Fine-tuning TimesFM on financial data

Introduction

TimesFM is a time series foundation model released by Google in 2024. This repo contains code following this work , fine-tuning TimesFM on financial data, aligning towards the task of price prediction.

Installation

The timesfm package can only be installed in Python 3.10 due to package conflicts. Ensure that you have the correct Python version installed, which in conda can be done with

conda create -n myenv python=3.10
conda activate myenv

and then installing the package:

pip install timesfm

To run the AR1 model in mock_trading.ipynb, you will also need the statsmodels package.

Data

The fine-tuning dataset is proprietary and not publicly available. However, you can download the necessary data using the following APIs:

• Binance API
• Yahoo Finance API

Fine-Tuning on Financial Data

To run the code in this repository, use the following command:

python src/main.py --workdir=/path/to/workdir --config=configs/fine_tuning.py --dataset_path=/path/to/dataset

Replace /path/to/workdir and /path/to/dataset with your local paths. Logs, tensorboard data and checkpoints will be stored in workdir. src/fine-tuning.py contains the necessary configurations for fine-tuning. A brief summary of the hyperparameter settings is found here:

Hyperparameter/Architecture	Setting
Optimizer	SGD
Linear warmup epochs	5
Total epochs	100
Peak learning rate	1e-4
Momentum	0.9
Gradient clip (max norm)	1.0
Batch size	1024
Max context length	512
Min context length	128
Output length	128
Layers	20
Hidden dimensions	1280

Mock trading

We provide our mock trading script and notebook used in calculating several evaluation metrics. To run the mock trading script, use the following command

python src/mock_trading.py --workdir=/path/to/workdir --data_path=/path/to/dataset

where workdir is where the positions.csv file, representing the buy/sell orders of each day, will be stored. data_path is the path to your test dataset location. mock_trading_utils.py contains some data loading functions but is mainly for PFN internal usage. Please adapt this to your own data cleansing needs.

Key benchmarks

For reference, we provide some key performance benchmarks attained by our experimental runs. We are able to achieve around a 30% of overall train/eval loss reduction. On our test set, we achieve the following performance on S&P500.

Horizon	Ann Sharpe	Max Drawdown	Ann Returns	Ann Volatility	Neutral Cost (%)
2	0.516	-0.0015	0.0125	0.0242	0.0025
4	-0.482	-0.0283	-0.0094	0.0194	-0.0055
8	0.227	-0.0168	0.0049	0.0215	0.0067
16	0.003	-0.0189	0.0001	0.0242	0.0002
32	0.420	-0.0155	0.0143	0.0339	0.0804
64	1.285	-0.0022	0.0333	0.0260	0.3472
128	1.679	-0.0009	0.0361	0.0215	0.6005

The following is a sharpe ratio comparison between our model and traditional benchmarks.

	Ours	Original TimesFM	Random	AR1
S&P500	1.68	0.42	0.03	1.58
TOPIX500	1.06	-1.75	0.11	-0.82
Currencies	0.25	-0.04	-0.03	0.88
Crypto Daily	0.26	-0.03	0.01	0.17

Weights

Pretrained weight is available: https://huggingface.co/pfnet/timesfm-1.0-200m-fin