dolphindb-highfreq-factor

name	dolphindb-highfreq-factor
description	MUST use when the user asks to generate DolphinDB code that computes a factor from intraday / tick / high-frequency data (高频因子 / 日内因子 / 降频到日) and aggregates down to daily frequency. Do NOT use for factors that operate directly on daily-frequency data — those use dolphindb-daily-factor.

DolphinDB High-Frequency Factor Generator

The Rule

Never present factor code to the user before the DolphinX platform returns a successful execution result.

When to Use This Skill (vs dolphindb-daily-factor)

Scenario	Which skill
Factor starts from intraday/high-frequency data and down-frequencies to daily	dolphindb-highfreq-factor ← this skill
Factor operates on daily-frequency data (daily bars)	dolphindb-daily-factor

The differentiator is the granularity of the source data, not the output frequency. Both produce daily-frequency output, but the high-frequency skill adds a down-frequency aggregation step.

If the user says "write a factor" without specifying the data source, ask whether the data is daily or intraday before choosing a skill.

4-Step Flow

1. probe — Inspect the user's cluster to confirm which data tables exist and their fields, and whether the factor database exists. See references/probing.md.

2. verify — For every operator / function you are not 100% sure maps to a DolphinDB built-in, check references/pitfalls.md first, then consult the DolphinDB documentation or run a minimal probe on the DolphinX platform. Division uses \, not /.

3. template — Read references/template.dos. Fill in <FactorName>, fields used, and the factor body. Choose the normal path or the adjust path depending on whether post-down-frequency processing is needed. Keep all sections intact.

4. execute — Write the code to a .dos file. Run it on the DolphinX platform. Check the job status. If execution fails, isolate the error, fix, re-run.

If the factor expression is ambiguous (e.g. the user's formula doesn't specify the data source or weighting scheme), ask the user one consolidated question before writing code.

Two Computation Patterns

Normal Factor (single down-frequency)

Intraday computation → aggregate to daily → store.

Factor function receives intraday table → computes per-stock per-day aggregates → returns result table
res = mr(ds, conf[`func]).unionAll()

Example: examples/normal_factor.dos

Adjust Factor (down-frequency + post-processing)

Intraday computation → down-frequency to daily → further processing on the daily result (local computation, non-distributed).

Factor function returns daily result → adjust function processes daily result
diffTB = mr(ds, conf[`func]).unionAll()
res = conf[`funcsec](diffTB)    // local computation, not distributed

Example: examples/adjust_factor.dos

Factor Storage

• Factor results are stored in dfs://factor_day / factor_day table (TSDB engine)
• Before storing, scan the cluster to see if the factor database exists. If not, create it.
• See references/factor_table.md for the create-table code.

Data Sources

The user's cluster may have different high-frequency data tables. Always probe before assuming. Common possibilities:

Data type	Possible DB path	Possible table name
Level2 snapshot	dfs://Level2	snapshot
Level2 entrust	dfs://Level2	entrust
Level2 trade	dfs://Level2	trade
Minute K-line	dfs://stockMinKSH	stockMinKSH

Reference schemas for field names are available in references/schema.md. Always verify actual fields against the user's cluster — the schema files are for field name reference only, not for creating tables or assuming availability.

The user may also specify custom database/table names (e.g. "dfs://my_level2", "my_snapshot"). Always use the names provided by the user; the table above is only a default reference.

If the user's specified source does not exist on the cluster, follow the fallback procedure in references/probing.md#fallback. Do NOT guess, do NOT halt — probe all candidates, surface findings, and continue with the best available match.

References (read on demand)

• references/probing.md — inspect cluster schema for high-frequency data and factor tables; includes fallback procedure when expected data sources are missing
• references/schema.md — reference field names from Level2 snapshot/entrust/trade and minute K-line tables
• references/pitfalls.md — DolphinDB syntax gotchas + red flags specific to high-frequency computation
• references/template.dos — the comprehensive template to fill in (normal + adjust paths)

Examples (read for shape, not for content)

• examples/normal_factor.dos — normal high-frequency factor: single down-frequency step
• examples/adjust_factor.dos — adjust factor: down-frequency + local post-processing

Related Skills

• Documentation questions about DolphinDB in general → dolphindb-rag skill
• Daily-frequency factor generation → dolphindb-daily-factor skill