Function reference
q.bt
LEAN backtest orchestration, exact native artifacts, and isolated interactive reports.
| bt | Backtest execution and reporting. |
| bt.lean | Typed local Lean CLI orchestration. |
q.experiment
Backend-neutral experiment runs, parameters, metrics, datasets, models, and artifacts with local and MLflow tracking.
| experiment | Reproducible experiment runs, metrics, datasets, models, and artifacts. |
q.ai
Generation, structured extraction, embeddings, retrieval, agents, batch inference, evaluation, local models, and serving specifications.
| ai | QRT’s curated generative-AI workbench. |
| ai.client | Configured client for repeatable QRT AI workflows. |
| ai.types | Shared messages, results, capabilities, usage, and provenance types. |
| ai.embeddings | Hosted and local embedding contracts. |
| ai.vector | LanceDB-backed embedded vector indexes. |
| ai.rag | Point-in-time retrieval-augmented generation workflows. |
| ai.prompts | Versioned deterministic prompt assets. |
| ai.tools | Typed, allowlisted tool definitions and execution. |
| ai.agents | Bounded Pydantic AI-backed agent workflows. |
| ai.batch | Stable-key, resumable batch inference. |
| ai.evals | Versioned evaluation suites and regression reports. |
| ai.tokenize | Model-aware token counting and context budgeting. |
| ai.local | Explicit context-managed local model engines. |
| ai.serve | Validated local inference-server specifications. |
| ai.finetune | LoRA and QLoRA adaptation specifications with artifact lineage. |
q.indicator
Single-instrument market measurements and explicit provider catalogs.
| indicator | Stateless market measurements for research, rules, and model inputs. |
| indicator.talib | TA-Lib indicators exposed with a pandas-friendly interface. |
| indicator.pandas_ta | pandas-ta-classic indicators exposed under the qrt indicator namespace. |
q.cross_section
Cross-sectional characteristics, rankings, and neutralization.
| cross_section | Cross-sectional characteristics and relative asset measurements. |
q.label
Future-aware financial labels, event detection, and overlap-aware sample weights.
| label | Leakage-explicit target construction for financial machine learning. |
q.dataset
Aligned model inputs, targets, sample weights, metadata, and split schemes.
| dataset | Aligned datasets that bridge feature and label research with model fitting. |
q.transform
Fitted transformations applied at the model-training boundary.
| transform | Fitted transformations applied at the model-training boundary. |
| transform.impute | Missing-data indicators and imputation. |
| transform.scale | Numeric feature normalization. |
| transform.outlier | Outlier detection and treatment. |
| transform.encode | Categorical feature encoding. |
| transform.reduction | Dimensionality reduction for feature matrices. |
| transform.selection | Feature selection methods. |
q.calendar
Exchange sessions, closures, and market-time operations.
| calendar | Exchange sessions, closures, and market-time operations. |
q.signal
Point-in-time investment intent derived from indicators, factors, models, and rules.
| signal | Point-in-time investment intent derived from scores, models, or rules. |
q.stats
Reusable return-stream, risk, and classification statistics, including performance, tail-loss estimators, rolling diagnostics, and multiclass evaluation curves (no plotting dependency).
| stats | Statistical analytics for return streams: performance, risk, and benchmark-relative metrics. |
q.plot
Interactive classification diagnostics, return-stream charts, trade visualizations, and performance reports.
| plot | Opinionated static and interactive plots for quantitative research. |
| plot.interactive | Interactive Plotly charts for quantitative research return streams. |
q.data
Loading/saving local files, market data sources (Yahoo Finance, Binance, DuckDB) each exposed as their own submodule, prepackaged sample datasets, and the master securities database.
| data | Data access: loading/saving local files, downloading from market data |
| data.clean | Deterministic cleaning and validation for market-data frames. |
| data.local | Local file loading and saving (parquet, csv, …) plus raw trade |
| data.datasets | Prepackaged sample datasets shipped with qrt for offline use – handy |
| data.sources | Data sources: network vendors, databases, and other backends – each |
| data.sources.yfinance | Yahoo Finance market data source (stocks, ETFs, indices). |
| data.sources.binance | Binance futures market data source. |
| data.sources.duckdb | Generic DuckDB-backed data source. |
q.model
ML model utilities: PyTorch helpers and a compatibility bridge for sklearn time-series position arrays.
| model | ML model utilities: helpers for building, inspecting, and training models. |
| model.torch | PyTorch model helpers. |
| model.selection | Compatibility helpers for array-based model selection. |
q.env
Environment variables, runtime inspection, hardware reports, and declarative requirements.
| env | Environment variable and .env file helpers. |
q.gym
Gymnasium-compatible financial environments for reinforcement learning.
| gym | Financial Gymnasium environments for reinforcement-learning agents. |
| gym.core | Gymnasium environments for financial reinforcement learning. |
q.utils
Shared utilities.
| utils | General-purpose helper functions. |