Research Workspace Dashboard
Central research home for recent strategies, active experiments, running jobs, validation states, alerts, and actionable workspace activity.
QuantLab — Quantitative Research Platform
A complete quantitative strategy research ecosystem for designing, backtesting, analyzing, optimizing, validating, and paper-trading systematic investment strategies.
About the project
A full-stack quantitative research platform designed to take trading ideas from initial hypothesis through strategy construction, historical experimentation, trade-level analysis, optimization, robustness validation, and paper trading. QuantLab combines visual and AI-assisted strategy creation with reproducible backtesting, experiment management, market-data exploration, portfolio research, and collaborative workspace controls.
Platform
Digital Business Platform
Frontend
React / Vite
Focus
SaaS · Commerce · Content
Problem Statement
Quantitative strategy research is often fragmented across notebooks, scripts, charting tools, data providers, optimization systems, and manual spreadsheets. This makes experiments difficult to reproduce, strategy versions hard to compare, backtest assumptions easy to overlook, and AI-generated insights difficult to trust. QuantLab needed a unified research environment where every strategy, dataset, experiment, trade, validation result, and optimization study remained traceable while still providing an approachable workflow for both visual and advanced quantitative researchers.
Solution
Designed a unified quantitative research ecosystem centered around a versioned Strategy IR and immutable experiments. Users can create strategies through AI, visual blocks, templates, or code, validate the resulting logic, configure reproducible backtests, monitor asynchronous research jobs, inspect quantitative performance and individual trades, compare strategy versions, run parameter optimization and out-of-sample validation, and forward-test validated strategies through paper trading. AI capabilities remain grounded in deterministic experiment data and platform tools instead of generating unsupported quantitative claims.
Strategic Impact
Creates a scalable foundation for systematic investment research by connecting strategy authoring, reproducible experimentation, quantitative analytics, AI-assisted reasoning, optimization, robustness testing, market-data exploration, paper trading, and portfolio-level analysis within one traceable research workflow.
Key Features
An end-to-end research environment covering strategy creation, quantitative experimentation, explainability, robustness analysis, and forward testing.
AI Strategy Builder
Transforms natural-language trading hypotheses into structured, editable strategy definitions with schema validation and explicit user approval.
Visual Strategy Builder
No-code strategy construction using typed conditions, nested logic groups, dynamic calculations, actions, sizing policies, and reusable blocks.
Strategy Library
Versioned research catalog for searching, organizing, duplicating, archiving, and continuing work across quantitative strategies.
Strategy Templates
Reusable quantitative strategy archetypes such as momentum, mean reversion, breakout, and trend-following models.
Strategy Validation
Pre-execution validation detects incomplete logic, unsupported features, contradictory rules, missing risk controls, and invalid configurations.
Backtest Configuration
Reproducible experiment setup covering historical periods, dataset versions, initial capital, commissions, slippage, benchmarks, and execution assumptions.
Asynchronous Backtesting
Durable background research jobs with real execution stages, progress monitoring, cancellation, retry handling, and refresh-safe status tracking.
Backtest Analytics
Comprehensive quantitative reports covering returns, drawdowns, Sharpe, Sortino, profit factor, trade statistics, rolling performance, and regime behavior.
Trade Explorer
Trade-level debugging and explainability with entries, exits, fills, fees, slippage, position changes, signal reasons, and historical chart context.
Strategy Comparison
Side-by-side comparison of strategy versions and experiments across performance, assumptions, equity curves, drawdowns, regimes, and structural logic differences.
AI Strategy Analysis
Evidence-grounded AI analysis explaining return drivers, drawdowns, regime dependence, trade clusters, potential weaknesses, and future research hypotheses.
Optimization Lab
Controlled parameter-search environment for studying candidate configurations, sensitivity, trade-offs, stable regions, and robustness rather than simply maximizing historical returns.
Validation Lab
Out-of-sample, holdout, and walk-forward testing designed to detect overfitting and measure whether strategy performance survives unseen market data.
Market Data Explorer
Research interface for inspecting instruments, OHLCV history, dataset coverage, features, indicators, corporate actions, adjustment policies, and data-quality issues.
Paper Trading
Forward-testing environment for validated strategies using simulated capital, live or delayed feeds, risk controls, positions, orders, and execution monitoring.
Portfolio Research Lab
Multi-strategy analysis covering allocations, combined returns, volatility, drawdowns, correlation, diversification, risk contribution, and stress scenarios.
Immutable Research History
Every historical experiment remains linked to its exact strategy version, configuration, dataset version, and engine assumptions for complete reproducibility.
Versioned Strategy Architecture
All strategy-authoring methods compile into the same canonical Strategy IR, ensuring consistency across AI, visual, template, and code workflows.
Workspace & Team Management
Workspace administration with team members, permissions, data providers, API access, integrations, security configuration, and audit history.
