qrt — Quant Research Tools
Getting Started
API Reference
Roadmap
q.preprocess — Preprocessing
Roadmap
Getting Started
q.bt — Backtesting
Roadmap
q.data — Data
Sample datasets
Data schemas
Local files
Market data sources
Roadmap
q.indicator — Indicators
QRT indicators
Simple moving average (SMA)
Exponential moving average (EMA)
Mean absolute deviation (MADEV)
Volume spike ratio
Price spikes
Log returns
Realized variance
Realized quarticity
Bipower variation
Median realized volatility
Minimum realized volatility
Realized volatility by session
Relative strength
Relative-strength moving average
Relative-strength days
Relative-strength phase
RS new high before price
TA-Lib
pandas-ta-classic
Roadmap
q.cross_section — Cross-sectional analysis
Roadmap
q.feature — Feature lifecycle
Roadmap
q.preprocess — Preprocessing
Missing data
Scaling
Outliers
Encoding
Reduction
Selection
Roadmap
q.calendar — Market time
Roadmap
q.signal — Signals
Roadmap
q.model — Models
Roadmap
q.env — Environment
Roadmap
q.plot — Plotting
Classification curves
Return charts
Tearsheet report
Simulation tests
Trade logs
Saving figures
Roadmap
q.portfolio — Portfolio
Roadmap
q.stats — Statistics
Performance & benchmark
Rolling & calendar returns
Factor analytics
Chained API
Roadmap
q.utils — Utilities
Roadmap
Roadmap
q.preprocess — Preprocessing
Roadmap
q.preprocess Roadmap
Define the shared pandas-friendly
fit
/
transform
protocol
Add deterministic and fitted imputers
Add standard, robust, min-max, and max-absolute scalers
Add clipping, winsorization, and fitted outlier detectors
Add categorical encoders with unseen-category policies
Add PCA and related reduction adapters
Add variance, mutual-information, and recursive feature selection
Serialize fitted state and validate training-boundary provenance
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Selection
q.calendar — Market time