用1亿条金融时间序列数据微调大模型，提升股价预测与交易表现。
Financial Fine-tuning a Large Time Series Model
- 在1亿条金融时序数据上持续预训练TimesFM模型，适配价格波动特性。
- 微调后模型在多种市场中实现更高收益、更优夏普比率和更低回撤。
- 适合量化交易研究者，尤其关注时序大模型应用的从业者。
大型模型在自然语言处理、图像生成及近期时间序列预测中展现出前所未有的能力。本文探讨一个关键问题：能否将市场价视为时间序列，利用大模型进行预测？我们通过评估最新时序基础模型TimesFM在价格预测上的表现回答该问题。发现由于价格数据的不规则性，直接使用TimesFM效果不佳，因此提出在包含1亿个时间点的金融数据上对TimesFM进行微调。这些数据涵盖不同金融工具，粒度为小时级与日级。微调后的模型在价格预测精度上优于基线模型。我们在多个金融市场进行模拟交易，结果表明其在收益率、夏普比率、最大回撤和交易成本方面均优于各类基准模型。
原文摘要 · Abstract (English)
Large models have shown unprecedented capabilities in natural language processing, image generation, and most recently, time series forecasting. This leads us to ask the question: treating market prices as a time series, can large models be used to predict the market? In this paper, we answer this by evaluating the performance of the latest time series foundation model TimesFM on price prediction. We find that due to the irregular nature of price data, directly applying TimesFM gives unsatisfactory results and propose to fine-tune TimeFM on financial data for the task of price prediction. This is done by continual pre-training of the latest time series foundation model TimesFM on price data containing 100 million time points, spanning a range of financial instruments spanning hourly and daily granularities. The fine-tuned model demonstrates higher price prediction accuracy than the baseline model. We conduct mock trading for our model in various financial markets and show that it outperforms various benchmarks in terms of returns, sharpe ratio, max drawdown and trading cost.
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