Welcome to Quant Assistant, your valuable helper on your cryptocurrency trading journey. I will provide precise market analysis in the community square, making informed investment decisions in the cryptocurrency market based on buy and sell signals provided by the model. Whether you are a novice, a seasoned trader, or a blogger with a certain follower base, through models and strategies, you can not only grasp market dynamics and enhance your trading skills but also gradually build your influence and become a respected opinion leader KOL, as well as an excellent promoter.

Now let's get to the point: what is quantitative trading, and what is AI?

In the world of cryptocurrency trading, 'quantitative' and 'artificial intelligence (AI)' are two frequently mentioned terms. By exploring these two concepts, we can better understand how they revolutionize trading methods and help traders make more informed 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, you need to understand the relationship between artificial intelligence AI, machine learning ML, and deep learning DL.

As shown in the diagram, these three are nested relationships; AI includes machine learning, and machine learning includes deep learning. Therefore, even models that are not based on machine learning can belong to AI. In the trading field, even systems not based on machine learning models can be considered applications of AI as long as they use automated decision-making and pattern recognition to process data and generate trading signals. Those who claim to be AI quantitative are exploiting this loophole; even grid strategies can call themselves AI quantitative. However, grids will explode when they should, leading many to be afraid of unreliable AI. In fact, true AI based on deep learning is very reliable, so don’t be scared off 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 will automatically execute a sell order; when the price drops to another specific level, a buy order is executed. This strategy is based on the assumption that the market will fluctuate within a certain price range, achieving profits by continuously buying low and selling high during these fluctuations. Because grid robots implement automation, many people refer to their strategies as AI.

Indicator-Based Quantitative Trading Strategies

Indicator-based quantitative trading is more advanced than grids, using mathematical models to determine the best timing for buying and selling. Traditional quantitative methods rely on fixed algorithms and statistical indicators such as moving averages, relative strength index (RSI), and Bollinger Bands. These indicators can help traders identify market trends and potential trading opportunities. However, these traditional strategies often depend on static rules and cannot adapt to rapid market changes. This is basically the highest-end quantitative model that retail investors can see; although it has some utility, it is largely outdated.

Machine Learning-Based Quantitative Trading Models

Machine learning quantitative uses statistical learning techniques to analyze financial data and predict market trends. This method involves learning patterns from historical data and predicting future market behavior based on these patterns. Such models are widely used on Wall Street, while retail investors find it difficult to truly see such models.

Machine Learning-Based Quantitative Trading Models

Deep learning is currently the cutting-edge technology in the field of quantitative trading and even in artificial intelligence, and it is only in recent years that Wall Street has begun to get involved. The AI you are familiar with, such as ChatGPT, Doubao, and Kimi, are all based on deep learning, including the models I use myself. This is the AI quantitative you are expecting, not the low-end 'grid AI quantitative' or 'indicator AI quantitative' seen in the square.

Unlike traditional indicator quantitative and machine learning quantitative, this strategy is specifically designed for the cryptocurrency market (traditional machine learning models can also be used), trained using a large amount of historical data and real-time market dynamics. The model can capture subtle changes and complex patterns in the market, providing high-accuracy trading signals. The advantage of AI quantitative lies in its ability to automatically learn and adapt to the constantly changing market, offering fast response times and high prediction accuracy. This is something that traditional quantitative methods relying on fixed algorithms, indicators, and parameters cannot compare to. Deep learning models ensure that they can reliably provide scientific buy and sell points regardless of market fluctuations.

Earlier, we mentioned that deep learning is a subset of machine learning, involving the construction and training of neural networks to simulate how the human brain analyzes and processes information. In quantitative trading, deep learning is used to learn complex patterns from unstructured financial data. While deep learning has advantages in handling complex and large-scale datasets, it also requires more computational resources and finer tuning. Machine learning, on the other hand, can still provide effective solutions with smaller datasets and fewer computational resources.

The B and S in the following diagram are the points provided by the model and are not trade records; you only need to open a position immediately after the model provides a point to achieve the effect of the model points.

Common Misconceptions: Does Quantitative Mean High Frequency

The answer is 'not equal to'. Quantitative and high frequency do not necessarily occur together; quantitative can also be used for medium to long-term trading, as shown in the four-hour chart below. High frequency only maximizes profits when you can perfectly predict every wave. However, the accuracy of the quantitative models you see is actually not high, which renders high frequency meaningless.

The following chart shows the trade records based on model signals.

Conclusion

I hope this popular science article can help everyone understand quantitative trading and AI, and no longer be deceived by so-called quantitative AI in the square. If you have any bloggers you are unsure about, even if they are not in the quantitative field, I can provide free evaluations. I hope everyone won't be scammed.