What is algorithmic trading and how does it work?

Main aspects

• Algorithmic trading uses computer algorithms to automate the buying and selling of financial instruments based on predefined criteria.

• Among the strategies used in algorithmic trading are the Volume Weighted Average Price (VWAP), the Time Weighted Average Price (TWAP), and the Percentage of Volume (POV).

• While it increases efficiency and eliminates emotional bias from trading, it also faces challenges such as technical complexity and the risk of system failures.

Introduction

Emotions often interfere with rational decision-making when trading. Algorithmic trading offers a solution by automating the process. In this article, we will explore its definition, how it works, advantages, and limitations.

What is algorithmic trading?

Algorithmic trading employs computer algorithms to generate and execute buy and sell orders in financial markets. These algorithms analyze market data and operate according to specific rules set by the trader. The goal is to optimize trading and eliminate the emotional bias that can negatively impact results.

How does algorithmic trading work?

There are various ways to implement algorithmic trading, not all of which are efficient or successful. However, for illustrative purposes, we will discuss some simple examples that can serve as a starting point and provide basic concepts of their practical functioning.

Definition of the strategy

The first step is to determine a trading strategy. These can be based on various factors, such as price movements or technical patterns. For example, a simple strategy could be to buy when prices drop by 5% and sell when they rise by 5%.

Algorithm Programming

The next step is to convert this strategy into a computer algorithm. The process involves coding rules and conditions into a program capable of monitoring the market and executing trades automatically.

Python is a popular programming language for this purpose due to its simplicity and the availability of powerful libraries. Here is an illustrative example of how a simple trading algorithm could be coded in Python to trade bitcoin:

This code uses the yfinance library to download historical data for bitcoin (BTC-USD) and the pandas library to process it. Trading strategies are determined by creating buy and sell signals based on price movements. Specifically, this algorithm generates a buy signal when the price drops by 5% compared to the previous day's close and a sell signal when it rises by 5%. The execute_strategy function iterates through the data and prints a buy or sell order based on the signal.

Backtesting

Before the launch, the algorithm undergoes backtesting using historical market data to evaluate its past performance. This helps refine the strategy and increase its effectiveness.

Here is an example of how to perform the backtesting of the previous strategy:

This code simulates the buying and selling of bitcoins based on signals generated by an algorithm to track balances over time. The backtest function initializes the account balance, iterates through the data to execute buy and sell orders, and prints the initial and final balances. This function helps to evaluate the historical performance of a strategy.

Execution

Once properly tested, the algorithm can connect to a trading platform or exchange to execute trades. The algorithms continuously monitor the market. When they identify an opportunity that meets their criteria, they automatically execute a trade.

Numerous platforms offer APIs (Application Programming Interfaces) that allow algorithms to interact programmatically with the market. Below is an example of how to place a market order using the Gate API:

This code uses the Gate_api library to connect to the Gate API. It initializes the client with an API key and a secret, then places a market buy order for a specific amount of bitcoin (BTC) using USDT. The API response will be printed, which includes the order details.

Monitoring

Once the algorithm is up and running, continuous monitoring is required to ensure it operates as intended. Adjustments may be necessary based on changes in market conditions or performance metrics.

This monitoring may include logging mechanisms that document the actions of the algorithm and performance metrics for your review. Here is an example of how to add logs to an algorithm:

This code sets up a logging mechanism using Python's logging library. It creates a log file named trading.log and logs buy and sell actions along with the timestamp and price when such actions occur. These logs help maintain a detailed history of all trades executed by the algorithm, facilitating performance analysis and diagnosing potential issues.

Algorithmic Trading Strategies

The following are examples of some indicators that could be potentially useful in algorithmic trading strategies.

Volume Weighted Average Price (VWAP)

The VWAP is an indicator that can be used in trading strategies that aim to execute orders as close as possible to the volume-weighted average price. The concept involves dividing the total order into small fragments and executing them over a specified period with the goal of matching the market's volume-weighted average price.

Time-Weighted Average Price (TWAP)

The TWAP strategy is similar to the VWAP, but it focuses on executing trades evenly over a specified period, rather than weighting them by volume. This strategy aims to minimize the impact of large orders on market prices by distributing them over time.

Volume Percentage (POV)

The POV involves executing trades based on a predetermined percentage of the market volume. For example, an algorithm might try to execute trades that represent 10% of the total market volume over a specific period. This strategy adjusts execution rates according to market activity to minimize its impact.

Advantages of algorithmic trading

Efficiency

Algorithmic trading can execute orders at high speed, often in milliseconds, allowing even small market movements to be exploited by traders.

Emotion-free trading

Algorithms operate based on predetermined rules and are not influenced by emotions such as FOMO or greed. They can reduce the risk of impulsive decisions that could negatively affect trading outcomes.

Limitations of algorithmic trading

Technical complexity

Developing and maintaining trading algorithms requires technical expertise in programming and financial markets. This can pose a barrier for many traders.

System failures

Algorithmic trading systems are susceptible to technical issues, such as software bugs, connectivity problems, and hardware failures. This issue can lead to significant financial losses if not managed properly.

Conclusion

Algorithmic trading involves the use of computer programs to automatically execute trades based on predefined rules and criteria. While it offers various advantages, such as increased efficiency and emotion-free trading, it also faces challenges such as technical complexity and the risk of system failures.