Fund Analysis - CST Case Study

This repository contains my solution to the CST Case Study. The program reads cashflow data, validates it with Pydantic models and performs three analyses:

1. Internal Rate of Return (IRR) - at both currency and fund level.
2. Net Asset Value (NAV) Schedule - cumulative cashflows per currency.
3. FX Hedge Proposals - generates suggested 3-month rolling FX forward contracts to hedge 100% of foreign NAV exposure.

Requirements

• Python >= 3.12
• Dependencies managed via pyproject.toml

Installation

1. Create + activate venv:

   python -m venv .venv

2. Install package

   pip install . # Runtime dependencies only
   pip install ".[dev]" # Runtime and development dependencies
   pip install -e ".[dev]" # Editable install for development

Usage

Run analysis via the CLI:

python -m fund_analysis.cli --help

CLI Options

Flag	Description	Default
-i, --input	Path to xlsx input file	data/input/CSTCase_Study_data__1.xlsx
-f, --fund	Fund name filter	Fund I
-o, --output	Output directory	outputs/
-e, --export	Export results to output files	off

Methodology

IRR Calculation

Internal Rate of Return is calculated using XIRR.

• Handles irregular cashflow timing by using exact dates
• Calculated separately for each currency
• Fund level IRR calculated using base currency cashflows

NAV Calculation

The NAV schedule is calculated as the present value of remaining cashflows at each time(t).

• NAV(0) = 0 (as specified in the requirements)
• Cashflows at time t are included in NAV(t)
• Discount rate = The period IRR which is calculated from the annualised XIRR

FX Risk Hedging

NAV Exposure(t) = NAV(t) - Cashflow(t)

Reasoning: Cashflows occuring at time t are converted to base currency, so should be excluded from the hedge.

Direction:

• Positive NAV Exposure (long foreign currency) = SELL FOREIGN / BUY BASE
• Negative NAV Exposure (short foreign currency) = BUY FOREIGN / SELL BASE

Implementation details:

• Hedging starts at t=1 and finishes at t=n-1
• 100% hedge ratio applied to NAV exposure

Results / Example Outputs

Pre-generated results can be found in:

examples/outputs/
- irr.csv
- nav_schedule.csv
- hedge_proposals.csv

Testing

Pytest testing framework is used to write and manage tests.

Run tests:

pytest .

NOTE: Requires fund_analysis package + dev dependencies installed in environment, see installation

Development & Tooling

• Formatting: Black
• Linting: ruff
• Type Checking: mypy
• Testing: pytest

Pre-commit is used to auto-run all of the above tools before each commit.

To install pre-commit hooks:

pre-commit install