Stock Exposure: Compute Portfolio Exposure to Individual Stocks Across Funds and Derivatives

Question

## Problem

A portfolio holds positions in individual stocks, ETFs (which have underlying stock weights), and options. Compute the net exposure to each underlying stock. An ETF position contributes its market value multiplied by each constituent's weight. An option contributes delta * notional to the underlying stock exposure.

```python
def compute_exposure(
    positions: list[dict],         # {symbol, qty, market_value, type}
    etf_compositions: dict,        # {etf_symbol: {stock: weight}}
    option_deltas: dict            # {option_symbol: {underlying, delta, notional}}
) -> dict:                         # {stock_symbol: net_exposure}
    pass
```

**Example:**
```
positions = [{symbol:"SPY", qty:10, market_value:5000, type:"etf"},
             {symbol:"AAPL_CALL", qty:5, market_value:300, type:"option"}]
etf_compositions = {"SPY": {"AAPL": 0.07, "MSFT": 0.06, ...}}
option_deltas = {"AAPL_CALL": {"underlying":"AAPL", "delta":0.6, "notional":150}}
Output: {"AAPL": 350 + 450, "MSFT": 300, ...}
```

## Follow-ups

1. How do you handle ETFs that hold other ETFs (nested funds)? What is the termination condition?
2. If option delta changes with market conditions (gamma), how do you mark exposure in real time?
3. How would you present this as a risk report grouped by sector?
4. What precision/rounding strategy do you use to avoid floating-point drift across thousands of positions?

Stock Exposure: Compute Portfolio Exposure to Individual Stocks Across Funds and Derivatives

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