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Let's get to the point. What is quantification and what is AI?
In the world of cryptocurrency trading, "quantitative" and "artificial intelligence (AI)" are two terms that are often mentioned. By exploring these two concepts, we can better understand how they can revolutionize trading and help traders make smarter decisions in complex markets. Many people call their strategies or indicators AI quantitative, which is actually to deceive those of you who don't understand AI.
The relationship between artificial intelligence AI, machine learning ML, and deep learning DL
First of all, you need to understand the relationship between artificial intelligence AI, machine learning ML and deep learning DL.
As shown in the figure, these three are inclusive. AI includes machine learning, and machine learning includes deep learning. Therefore, even models that are not machine learning can belong to AI. In the field of trading, even systems that are not based on machine learning models can be regarded as applications of AI as long as they use automated decision-making and pattern recognition to process data and issue trading signals. The people you see calling themselves AI quantitative are taking advantage of this loophole. Even grids can call themselves AI quantitative. However, the grid will explode, which causes many people to be afraid of AI because it is unreliable. In fact, real AI based on deep learning is very reliable. Don't be scared by some unscrupulous scammers.
Grid Trading Strategy
The core idea of the grid trading strategy is to set buy and sell orders at predetermined price intervals. When the market price rises to a certain level, the system automatically executes the sell order; when the price falls to another specific level, the buy order is executed. Such a strategy is based on the assumption that the market will fluctuate within a certain price range, and profits are achieved by constantly buying low and selling high during these fluctuations. Since grid robots are automated, many people call their strategies AI.
Quantitative trading strategies based on indicators
Indicator-based quantitative trading is more advanced than grids and uses mathematical models to determine the best time to buy and sell. Traditional quantitative methods rely on fixed algorithms and statistical indicators, such as moving averages, relative strength index (RSI), Bollinger bands, etc. These indicators can help traders identify market trends and potential trading opportunities. However, these traditional strategies often rely on static rules and cannot adapt to rapid changes in the market. However, this is basically the most advanced quantitative model that retail investors can see. Although it has a certain effect, it is basically outdated.
Quantitative trading model based on machine learning
Machine learning quantitative analysis is the use of statistical learning techniques to analyze financial data and predict market trends. This approach involves learning patterns from historical data and predicting future market behavior based on these patterns. Such models are widely used on Wall Street, but it is difficult for retail investors to actually see such models.
Quantitative trading model based on deep learning
Deep learning is the most cutting-edge technology in the current quantitative field and even in the field of artificial intelligence. Even Wall Street has only recently begun to touch upon it. The AI you know, such as ChatGPT, Doubao, and Kimi, are all based on deep learning, including my model. This is the AI quantification you expect, not the low-end "grid AI quantification" or "indicator AI quantification" on the market.
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Different from traditional indicator quantification and machine learning quantification, it is specially designed for the cryptocurrency market (traditional machine learning models can also be used). It is trained with a large amount of historical data and real-time market dynamics. My model can capture subtle changes and complex patterns in the market, thereby providing high-precision trading signals. The advantage of AI quantification is that it can automatically learn and adapt to the constant changes in the market. It not only has a fast response speed, but also has high prediction accuracy. This is unmatched by traditional quantitative methods that rely on fixed algorithms, indicators and parameters. My AI model ensures that no matter how the market fluctuates, it can provide you with scientific buying and selling advice stably.
We mentioned earlier that deep learning is a subset of machine learning, which involves building and training neural networks to simulate the way the human brain analyzes and processes information. In quantitative trading, deep learning is used to learn complex patterns from unstructured financial data. Although deep learning has advantages in processing complex and large-scale data sets, it also requires greater computing resources and more sophisticated tuning. Machine learning can still provide effective solutions with smaller data sets and fewer computing resources.
Common misunderstanding: Is quantization equal to high frequency?
The answer is no. Quantitative and high frequency are not bound together. Quantitative can also be used for medium and long-term trading, as shown in the four-hour chart below. High frequency is the most profitable only when you can perfectly predict every band. However, the accuracy of the quantitative model you can see is not high, so high frequency loses its meaning.
Conclusion
I hope this popular science article can help you understand quantitative AI and stop being fooled by the so-called quantitative AI in the market. If you are unsure about a blogger, even if he is not a quantitative type, I can also identify him for free. I hope you will not be fooled. Finally, if you want to build your own trading system and community, follow me. I will be your assistant to start your journey as a KOL in the cryptocurrency market, and get all the necessary tools and support to achieve a double increase in trading success and influence.
