Professional-grade quantitative research platform powered by local-first computing, statistical validation and native execution.
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At the heart of AlphaBoundary is a single, continuous research loop. Every stage feeds the next. Nothing is optional. Nothing is skipped.
Idea
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Feature Engineering
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CALIBRATE
(Bayesian TPE · Walk-forward · IC screening · Regime detection)
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Monte Carlo Simulation
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Stress Testing
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Validation Report
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Backtest
(Real-cost fills · Slippage · Funding)
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Deploy (Paper → Demo → Live)
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Execution
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Drift Detection
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RECALIBRATE
(When drift exceeds threshold)
Calibration is the core. Every strategy is Bayesian-calibrated across multiple walk-forward windows, with full IC screening and regime-adaptive parameter tuning, before it can be deployed. The backtest is a consequence of good calibration — not a substitute for it.
Coming soon.
Most quantitative platforms focus on one stage of the workflow.
Some stop at backtesting.
Others stop at execution.
AlphaBoundary was designed around a different idea:
A trading strategy should pass a rigorous validation pipeline before reaching live markets.
Research. Validate. Deploy. Monitor. Improve. Repeat.
AlphaBoundary is a local-first quantitative research platform for systematic traders.
It covers the entire strategy lifecycle — from feature engineering to live execution — through a structured, statistically rigorous workflow.
Unlike traditional platforms that focus on either research or execution, AlphaBoundary connects every stage through a single, continuous workflow.
The platform enforces one rule: what you test is what you trade.
Every backtest uses realistic fill models. Every feature is screened for predictive power. Every strategy is validated through walk-forward analysis before deployment.
No shortcuts. No optimistic assumptions. No black boxes.
| Capability | No-Code Bot Builders | Cloud Research Platforms | AlphaBoundary |
|---|---|---|---|
| Code required | No | Yes | No |
| Statistical validation | None | Partial | Full pipeline |
| Real-cost backtesting | No | Approximate | Exchange-realistic |
| Live execution bridge | Manual | Self-built | One click |
| Drift detection | No | No | Built-in |
| Local compute | N/A | Cloud-only | Native C# agent |
| AI research assistant | No | No | Quant Copilot |
- Research — Feature engineering, information coefficient screening, correlation analysis, regime detection
- Calibration — Bayesian TPE optimization, walk-forward validation, parameter stability scoring, regime-adaptive weighting
- Validation — Monte Carlo simulation, stress testing, deflated Sharpe ratio, robustness analysis
- Execution — Native execution engine, position management, portfolio allocation, realistic order simulation with slippage and partial fills
- Monitoring — Strategy health tracking, drift detection, exchange reconciliation, risk engine with kill-switches and daily loss limits
- Quant Copilot — AI assistant that understands your research context, explains statistical metrics, interprets validation results and guides your workflow
Most quantitative platforms give you tools. AlphaBoundary gives you a research partner.
What it does:
- Explains statistical results in plain English
- Guides you through the research workflow step by step
- Interprets validation outputs and flags concerns
- Assists during live execution with real-time context
- Answers questions about methodology and platform features
Example interactions:
"Why did this feature fail IC screening?"
"Is this walk-forward result statistically significant?"
"Should I be concerned about this drift alert?"
Privacy model: The Copilot operates on metadata and computed metrics — never on raw market data. Your ticks and positions stay local.
Your Browser
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Cloud API (orchestration + state)
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Desktop Agent (C# native)
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Research Engine (local compute)
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Exchange
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Execution Reports
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Dashboard
Key principles:
- Heavy compute runs locally — Calibration, IC analysis and regime detection execute on your hardware via a native C# agent. No server bottlenecks. No RAM ceilings.
- Cloud orchestrates, never computes — The cloud layer manages sessions, stores configurations and synchronizes state. Raw market data never touches our servers.
- Your data stays yours — Ticks, fills and positions remain on your machine unless you explicitly choose to export them.
- Event-driven pipeline — Feature updates propagate in real time. No polling. No batching delays.
Heavy computations are executed locally through a native C# desktop agent.
- Runs locally — Your hardware, your rules
- Native C# — Zero-GC overhead, direct memory structs
- Docker-ready — Deploy on laptop, workstation or cloud VM
- Cross-machine deployment — Same agent, same performance, anywhere
- Multi-core execution — Parallel calibrations with shared state
The agent handles the heavy lifting so the cloud only orchestrates. Result: lower costs, better privacy, no compute limits.
AlphaBoundary is built around a few simple principles:
- Local-first computing — Heavy math runs on your machine, not our servers
- Research before execution — No strategy reaches live markets without passing validation
- Statistical rigor over optimistic backtests — Realistic fills, deflated metrics, walk-forward analysis
- Realistic execution models — What you test is what you trade
- Explainability before automation — Every decision is inspectable and attributable
- AI as an assistant, never a decision maker — Quant Copilot guides, you decide
- User-owned compute — Your hardware, your performance ceiling
- User-owned data — Raw ticks and positions never leave your environment
| Layer | Technology |
|---|---|
| Frontend | React |
| Desktop Agent | C# (.NET, native structs, zero-GC) |
| Backend | Node.js |
| Communication | gRPC (binary, high-throughput) |
| Storage | Parquet (columnar, compressed) |
| Database | PostgreSQL |
| Streaming | WebSockets |
| AI | Local context-aware LLM (metadata only, no raw data) |
- Web platform (React + local-first architecture)
- Desktop agent — Windows (C# native)
- Binance integration (testnet + live)
- Core research pipeline (feature engineering → validation → execution)
- Quant Copilot (context-aware AI assistant)
- UI polish and responsive refinements
- Documentation suite
- Onboarding workflow
- Bybit and OKX integrations
- macOS desktop agent
- Public beta release
- Multi-broker support (10+ exchanges)
- Portfolio-level analytics
- Strategy marketplace
- Team collaboration features
- Getting Started — From zero to first backtest
- Research Guides — Feature engineering, IC testing, regime analysis
- Calibration — Bayesian TPE, walk-forward validation, parameter stability
- Validation — Monte Carlo, stress testing, deflated Sharpe
- Execution — Paper, demo and live trading
- Desktop Agent — Installation and configuration
- Quant Copilot — Capabilities and privacy model
- API Reference — Programmatic access to all operations
| Channel | Status |
|---|---|
| Website | Live |
| GitHub | Active |
| Documentation | Planned |
| YouTube | Planned |
| Discord | Planned |
The Local-First Quant Research Platform.
AlphaBoundary — Research first. Execution second. Marketing last.