Insights & Perspectives
Ideas, observations and perspectives from Alphigma.
Alphigma’s publishing and communication layer: in-depth articles, technology discussions, engineering architecture, market structure commentary, and quantitative tutorials.
While Research focuses on structured empirical investigations, backtests, and statistical evidence, Insights represents our broader commentary, architectural breakdowns, tutorials, and industry observations.
6 Themes
Publishing TracksAI Infra & Quant
Focus AreasProduction Engineering
Technical DepthAlphigma's AI Stack
A comprehensive exploration of the 5-layer architecture powering Alphigma: from curated datasets and multi-model routing via OmniRoute to collaborative agentic research and production products.
5-tier decoupled stack: Data, Models, OmniRoute Gateway, Agentic Research, and Products.
Continuous knowledge flywheel: Data → AI → Research → Products → Knowledge.
Specialized agent teams for signal discovery, anomaly detection, and backtest robustness.
Unified OpenAI-compatible gateway abstracting multi-provider execution.
Articles, Architecture & Commentary
Explore our 6 primary insight publications spanning AI infrastructure, quantitative theory, machine learning guardrails, market microstructure, and low-latency engineering.
Alphigma's AI Stack
A comprehensive exploration of the 5-layer architecture powering Alphigma: from curated datasets and multi-model routing via OmniRoute to collaborative agentic research and production products.
Why OmniRoute Is the AI Gateway Behind Alphigma Quant AI
How Alphigma uses a multi-provider AI architecture to power its research, coding and content workflows with dynamic model routing, token optimization, and high-availability fallbacks.
Deconstructing Modern Portfolio Theory: Limitations in Fat-Tailed Regimes
An exploration of why quadratic variance optimization breaks down during non-Gaussian liquidity crises and how Conditional Value-at-Risk (CVaR) bridges the gap.
Why LLMs Need Guardrails When Generating Quantitative Backtests
Analyzing common pitfalls in AI-generated code—such as subtle lookahead biases, survivorship leakage, and overfitting to historical anomalies.
The Evolution of US Equity Options Market Structure & Zero-DTE Dominance
Observations on dealer gamma concentration, intraday volatility spikes, and structural shifts caused by same-day expiration options volume.
Building Low-Latency Tick Processing Pipelines in Rust and Apache Arrow
Deep dive into Alphigma’s data infrastructure: processing tens of millions of financial market events per second with zero-copy SIMD columnar operations.
Step-by-Step Guide: Implementing Purged K-Fold Cross-Validation in Python
A practical, code-driven guide with ready-to-run Python examples for splitting financial time-series data without lookahead or serial correlation bias.
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