The Bloomberg of Crypto
Market intelligence infrastructure for digital assets.
Cross-venue positioning. Explainable signals. One platform.
Crypto traders and desks operate across fragmented venues — CEX perps, perp DEXs, on-chain flows, prediction markets. Today they stitch together 10–15 tools and still lack a unified view.
The Problem
New platforms, new primitives — but tools from the past
Hyperliquid, prediction markets, perp DEXs, on-chain flows, social sentiment, news — everyone tracks them individually, but not together in context.
Tools stitched together
Expensive APIs, no visualization
Unified cross-venue context
Liquidity, order books, liquidations, sentiment — each signal helps.
But signals together, in context, help more. That's what's missing.
Not every feature works for every model. Not every model improves every ensemble. Traders need flexibility to maximize the value of their own systems — not one-size-fits-all dashboards.
Traditional finance has Bloomberg. Crypto has... chaos.
The Solution
One platform. Feature engineering made accessible.
We aggregate data across venues with a proprietary unified schema. We visualize what others only offer as expensive APIs — human-readable, explainable, in context.
Features that work together
Liquidity + sentiment + positioning — individually useful, but combined they reveal what neither shows alone. Interaction effects, ratios, cross-venue comparisons.
Pick, search, understand
Users browse and select features useful to them. Use them directly in dashboards, or plug into your own models. We handle the data engineering.
The Full SuiteVisualization + API + Predictor Library — one unified offering
Like features in a machine learning model — individually they predict weakly, but combined they predict strongly.
Wangr's modules share underlying predictors, so the combined view is stronger than any single dashboard. This isn't just aggregation — it's engineered signal clarity.
What's Shipped
30+ modules. Live.
This isn't a roadmap. It's shipped.
AI Agents with MCP server access to on-chain data and proprietary features
Real-time scanners showing arbitrage opportunities with fees and liquidity factored in
Our proprietary engine normalizes fragmented venue logic (e.g., Hyperliquid vs. Binance) into a standard risk model.
This allows us to scale and adapt to new exchanges faster than competitors building point-to-point integrations.
The Innovation
Predictor Library
Hundreds of engineered features that turn raw market data into high-context signals.
Connected by design. Explainable AI by design.
Raw Data
CEX, DEX, On-chain
Feature Engineering
Transformations, Ratios, Interactions
Predictors
Explainable, Actionable
Illustrative Example
Instead of looking at "number of longs" and "number of shorts" as two disconnected metrics, Wangr builds a single predictor like the Long/Short Ratio of top traders, then tracks its slope and rate of change. That captures not just the level of positioning, but how quickly sentiment is changing.
The Moat
Competitors can access the same raw data. But without significant investment, they can't replicate predictors built with the community and customers of Wangr.
Delivery Methods
- + API (plug into pipelines)
- + TradingView-style indicators
- + In-app dashboards & alerts
- + Proprietary feature engineering pipeline and regime classification models as trade secrets
- + Hundreds of hand-crafted predictors created, curated, and empirically validated
- + Competitors can access raw data but can't replicate 2+ years of optimization without significant R&D
"Most of the value in machine learning comes from the data and feature engineering; it's a treasure hunt and a craft."
Traction
Built fast. Growing faster.
Impressions
organic reach
Monthly Visitors
organic traffic
Daily Sessions
logged-in users
Registered Users
and growing
CAC
product-led growth
Growth Engine
Demand is validated by organic adoption and social distribution. Whale Watch acts as the entry point into a broader daily workflow — users arrive for one tool and discover the full platform.
Dozens of viral posts
Organic social distribution across platforms
Waitlist for paid features
Demand validated before monetization
Daily API requests
Consistent demand for paid API access
Validation & Pipeline
Unprompted user donations before any monetization push
Daily requests for paid API access
7 users enrolled in copy-trading prerequisites within 4 days (from ~200 newsletter subscribers)
Early discovery conversations with digital-asset desks at several top-tier global trading firms.
Focus: Understanding their stage of development and how to make the most impact with Wangr products.
Timing advantage: Several target firms are actively building or have recently launched digital-asset capabilities.
Market Opportunity
Why Now
Digital-asset markets are structurally shifting. New venues create new opportunities.
Crypto Derivatives Volume (2025)
Active Crypto Users
Prediction Market Volume (2025)
Hyperliquid Revenue (2025)
High-Growth: Prediction Markets
From <$100M monthly (early 2024) to >$13B (end 2025). Kalshi valued at $11B. Polymarket raising at >$1B. Wangr treats these as first-class market structure.
High-Growth: Perp DEXs
Hyperliquid: 609k new users in 2025, $2.95T cumulative volume. Perp DEX growth increases fragmentation — and the value of unified tooling.
Nobody offers a productized predictor library — customers build and maintain these features themselves, along with the data pipelines. Wangr does the engineering so they don't have to.
Business Model
Three Revenue Lanes
Retail Pro
Active traders
- + Full platform access
- + Alerts & notifications
- + API access
Mid-tier API
Prosumers, VCs, Media
- + Institutional-grade data
- + No enterprise overhead
- + Direct feed access
Enterprise
Funds & Desks
- + Custom integrations
- + Dedicated feeds
- + SLAs & compliance
24-Month Target: $10M+ ARR
Copy trading and API access generate ongoing exchange referral fees.
Market shows >$1M/year potential with multiple copy trading services earning this on Hyperliquid alone. Wangr's trading infrastructure positions us to capture this at scale.
Planned fund allocation — no additional hires in this phase
Team
Built to Execute
Oliver Holl
Founder & CEO
- + ETH Zurich, Computer Science
- + Kaggle Expert (peak rank 565/200k)
- + TA: Soccer Analytics, Data Viz, ML
- + Built 30+ modules in 3 months solo
"Passionate ML engineer and data visualization developer. Making complex data understandable."
Illia Mykhailov
Co-Founder, Engineering
- + PhD, University of Sumy
- + Senior Software Engineer
- + Low-latency systems expert
- + Blockchain/on-chain infrastructure
"Scaling data pipelines and building the real-time infrastructure layer."
Execution is the moat: 30+ modules. 3 months. Zero funding. That velocity compounds.
- Regulatory: Clear product boundaries; automation tooling gated via education/disclosures
- Copy trading: Require education and risk framing before access; user-controlled infrastructure
- Data correctness: Provenance tracking, sanity checks, monitoring, user-facing caveats
- Vendor dependency: Diversified data sources; resilient architecture
Long-term vision: Become the market intelligence infrastructure layer for digital assets. A several-hundred-million-dollar opportunity at maturity.
Join the future of crypto intelligence
Building the market intelligence infrastructure layer for digital assets.