How does algorithmic trading work?

Algorithmic trading (also known as automated trading, black-box trading, or algo-trading) uses a computer program to execute trades at specific times. Algorithmic trading can generate profits at a speed that is impossible for human traders with algorithms.

In this article, we’ll look at algorithmic trading, how it works, pros and cons, if it’s profitable, and more.

When were computerised trading systems introduced?

Computerized trading systems were introduced to the US pasar keuangan in the 1970s, leading to a rise in the use of trading algorithms. In 1976, the New York Stock Exchange implemented a designated order turnaround system to route orders from traders to exchange floor specialists.

Over the next few decades, exchanges improved their ability to accept electronic trading. By 2009, computers executed over 60% of all US trades.

Algorithmic trading types

In financial trading, algorithms are rules or instructions that make trading decisions automatically. Among these are simple single-stock and more complex black-box algorithms that analyse market conditions, price moves, and other financial data to execute trades at the best times for the lowest cost-to-maximum profit ratio.

Arrival price algorithms:

These aim to execute trades in the closest possible range to the stock price at the time the order was placed. They help to minimise the effect on the market and reduce the risk of price fluctuations after the order is placed.

Basket for algorithmic trading:

Also referred to as portfolio algorithms, basket algorithms execute orders while evaluating how they will affect other decisions and securities within a portfolio.

For instance, even when a security is offered at the best price, the algorithm may decide to delay trading if doing so would raise risk for the overall portfolio. The algorithm incorporates minimum and maximum participation rates, self-financing, and cash balancing constraints.

Implementation shortfall:

These automated rules are designed to reduce implementation shortfall, which is the cost of executing an order when it differs from the decision price.

Percentage of volume:

These algorithms modify order sizes based on real-time market trading volume. The goal is to maintain a predetermined percentage of the total market volume while balancing market impact and timing.

Single-stock algorithms:

These algorithms are designed to optimise the execution of trades for a single security, taking into account factors like order size and market conditions.

Volume-weighted average price (VWAP):

These algorithms execute orders at a price that is close to the stock’s volume-weighted average price over a specified period.

Time-weighted average price (TWAP):

Time-weighted average price algorithms evenly distribute trades over a set period to achieve an average price reflecting the time-weighted average of the stock price. They are used to reduce market disruption when executing large orders.

Risk-aversion parameter:

This varies according to the trader and the strategies used. It’s often incorporated with other algorithms to adjust trading aggressiveness in line with the trader’s or client’s risk tolerance.

Pros and Cons of algorithmic trading

Keunggulan

Trades are often executed at the best possible prices.
Trade orders can be placed instantly and accurately, with a high probability of execution at the levels required. Trades are executed at the right time to prevent significant price swings.
Lower transaction costs.
Automated checks on multiple market conditions simultaneously.
There is no risk of human error when placing trades.
Algorithmic trading can be backtested using historical and real-time data.

Kekurangan

Computer-assisted trading depends on quick execution times and low latency, which is the delay in a trade execution. There may be missed opportunities from slow trade execution.
Algorithmic trading uses historical data and mathematical models to forecast future market movements. But unexpected market disruptions, also known as black swan events, can happen and result in losses.
Computerized trading depends on technology, such as computer programs and fast internet connections. Technical issues can negatively affect the trading process and lead to losses.
Large algorithmic trades can drastically affect market prices; traders who cannot modify their trades risk losing money. At times, algorithmic trading may also cause increasing market volatility, occasionally resulting in flash crashes.
Many of the complex rules and regulations that apply to algorithmic trading can be very time-consuming to follow.
The cost of creating and implementing algorithmic trading systems can be high, and traders might have to pay extra for data feeds and software.
Algorithmic trading systems rely on pre-defined rules and instructions, which can affect traders’ ability to customise their trades to meet their unique needs.
Computer-assisted trading relies on mathematical models and past data, often overlooking the subjective and qualitative elements that can impact market movements. The absence of human judgment might not suit traders who prefer a more intuitive approach to trading.

Algorithmic trading technical requirements

The final component of algorithmic trading is applying the algorithm using a computer program and backtesting it to see if it would have generated profits on historical periods of past stock-market performance. It involves transforming the selected strategy into an integrated computerised process with access to a trading account for placing orders.

The requirements for algo-trading are as follows:

Computer-programming knowledge to develop the required trading strategy through hiring programmers or using pre-made trading software.
Access to networks and platform perdagangan to place orders.
Availability of market data feeds that the algorithm will monitor for opportunities to place orders.
Infrastructure and ability to backtest the system before launching it on real markets.
Historical data is available for backtesting based on the complexity of the algorithm’s rules.

Is algorithmic trading profitable?

Yes, algorithmic trading can be profitable. With algorithmic trading, you can execute trades more quickly than a human trader, as this type of trading provides a more disciplined, systematic approach. Algorithmic trading also allows you to execute trades at the best prices and eliminates the impact of human emotions on trading decisions.

It is important to keep in mind that you may still have losses even when using an algorithmic trading system. Algorithmic trading carries the same risks and uncertainties as other forms of trading.

In addition, most regular traders lack the funds to create and implement an algorithmic trading system, and they might have to pay fees for software and data feeds. As with any type of investing, it is important to fully research and understand the potential risks and rewards before you make any decisions.

How do I get started in algorithmic trading?

You need to learn programming (C++, Java, or Python) and develop or select a trading strategy before beginning algorithmic trading. Next, use historical data to backtest your strategy. After you’re satisfied with this, put it into practice via a broker that supports algorithmic trading.

Additionally, there are open-source platforms where programmers and traders exchange software and have discussions and advice for beginners.

Final thoughts

Algorithmic trading provides speed, efficiency, and objectivity in trading decisions. It can automate entry and exit points, reduce the risk of human error, and reduce information leaks.

However, there are also significant risks: Alpha trading relies on complex technology that can break down or be hacked, while high-frequency trading can increase system risk. Other potential risks include execution errors, market volatility, and technical glitches.

Disclaimer:
This information is not considered investment advice or an investment recommendation, but instead a marketing communication. IronFX is not responsible for any data or information provided by third parties referenced or hyperlinked in this communication.