Complete platform reference — methodology, data pipeline, signal framework, research process, and module documentation.

# Sharpe Mind — Moneda Research & Zero Ai Labs | Last updated: March 2, 2026

SHARPE MIND is a market data superintelligence terminal built by Zero Ventures. It integrates traditional trading data (options chains, equities, macro) with alternative intelligence sources (foot traffic, prediction markets, satellite imagery, social sentiment) to generate high-signal, cross-referenced trade ideas.

Unlike conventional terminals that present data in silos, SHARPE MIND is designed as a convergence engine — every module feeds into every other module. A foot traffic anomaly at Walmart connects to options IV surfaces, which connects to Kalshi prediction market probabilities, which connects to the signal engine thesis builder. No data point exists in isolation.

Core Principles

Module Architecture — 13 Modules

SHARPE MIND's methodology is built on a single thesis: alternative data, when properly cross-referenced against traditional market signals, produces a measurable information advantage. This advantage is strongest in the 2-8 week window before earnings announcements, when foot traffic and consumer behavior data leads sell-side estimates.

The Information Advantage Chain

Traditional sell-side research relies on survey data, management guidance, and historical financial models. These inputs lag reality by weeks to months. SHARPE MIND inverts this by collecting real-time physical-world signals and mapping them to financial outcomes before the consensus catches up.

Three Layers of Intelligence

Convergence Scoring

No single data source is sufficient for a trade thesis. SHARPE MIND uses convergence scoring — counting the number of independent signals that align on the same directional bias. The scoring framework:

SHARPE MIND collects data from 6 primary sources, normalizes it into a common schema, and distributes it across all 9 modules. The pipeline is designed for freshness — live API data where available, intelligent caching where API calls are expensive, and industry-modeled fallbacks where live data doesn't exist.

Data Sources

Pipeline Architecture

Caching Strategy

BestTime.app API calls cost credits ($29/month plan). Rather than calling the API on every page load, SHARPE MIND pre-fetches traffic data for all 25 tickers and stores it in a static JSON cache (besttime-cache.json). This cache is refreshed weekly or on-demand when new data is needed. For the 9 tickers without BestTime coverage (DG, DPZ, TJX, ROST, ULTA, KR, CZR, TSLA, COIN), the system generates synthetic heatmaps using sector-specific 24-hour traffic curves with deterministic per-ticker noise to ensure each ticker looks distinct.

BestTime.app Coverage

SHARPE MIND uses a 10-source signal matrix. Each source has a defined lead time, connection mechanism, and confidence level. The goal is multiple independent signals converging on the same thesis — not relying on any single data point.

10-Source Intelligence Matrix

Kalshi → Traffic Transmission Channels

Each Kalshi prediction market maps to specific foot traffic tickers through economic cause-and-effect chains:

Detailed documentation for each module — what it does, what data it uses, what tabs/features are available, and how it connects to other modules.

SHARPE MIND follows a structured 4-phase research process designed to minimize bias and maximize signal quality. Every trade idea must pass through all 4 phases before capital is committed.

Every SHARPE MIND research output is framed as a testable hypothesis. This is what separates institutional process from retail speculation. A hypothesis must be falsifiable — you must define what would prove you wrong before entering the trade.

Hypothesis Framework

Tracking Hypothesis Outcomes

Over time, SHARPE MIND tracks which hypotheses were confirmed vs denied, building a statistical record of which signal combinations produce the highest win rates. This feedback loop is the foundation for the platform's agentic intelligence — the system learns which data sources are most predictive for each sector and adjusts convergence weighting accordingly.

The Thesis Builder is SHARPE MIND's institutional-grade trade documentation system. Every trade idea is structured into 8 sections, designed to be presentable to investment committees, portfolio managers, or internal review.

8-Section Template

Automatic Red Flags

If any of these conditions exist, the thesis requires re-evaluation before proceeding:

Risk management is not optional — it's the foundation of every trade. SHARPE MIND enforces structured risk thinking through the thesis builder and provides tools for position sizing, stop placement, and portfolio correlation analysis.

Position Sizing Rules

Sharpe Mind Research publishes periodic research papers documenting findings from the platform's data collection, hypothesis testing, and signal analysis. These papers serve as both internal knowledge base and external credibility for the platform's methodology.

Published Research

Research Paper Pipeline

All research findings that prove statistically significant are integrated back into SHARPE MIND's convergence scoring weights, creating a feedback loop between research and platform intelligence.

SHARPE MIND's development roadmap is driven by one question: what reduces noise and increases signal quality? Features are prioritized by their contribution to trade conviction, not by visual impressiveness.

SHARPE MIND is designed as a modular system. New data sources can be connected through the existing API route framework, and new modules can be added without disrupting existing functionality.

Current Integrations

Adding New Data Sources

To integrate a new data source into SHARPE MIND: (1) Create an API route in /pages/api/ that proxies the external API, (2) Add a component or tab in the relevant module that consumes the data, (3) Define the cross-module connection — how does this data feed into convergence scoring?, (4) Update the Signal Engine matrix to include the new source.

Tech Stack