MF Portfolio Processor

Processes two-point mutual fund portfolio holdings (e.g. May -> Aug 2025).

Input = standardized CSV in the required format (month_end, scheme, ISIN, market value, quantity).

Runs a Python pipeline behind the scenes:

• Validates totals (Market Value & Quantity)
• Builds ISIN mapping file (deduplication + standardized names)
• Checks for duplicate or conflicting names (name_cut and ISIN overlaps)
• Generates final summary table with % shares, counts, and % changes

Validation Reports

• Totals by scheme and period
• Duplicate name detection (via name_cut)
• ISIN mapping + exceptions

Final Summary CSV

• Company-level holdings (MV, Qty, % shares, MF counts)
• % change in MV and Qty between periods
• Manual comments (e.g. "Split", "Rights", "Bonus") preserved from ISIN mapping

Outputs are CSV-ready for Excel or BI tools. Results can be pushed into any dashboard - e.g. HTML/JS apps for interactive visualization.

1. Prepare data file
   • Convert monthly portfolio disclosures into the required CSV format
   • Either format manually or use the MF Conversion Tool at app.tigzig.com/mf-files-ai
   • Ensure both months' data are appended into one CSV
2. Upload file to this GPT
   • Immediately receives 3 validation CSVs: Validation totals, ISIN mapping, Duplicate name/ISIN checks
3. Confirm or adjust
   • Review validation files
   • If needed, give GPT corrections (e.g. update ISIN mapping, fix duplicates)
   • GPT re-runs pipeline and regenerates outputs
4. Get final results
   • Download the final summary CSV
   • Analyze in Excel, or feed into custom dashboards

To replicate this GPT:

Quick Overview

1. Get these two files (available in the resources section):
   • pipeline.py (full reproducible pipeline)
   • custom_instructions.md (system instructions)
2. In the Custom GPT builder:
   • Apply custom_instructions.md as system prompt
   • Upload pipeline.py under Actions schema
3. That's it - your GPT will now accept CSV uploads, generate validation reports, wait for user confirmation, and create the final summary.
4. Extend/customize as needed - the full Python pipeline is open for editing.