What is Algorithmic Trading?

Algorithmic trading, automated trading, algo-trading, or black-box trading, all refer to the same thing: a computer program that places a trade based on a defined set of instructions. Theoretically, the deal can make money more quickly and more frequently than a human trader could. In this article, we discuss the works of algorithmic trading.

What is algorithmic trading?

Trading with algorithms entails opening and closing deals in response to predetermined criteria, such as predetermined points in the underlying market's price movement. You can save time by not manually scanning the markets. Instead, trading algorithms (algos) can do it for you when the current market conditions match any predefined criteria.

Schedules, budgets, quantities, or mathematical models form the basis of the specified sets of instructions. By removing the influence of human emotions from trading, algo-trading makes markets more liquid and trading more methodical, in addition to providing traders with profit opportunities.

How algorithmic trading works

Let's pretend a trader follows these basic guidelines:

If a stock's 50-day moving average rises above its 200-day moving average, you should purchase 50 shares. If the stock's 50-day moving average falls below its 200-day moving average, you should sell your shares.

When the necessary conditions are satisfied, a computer programme will automatically place the buy and sell orders while monitoring the stock price and moving average indicators. All it takes are two simple instructions.

For traders, live price and graph monitoring, as well as manual order entry, are now things of the past. Because it is able to accurately detect trading opportunities, the algorithmic trading system accomplishes this task automatically.

Algo-trading time scales

Modern algorithmic trading is largely characterised by high-frequency trading (HFT), which seeks to profit from the simultaneous execution of numerous orders in different markets using a variety of decision parameters according to pre-programmed instructions.

Algorithmic trading is employed in a wide range of trading and investing activities, including:

• For large-scale stock purchases that do not intend to impact stock prices, algo-trading is a beneficial tool for buy-side corporations, pension funds, insurance companies, and other long-term investors.
• Market makers (such as brokerage firms), speculators, arbitrageurs, and short-term traders all benefit from automated trade execution, and algo-trading helps sellers have sufficient liquidity.
• Programming trading rules and letting the program trade automatically is far more efficient for systematic traders, hedge funds, and pair traders. Pairs trading is a market-neutral trading strategy that matches a long position with a short position in two highly correlated instruments, like two stocks, exchange-traded funds (ETFs), or currencies.

Instead of relying on gut feelings or intuition, algorithmic trading offers a more methodical way to actively trade.

Algorithmic trading strategies

Finding a beneficial opportunity, whether in terms of increased profits or decreased costs, is essential for any algorithmic trading approach. Some typical approaches to algo-trading are as follows:

Arbitrage opportunities

Arbitrage, or the practice of buying a dual-listed stock at a discount in one market and selling it at a premium in another, allows investors to profit from price differentials without taking any risks. When price differentials occasionally arise, investors can apply the same process to futures instruments, unlike stocks. We can realize valuable possibilities by placing orders rapidly and using an algorithm to detect price differentials.

Trend-following strategies

The most common algorithmic trading methods follow trends in moving averages, price level fluctuations, channel breakouts, and related technical indicators. These methods, due to the lack of prediction or price forecasting involved, are the most straightforward and easy to apply through algorithmic trading. Without getting into the specifics of predictive analysis, algorithmic trading initiates trades when favourable trends emerge; these trends are straightforward and simple to implement. One common method for detecting trends is to use moving averages of 50 and 200 days.

Index fund rebalancing

To ensure that their holdings are in line with their benchmark indices, index funds rebalance their portfolios at predetermined intervals. Algorithmic traders can benefit from this because they can anticipate trades that will yield a gain of 20 to 80 basis points, depending on the number of stocks in the index fund, right before rebalancing. Algorithmic trading techniques execute these deals quickly and at the best possible price.

Mean reversion

The mean reversion strategy relies on the transitory nature of asset price extremes and their regular return to their average or mean value. Automatic trades can be executed whenever an asset's price enters or exits a predefined range that has been determined by identifying and implementing an algorithm based on said range.

Mathematical model-based strategies

You can trade options and the underlying security simultaneously using proven mathematical models, such as the delta-neutral trading method. In a delta-neutral portfolio, the positive and negative deltas of each position work together to bring the total delta of all the assets in the portfolio to zero. A delta is a ratio that compares the price change of an asset (often a marketable security) to the price change of its derivative.

Time-weighted average price (TWAP)

The TWAP average pricing technique dynamically releases smaller parts of a large order to the market by uniformly distributing time slots between a start and end time. We execute the order near the average price between the start and end hours to minimise market impact.

Volume-weighted average price (VWAP)

The VWAP average pricing technique uses stock-specific historical volume patterns to dynamically release smaller pieces of a large order to the market. Ideally, the order's execution price will be near the volume-weighted average.

Implementation shortfall

The goal of the implementation shortfall approach is to minimise the order execution cost as much as possible by taking advantage of the opportunity cost of delayed execution and trading off the real-time market. When the stock price goes up, the approach will raise the targeted participation rate; when it goes down, it will lower it.

Percentage of volume (POV)

This algorithm will continue to send partial orders based on the market volume and the specified participation ratio until the trade order fills completely. When the stock price reaches user-defined levels, the corresponding "steps strategy" raises or lowers this participation rate, which sends orders at a proportion of market volumes that the user defines.

Technical requirements for algorithmic trading

The last step in algorithmic trading is to put the algorithm into action using a computer program. Back testing, which tests the algorithm on historical stock-market performance periods to determine its profitability, precedes this step. The task at hand is to integrate the discovered technique into an automated procedure that can access a trading account to place orders. Algorithmic trading necessitates the following:

• The amount of available historical data for back testing is directly proportional to the algorithm's rule complexity.
• The algorithm can identify potential order placement opportunities by accessing feeds of market data.
• You can either have experience in computer programming, have the capacity to employ programmers, or utilize pre-made trading software to formulate the essential trading strategy.
• Before entering live markets, ensure you have the infrastructure and capability to carry out system back tests.
• Internet connectivity and the ability to execute orders using online trading platforms are essential.

Advantages of algorithmic trading

• We frequently execute trades at the best possible price.
• The minimal lag time between placing a trade order and its execution increases the likelihood of its execution at the specified levels. We precisely time trades and execute them promptly to avoid large price fluctuations.
• Cut down on transaction fees.
• The system provides autonomous monitoring of various market circumstances in real time.
• The reduced potential for human error in trade placement means there is no room for human error. It also disproves human traders' propensity to let their emotions and psychology influence their trading decisions.
• To determine if algo-trading is a good trading technique, it can be back-tested using both historical and real-time data.

Disadvantages of algorithmic trading

• Large algorithmic trades can cause market prices to fluctuate wildly, which is disastrous for traders who can't react quickly enough to change their trades. Some have speculated that algorithmic trading causes flash crashes because it makes markets more volatile.
• Algorithmic trading requires low latency, or the time it takes to execute a trade. Trades that execute too slowly could result in missed opportunities or losses.
• Algorithmic trading relies on technology, particularly computer programs and fast internet connections. Technical difficulties or breakdowns can halt the trading process and sustain losses.
• The numerous rules and regulations that algorithmic traders must follow can be difficult and time-consuming to understand and implement.
• Algorithmic trading uses past data and statistical models to foretell how the market will behave. But algorithmic traders run the risk of losing money due to black swan events, which are unexpected disturbances to the market.
• Since algorithmic trading is based on mathematical models and past data, it disregards the subjective and qualitative elements that can impact market fluctuations. For traders who lean towards an instinctual or intuitive trading style, this absence of human judgement might be problematic.
• The inability to tailor trades to individual tastes or requirements is a potential drawback of algorithmic trading systems, which operate on a foundation of pre-established rules and instructions.

Pros and cons of algorithmic trading

Conclusion

In order to open and execute trades based on pre-programmed code, algorithmic trading combines financial markets with computer software. It is up to traders and investors to decide when they want deals to open or close.

They can also carry out high-frequency trading using computer power. Today's financial markets widely use algorithmic trading, providing traders with a wide range of tactics. Prepare yourself with computer hardware, programming knowledge, and expertise in the financial markets before you begin.