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Found 62 Skills
Use daily frequency data of natural gas and fertilizer prices to verify whether the narrative of "natural gas price surge → fertilizer supply constraints/breach of contract → fertilizer price surge" holds, and output key turning points and lead-lag analysis that can be marked on charts.
Predictive analytics for Dynatrace — time series forecasting with the timeseries-forecast tool, capacity saturation planning, trend and anomaly detection across hosts, services, and infrastructure.
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
Pandas data manipulation with DataFrames. Use for data analysis.
Cointegration testing for pairs trading using Engle-Granger, Johansen, and rolling stability analysis
Guides quantitative research for markets and finance—research question framing, data sourcing and quality checks, descriptive and inferential statistics, time series and panel methods (high level), factor and signal research, backtest design and pitfalls (lookahead, survivorship), risk metrics (volatility, drawdown, Sharpe limitations), regime and stress analysis, and reproducible notebooks or reports with explicit limitations and uncertainty communication. Use when the user mentions "quantitative research", "quant researcher", "factor research", "signal backtest", "time series analysis", "panel regression", "alpha research", "Sharpe ratio analysis", "survivorship bias", "lookahead bias", "econometric analysis", or "risk factor model". Not for production ML pipelines (data-scientist, ml-research-engineer), equity narrative reports (equity-research skills), SOX accounting (financial-statements), legal investment advice, or trading execution systems (senior-software-engineer).
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Expert-level data science, analytics, visualization, and statistical modeling
InfluxDB Cloud integration. Manage data, records, and automate workflows. Use when the user wants to interact with InfluxDB Cloud data.
Comprehensive guide for implementing Syncfusion WPF Range Selector (SfDateTimeRangeNavigator) for time-bound data visualization with interactive scrolling, zooming, and range selection. Use this when working with range selectors, date-time range navigation, or time-bound data visualization. This skill covers interactive data range selection, chart range zooming, and dashboard time navigation features for large time-based datasets in WPF applications.
Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.