All case studiesTooling · Python + SQL

[e.g., "Automating debt sculpting in Python" OR "A BESS dispatch & revenue simulator on ERCOT/CAISO data"]

[one line: replacing manual goal-seek with auditable code, or simulating BESS revenue from market data]

[e.g., "Automating debt sculpting in Python" OR "A BESS dispatch & revenue simulator on ERCOT/CAISO data"]
01

Project Snapshot

Stack
[Python (pandas/NumPy), SQL, Tableau]
Data
[e.g., ERCOT/CAISO market prices]
Output
[sculpted debt profile / dispatch revenue]
02

The Challenge

[The manual, error-prone parts of project finance — iterative debt sculpting by goal-seek, or estimating BESS revenue across thousands of price intervals — and how code makes them fast, auditable, and repeatable.]

03

The Approach

  1. 01

    [Ingest data via SQL / API]

  2. 02

    [Implement the core algorithm: sculpting loop to target DSCR, or dispatch across price spreads]

  3. 03

    [Validate against an Excel benchmark]

  4. 04

    [Visualize results]

04

Inside the Model

[Describe the tool.]

[ Image placeholder — Code snippet + output chart / Tableau dashboard ]

[Repo link placeholder]

05

Results at a Glance

[Runtime vs manual][Intervals processed][Revenue estimate][Accuracy vs Excel]
06

What This Demonstrates

  • Python
  • SQL
  • Data pipelines
  • Automation of finance workflows
  • Validation discipline
07

Key Takeaway

[Idea + mechanism, two sentences.]

Illustrative case built on representative data; not based on any confidential or client transaction.