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AriaMF/
anomaly-gpt

Name: anomaly-gpt
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anomaly-gpt

GPT-based financial anomaly detector. Uses next-token perplexity as a market surprise score, evaluated on 7 documented crises against rolling volatility, EWMA, and Isolation Forest baselines.

Model Description

Decoder-only GPT (200,768 parameters) trained on discretized SPY log-returns (2010–2019). Anomaly score = negative log-likelihood of the observed return token given a 20-day context:

Anomaly Score(t) = -log P_θ(r_t | r_{t-19}, ..., r_{t-1})

Scores are smoothed with a 5-day rolling mean before thresholding.

Results

GPT perplexity detects 4/7 documented crises vs 1/7 for rolling volatility.

Event	GPT	Rolling Vol	EWMA	Isolation Forest
Flash Crash 2010	—	—	—	—
China Devaluation 2015	YES	—	YES	—
XIV Vol Cascade 2018	YES	—	—	—
COVID Crash 2020	YES	YES	YES	YES
Meme Stock Squeeze 2021	YES	—	—	—
Fed Rate Shock 2022	—	—	—	YES
SVB Collapse 2023	—	—	—	—
Total	4/7	1/7	2/7	2/7

A fixed z > 2 threshold is not a common operating point across detectors with different score skew. Matched to a common 2.11% flag rate, GPT stays at 4/7 while every baseline drops to 1/7. Against a circular-shift null (1000 shifts), GPT's count is the only one significant at the 1% level (p = 0.004; rolling volatility p = 0.148).

Events detected only by GPT at the matched flag rate: China Devaluation 2015, XIV Vol Cascade 2018, Meme Stock Squeeze 2021. No mechanism is claimed for why these and not others — with n=7 there is no power to test one.

Architecture

vocab_size: 3 (down / flat / up)
context_length: 20 trading days
n_layer: 4, n_head: 4, n_embd: 64
Parameters: 200,768
Weight tying: token embedding ↔ output head
Optimizer: AdamW (β=0.9, 0.95) + cosine LR schedule with linear warmup
Training data: SPY log-returns 2010–2019, 2,264 tokens (251 held out for validation)
Evaluation: SPY 2010–2024

The model is heavily over-parameterised relative to its training set. This is a simplicity choice, not a tuned one; no capacity ablation has been run.

Usage

python

import torch
from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(repo_id="AriaMF/anomaly-gpt", filename="final_model.pt")
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)

# Clone the repo first: github.com/ariamousavifar/anomaly-gpt
from gpt.model import GPT
model = GPT(vocab_size=3, context_length=20)
model.load_state_dict(ckpt["model"])
model.eval()

from data.tokenizer import ReturnTokenizer
from anomaly.scorer import AnomalyScorer
from data.loader import download_returns

tok     = ReturnTokenizer(vocab_size=3)
scorer  = AnomalyScorer(model, tok, context_length=20)
returns = download_returns("SPY")
scores  = scorer.rolling_score(returns)
print(scores.tail())

Limitations

Thresholds are computed in-sample over the full evaluation series, which contains the event windows being tested. Not a forward-looking backtest.
7 events is too few to support fine distinctions between detectors.
SPY only, daily frequency — intraday events are undetectable by construction.
Ternary tokenization discards within-bin magnitude.
This checkpoint predates the repo's seeding fix, so its exact training run is not reproducible. scripts/score_assets.py reproduces every published number from these weights; scripts/train_final.py documents the config.
Not a trading signal — research platform only.

Attribution

The transformer implementation is adapted from coursework for Deep Learning, University of Geneva (2024). The return tokenizer, perplexity scorer, baseline detectors, event registry and evaluation harness are original.

AriaMF/anomaly-gpt