation
Algorithms are often tested and optimized through backtesting and simulation to assess their historical performance before they’re deployed in live markets.
Monitoring and Oversight
Algorithms must be monitored continuously to ensure they operate as intended. Human oversight is often necessary to intervene in unexpected market conditions.
Example of a Moving Average Trading Algorithm
Moving average trading algorithms are prevalent and extremely easy to implement. The algorithm buys a security (e.g., stocks) if its current market price is below its average market price over some period and sells a security if its market price is more than its average market price over some period. Here, we consider a 20-day moving average trading algorithm.
A Simple Trading Strategy: Buy Low, Sell High
The algorithm buys shares in Apple (AAPL) if the current market price is less than the 20-day moving average and sells Apple shares if the current market price is more than the 20-day moving average. The green arrow indicates a point when the algorithm would’ve bought shares, and the red arrow indicates a point when this algorithm would’ve sold shares.
7 Advantages of Algorithmic Trading
1. Minimize Market Impact
A large trade can change the market price, which is a distortionary trade. To avoid this situation, traders usually open large positions that may move the market in steps.
Staggered Buying to Minimize Market Impact
For example, an investor wanting to buy one million shares in Apple might buy the shares in batches of 1,000 shares. The investor might buy 1,000 shares every five minutes for an hour and then evaluate the trade’s impact on Apple stocks’ market price. If the price remains unchanged, the investor will continue with his purchase.
Cost, Time, and Market Impact
Such a strategy allows the investor to buy Apple shares without increasing prices. Nevertheless, the strategy comes with two main drawbacks:
- If the investor needs to pay a fixed fee for every transaction he makes, the strategy might incur significant transaction costs and take considerable time to complete.
- If the investor buys 1,000 shares every five minutes, it would take him just over 83 hours (more than three days) to complete the trade.
A trading algorithm can solve the problem by buying shares and checking if purchasing has impacted the market price. It can significantly reduce the number of transactions needed to complete the trade and the time taken to complete the trade.
2. Ensures Rules-Based Decision-Making
Traders and investors often get swayed by sentiment and emotion and disregard their trading strategies. For example, in the lead-up to the 2008 Global Financial Crisis, financial markets showed signs that a crisis was on the horizon.
Nevertheless, many investors ignored the signs because they were caught up in the “bull market frenzy” of the mid-2000s and didn’t think a crisis was possible. Algorithms solve the problem by ensuring that all trades adhere to a predetermined set of rules.
3. Best Execution
Trades are often executed at the best possible prices.
4. Low Latency
Trade order placement is instant and accurate (there is a high chance of execution at the desired levels). Trades are timed correctly and instantly to avoid significant price changes.
5. Reduced Transaction Costs
Lowering fees and expenses associated with transactions to improve cost-efficiency and overall profitability for businesses and consumers.
6. A Multi-faceted Approach
Simultaneous automated checks on multiple market conditions.
7. Backtesting
Algo trading can be backtested using available historical and real-time data to see if it is a viable trading strategy.
6 Disadvantages of Algorithmic Trading
1. Miss Out on Trades
A trading algorithm may miss out on trades because the latter doesn’t exhibit any of the signs the algorithm’s been programmed to look for. It can be mitigated to a certain extent by simply increasing the number of indicators the algorithm should look for, but such a list can never be complete.
2. Latency
Algorithmic trading relies on fast execution speeds and low latency, which is the delay in the execution of a trade. If a trade is not executed quickly enough, it may result in missed opportunities or losses.
3. Black Swan Events
Algorithmic trading relies on historical data and mathematical models to predict future market movements. But unforeseen market disruptions, known as black swan events, can occur, resulting in losses for algorithmic traders.
4. Dependence on Technology
Algorithmic trading relies on technology, including computer programs and high-speed internet connections. Technical issues or failures can disrupt the trading process and result in losses.
5. Regulation
Algorithmic trading is subject to various regulatory requirements and oversight, which can be complex and time-consuming to comply with. High Capital Costs: Developing and implementing algorithmic trading systems can be costly, and traders may need to pay ongoing fees for software and data feeds.
6. Lack of Human Judgment
Algorithmic trading relies on mathematical models and historical data, so it does not consider the subjective and qualitative factors that can influence market movements. This lack of human judgment can disadvantage traders who prefer a more intuitive or instinctive approach to trading.
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11 Algorithmic Trading Strategies
1. Decoding Automated Trading Systems: Algorithmic Trading Strategies
Automated trading systems, also known as algorithmic trading systems, operate by using computer programs to buy and sell securities automatically based on predetermined criteria.
Algorithmic trading strategies offer endless possibilities. Any strategy requires an identified profitable opportunity, such as improved earnings or cost reduction.
2. Trend-Following Strategies
The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements, and related technical indicators. These are the easiest and simplest strategies to implement through algorithmic trading because these strategies do not involve making any predictions or price forecasts.
A Simple Yet Effective Approach
Trades are initiated based on desirable trends, which are easy to implement through algorithms without getting into the complexity of predictive analysis. Using 50- and 200-day moving averages is a popular trend-following strategy.
3. Arbitrage Opportunities
Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks vs. futures instruments as price differentials do exist from time to time. Implementing an algorithm to identify such price differentials and placing the orders efficiently allows profitable opportunities.
4. Index Fund Rebalancing
Index funds have defined rebalancing periods to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20 to 80 basis points profits depending on the number of stocks in the index fund just before index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and the best prices.
5. Mathematical Model-Based Strategies
Proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and the underlying security. (Delta neutral is a portfolio strategy consisting of multiple positions with offsetting positive and negative deltas, a ratio comparing the change in the price of an asset, usually marketable security, to the corresponding change in the price of its derivative, so that the overall delta of the assets in question totals zero.)
6. Trading Range (Mean Reversion)
Mean reversion strategy is based on the concept that an asset’s high and low prices are a temporary phenomenon that revert to their mean value (average value) periodically. Identifying and defining a price range and implementing an algorithm based on it allows trades to be placed automatically when the price of an asset breaks in and out of its defined range.
7. Volume-Weighted Average Price (VWAP)
Volume-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock-specific historical volume profiles. The aim is to execute the order close to the volume-weighted average price (VWAP).
8. Time Weighted Average Price (TWAP)
A time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times, thereby minimizing market impact.
9. Percentage of Volume (POV)
Until the trade order is filled, this algorithm sends partial orders according to the defined participation ratio and the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels.
10. Implementation Shortfall
The implementation shortfall strategy aims to minimize the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when it moves adversely.
11. Beyond the Usual Trading Algorithms
A few special classes of algorithms attempt to identify happenings on the other side. These sniffing algorithms—used, for example, by a sell-side market maker—have the built-in intelligence to identify the existence of any algorithms on the buy side of a large order. Such detection through algorithms will help the market maker identify large order opportunities and enable them to benefit by filling the orders at a higher price. This is sometimes identified as high-tech front-running.
Depending on the circumstances, the practice can be considered illegal and is heavily regulated by the Financial Industry Regulatory Authority (FINRA).
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- Futures Trading Algorithms
Technical Requirements for Algorithmic Trading
The final component of algorithmic trading is implementing the algorithm using a computer program, accompanied by backtesting (trying out the algorithm on historical periods of past stock-market performance to see if using it would have been profitable).
The challenge is to transform the identified strategy into an integrated computerized process with access to a trading account for placing orders. The following are the requirements for algorithmic trading:
What Tools Are Needed for Algorithmic Trading?
Computer-programming knowledge to program the required trading strategy, hired programmers, or pre-made trading software. Network connectivity and access to trading platforms to place orders. Access to market data feeds that will be monitored by the algorithm for opportunities to place orders.
The ability and infrastructure to backtest the system once it is built before it goes live on real markets. Available historical data for backtesting depends on the complexity of rules implemented in the algorithm.
An Example of Algorithmic Trading
Royal Dutch Shell (RDS) is listed on the Amsterdam Stock Exchange (AEX) and London Stock Exchange (LSE). We start by building an algorithm to identify arbitrage opportunities. Here are a few interesting observations:
- AEX trades in euros while LSE trades in British pound sterling due to the one-hour time difference.
- AEX opens an hour earlier than LSE followed by both exchanges trading simultaneously for the next few hours and then trading only in LSE during the last hour as AEX closes.
Can we explore the possibility of arbitrage trading on the Royal Dutch Shell stock listed on these two markets in two different currencies?
What Happens When a Strategy is Executed?
The computer program should perform the following:
- Read the incoming price feed of RDS stock from both exchanges. Using the available foreign exchange rates, convert the price of one currency to the other. Suppose a large enough price discrepancy (discounting the brokerage costs) leads to a profitable opportunity. In that case, the program should place the buy order on the lower-priced exchange and sell the order on the higher-priced exchange.
- If the orders are executed as desired, the arbitrage profit will follow, and it will be simple and easy! Nevertheless, algorithmic trading is more complex to maintain and execute. Remember, if one investor can place an algorithm-generated trade, so can other market participants. Consequently, prices fluctuate in milliseconds and even microseconds.
In the above example, what happens if a buy trade is executed but the sell trade does not because the sell prices change when the order hits the market? The trader will be left with an open position making the arbitrage strategy worthless.
Additional risks and challenges include:
- System failure
- Network connectivity errors
- Time lags between trade orders and execution
- Imperfect algorithms
The more complex an algorithm, the more stringent backtesting is needed before it is implemented.
Deploy a Trading VPS Today
Algorithmic trading uses computer programs to automate trading decisions. Traders create algorithms, or sets of rules, that instruct their computers when to buy and sell based on market data. Algorithmic trading helps remove emotions from trading and can execute orders faster than humans.