They are two ways of saying the same concept. Quantitative trading is mostly used abroad, or quantitative trading is a word introduced from abroad. Programmed trading is domestic quantitative trading. In China, it is often called programmed trading.

The biggest difference between quantitative trading and programmed trading is: during the transaction process, if it is a manual transaction, it is a quantitative transaction, if it is a transaction automatically completed by a computer, it is a programmed transaction.

Programmed trading

The meaning is very simple, which means that it corresponds to manual trading and uses computer programs (programs) to assist, make decisions and execute transactions.

Procedural trading refers to the trading behavior that automatically generates or executes trading instructions through established programs or specific software.

The specific trading timing, positions, stop loss and profit, and profit standards in programmed trading may be written into the trading program, or may be independent of the program. Programmatic is just one way trades are executed.

Generally, there are some well-known advantages of using program trading, such as faster transaction speed, avoiding the influence of human emotions and better execution guarantee, etc.

At the same time, we should also pay attention to the difference between trading procedures and trading systems. The trading system is a complete system, and the specific execution program may be only a part of it. A good trading system should also include risk control, capital utilization, position management, etc., not just the generation of buy and sell signals.

Quantitative trading

It is more based on data and historical statistics, using mathematical tools to study the factors and causes of assets and prices in the market to make some trading decisions. Quantitative trading does not necessarily require the use of computers to execute transactions. However, transactions based on quantitative changes in transaction factors can be called quantitative transactions. General quantitative investment involves relatively complex mathematical models, which requires high mathematical abilities from investors. However, this does not mean that quantitative investment will definitely make money. It also depends on whether the model is effective.

I have to mention “artificial intelligence” and “machine learning” that have been very popular in the past two years. They are too easy to mention at the same time as quantitative trading. But specifically, they include each other, but are different. Quantitative trading looks for relative laws with a certain logical basis. These laws are not static, and the concept of "learning" in machine learning is: If a system can improve its performance by executing a certain process, it is "learning". So for the machine, it can only "execute the process". This process must be deterministic. But this does not fully summarize the relationship between quantification and artificial intelligence. Because machine learning is only one of the ways of artificial intelligence.

Classification of quantitative trading

1. Trend trading

It is suitable for some masters of subjective trading to use technical indicators as auxiliary tools, but if you only use various Technical indicators and indicator combinations are used as core algorithms to build models, and long-term profitability has never been seen.

2. Market-neutral transactions

Have higher correlation with the market, lower risks, higher income stability, and require larger capital capacity. It is suitable for some quantitative traders who find the alpha factor in the market to earn extra returns above the market average return.

3. High-frequency trading

Buying and selling frequently in a very short period of time, completing multiple large transactions. This type of trading method has extremely high requirements on hardware systems and market environment. It is only suitable for professional institutions in mature markets and requires algorithm experts. C/C++ is generally used for algorithmic trading.