GitHub - dy9gzbph7m-cpu/quant-frontier-capacity-solver: I engineered a closed-loop, mid-frequency covariance optimization system that dynamically anchors high-growth tech alpha against structural mega-cap defensive liquidity, sustaining institutional efficiency up to a mathematically modelled capacity ceiling. · GitHub
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Multi-Asset Cross-Covariance Optimization & Path-Dependent Capacity Engine
An institutional-grade portfolio management and risk-modeling framework engineered in Python. This repository contains two core execution systems: a Production Engine utilizing a stabilized Moore-Penrose pseudo-inverse matrix for mid-frequency portfolio optimization, and a Capacity Stress-Test Simulator mapping strategy degradation using the Square-Root Law of Market Impact.
🏛️ Strategy Architecture & Methodology
1. The Production Engine (production_engine.py)
Optimization Framework: Employs a modern Mean-Variance (Max-Sharpe Ratio) framework to dynamically isolate optimal weights across a highly liquid 49-asset global equity pool.
Matrix Stabilization: Uses a Moore-Penrose pseudo-inverse mapping layer (np.linalg.pinv) to manage and stabilize empirical cross-covariance matrices, neutralizing near-singular data structures and colinearity issues.
Alpha Anchoring & Sector Tilting: Enforces structural technology and luxury infrastructure tilts (specifically targeting NVDA, AMD, and RACE) while executing an absolute weight normalization loop after every portfolio re-balancing sequence.
Execution Horizon: Operates at a mid-frequency cycle, simulating forward performance via chunk-paged vectorized Monte Carlo (100,000 paths) over a standard 263-day horizon.
2. The Capacity Stress-Test Simulator (capacity_stress_test.py)
To isolate the exact operational constraints of the alpha engine, the framework features a path-dependent sequential simulation across a multi-decade horizon.
The Market Impact Layer: Rather than evaluating transaction fees in a vacuum, trades are subjected to a sequential implementation of the Square-Root Law of Market Impact :
$$\Delta p = Y \cdot \sigma \cdot \sqrt{\frac{Q}{V}}$$
Slippage Dynamics: The algorithm pulls real-world Average Daily Volume (ADV) and historical volatility metrics via live API feeds. As capital scales, the engine penalizes executions proportionally to trade size relative to underlying order book depth.
Equilibrium Discovery: The model successfully locates a structural system ceiling at $1.69 Trillion . At this threshold, transaction slippage perfectly counterbalances strategy alpha generation, creating a natural asset capacity equilibrium.
💻 Tech Stack & Requirements
Language: Python 3.8+
Core Libraries: numpy, pandas, yfinance
To install dependencies locally, run:
pip install numpy pandas yfinance
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I engineered a closed-loop, mid-frequency covariance optimization system that dynamically anchors high-growth tech alpha against structural mega-cap defensive liquidity, sustaining institutional efficiency up to a mathematically modelled capacity ceiling.
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