FinGPT is an financial AI framework designed to learn from the wisdom of the market


FinGPT is an AI framework designed to facilitate access to optimized language models for financial tasks. It is open source and can be used commercially.

With FinGPT, the research team from Columbia University and New York University (Shanghai) aims to democratize access to language models optimized for financial markets.

Proprietary models such as BloombergGPT would benefit from access to exclusive financial data, the researchers write. In addition, they say, BloombergGPT is too expensive, estimated at five million US dollars for training, and too inflexible.

Instead, FinGPT uses pre-trained language models and fine-tuning using the efficient low-rank adaptation (LoRA) method. The LoRA method can reduce the number of trainable parameters from 6.17 billion to just 3.67 million, according to the team. This makes the fine-tuning process much faster and less computationally intensive, while still allowing the model to efficiently understand and produce financial text.


Focus on high quality data pipeline

The researchers argue that the success of a financial language model depends as much on the capabilities of the language model as it does on the quality of the data. They see FinGPT as a direct response to BloombergGPT, and therefore place a strong emphasis on data quality and preparation.

The team first developed an automated pipeline of curated, high-quality financial data. They draw from established sources such as Yahoo Finance and Bloomberg, as well as content from Twitter, Reddit, and SEC filings. They also pull information from trend barometers like Google Trends and established datasets like AShare and Stocknet.

FinGPT’s data sources. | Image: FinGPT

According to the team, this data goes through a thorough cleaning and formatting process to ensure its quality and usability.

The data is then processed with language models using the FinGPT framework. Depending on the application, LLMs from well-known companies can be used, or trainable or fine-tunable models can be enriched with custom data. Since fine-tuning is faster than fully training a model, FinGPT is said to be more up-to-date and dynamic than BloombergGPT.

Automated human feedback via a detour

Fine-tuning a model typically requires a large amount of high-quality, labeled data. Labeled data means that the data contains additional information from which the model can learn, such as whether a news story is considered good or bad. Such data can be difficult and expensive to obtain, especially in specialized areas such as finance.


FinGPT as open source under the MIT license on Github. Commercial use is permitted. The developers do not guarantee or take responsibility for financial decisions based on the model.

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