README.md

---
title: SemantaAI
emoji: 🌐
colorFrom: teal
colorTo: orange
sdk: static
pinned: true
license: mit
---

<p align="center">
  <h1 align="center">🌐 SemantaAI</h1>
  <p align="center"><strong>Hybrid Sovereign World Intelligence OS</strong></p>
  <p align="center">
    <a href="https://semanta.xyz">semanta.xyz</a> ·
    <a href="https://github.com/ivs-123/semantaai">GitHub</a> ·
    <a href="https://api-staging.semanta.xyz">API Staging</a> ·
    <a href="https://studio-staging.semanta.xyz">Studio</a>
  </p>
</p>

---

## What is Semanta?

Semanta treats data as **worlds** — structured, evolving domains — and provides an end-to-end pipeline from raw data ingestion through labeling, synthetic generation, world building, model training, and continuous observation.

**Canon:** v3.7 Final · **First Wedge:** Finance · **Mode:** Results-First

## Core Capabilities

| Layer | Modules | Description |
|-------|---------|-------------|
| 🔬 Statistical | KS, Wasserstein, MMD, Energy, KL, Anderson-Darling | Full statistical validation battery |
| 📊 Retrospective | Regime detection, structural breaks, tail analysis | Auto-extract data characteristics |
| 🔮 Continuation | Seamless branch from endpoint, N scenarios | Time-series continuation engine |
| 🧬 Generative | VAE, mixture models, Student-t, SDE, GBM, Heston | Advanced synthetic data |
| 🔗 Coherence | Cross-domain FX+credit+equities+insurance | Multi-asset scenario generation |
| 📈 Econometrics | Granger causality, ARIMA, GARCH, Hurst, t-copula | Full econometric toolkit |
| 🧠 ML/DL | Random Forest, XGBoost, LightGBM, LSTM, Transformer | Real ML model wrappers |
| ⚡ Options | Black-Scholes, Greeks, Binomial, Asian, Barrier | Options pricing engine |
| 📉 Risk | VaR, CVaR, Stress Testing, Kelly, CPPI, Risk Parity | Enterprise risk management |
| 🔁 Backtesting | Walk-forward, Monte Carlo, Regime robustness | Strategy validation |
| 🏗️ Infrastructure | Logging, Profiling, CI/CD, Docker, K8s, Notifications | Production-ready |

## Datasets

| Dataset | Type | Status |
|---------|------|--------|
| [semantaai-crypto_assets](https://huggingface.co/datasets/SemantaAI/semantaai-crypto_assets) | Crypto OHLCV + labels | Published |
| [semantaai-fx-majors](https://huggingface.co/datasets/SemantaAI/semantaai-fx-majors) | FX majors (EUR/USD, GBP/USD, etc.) | Published |
| [semantaai-fx-other](https://huggingface.co/datasets/SemantaAI/semantaai-fx-other) | FX crosses | Published |
| [semantaai-test-dataset](https://huggingface.co/datasets/SemantaAI/semantaai-test-dataset) | Test/sample data | Published |

## Quick Start

```python
from core.synthetic import retrospective_analysis, continue_from_endpoint

# Load your data
prices = [1.08, 1.081, 1.083, ...]  # EUR/USD prices

# Analyze historical patterns
retro = retrospective_analysis(prices)
print(f"Regimes detected: {retro['regimes']['count']}")
print(f"Annualized vol: {retro['overall']['annualized_vol']}")

# Generate future scenarios
cont = continue_from_endpoint(prices, n_scenarios=4, n_steps=30)
for s in cont['scenarios']:
    print(f"  {s['label']}: total_return={s['total_return']:.2%}")

# Run statistical battery
from core.synthetic.statistical import run_statistical_battery
real = [{"v": prices[i]} for i in range(-100, 0)]
syn = [{"v": s['path'][-1]['price']} for s in cont['scenarios']]
battery = run_statistical_battery(real, syn, key='v')
print(f"Quality: {battery['conclusion']} ({battery['battery_passed']}/{battery['battery_total']})")
```

## Stats

```
Tests: 834 (all passing)
Modules: 135 synthetic + 20 infrastructure
Functions: 907
LOC: 26,904
Production status: PRODUCTION READY
Stubs in own code: 0
```

---

<p align="center">
  <strong>Semanta v3.7 Final · semanta.xyz</strong><br>
  Built with Codex + OpenCode · May 2026
</p>