Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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python3 scripts/one_fund_analysis.py \
--accession_number ... \
--quarter 2025-q2 \
This script will print out several basic information of a given fund on 2025-q2, including total number of holdings, AUM, total number of stock holdings, etc.
python scripts/one_fund_analysis.py \
--quarter 2025-q3 \
--accession_number <accession number assigned in q3> \
--baseline_quarter 2025-q2 \
--baseline_accession_number <accession number assigned in q2>
This script will print out the dynamic changes of holdings from 2025-q2 to 2025-q3. Such as newly purchased stocks ranked by notional value, and newly sold stocks ranked by notional value.
python scripts/holding_analysis.py \
--cusip <stock cusip> \
--quarter 2025-q3 \
--topk 10
This script will print out the top 10 hedge funds who hold a particular stock with highest notional value.
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AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
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Redact text from PDF documents for blind review anonymization
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Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
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Analyze failed GitHub Action jobs for a pull request.
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Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
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Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.
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