Say goodbye to emotional trading: algorithmic trading automates your decision-making.

What is the biggest enemy of trading? It is not market fluctuations, but your own emotions. FOMO, greed, fear—these emotions are consuming traders' profits every day. Is there a way to completely avoid this problem? Yes, with algorithmic trading.

What is algorithmic trading? In one sentence.

Algorithmic trading allows computers to automatically place orders according to the rules you set. No need to monitor the market, no manual operation required; the program will monitor the market 24/7 and execute immediately once the conditions are met. The current price of Bitcoin (BTC) is $88.07K, and this real-time fluctuation is where the algorithm comes into play—price changes that humans cannot react to, computers can execute trades in milliseconds.

From Idea to Practice: Five Key Steps

Step 1: Define Your Trading Logic

For example, set the rules: buy when the price drops by 5%, and sell when it rises by 5%. This is your trading strategy. It sounds simple, but executing this strategy requires monitoring the market around the clock without rest—this is where the value of the Algorithm lies.

Step 2: Implement this logic with code

Python is the preferred language because it has strong support for financial databases. You need to code the trading rules into a program so that it can read real-time market data and generate buy and sell signals. This step requires a certain level of programming knowledge, but there are plenty of tutorials available online for reference.

Step 3: Validate the strategy with historical data (backtesting)

Before real trading, simulate your algorithm using past market data. Assume it has been running from the beginning of 2024 to now, and see whether this strategy would make a profit or a loss. This step is crucial—it can help you identify vulnerabilities in the strategy and avoid paying tuition with real money.

Step 4: Connect to the trading platform's API

Choose a trading platform that supports API, and use the API key to enable your program to place orders. The program will automatically execute buys and sells, and all transactions will be recorded for reference.

Step 5: Continuous Monitoring and Optimization

After the algorithm goes live, it’s not enough to just “play dead”. You need to regularly check the logs, track performance metrics, and adjust parameters according to market changes. The market is evolving, and your algorithm must evolve as well.

Detailed Explanation of the Three Main Algorithmic Trading Strategies

Volume Weighted Average Price (VWAP)

The core logic of this strategy is to split large orders into smaller chunks and execute them throughout the trading day, allowing your average execution price to approach the market's volume-weighted average price. The benefits are lower costs and reduced market impact.

Time-Weighted Average Price (TWAP)

Similar to VWAP, but does not consider trading volume, instead evenly disperses orders over time. This strategy is useful if you want to complete a large trade within a fixed time but are afraid of impacting the market.

Percentage of Volume Algorithm (POV)

Execute trades based on a certain percentage of the total market volume. For example, if the current 24-hour trading volume is $274.69M, your algorithm might aim to execute 10% of that. This way, you can follow the market rhythm without appearing abrupt.

Why do traders use algorithms?

Speed Advantage

Human reaction time is measured in seconds, while algorithms operate in milliseconds. Minor price fluctuations mean nothing to human traders, but can represent a complete profit cycle for algorithms.

Eliminate emotions completely

Greed prevents you from holding onto the gains, while fear causes you to panic sell. Algorithms do not have these issues—they execute according to rules, and will not change strategies due to a positive news event, nor will they panic and close positions because of a market crash.

But it's not that simple: the difficulties of the Algorithm

High technical threshold

Writing trading algorithms requires both programming and financial knowledge. A small bug can lead to a complete failure of the strategy, even causing losses. This is an insurmountable barrier for many traders.

systemic risk

Network disconnection, server failure, API timeout - any technical issue can disrupt your plans. Algorithmic market flash crash events occur from time to time, sometimes simply because a certain system hasn't implemented proper fault tolerance.

Final Recommendations

Algorithmic trading is not a “set it and forget it” money-making machine. It is a tool, powerful but requiring careful maintenance. If you want to use it, either invest time in learning programming and financial knowledge, or spend money on ready-made algorithm services. The key is to understand its principles—this way, you can react quickly when it encounters problems, and also assess whether it truly fits your trading style.