qrt — Quant Research Tools
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q.indicator — Indicators
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Getting Started
q.bt — Backtesting
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q.data — Data
Sample datasets
Data schemas
Local files
Market data sources
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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
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q.cross_section — Cross-sectional analysis
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q.feature — Feature lifecycle
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q.preprocess — Preprocessing
Missing data
Scaling
Outliers
Encoding
Reduction
Selection
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q.calendar — Market time
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q.signal — Signals
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q.model — Models
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q.env — Environment
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q.plot — Plotting
Classification curves
Return charts
Tearsheet report
Simulation tests
Trade logs
Saving figures
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q.portfolio — Portfolio
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q.stats — Statistics
Performance & benchmark
Rolling & calendar returns
Factor analytics
Chained API
Roadmap
q.utils — Utilities
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Native indicators
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q.indicator — Indicators
Roadmap
q.indicator Roadmap
Only open work is listed here.
Native indicators
Add causal-window and batch/online equivalence tests to native indicators
Providers
Evaluate tsfresh as an explicit provider for automated time-series measurements
Keep provider dependencies optional and surface actionable installation errors
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pandas-ta-classic
q.cross_section — Cross-sectional analysis