About · alphabench · alphabench

alphabench is an autonomous quant research assistant that helps teams and individual researchers move from idea to insight faster. It unifies data access, feature engineering, modeling, backtesting, risk, execution, and reporting into a single, conversation‑driven workflow.

Who we serve

Quant researchers exploring new alpha ideas
Data scientists building signals, factors, and features
Portfolio managers evaluating strategies and risk
Engineers integrating models into production
Educators and students learning modern systematic investing

What you can do with alphabench

Design research workflows in natural language, then refine with precise controls
Pull historical and realtime market data, options chains, futures curves, and macro series
Engineer features: technical indicators, factor models, regime detection, event windows, NLP signals
Train and evaluate models: time‑series ML, deep learning, reinforcement learning, causal and Bayesian methods
Backtest strategies with realistic frictions: slippage, fees, borrow, short constraints, latency
Quantify risk: factor exposures, VaR/ES, drawdown profiles, stress and scenario analysis
Execute: simulate, paper trade, or connect to execution adapters
Produce artifacts: research notes, charts, tables, code snippets, and full reports ready to share

How it works

Start a chat and describe your goal (for example, build a momentum strategy on liquid equities with weekly rebalancing).
The assistant drafts a plan and proposes tools to use (data, features, modeling, backtests, reporting).
You approve or edit the plan; steps run with transparent logs and provenance.
Results are captured as artifacts: datasets, charts, metrics, diffable code, and reports.
Iterate quickly: tweak parameters, swap models, expand universes, or compare variants side‑by‑side.

Capabilities by domain

Data access

Equities, ETFs, indexes, FX, futures, options (chains, greeks, IV surfaces)
Corporate actions, fundamentals, estimates, and events
Alternative and macroeconomic indicators
Built‑in snapshotting for reproducible runs

Feature engineering

Technical: momentum, volatility, trend, seasonality, market‑microstructure features
Factors: value, quality, size, low‑volatility, growth, profitability, investment
NLP and news: keyword windows, sentiment, topic signals
Event studies: pre/post windows, cumulative abnormal returns, bootstrapped significance

Modeling and evaluation

Classical time‑series and cross‑sectional ML
Deep sequence models for multi‑horizon forecasting
Policy learning and reinforcement learning for signal‑to‑execution mapping
Robust evaluation: walk‑forward, expanding windows, cross‑sectional folds, bootstrap CI

Backtesting and portfolio construction

Single‑asset and multi‑asset portfolios with constraints
Transaction cost models, borrow costs, partial fills and slippage
Position sizing, risk parity, volatility targeting, leverage and exposure controls
Benchmarking and risk decomposition

Risk and monitoring

Real‑time and historical risk metrics
Factor and scenario analysis with stress templates
Alerting on drift, regime breaks, or model underperformance

Execution and reporting

Paper and simulated execution with latency and queue modeling
Adapters for broker/execution venues (extensible)
Research reports with clear data lineage and reproducibility notes

Architecture overview

Conversation orchestrator that turns natural language into structured research plans
Tool registry for data, compute, risk, and reporting primitives
Artifact store that tracks results, parameters, and lineage for reproducibility
Data governance layer for licensing, entitlements, and audit trails
Safety guardrails with resource limits and fallbacks

Security and compliance at a glance

Principle of least privilege for data and compute access
Encryption in transit and at rest for supported storage backends
Configurable retention windows and secure deletion workflows
Audit logs for tool invocations and material outputs

For details, see our Privacy Policy and Terms of Service.

Our principles

Accuracy and reproducibility over hype
Clear provenance and compliance for data usage
Safe defaults with graceful fallbacks
First‑class visualization of results
Extensible architecture for proprietary workflows

Roadmap highlights

Expanded broker/execution adapters and OMS integrations
More factor libraries and portfolio construction methods
Inline notebooks and experiment tracking
Multi‑tenant workspaces with fine‑grained roles

Frequently asked questions

Is this investment advice? No. The platform assists research and education and does not provide investment advice. You are responsible for validating and using outputs.

Can I bring my own data? Yes. You can connect external data sources subject to licensing and compliance.

Can I export results? Yes. Artifacts, charts, and tables can be exported, and code can be downloaded for offline runs.

How do you handle private IP? Workspaces and artifact visibility controls help protect proprietary research.

If you have feedback or would like to collaborate, please reach out.