Is AI Model Trading Reliable? An Analysis

Today, I will share the application of AI large model technology in financial analysis and quantitative trading. Based on Alibaba’s latest Qwen2 large model, I have built an automated technical analysis and visualization system for stocks.

This system mainly uses various technical tools and libraries such as talib, streamlit, and openai to obtain and analyze stock data, and intelligently analyze and visualize stock technical indicators through the Qwen2 model interface provided by SiliconCloud. By simply inputting the stock code, you can obtain the technical analysis data visualization, AI intelligent analysis, and investment reference results as shown in the figure below:

Taking NVIDIA (NVDA) stock as an example, we input the stock code NVDA and select the analysis time range. The system automatically outputs results based on the Qwen2 model and visualizes technical analysis indicators such as moving averages, MACD, RSI, and OBV:

Due to space limitations, this article only shows part of the code. Click here to obtain the complete code and technical documentation download method (you can also scan the code at the end of the article).Next, I will gradually explain each part of the code to give everyone a clearer understanding of this system.

Importing Libraries

First, we need to import several libraries, each with its specific purpose:

yfinance helps us obtain stock data, tablib is a Python library for processing stock data and calculating technical indicators, and streamlit enables API interaction and web interface display, among other functions.

Defining Large Model API and AI Analysis Functions

We can call the Qwen2 large model through the SiliconFlow platform, which has recently been launched for public testing and allows free access to a quota of 42 yuan, approximately 300 million tokens. It is compatible with the OpenAI interface and can be used after installing the OpenAI Python library. After that, we can directly call the relevant interfaces of OpenAI, as the platform supports most parameters related to OpenAI. By modifying the base_url, model, and api_key parameters, it can be used directly. The supported model parameters are shown in the figure.

To interact with the SiliconFlow API, we define a function: siliconflow_run, which communicates with SiliconFlow via the API key, sends requests, and receives responses:

Then we define a function to call the AI model for stock technical analysis. This function receives technical indicator data, constructs a request, and calls the SiliconFlow API for analysis:

User Input and Stock Data Acquisition

Through the sidebar of Streamlit, we obtain the stock code and date input by the user. This part of the code allows users to input parameters through the web interface. We define functions to obtain stock names and stock data, which retrieve stock names and stock data via API and implement a retry mechanism.

Technical Analysis Data Analysis and Display

After obtaining the data, we calculate technical indicators and call the AI analysis function, then display the results on the web interface:

Through this system, we can obtain stock data in real-time, calculate various technical indicators, and combine them with the latest AI large model for intelligent analysis. It can quickly provide intelligent support for our investment decisions, especially for investors who do not fully understand complex financial indicators, helping them better understand market trends. However, in practice, we also need to consider various factors such as fundamentals for a comprehensive analysis, and then make scientific investment decisions.

Long press to scan the code to obtain the complete code and document download password for this article: