Market Data
Price, volume, alternate data
Structured Data · Real-time
Emergent risk observatory
Turn unknown unknowns into tradable factors.
ARKA identifies hard-to-detect emergent market themes, quantifies them, and constructs institutional-grade equity factors before they become mainstream.
emergence field / x: evidence / y: affinity
96 particles · field initializing
1,800+securities scanned per theme
50K+ / 10M+unstructured docs & structured rows analyzed per run
10M+API calls orchestrated per factor
End-to-Endtradable factors with weights & backtest
Multimodal evidence layer
Every signal. One graph.
Structured and unstructured evidence is normalized through symbology mapping into an entity graph—the common substrate for emergent factor discovery.
Fundamentals
Revenue, R&D, earnings
Structured Data · Daily
SEC Filings
10-K, 10-Q, 8-K text
Unstructured Data · As filed
Patent Data
CPC codes, citations
Alternative Data · Weekly
News & Sentiment
Keyword coverage, sentiment
Unstructured Data · Real-time
Academic Research
Papers, citations, authors
Unstructured Data · Daily
6data sources
13AI agents
100K+LLM calls per run
Controlled factor construction
From signal to portfolio-ready factor.
Eleven steps move from discovery to delivery. Humans choose the thesis, review the execution graph, and authorize every run.
01
AI agentDiscovery
Scan Emerging Signals
AI agents monitor policy documents, news, filings, patents, and research in real-time. They cluster emerging risk signals before consensus.
02
AI agentDiscovery
Recommend Themes
Ranked theme candidates arrive with evidence dossiers based on novelty, momentum, breadth, and economic impact.
03
You decideDiscovery · branch
Select a Recommended Theme
Inspect a surfaced theme and its evidence dossier, then select the prepared specification.
Define Custom Factor via Prompt
Describe a thesis in natural language and construct the specification from scratch.
04
AI agentResearch & design
Perform Literature Review
Automated academic and industry research is synthesized into a mathematical framework for factor construction.
05
AI agentResearch & design
Generate Pseudocode
The framework becomes an executable directed acyclic graph of operations, inputs, outputs, and dependencies.
06
You decideResearch & design
Review & Edit Pseudocode
You inspect and edit the plan before committing: add sources, adjust weights, or remove operations.
07
You decideExecution
Initiate Execution
Nothing executes without explicit human approval. Choose thinking mode and confirm the run.
08
AI agentExecution
Orchestrate Millions of LLM Calls
13 specialized agents execute concurrently with dependency tracking, checkpoints, and retry logic.
09
AI agentExecution
Generate Factor
Security-level scores, historical weights, category breakdowns, and a full benchmark backtest are generated.
10
ARKA QAReview & delivery
Internal Review by ARKA
Trust Score validation covers decomposition, bootstrap significance, concentration, liquidity, and knockout scenarios.
11
DeliveredReview & delivery
Factor Ready for User
Historical weights, full backtest, risk decomposition, model card, hedge recommendations, and integration-ready output.
Live factor library
Factors the platform has already built.
Each factor is an executable research output with holdings, performance history, and a transparent fact sheet.
ARKA-MEM
Arka AI Memory Tax
Holdings10
As of2026-10-09
ARKA-OSA
Arka OSAT Bottleneck
Holdings25
As of2026-10-15
ARKA-ODC
Arka Orbital Data Centers
Holdings13
As of2026-10-08
ARKA-PHO
Arka Photonics
Holdings14
As of2026-10-09
ARKA-GLS
Arka Glass Substrates
Holdings16
As of2026-10-15
ARKA-AIF
Arka AI Factory Infrastructure
Holdings15
As of2026-10-13
ARKA-ASP
Arka Advanced Semiconductor Packaging
Holdings49
As of2026-10-13
Validated and explainable
Built for risk committees.
Every factor ships with lineage, decomposition, access control, and statistical validation in a reviewable model card.
Example model cardvalidated
82Trust Score / 100
✓Sharpe > 1.0pass
✓Max DD < 10%pass
✓Win Rate > 50%pass
✓HHI < 0.15pass
Model Cards & Explainability
Every factor comes with documentation for risk committees and regulators
Full Audit Trails
Complete lineage from data source to portfolio action
Trust Score Validation
Five-component quality gate across performance, orthogonality, concentration, stability, and liquidity
Factor Decomposition & Risk Attribution
Multi-model attribution isolates genuine alpha from style, market, and sector exposure
Role-Based Access Control
Granular permissions for PMs, risk managers, quants, and compliance
Bootstrap Significance
Statistical testing with 95% confidence intervals and p-values
Institutional coverage
Built for institutions that manage risk at scale.
ARKA adds an emergent-risk layer to the systems institutions already trust.
$260T+addressable AUM
Hedge Funds
~8,400 funds globally
- Pain point
- Generating new factors from unstructured data
- ARKA value
- Factor zoo + risk overlay, integrated with existing models
Asset Managers
Fast-growing active ETF segment
- Pain point
- Limited tools for new product design and hidden risk monitoring
- ARKA value
- Designs thematic/hedged products, improves risk disclosures
Private Equity & VC
Expected to double by 2030
- Pain point
- Quantifying thematic risk, mapping private holdings to public hedges
- ARKA value
- Maps private holdings to public proxies, emergent risk dashboards
Commercial Banks
Largest 1,000 banks
- Pain point
- Project finance, trade finance, and balance-sheet stress testing
- ARKA value
- Emergent risk quantification for complex projects and cross-border trade
Institutional interoperability
Plugs into your existing stack.
ARKA POINT complements portfolio, risk, and analytics platforms as the emergent-risk discovery layer—without a rip-and-replace.
Bring your own data
Integrate proprietary internal factors, signals, and datasets through ARKA’s plug-and-play API with zero pipeline changes.
Talk to Engineeringon_prem
banks, regulated clients
vpc_single
hedge funds, AMs
cloud_multi
mid-tier, PE/VC
All modes: RBAC + audit trails + model cards
Programmatic by design
API-first. Built for quants.
Create factors, compute risk overlays, and generate hedges from code, or use the low-code studio.
Compound AI Orchestration
Orchestrates tens of millions of API calls (10M+) per factor and decomposes complex themes into granular tasks.
Multiple Specialized Sub-Agents
Universe selection, extraction, classification, scoring, aggregation, and code generation are plug-and-play.
DAG-Based Execution
Operations run as a directed acyclic graph with dependency tracking, checkpointing, and resume.
Institutional-Grade Factor Analytics
A full tearsheet covers performance, robustness, attribution, liquidity, stress, and capacity.
from arka import FactorEngine
engine = FactorEngine(
api_key=os.environ["ARKA_API_KEY"]
)
factor = engine.create(
theme="quantum computing",
universe="russell_2000",
thinking_mode="HIGH"
)
print(f"Trust Score: {factor.trust_score}")
print(f"Holdings: {factor.num_holdings}")
print(f"Sharpe: {factor.sharpe_ratio:.2f}")overlay = engine.risk_overlay(
portfolio="macro_pod_1",
factors=["tariff_exposure",
"ai_datacenter_stress",
"quantum_compute"],
horizon="6m"
)
for exposure in overlay.exposures:
print(f"{exposure.factor}: "
f"{exposure.beta:.2f} beta, "
f"{exposure.pnl_impact:.1f}% P&L")hedges = engine.optimize_hedge(
portfolio="macro_pod_1",
factor="tariff_exposure",
target="min_tracking_error",
instruments=["futures", "etf"]
)
for h in hedges.candidates:
print(f"{h.instrument}: "
f"{h.notional:,.0f} notional, "
f"{h.te_reduction:.0f}bps TE reduction")$ pip install arka-pointSuccessfully installed arka-point-0.1.0
The next risk is already forming
See ARKA POINT in action.
From emergent risk detection to portfolio-ready hedges, see how institutional teams turn unknown unknowns into quantifiable, tradable factors.
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