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license: mit
datasets:
  - helloadhavan/Stock-pile
language:
  - en
pipeline_tag: time-series-forecasting
library_name: pytorch
tags:
  - lstm
  - time-series
  - stock-prediction

LSTM Stock Next-Day Predictor

This model was trained on the Stock-Pile dataset. It is a custom LSTM architecture with a learned ticker embedding, trained on about 5 million rows of daily stock data covering hundreds of companies across US, European and Asian markets. Given the previous 30 trading days, it predicts the next day's Open, High, Low, Close and log-Volume. It is a learning and experimentation project, not financial advice.

Files

File	Purpose
`loader.py`	`Model`, `Normalizer` and `load_model`
`config.json`	Ticker ids, per-ticker normalization statistics and training logs
`stock_predictor/model.pth`	Trained weights
`MODEL_CARD.md`	Architecture, data, evaluation and limitations

Usage

Download the repo by running:

huggingface-cli download helloadhavan/stock-LSTM

import torch
from .stock-LSTM.loader import load_model, Normalizer

model = load_model("stock_predictor/model.pth", "config.json")
normalizer = Normalizer("config.json")

window = {
    "Open": open_t,
    "High": high_t,
    "Low": low_t,
    "Close": close_t,
    "Volume": torch.log1p(volume_t),
}

x = normalizer(window, "AAPL")
ticker_id = normalizer.ticker_ids["AAPL"]
ids = torch.full((x.shape[0], 1), float(ticker_id))
inputs = torch.cat([x, ids], dim=-1).unsqueeze(0)

with torch.no_grad():
    prediction = model(inputs)

result = normalizer.denormalize(prediction[0], "AAPL")
result["Volume"] = torch.expm1(result["Volume"])

Each tensor in window has shape (30,). denormalize returns a dict of prices and log-Volume, so apply torch.expm1 to get raw volume.

Notes

Volume must be log1p-transformed before normalizing.
Only tickers listed in config.json are supported.