Data Cleaning
Cleans and preprocesses financial time series data. Handles missing values, outliers, corporate actions (splits, dividends), date alignment, and data quality checks. Trigger when the user has messy financial data or needs to prepare data for analysis.
Install
$ agentstack add skill-qunyou-agent-finance-skills-data-cleaning ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-qunyou-agent-finance-skills-data-cleaning) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Data Cleaning
Prepares financial time series data for analysis by handling common data quality issues.
Real Code Reference
tradinglearn/utils/data_fetcher.py—fetch_stock_data()normalizes TDX raw data to clean DataFrametradinglearn/utils/simple_pytdx2.py— mock data generator with controllable noisetradinglearn/pytdx2/client/quotationClient.py— raw data source (prices need scaling adjustments)
Capabilities
- Missing values: forward fill (ffill), linear interpolation, drop, or flag
- Outlier detection: z-score, IQR, moving average deviation
- Corporate actions: adjust prices for splits and dividends using adjustment factors
- Date alignment: merge multiple series on common trading dates (not calendar dates)
- Data quality: volume anomalies, price gaps > N%, stale data, duplicate rows
Typical Workflow
- Load raw data →
pd.DataFramewith columns: date, open, high, low, close, volume - Check nulls →
df.isnull().sum(), decide fill strategy - Detect outliers → flag bars where
abs(zscore(returns)) > 3 - Adjust for splits → apply adjustment factor series
- Align dates → reindex to intersection of all trading calendars
- Validate → no NaNs, no suspicious jumps, no future dates
Edge Cases
- Non-trading days: use trading calendar, not calendar days
- IPO dates: trim to actual listing date (data before is invalid)
- Suspended stocks: forward-fill last price or mark as NaN
- Pre/after-hours: decide whether to include or filter
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: qunyou-agent
- Source: qunyou-agent/finance-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.