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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
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# Model Card for Model ID

The Hybrid Image-Numerical Stock Prediction Model is a multi-input neural network designed to predict binary stock price movements (up/down) based on historical chart images and technical indicators. It processes both image data (stock charts) and numerical data (technical indicators like RSI, MACD, Bollinger Bands, ATR, and OBV) to make predictions.


This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Uses

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### Direct Use

The model can be used for predicting short-term stock price movements based on historical data. It is intended for use in financial analysis when combined with other tools and strategies.

## Bias, Risks, and Limitations

The model may exhibit biases based on the training dataset's characteristics. It may not perform well during sudden market shifts caused by external factors. Users should be cautious about over-reliance on the model's predictions without additional analysis.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model



## Training Details

### Training Data

The model was trained on the Kaylah072001/Stock_Graphs_Dataset, which includes stock chart images and corresponding price data. Technical indicators were extracted using the ta library.



### Training Procedure

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#### Preprocessing [optional]

Image data was rescaled to a range of 0–1 and augmented using rotation, shift, zoom, and flip techniques. Numerical features were extracted dynamically during training using technical indicator calculations.


#### Training Hyperparameters

- **Training regime:** Training regime: Mixed precision (fp32)
Optimizer: Adam
Loss function: Binary crossentropy
Metrics: Accuracy, AUC-ROC
Cross-validation: 5-fold stratified k-fold



### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

Performance was evaluated across different market conditions and stock types.

[More Information Needed]

#### Metrics

Accuracy