What is Algorithmic Trading?

Algorithmic trading involves using computer algorithms to automate trading decisions and execute orders efficiently. It focuses on the “how” of trading, emphasizing efficiency and speed in executing decisions using technology. Putting pre-programmed trading in place means trading orders can be put in place quicker, and trades can take place faster than a human could do it. It is commonly used in all major financial institutions such as investment banks and pension funds. It is possible for retail investors to access this type of trading tool as well.

How Algorithmic Trading Works

Algorithmic trading works by using computer algorithms to automate the trading process. These algorithms can analyze vast amounts of historical and real-time data to identify potential trading opportunities and execute trades efficiently.

Automated or algorithmic trading relies on bespoke software and the creation of mathematical formulas and instructions to enable the rapid execution of trades when the suitable market opportunity arises. These software-driven trades are capable of generating multiple trades at high speed, which in turn can provide the markets with more liquidity.

Advantages of Algorithmic Trading

Algorithmic trading offers several advantages, including:

• Minimized market impact: When large trading orders hit the market, they can cause significant price shifts – algorithms cleverly break down these orders into smaller portions, making their market entrance more subtle and thus reducing any dramatic influence on the stock’s price.
• Better execution prices: Algorithms, through their ability to analyze real-time market data and historical patterns, can pinpoint optimal moments to buy or sell, securing the most favorable prices for traders.
• Optimized speed: Given that they can act faster than any human, algorithms capitalize on fleeting market opportunities by making instantaneous decisions grounded in vast sets of data.
• Enhanced consistency: Algorithms ensure emotionless, data-driven trading, which not only enhances the uniformity of trading actions but also significantly diminishes the chance of costly human errors.

Disadvantages of Algorithmic Trading

• Market Risk: Impact-driven algorithms, which split larger orders into smaller child orders to minimize market impact, expose the user to larger market risk – this is because the order is executed over a longer period, during which market conditions can change.
• Dependence on Technology: Algorithmic trading relies heavily on technology and data accuracy – any technical glitches, such as system failures or connectivity issues, can lead to significant losses.
• Market Manipulation: There is a possibility of market manipulation by other participants who might anticipate the next move of the algorithm and act accordingly.
• Completion Uncertainty: For algorithms like the Percentage of Volume (POV) algorithm, the completion of the total order is not always certain – there might also be potential competition for liquidity.

Algorithmic Trading Types

There are several commonly used trading types which are used to form algorithmic trading strategies:

TWAP (Time-Weighted Average Price) algorithm

The Time-Weighted Average Price (TWAP) is a trading algorithm that aims to execute orders close to the average price of a security over a specified period. This is achieved by dividing the total order into smaller, equally sized chunks and executing them at regular intervals throughout the set duration. The primary goal of TWAP is to minimize market impact and ensure even execution over time.

Download the Financial Edge Algorithmic Trading Types template to follow these steps to completing these strategies.

VWAP (Volume-Weighted Average Price) Algorithm

VWAP (Volume-Weighted Average Price) algorithm is a fundamental concept in the trading world. It gives the average price a security has traded at throughout the day based on both volume and price. It acts as a benchmark that many traders use to gauge whether they’re getting a good execution price.

This template provides an example of calculating VWAP. We have been provided with information on 8 trades during an observation period: to calculate the VWAP traders must:

1. Sum the total dollar amount traded over the period = $19,944,109.90
2. Sum the total volume traded over this time = 243,960
3. Dividing the amount by the volume will give the VWAP = $81.7516

POV (Percentage of Volume) Algorithm

The Percentage of Volume (POV) algorithm is a trading strategy designed to execute orders based on a predefined percentage of the total market volume. This algorithm aims to minimize market impact by adjusting the execution rate according to the actual market activity.

Impact-Driven Algorithms

Impact-driven algorithms are designed to minimize the market impact of an order by splitting larger orders into smaller child orders.

Cost-Driven Algorithms

Cost-driven algorithms adjust execution strategy based on real-time market conditions, aiming to strike the right balance between cost and execution.

Example of a Moving Average Trading Algorithm

A moving average trading algorithm might involve calculating the average price of a security over a set period and using this information to make trading decisions. For example, the TWAP algorithm calculates the average price of a security over a set period and executes orders at regular intervals to minimize market impact.

An example of TWAP is provided in the Financial Edge template:

The TWAP calculation takes the total price of the trades undertaken, in this example its $654.04 and averages it over the number of trades undertaken, in this case it is 8 trades.

The formula is $654.04 / 8 = $81.7550 TWAP

Impact vs. Cost-Driven Algorithms

Impact-driven and cost-driven algorithms are two types of execution strategies used in algorithmic trading, each with distinct objectives and methodologies.

Impact-driven algorithms

Impact-driven algorithms are designed to minimize an order’s market impact. These algorithms split larger orders into smaller ‘child’ orders to avoid significant price movements caused by executing a large order all at once. By doing so, they aim to achieve better execution prices and reduce the risk of market manipulation by other participants.

However, impact-driven algorithms expose the user to larger market risk because the order is executed over a longer period, during which market conditions can change.

Cost-driven algorithms

Cost-driven algorithms, on the other hand, adjust their execution strategy based on real-time market conditions. These algorithms strive to strike the right balance between minimizing market impact and achieving the best possible execution price. They continuously monitor market conditions and adjust their execution strategy accordingly.

Cost-driven algorithms are designed to optimize the overall cost of execution, taking into account factors such as market volatility, liquidity, and order size.

Getting Started with Algorithmic Trading

To get started with algorithmic trading:

• Develop a trading strategy or algorithm
• Use historical data to back-test the strategy
• Implement the algorithm using the appropriate technology
• Monitor and adjust the algorithm based on real-time market conditions

Once tested, a team can establish that the algorithm is working as planned and delivering what it set out to do. Then, it can be implemented in real-life markets. Once established, there may be instances where algorithms need changing or updating to suit new market or trading conditions.

Conclusion

Algorithmic trading leverages computer algorithms to automate and optimize trading decisions, offering significant advantages such as efficiency, speed, and better execution prices. However, it also presents challenges, including increased market risk and dependence on technology and data accuracy. Balancing these benefits and drawbacks is crucial for successful implementation.