Recently, many people have been discussing how to avoid emotional decision-making in trading; in fact, algo trading (algorithmic trading) is a very good solution. Instead of relying on willpower to fight FOMO and greed, it's better to let the machine make decisions for you.

In simple terms, algo trading is using computer programs to automatically execute buy and sell orders. You need to first set a set of trading rules—such as buying when the price drops 5%, selling when it rises 5%—and then program these rules into an algorithm that monitors the market 24/7. Once the conditions are met, it automatically places orders. The obvious benefit of this approach is that trading speed can reach milliseconds, completely unaffected by emotions, and it won't make stupid decisions out of impulse.

However, the actual operation of algo trading isn't that simple. First, you need to determine a trading strategy, which could be based on price trends, technical patterns, or other factors. Then, you have to convert the strategy into code—Python is a popular choice because of its powerful data processing libraries. After writing the code, you must backtest it with historical data to see how the strategy performed in the past. Only if the backtest results are satisfactory do you dare to connect it to a real trading platform's API, allowing the algorithm to start trading.

There are several common algo trading strategies in the market. VWAP (Volume Weighted Average Price) involves splitting large orders into smaller chunks and executing them according to market volume, aiming to get as close as possible to the VWAP. TWAP (Time Weighted Average Price) is similar but spreads orders evenly over time rather than based on volume. Another is POV (Percentage of Volume), which executes trades equivalent to a certain percentage of the total market volume. These strategies are designed to minimize market impact when executing large orders.

The advantages of algo trading are indeed numerous. In terms of efficiency, algorithms can detect tiny price fluctuations within milliseconds. Psychologically, because machines execute trades, it completely avoids human greed and fear. But there are also issues: developing and maintaining algorithms requires programming and financial knowledge, which can be a high barrier for many traders. Additionally, the system itself can encounter problems—software bugs, network outages, hardware failures—that could lead to significant losses.

Therefore, algo trading is not a magic pill; it is a tool. When used well, it can improve efficiency and discipline; if misused, it can also cause serious losses. If you're interested in this area, it's recommended to start with simple strategies, thoroughly backtest them, and then try small live trades to test the waters.