What Is Quantitative Research?

Define the rule before historical analysis

Quantitative research can be understood as a process: first translate market observations into clear rules, then use data and programs to review, record, and verify those rules.

In discretionary judgment, many conclusions come from experience and real-time impressions. Rule-based research tries to break those judgments into explicit conditions, such as observation targets, signal records, invalidation boundaries, pause conditions, and review triggers.

Its core value is not to create automatic certainty, but to make research logic clearer, more testable, and easier to review.

Data, rules, and signals form the basic structure

• Data: market information used for observation, such as price, volume, volatility range, and technical indicators.
• Rules: pre-defined judgment conditions, such as recording a research prompt when price forms a defined relationship with an observation range.
• Signals: research records generated when a rule condition is met. They can mark status, observation, invalidation, or review pause.

A simple example: the data is closing price and a trend-reference indicator; the rule is their defined relationship; the signal records a trend-observation prompt. This is the basic logic of quantitative research.

Understand applicable conditions first

• Trend framework: observes whether price or indicators show persistent directional features.
• Mean-reversion framework: observes whether short-term deviations return to a normal range.
• Range-observation framework: observes the relationship between price and a predefined observation range.
• Multi-factor framework: observes several variables such as price, volume, volatility, and state indicators together.

No framework is inherently superior. The key is to define applicable conditions, sample range, and review method.

Quantitative research is not a deterministic conclusion

• Assuming rule-based research can automatically create reliable conclusions.
• Looking only at outcome metrics while ignoring process risk.
• Treating one positive historical sample as proof of rule stability.
• Adjusting parameters too frequently until the rule overfits past samples.
• Ignoring cost, delay, and process differences in subsequent monitoring conditions.

The value of quantitative research is to help learners define rules, inspect sample behaviour, and identify risk boundaries. It cannot remove uncertainty or replace ongoing review and risk-management frameworks.