Quantitative Trading is a method of using computer programs to automatically trade according to set rules. Its main features include:
Establish quantitative trading models - establish trading strategy models based on historical data, statistical analysis, etc., and encode them in programming languages;
Backtest trading strategies - repeatedly backtest on historical data to verify the effectiveness of the strategy;
Real-time automation - connect the strategy program to the trading interface to realize automatic order trading;
Continuous optimization - Continuously optimize trading strategy algorithms based on backtesting and real trading results;
Strict risk control - set stop loss, position limit and other rules to strictly control transaction risks;
High-frequency trading - typically using computers to perform large-scale, high-frequency transactions;
Data-driven - quantitative trading relies on statistical analysis of historical data;
Reduce Human Emotions - Reduce the impact of personal emotions on trading.
Quantitative trading achieves rule-based systematic trading through programming, but it also requires a lot of time to research and optimize strategy programs.
It is indeed feasible for ordinary people to learn programming by themselves and make money through quantitative trading, but there are certain difficulties. You need to pay attention to the following points:
Quantitative trading requires programming to implement strategy backtesting and automatic trading, which has a relatively high threshold and a steep learning curve.
You need to learn relevant knowledge such as data analysis, statistics, machine learning, etc. The programming language itself is not enough.
When writing strategies, you need to ANALYZE AND BACKTEST a large amount of historical market data, which requires a relatively professional quantitative analysis platform.
Even after writing a strategy, it is still necessary to monitor the trading results and continuously optimize it. It will not be achieved overnight.
Quantitative trading requires taking on technical risks and market risks, and there is a possibility of losses.
Developing and operating a quantitative trading system on your own also requires significant capital and time costs.
You should have a clear assessment of your own programming ability, analytical ability, and strategic level, and don't be too impatient.
It is best to find a professional quantitative trading team or institution for training and guidance.
You can test the effect in simulated trading first, and then try it in real trading after accumulating enough experience.
We must control the scale of trading funds, strictly implement risk control, and develop gradually.
Therefore, it is feasible for ordinary people to conduct quantitative trading by self-learning programming, but it requires long-term hard learning and understanding of the risks and difficulties. It needs to be treated cautiously and rationally.
I have also tried to put my own trading strategy into the code for automatic trading. Since I don’t understand the code, I used GPT-4 to write it before, and I was able to write it, but I still felt that the risk was too high, so I never implemented it. At present, I only use GPT-4 to write some simple strategies and put them in TradingView as some signals, and at the same time, I place orders independently according to the market situation.