Roadmap

Open work is maintained alongside each submodule’s documentation. Completed items are removed from these pages.

Module roadmaps

Area	Scope	Roadmap
q.bt	Backtesting	View roadmap
q.data	Data access and datasets	View roadmap
q.env	Environment configuration	View roadmap
q.calendar	Exchange sessions and market time	View roadmap
q.indicator	Single-instrument market measurements	View roadmap
q.cross_section	Cross-sectional characteristics	View roadmap
q.feature	Feature definitions and materialization	View roadmap
q.preprocess	Fitted model-input transformations	View roadmap
q.signal	Investment intent and rule outputs	View roadmap
q.model	Model utilities and selection	View roadmap
q.plot	Plotting and reports	View roadmap
q.portfolio	Portfolio analysis	View roadmap
q.stats	Return-stream and factor statistics	View roadmap
q.utils	Shared utilities	View roadmap

Project-wide work

• Package scaffolding (pyproject.toml, CI, tests) — pyproject.toml and tests/ are in place; CI (GitHub Actions or similar) is still missing
• q.load_settings("settings.json") — project-level defaults (rolling window, annualization, theme) that override library defaults without cluttering call sites

Ideas

Exploratory candidates that fit qrt’s unified quant-research API. These are not yet committed roadmap items. Candidates with a clear existing owner live on that module’s roadmap; the items below would introduce or span namespaces.

• q.config — layer defaults, JSON/TOML files, .env values, and explicit overrides with validation
• q.calendar — exchange calendars, sessions, holidays, market hours, and timestamp alignment using exchange-calendars
• q.risk — VaR/CVaR, volatility targeting, drawdown limits, risk budgets, and position sizing
• q.optimize — mean-variance, risk-parity, minimum-variance, and constrained portfolio optimization
• q.experiment — record parameters, dataset fingerprints, random seeds, metrics, and artifacts for reproducible research
• q.validate — validate OHLCV/trade schemas, timestamps, missing sessions, suspicious returns, and look-ahead risks
• q.execution — shared fee, slippage, spread, order-sizing, and fill models for backtests
• q.signal — signal normalization, ranking, neutralization, decay, turnover, and information-coefficient analysis

Inspiration

Libraries we take inspiration from (and in some cases wrap or borrow ideas from):

Library	What we borrow
tulipy	technical indicators
feature-engine	feature engineering
quantstats	tearsheets, return-stream metrics
pyfolio-reloaded	portfolio/performance analysis
alphalens-reloaded	alpha-factor evaluation
empyrical-reloaded	risk/performance statistics
tsfresh	automated time-series feature extraction
skfolio	sklearn-style portfolio optimization
mlfinlab	purged CV, embargo, financial ML (López de Prado)
pandas-ta	technical indicators
pandas-ta-classic	maintained fork of pandas-ta indicators
Riskfolio-Lib	portfolio optimization & risk measures
qlib	end-to-end quant ML platform design
pytorch-forecasting	PyTorch time-series model wrappers
sktime	unified time-series API design
alphatools	alpha research & factor tooling on a securities master
PyStats	statistical distribution functions (pdf/cdf/quantile/sampling)