Learn Factors
Study feature definitions, IC, quantiles, missingness and correlation while keeping predictive diagnostics separate from trading results.
What you will learn
A factor measures a security characteristic. A high score does not establish profitability. Specify its definition and availability, separate future-return labels, and then inspect the evaluation sample.
Follow the sequence below from definitions to input checks and public lessons. Distinguish numerical illustrations from actual run results.
Definitions and evaluation
What Is a Factor? Comparing Security Characteristics with NumbersLearn what a factor measures, how scores differ from rankings and trading orders, and why factor evaluation does not establish investment profitability. Public KabuForge examples keep features separate from future-return labels.What Is IC? Reading the Relationship Between Factors and Future ReturnsCompare IC and Rank IC using fictional data, and learn how labels, missing values, sample size and costs affect interpretation.What Is Quantile Analysis? Evaluating Factors by GroupsGroup securities by factor score and examine future returns, group sizes, missing data and membership changes using an explicitly fictional example.
Inspect the diagnostics
Factor Coverage: Check Which Securities and Dates Reach EvaluationSeparate eligible securities, valid features and future labels before interpreting changes in the evaluation sample.Rank Normalization: Comparable Scales Still Need Clear ConventionsCompute an illustrative rank transformation and inspect ties, missing values, direction and weights.Factor Correlation: Check Whether Features Repeat the Same InformationDistinguish feature-to-feature correlation from IC and check the common sample and invalid observations.ICIR: What the Mean and Variation of IC Can Tell YouCalculate an illustrative mean-to-standard-deviation ratio and inspect sample size, overlapping labels and inferential limits.
Design a study
Factor Analysis with J-Quants Data: Inputs and EvaluationFactor analysis turns security characteristics into values and evaluates their relationship with later returns.Multifactor research: check scale and overlap before combining scoresCombine fictional normalized scores while preserving direction, timing and validation rules.Reproduce a Study: Preserve Data, Settings and Run EvidenceBuild a reviewable evidence package linking input identity, recipes, software and receipts.
Explore connected topics
Applies to: main source candidate (unpublished) · Public source docs/en_US/GUI_RESEARCH_COURSES.md
Reviewed · KabuForge