Function reference
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.feature
Named, versioned feature definitions, computation, and materialization.
| feature | Named, versioned feature definitions and materialization. |
| feature.ops | Generic operations used to construct feature columns. |
q.preprocess
Fitted transformations applied at the model-training boundary.
| preprocess | Fitted transformations applied at the model-training boundary. |
| preprocess.impute | Missing-data indicators and imputation. |
| preprocess.scale | Numeric feature normalization. |
| preprocess.outlier | Outlier detection and treatment. |
| preprocess.encode | Categorical feature encoding. |
| preprocess.reduction | Dimensionality reduction for feature matrices. |
| preprocess.selection | Feature selection methods. |
q.calendar
Exchange sessions, closures, and market-time operations.
| calendar | Exchange sessions, closures, and market-time operations. |
q.signal
Investment intent derived from indicators, factors, models, and rules.
| signal | Investment intent derived from indicators, factors, models, or rules. |
q.stats
Reusable return-stream and classification statistics, including performance, 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.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 (model summaries via torchinfo) and leakage-aware CV splitters named after scikit-learn’s model_selection.
| model | ML model utilities: helpers for building, inspecting, and training models. |
| model.torch | PyTorch model helpers. |
| model.selection | Model selection: leakage-aware CV splitters for time-series data. |
q.env
Environment variables, runtime inspection, hardware reports, and declarative requirements.
| env | Environment variable and .env file helpers. |
q.utils
Shared utilities.
| utils | General-purpose helper functions. |