Building a Small Financial Text Dataset for CPU-Based LLM Training

Summary

The article walks through preparing financial time-series data and training a small language model on a CPU. It introduces tokenizer roles and distinctions between encoder, decoder, and encoder-decoder models, then describes collecting price data from MetaTrader, splitting close-price observations into fixed-length sequences, saving the dataset, tokenizing it, and preparing training and validation data. The stated goal is to make a basic experiment possible without GPU hardware.

The author emphasizes that the example uses a small dataset and limited computing resources, so model quality may be weak and results are intended as a demonstration. Better outcomes may require more or more suitable data, additional processing, and adjusted model parameters. The article positions CPU-trained models as potentially useful for narrow tasks, but it does not provide evidence that the sample model generates reliable trading signals or improves strategy performance.

Key ideas

• A tokenizer converts text into tokens that a language model can process, and tokenizer choice depends on the model task.
• The example gathers MetaTrader price history and divides close prices into sequences for model training.
• The tutorial prepares tokenized training and validation data using a GPT-2 tokenizer.
• CPU training can demonstrate a workflow, but hardware limits and a small dataset constrain model quality.
• The dataset construction and model parameters should be adapted to the specific task and tested empirically.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.