# Using Large Language Models to Predict Bitcoin's Short-Term Trends Based on Online Trends

> This is a 2026 graduation project that explores how to use large language models to analyze online trend data, predict Bitcoin's short-term price behavior, and combine natural language processing with financial forecasting.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-19T10:13:52.000Z
- 最近活动: 2026-05-19T10:22:20.065Z
- 热度: 141.9
- 关键词: 大语言模型, 比特币, 加密货币, 金融预测, 情感分析, 网络趋势, 量化交易, 毕业设计
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-an913478-using-large-language-models-to-predict-short-term-behaviour-of-btc-base
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-an913478-using-large-language-models-to-predict-short-term-behaviour-of-btc-base
- Markdown 来源: floors_fallback

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## [Graduation Project Sharing] Using Large Language Models to Predict Bitcoin's Short-Term Trends Based on Online Trends

This is a 2026 graduation project that explores how to use large language models to analyze online trend data and predict Bitcoin's short-term price behavior, combining natural language processing with financial forecasting. The project aims to address prediction challenges in the cryptocurrency market such as high volatility and emotion-driven movements. Through multi-source data fusion and the advantages of large language models, it provides references for trading decisions and risk management.

## Project Background: Challenges in Cryptocurrency Prediction and Its Connection to Online Trends

## Challenges in Cryptocurrency Prediction

The price volatility of Bitcoin and other cryptocurrencies has long been one of the most difficult phenomena to predict in financial markets. Unlike traditional assets, the cryptocurrency market has unique characteristics:

**Extremely high volatility**: Bitcoin prices can fluctuate drastically in a short period; a daily change of more than 10% is not uncommon.

**24/7 trading**: The cryptocurrency market operates around the clock, with no opening or closing time restrictions like traditional markets, so information spreads and reacts extremely quickly.

**Emotion-driven**: Cryptocurrency prices are largely influenced by market sentiment; discussions on social media, celebrity remarks, and regulatory news can all trigger drastic fluctuations.

**Retail-dominated**: Compared to traditional financial markets, the cryptocurrency market has a higher proportion of retail investors, who are more susceptible to group sentiment and behavioral biases.

## Relationship Between Online Trends and Prices

Studies show that there is a significant correlation between online trend data and cryptocurrency prices. These data sources include:

**Social media discussion volume**: The popularity of Bitcoin discussions on platforms like Twitter and Reddit often precedes price changes.

**Search trends**: Changes in search trends for keywords like "Bitcoin" and "cryptocurrency" on Google reflect fluctuations in public attention.

**Sentiment indicators**: By analyzing the emotional tendency of social media text using natural language processing technology, shifts in market sentiment can be captured.

**News streams**: The quantity and emotional tone of cryptocurrency-related news have a direct impact on prices.

## Technical Approach: Advantages of Large Language Models and Project Scheme Design

## Advantages of Large Language Models

Traditional time series models (such as ARIMA and LSTM) have certain capabilities in processing numerical price data, but they struggle to effectively utilize text information. The emergence of large language models has changed this situation:

### Multimodal Understanding Ability

Large language models can process both numerical and text data simultaneously, converting text information from social media posts, news articles, and forum discussions into quantifiable features.

### Contextual Understanding

Unlike simple keyword counting, large language models can understand the context and semantics of text. For example:
- The emotional polarity of "Bitcoin skyrockets" and "Bitcoin plummets" is completely different
- Sarcasm and irony can be identified
- Professional terms and slang can be correctly understood

### Long Text Processing

Large language models can handle long documents, capture key information, generate summaries and sentiment scores, and provide rich feature inputs for prediction models.

## Project Technical Scheme

### Data Collection Layer

The project needs to collect multi-source data:

**Price data**: Obtain historical price data from cryptocurrency exchange APIs, including opening price, closing price, highest price, lowest price, trading volume, etc.

**Social media data**: Obtain relevant posts and comments through Twitter API, Reddit API, etc., including text content and interaction data (likes, retweets, comment counts).

**News data**: Crawl headline news from cryptocurrency news websites and extract titles and summaries.

**Search trend data**: Obtain search popularity data through Google Trends API.

### Feature Engineering Layer

**Text feature extraction**:
- Use large language models to perform sentiment analysis on social media posts and news
- Extract topics and keywords
- Generate text embedding vectors
- Calculate discussion popularity and spread speed

**Numerical feature construction**:
- Technical indicators: Moving averages, RSI, MACD, etc.
- Volatility indicators
- Trading volume changes

### Prediction Model Layer

The project may adopt the following model architectures:

**Multimodal fusion model**: Fuse text features and numerical features and input them into the prediction model.

**Time series model**: Use Transformer or LSTM to handle time series dependencies.

**Ensemble method**: Combine prediction results from multiple models to improve robustness.

### Prediction Objectives

Since it is short-term prediction, the project may focus on:
- Price direction (up/down/flat) in the next hour/day
- Price fluctuation range
- Trading volume prediction

## Technical Challenges and Countermeasures

## Technical Challenges and Solutions

### Data Quality Issues

Social media data has a lot of noise, including a large amount of irrelevant information and spam content. Solutions include:
- Using large language models for content filtering
- Identifying and removing bot accounts
- Weighted processing of remarks from high-influence users

### Time Synchronization

Timestamps from different data sources may be inconsistent and need to be accurately aligned. The global distribution of the cryptocurrency market also makes time zone handling a challenge.

### Overfitting Risk

Financial market data is non-stationary, and historical patterns may not predict the future. Need to:
- Strict cross-validation
- Rolling window testing
- Regularization techniques

### Latency Issues

Real-time prediction needs to consider the latency of data acquisition and model inference. For high-frequency trading, even millisecond-level latency can affect strategy effectiveness.

## Application Value and Analysis of Project Limitations

## Practical Application Value

### Trading Decision Support

Although it cannot guarantee profits, such models can provide reference signals for traders to assist decision-making:
- Identify turning points in market sentiment
- Warn of potential drastic fluctuations
- Confirm trend directions

### Risk Management

For institutions and individuals holding Bitcoin, the model can help:
- Evaluate the current market risk level
- Optimize position management
- Set stop-loss points

### Research Value

From an academic research perspective, such projects help:
- Understand the microstructure of the cryptocurrency market
- Quantify the impact of social media on prices
- Explore the application boundaries of large language models in the financial field

## Limitations and Risks

### Efficient Market Hypothesis

If this prediction method is truly effective, as more users adopt it, the market will adjust quickly and the method may become ineffective. This is a common phenomenon in the quantitative investment field.

### Black Swan Events

Models are trained based on historical data and cannot predict unprecedented emergencies (such as major regulatory policies, exchange bankruptcies, etc.).

### Ethical and Legal Considerations
- Using social media data needs to comply with platform rules and user privacy policies
- Automated trading may involve regulatory compliance issues
- Prediction results should not be released as investment advice to the public

## Project Summary and Outlook

## Conclusion

This project demonstrates the innovative application of large language models in the field of financial forecasting. Combining natural language processing with quantitative finance is a research direction full of potential. Although cryptocurrency prediction remains an extremely challenging problem, such attempts help us better understand market dynamics and information dissemination mechanisms.

For researchers and developers interested in exploring the intersection of AI and finance, this is a graduation project worth paying attention to.