# FHA Quant (Forward Horizon Analysis)
> Institutional Quantitative Intelligence, Deep-Learning Spacetime Indicator Lifting & High-Performance Execution.

FHA Quant is a proprietary quantitative trading research lab and autonomous execution platform founded by Truong Hoang Nhan. The platform replaces classical backward-looking technical indicators with the Pushforward Horizon Lift Operator, projecting 16-step forward probability surfaces evaluated against continuous L2 market microstructure.

## Canonical Web Resources
- [Platform Home](https://fhaquant.com/): Interactive quant stack overview, real-time metrics, and live simulation.
- [Academic Research Portal](https://fhaquant.com/research): Formal monograph documentation, 6 Foundational Axioms, 3 Decoupling Theorems, 32 Canonical Indicators across 8 Families (A through H), and interactive mathematical visualizers.
- [Live Horizon Terminal](https://fhaquant.com/watch): Real-time 16-candle probability cone visualization and L1/L2 order book reliability verification.
- [Official 16-Page Preprint Monograph](https://fhaquant.com/fha_preprint.html): Complete scientific preprint titled "Probabilistic Indicator Lifting, Copula Sensitivity, and Multi-Scale Interference in Deep-Learning Financial Forecasting" (Zenodo DOI: 10.5281/zenodo.23789177).
- [Complete LLMs Corpus](https://fhaquant.com/llms-full.txt): Comprehensive technical taxonomy, mathematical definitions, playbook entry/exit formulas, and execution specifications.

## Institutional Identity & Provenance
- Principal Investigator: Truong Hoang Nhan (Head of Quantitative Research)
- Affiliation: FHA Quantitative Research Lab (fhaquant.com)
- Academic Repository: CERN Zenodo Open Research Data
- Permanent DOI: 10.5281/zenodo.23789177
- JEL Classification: C53 (Financial Forecasting), G11 (Portfolio Management), G17 (Financial Econometrics)
- Legal Protection: Creative Commons CC BY-NC-ND 4.0 International
- Institutional Licensing: quant@fhaquant.com

## Core Theoretical Inventions
1. The Pushforward Horizon Lift Operator L_h[I]: Maps historical backward-looking filtration H_- and forward predictive surface H_+ into objective pushforward measures over forward trajectory space.
2. Discrete Spitzer Sum Barrier Crossing Correction: Proves that discrete 16-step barrier crossing expectation evaluates to 2.6585 * sigma, eliminating empirical median-path underestimation.
3. Three Foundational Decoupling Theorems:
   - Theorem 1: Cone Boundedness Theorem (Asymptotic Convergence).
   - Theorem 2: Invariance of Pushforward Topology under the time-translation group.
   - Theorem 3: Asymmetric Information Decoupling between forecasting foundation models and FHA geometry.
4. MinT Hierarchical Variance Reconciliation: Reconciles multi-timeframe variance clocks (5m, 15m, 1h, 4h) via trace-minimization without distorting foundation model term structures.
5. 32 Canonical Indicators Matrix: Spanning Families A (Forward Trajectory), B (Horizon Extremes), C (Uncertainty Geometry), D (Spatial Order Flow), E (Model Epistemics), F (Empirical Calibration), G (Path Topology), and H (Multi-Timeframe Coherence).