Update README.md

README.md

@@ -1,46 +1,53 @@
-# Enhanced Spectral Stock Analysis (Adaptive Quant Engine)
-
-Professional-grade Hugging Face Space integrating advanced DSP-based spectral cycle analysis with a rolling adaptive backtest and Bayesian optimization of meta-parameters.
-
-## Key Features
-- Adaptive Anti-Aliasing Filter (Butterworth) with volatility and regime sensitivity
-- Multi-Frequency Momentum Extraction (phase + amplitude weighted)
-- Market Regime Detection (ADX-based)
-- Dynamic Parameter Heuristics + Bayesian Meta-Optimization (cutoff, order, thresholds, frequency weighting)
-- Rolling Window Backtest (Train/Validation) with sliding window + weekly step
-- Statistical Signal Validation (forward return success rate, Sharpe, Sortino)
-- Performance Metrics: Sharpe, Sortino, Hit Rate, Max Drawdown
-- Live Adaptive Signal Projection using last optimized window
-- Data Caching Layer for efficiency
-
-## Project Structure
-```
-src/
-  core/
-    spectral_analyzer.py
-    rolling_engine.py
-    bayes_optimizer.py
-    config.py
-  utils/
-    metrics.py
-app.py
-```
-
-## Running Locally
-Install dependencies:
-```bash
-pip install -r requirements.txt
-```
-Run app:
-```bash
-python app.py
-```
-
-## Hugging Face Space
-Space type: Gradio. Set `app.py` as entry point.
-
-## Disclaimer
-Educational & research tool. Not investment advice. Use at your own risk.
-
-## License
-MIT
+---
+license: mit
+title: spectral analysis
+sdk: gradio
+emoji: 🏆
+colorTo: red
+---
+# Enhanced Spectral Stock Analysis (Adaptive Quant Engine)
+
+Professional-grade Hugging Face Space integrating advanced DSP-based spectral cycle analysis with a rolling adaptive backtest and Bayesian optimization of meta-parameters.
+
+## Key Features
+- Adaptive Anti-Aliasing Filter (Butterworth) with volatility and regime sensitivity
+- Multi-Frequency Momentum Extraction (phase + amplitude weighted)
+- Market Regime Detection (ADX-based)
+- Dynamic Parameter Heuristics + Bayesian Meta-Optimization (cutoff, order, thresholds, frequency weighting)
+- Rolling Window Backtest (Train/Validation) with sliding window + weekly step
+- Statistical Signal Validation (forward return success rate, Sharpe, Sortino)
+- Performance Metrics: Sharpe, Sortino, Hit Rate, Max Drawdown
+- Live Adaptive Signal Projection using last optimized window
+- Data Caching Layer for efficiency
+
+## Project Structure
+```
+src/
+  core/
+    spectral_analyzer.py
+    rolling_engine.py
+    bayes_optimizer.py
+    config.py
+  utils/
+    metrics.py
+app.py
+```
+
+## Running Locally
+Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+Run app:
+```bash
+python app.py
+```
+
+## Hugging Face Space
+Space type: Gradio. Set `app.py` as entry point.
+
+## Disclaimer
+Educational & research tool. Not investment advice. Use at your own risk.
+
+## License
+MIT