diagnostic
Unverified ML strategy on Multi by ml4t. BotFinder score 18 out of 100.
Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows.
Source: github
BotFinder analysis pending.
diagnostic
ml4t-diagnostic Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows. Use ml4t-diagnostic to evaluate cross-sectional signals, construct purged time-series validation folds, correct strategy statistics for selection bias, analyze feature and trade behavior, and produce backtest reports. ML4T Library Ecosystem ml4t-diagnostic is one of seven libraries supporting the workflow described in Machine Learning for Trading. It accepts engineered features, predictions, and backtest results from the other ML4T libraries, but the primary signal-analysis workflow below has no external service or special hardware requirement. Installation and Support The supported Python versions are 3.12, 3.13, and 3.14 on Linux, macOS, and Windows. Python 3.15 is not currently supported because required dependency wheels are still being qualified. Progress is tracked in issue #45. Quick Start This example creates a synthetic cross-sectional factor whose score affects the next price change, then measures its information coefficient and quantile spread. analyzesig
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How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
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Alerts on changes: coming soon
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Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)