Turn unknown unknowns
into tradable factors.
ARKA platform identifies hard to detect emergent market themes, quantifies and constructs it into institutional-grade equity factors before they become mainstream.
// DATA LAYER
Data Fusion of Diverse Structured, Unstructured,
and Multimodal Data
Fusing market data, regulatory filings, patents, news, earnings, and academic research into a single entity graph — one source of truth for emergent risk detection.
// TECHNOLOGY
From signal to portfolio-ready factor.
Eleven steps. Three human checkpoints. Millions of orchestrated LLM calls. Your factor, your control.
DISCOVERY
Scan Emerging Signals
AI agents monitor policy documents, news, filings, patents, and research in real-time. Cluster emerging risk signals with confidence scores before they become consensus.
AI AGENTDISCOVERY
Recommend Themes
Surface ranked theme candidates based on novelty, momentum, breadth, and economic impact. Each theme comes with an evidence dossier and suggested factor specification.
AI AGENTUSER DECISION POINT — CHOOSE ONE PATH
Select a Recommended Theme
Browse the AI-surfaced themes, review the evidence dossier, and click to select. The engine pre-populates the factor spec from the dossier.
Define Custom Factor via Prompt
Describe your own factor thesis in natural language. The engine interprets your prompt and constructs the specification from scratch.
RESEARCH & DESIGN
Perform Literature Review
Automated academic and industry literature search. Discovers relevant papers, methodologies, and existing frameworks. Synthesizes into a mathematical framework for factor construction.
AI AGENTRESEARCH & DESIGN
Generate Pseudocode
LLM decomposes the mathematical framework into an executable directed acyclic graph (DAG) of operations. Each operation specifies its agent, inputs, outputs, and dependencies.
AI AGENTRESEARCH & DESIGN
Review & Edit Pseudocode
User inspects the generated execution plan. Accept the DAG as-is, or edit individual operations — add data sources, adjust scoring weights, remove unnecessary steps — before committing.
USER CHECKPOINTEXECUTION
Initiate Execution
User triggers the pipeline run. Nothing executes without explicit human approval. Choose thinking mode (LOW/HIGH) and confirm.
USER CHECKPOINTEXECUTION
Orchestrate Millions of LLM Calls
13 specialized agents execute the DAG concurrently. Data extraction, entity classification, relevance scoring, qualitative analysis, code generation, aggregation — all with checkpointing and retry logic.
13 AI AGENTSEXECUTION
Generate Factor
Final factor construction: security-level scores, historical weights by quarter, category breakdowns, and full backtest against benchmarks. Complete with cumulative returns, drawdown analysis, and rolling metrics.
AI AGENTREVIEW & DELIVERY
Internal Review by ARKA
Trust Score quality gate: Fama-French decomposition, bootstrap significance testing, knockout scenarios, concentration analysis, and liquidity checks. Only statistically meaningful factors pass.
ARKA QADELIVERY
Factor Ready for User
Complete factor delivered: historical weights, full backtest, risk decomposition, model card, hedge recommendations, and integration-ready output. Portfolio-ready.
DELIVERED// DATA FUSION LAYER
Every signal. One graph.
Structured and unstructured data unified via factor graphs. No silos — entities are normalized via symbology mapping into a single source of truth for emergent risk detection.
S
Structured Data
Market data, fundamentals, risk factors, holdings — all normalized
U
Unstructured Data
News, regulatory text, filings, earnings calls, research, social
A
Alternative Data
Patent filings, consortium memberships, supply chain networks
Market Data
Real-timeFundamentals
DailySEC Filings
As filedPatent Data
WeeklyNews & Sentiment
Real-timeAcademic Research
Daily// USE CASES
Built for institutions that
manage risk at scale.
Hedge Funds
$5TGenerating new factors from unstructured data
Factor zoo + risk overlay, integrated with existing models
Asset Managers
$69T+Limited tools for new product design and hidden risk monitoring
Designs thematic/hedged products, improves risk disclosures
Private Equity & VC
$22TQuantifying thematic risk, mapping private holdings to public hedges
Maps private holdings to public proxies, emergent risk dashboards
Commercial Banks
$164TProject finance, trade finance, and balance-sheet stress testing
Emergent risk quantification for complex projects and cross-border trade
// ECOSYSTEM
Plugs into your
existing stack.
ARKA POINT complements your existing risk and portfolio platforms as the emergent-risk discovery layer. No rip-and-replace required.
┌B┐ └─┘
Bloomberg PORT
Risk Analytics
╔═╗ ║A║ ╚═╝
BlackRock Aladdin
Portfolio Mgmt
[F] [S]
FactSet
Data & Analytics
┌M┐ │B│ └─┘
MSCI Barra
Factor Models
╔A╗ ╚X╝
Axioma
Risk Models
⟨⟩
Bring your own data
Integrate proprietary internal factors, signals, and datasets via ARKA's plug-and-play API. MSCI Barra custom factors, Axioma risk models, or your in-house alpha signals — all connect with zero pipeline changes.
Flexible deployment models
On-prem for banks and regulated institutions. Private cloud/VPC for hedge funds and asset managers. Fine-grained RBAC and full audit trails built in.
Talk to Engineering// GOVERNANCE & EXPLAINABILITY
Built for risk committees.
Every factor is transparent, auditable, and statistically validated. Model cards and explainability satisfy regulators and investment committees alike.
╔═══╗ ║ M ║ ╚═══╝
Model Cards & Explainability
Every factor comes with documentation for risk committees and regulators
┌───┐ │ T │ └───┘
Full Audit Trails
Complete lineage from data source to portfolio action
╭───╮ │82 │ ╰───╯
Trust Score Validation
5-component quality gate: performance, orthogonality, concentration, stability, liquidity
[α/β] [R² ]
Factor Decomposition & Risk Attribution
Multi-model attribution against Fama-French, Barra, and macro betas isolates genuine alpha from style, market, and sector exposure
◉─◉─◉ │ │ │
Role-Based Access Control
Granular permissions for PMs, risk managers, quants, and compliance
▪ p ▪ ▪ < ▪
Bootstrap Significance
Statistical testing with 95% confidence intervals and p-values
Validation Badges
Factors must pass all quality gates before reaching production
// FACTOR STUDIO
API-first.
Built for quants.
Full programmatic access to the factor engine. Create factors, compute risk overlays, and generate hedges — all from code. Or use the low-code studio for drag-and-drop workflows.
Compound AI Orchestration
Orchestrates tens of millions of API calls (10M+) per factor. Decomposes complex themes into granular sub-tasks.
Multiple Specialized Sub-Agents
Universe selection, extraction, classification, scoring, aggregation, code generation — each agent is plug-and-play.
DAG-Based Execution
Operations as a directed acyclic graph with dependency tracking, checkpointing, and resume from any point.
Institutional-Grade Factor Analytics
Auto-generates a full tearsheet per factor: performance, robustness, style attribution, and liquidity diagnostics — drawdowns, bootstrap p-values, Fama-French decomposition, regime stress, knockout scenarios, capacity curves.
1from arka import FactorEngine23engine = FactorEngine(4 api_key=os.environ["ARKA_API_KEY"]5)67factor = engine.create(8 theme="quantum computing",9 universe="russell_2000",10 thinking_mode="HIGH"11)1213print(f"Trust Score: {factor.trust_score}")14print(f"Holdings: {factor.num_holdings}")15print(f"Sharpe: {factor.sharpe_ratio:.2f}")See ARKA POINT in action.
From emergent risk detection to portfolio-ready hedges. See how institutional teams are turning unknown unknowns into quantifiable, tradable factors.
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