Statistics Packages with Quantamental Indicators
Data analysis with macro quantamental indicators can use standard data science packages in Python and R. These notebooks contain entry-level analysis examples focusing on standard time series and panel analysis.
JPMaQs with Seaborn
The notebook illustrates how to use the popular data visualization library Seaborn with quantamental data. In particular, it shows how to use the package to display historical distributions, panels of timelines, bivariate relations, and various types of heatmaps.
JPMaQS with Statsmodels
The Statsmodels library provides models and tools for statistical analysis. The notebook illustrates how to use it for various types of simple regression and time series analysis of quantamental data.
Panel regression with JPMaQS (Python)
The notebook illustrates various types of quantamental panel analysis in Python. In particular, it shows the application of pooled regression, fixed-effects regression, random-effects regression, linear mixed-effects models, and seemingly unrelated regressions.
Panel regression with JPMaQS (R)
The notebook illustrates various types of quantamental panel analysis in R. In particular, it shows the application of pooled models, fixed effects models, and linear mixed-effects models.
Trading strategies with JPMaQS
This notebook gives a step-by-step strategy research example using quantamental data and the Macrosynergy package. It shows how to check data, how to construct panels with plausible trading factors, and how to value the predictive power and economic value of such factors.