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--- |
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license: mit |
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title: Synthetic Stock Data Generator & Visualizer |
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emoji: 🧠 |
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colorFrom: blue |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 4.0 |
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app_file: app/app.py |
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pinned: false |
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--- |
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# 🧠 Synthetic Stock Data Generator & Visualizer |
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This project builds a **synthetic stock market data generator** using a combination of **Autoencoders (AE)** and **Generative Adversarial Networks (GANs)**. |
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The goal is to create realistic synthetic financial time-series data and compare model performance between **real** and **synthetic** datasets. |
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--- |
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## 🚀 **Project Overview** |
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### 🔹 Workflow |
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1. **Autoencoder (AE):** |
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- Encodes stock price data into a compressed **latent space**. |
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- Captures temporal and feature-based dependencies between Open, High, Low, Close, and Volume. |
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2. **GAN (Generator + Discriminator):** |
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- Learns to generate **synthetic latent vectors** that mimic the AE latent representations. |
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- Generator produces fake latent vectors. |
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- Discriminator learns to distinguish between real (from AE encoder) and fake (from Generator). |
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3. **Synthetic Data Reconstruction:** |
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- The **synthetic latent vectors** are passed through the **AE Decoder**. |
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- This recreates **synthetic stock market data** at the feature level (Open, High, Low, Close, Volume). |
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4. **Model Evaluation:** |
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- A downstream **neural network classifier** is trained on: |
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- Real data |
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- Synthetic data |
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- Performance metrics and comparison charts are saved in the `/charts` folder. |
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--- |
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## 📊 **Visualization App** |
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The project includes a **Gradio-powered dashboard** to visualize stock time series for real and synthetic data. |
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### 🖥️ Try it on Hugging Face |
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If you’re viewing this on Hugging Face, launch the app directly below 👇 |
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[](https://huggingface.co/spaces/Raheel31/Synthetic_Stock_Data) |
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### 🔍 App Features |
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- Select any stock ticker and feature (Open, High, Low, Close, Volume) |
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- View **5-year time series** comparisons of **original vs synthetic data** |
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- Interactive plots rendered with `matplotlib` |
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--- |
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## 📂 **Repository Structure** |
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