SemantaAI/semanta-main

🌐 SemantaAI

Hybrid Sovereign World Intelligence OS

semanta.xyz · GitHub · API Staging · Studio

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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	Crypto OHLCV + labels	Published
semantaai-fx-majors	FX majors (EUR/USD, GBP/USD, etc.)	Published
semantaai-fx-other	FX crosses	Published
semantaai-test-dataset	Test/sample data	Published

Quick Start

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

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Semanta v3.7 Final · semanta.xyz
Built with Codex + OpenCode · May 2026