You can deploy this model and start earning money today!

ZiyaZhao/
FinAlogy

Views

No views yet

FinAlogy — VICReg Morphology-Aware Encoder

This repository contains the pretrained visual encoder and demo data for FinAlogy, a visual analogy retrieval system for financial K-line analysis.

💻 Code: https://github.com/nice-zzy/FinAlogy
▶️ Demo Video: https://youtu.be/SIreBcl__WU

Files

File	Description
`checkpoint_best.pth`	Pretrained VICReg encoder checkpoint (CLIP-ViT-B/32 backbone, 675 MB)
`finalogy_demo_instances.zip`	Demo K-line instances for quick inference testing

Model Description

The encoder is a CLIP-ViT-B/32 backbone fine-tuned with VICReg self-supervised learning on DOW30 historical K-line data (2010–2021). It maps candlestick chart images into a 512-dimensional morphology-aware embedding space, capturing shape-level properties (body size, shadow structure, local trend direction) while remaining invariant to absolute price scales.

Training details:

Backbone: CLIP-ViT-B/32
Loss: VICReg (λ = µ = 5)
Data: DOW30 components, 2010–2020 (train), 2021 (test)
Window size: W = 5 trading days
Silver-pair threshold: τ = 0.98

Evaluation (test set, 2,075 unique anchors):

Method	52D Alignment
Barlow Twins	0.785
SimSiam	0.814
VICReg (ours)	0.910

52D Alignment measures cosine similarity between retrieved results and the query in the 52-dimensional morphological feature space — higher is better. VICReg achieves the best morphological alignment among all compared methods.

Usage

python

import torch
from huggingface_hub import hf_hub_download

# Download checkpoint
ckpt_path = hf_hub_download(
    repo_id="ZiyaZhao/FinAlogy",
    filename="checkpoint_best.pth"
)

# Load encoder
checkpoint = torch.load(ckpt_path, map_location="cpu")
# See https://github.com/nice-zzy/FinAlogy for full inference pipeline

For the full retrieval and report generation pipeline, see the GitHub repository.