// PLATFORM

The Emergent Risk Intelligence Platform

Four core layers — data fusion, factor discovery, portfolio integration, and blueprint studio — working together to detect, quantify, and hedge risks that traditional models miss.

Data Fusion
Factor Discovery
Portfolio Integration
Blueprint Studio

// 01 DATA FUSION LAYER

Agentic Data Fusion

Fuses structured market data, fundamentals, SEC filings, patent records, news sentiment, and academic research into a unified entity graph. No data silos — entities are normalized via symbology mapping into a single source of truth.

Structured Data

Market data, fundamentals, risk factors, holdings — all normalized and linked

Unstructured Data

News, regulatory text, filings, earnings calls, research papers, social sentiment

Alternative Data

Patent filings, consortium memberships, supply chain networks, CPC codes

6
Data Sources
13
AI Agents
100K+
LLM Calls/Run

Market Data

Real-time
OHLCV, market cap, volumePolygon.io

Fundamentals

Daily
Revenue, R&D, earningsFMP

SEC Filings

As filed
10-K, 10-Q, 8-K textEDGAR

Patent Data

Weekly
CPC codes, citationsGoogle Patents

News & Sentiment

Real-time
Keyword coverage, sentimentMulti-source

Academic Research

Daily
Papers, citations, authorsOpenAlex
factor_pipeline.dag
01
Universe Selection
02
Entity Extraction
03
Classification
04
Scoring
05
Aggregation
06
Code Generation

Trust Score Quality Gate

82/100
Performance

Sharpe ratio, information ratio, and risk-adjusted returns against benchmark

Orthogonality

Independence from existing Fama-French and Barra factors via decomposition

Concentration

Herfindahl index and top-N weight limits to prevent single-name dominance

Stability

Rolling window consistency, regime-change resilience, and bootstrap significance

Liquidity

ADV capacity analysis, market impact estimation, and execution feasibility

// 02 FACTOR DISCOVERY ENGINE

Emergent Factor Discovery

AI agents scan for risk clusters in real-time. Themes are scored on novelty, momentum, breadth, and economic impact. The engine automates factor definition end-to-end — from universe selection through entity extraction, classification, scoring, aggregation, and code generation.

Real-Time Risk Clustering

AI agents continuously monitor data streams, detecting emerging themes and scoring them on novelty, momentum, breadth, and economic impact.

Automated Factor Definition

Six-stage pipeline: universe selection, entity extraction, classification, scoring, aggregation, and code generation. No manual intervention required.

Fama-French Decomposition

Every factor is decomposed against known risk premia. Bootstrap significance testing ensures only statistically meaningful factors pass the quality gate.

Trust Score (5 Components)

Performance, orthogonality, concentration, stability, and liquidity. Only factors that clear all five gates ship to production.

// 03 PORTFOLIO INTEGRATION

Portfolio Integration & Optimization

Connect to your existing portfolio and risk management systems. ARKA computes exposures, decomposes risk across emergent factors, and generates minimal-impact hedges ready for execution.

Connect to Existing Systems

Plug into your portfolio management, risk, and order management systems. Import holdings, benchmarks, and constraints via API or file upload.

Exposure & Risk Decomposition

Measure portfolio exposure to each emergent factor and sub-factor. Decompose risk into traditional and ARKA-discovered components side by side.

Minimal-Impact Hedge Generation

Generate optimal hedge candidates across asset classes — index futures, sector ETFs, and options structures — with minimal tracking error and turnover.

Push to OMS

Approved hedges are formatted and routed to your order management system for execution. Full audit trail from signal to trade.

portfolio_overlay.py
Portfolio Import
1,247 positions loaded
Exposure Computation
12 emergent factors analyzed
Hedge Optimization
Min tracking error: 18bps
OMS Routing
3 candidates ready for approval
Hedge Instruments
45%
Futures
35%
ETFs
20%
Options

Tariff Shocks

Model cascading tariff impacts across supply chains. Identify exposed sectors, quantify revenue-at-risk, and generate hedges before consensus reprices.

Sector Rotation

Detect early rotation signals from flow data, earnings revisions, and macro indicators. Build dynamic allocation factors that adapt in real-time.

Regulatory Changes

Track pending legislation, agency rulemakings, and enforcement actions. Score companies on regulatory exposure and compliance readiness.

ESG & Climate

Construct forward-looking ESG factors from patent filings, emissions data, and supply chain analysis. Go beyond backward-looking ESG ratings.

Drag & Drop
Visual factor builder
Playbooks
Pre-built templates
Multi-Step
Chained pipelines

// 04 BLUEPRINT STUDIO

Blueprint Studio

For teams that want custom workflows without code. Drag-and-drop factor construction with multi-step pipelines, built-in playbooks, and scenario modeling — all backed by the same engine that powers the API.

Drag-and-Drop Factor Workflows

Visually compose factor pipelines by connecting data sources, transformations, scoring functions, and output nodes on a canvas.

Multi-Step Pipelines & Playbooks

Chain multiple pipeline stages with branching logic. Start from pre-built playbooks for common scenarios and customize as needed.

Scenario Modeling

Tariff shocks, sector rotation, regulatory changes, ESG screening — model any scenario with configurable parameters and instant backtesting.

Team Collaboration

Share pipelines across your team. Version control, approval workflows, and audit trails built in. From research to production in one tool.

// GET STARTED

Ready to see the platform?

Schedule a personalized walkthrough of the four layers. See how ARKA detects emergent risks, constructs quantitative factors, and generates hedges for your portfolio.