# 🔷 Prism

A local financial analysis dashboard. Enter any stock ticker to get a formatted view of financial statements, valuation metrics, a DCF model, and a news feed — all in your browser.

---

## Features

- **Market Bar** — Live S&P 500, NASDAQ, DOW, and VIX at the top of every page
- **Overview** — Price chart (1M / 3M / 6M / 1Y / 5Y), key stats (market cap, P/E, 52W range, beta)
- **Financials** — Annual and quarterly Income Statement, Balance Sheet, and Cash Flow Statement with year-over-year % change columns
- **Valuation** — Key ratios grid (P/E, EV/EBITDA, margins, ROE, etc.), DCF model with adjustable WACC/growth/years, EV/EBITDA implied price, comparable companies table, analyst price targets, and earnings history (EPS actual vs. estimate)
- **News** — Recent articles with heuristic Bullish / Bearish / Neutral tags and a 7-day sentiment summary

---

## Setup

### 1. Clone / navigate to the project

```bash
cd ~/Work/prism
```

### 2. Activate the virtual environment

```bash
source .venv/bin/activate
```

### 3. Add API keys

```bash
cp .env.example .env
```

Open `.env` and fill in your keys:

```
FMP_API_KEY=your_key_here
FINNHUB_API_KEY=your_key_here
```

Both are **free**:
- **FMP** (Financial Modeling Prep) — [financialmodelingprep.com](https://financialmodelingprep.com/developer/docs) — 250 requests/day
- **Finnhub** — [finnhub.io](https://finnhub.io) — 60 requests/minute

> **No keys?** The app still works. Price data, financials, and market indices are sourced from `yfinance` (no key required). Ratios, comps, and news won't load until keys are added.

### 4. Run the app

```bash
streamlit run app.py
```

Opens at `http://localhost:8501`

---

## Daily Usage

```bash
cd ~/Work/prism
source .venv/bin/activate
streamlit run app.py
```

---

## Project Structure

```
prism/
├── app.py                    # Entry point
├── requirements.txt
├── .env                      # Your API keys (not committed)
├── .env.example              # Key template
├── .streamlit/
│   └── config.toml           # Dark theme + layout settings
├── services/
│   ├── data_service.py       # yfinance — price, financials, indices
│   ├── fmp_service.py        # FMP API — ratios, peers
│   ├── news_service.py       # Finnhub — news + sentiment
│   └── valuation_service.py  # DCF engine (Gordon Growth Model)
├── components/
│   ├── market_bar.py         # Index metrics row
│   ├── overview.py           # Company header + price chart
│   ├── financials.py         # Statement tables
│   ├── valuation.py          # Ratios, DCF, comps
│   └── news.py               # News feed
└── utils/
    └── formatters.py         # Number formatting helpers
```

---

## DCF Model Notes

The DCF model uses historical **Free Cash Flow** from yfinance, computes a capped median growth rate from valid positive-FCF periods, and then projects forward using your chosen assumptions:

| Input | Default | Range |
|---|---|---|
| WACC | 10% | 5–20% |
| Terminal Growth Rate | 2.5% | 0.5–5% |
| Projection Years | 5 | 3–10 |
| FCF Growth Rate | Historical median | -20–30% |

The model uses the **Gordon Growth Model** for terminal value. It first estimates **enterprise value**, then bridges to **equity value** using debt and cash before calculating value per share. Terminal growth must remain below WACC. The FCF growth slider defaults to the historical median but can be freely overridden.

The **EV/EBITDA** tab derives an implied price by applying a target multiple to trailing EBITDA, subtracting net debt, and dividing by shares outstanding. The slider defaults to the company's current market multiple.

---

## API Rate Limits

| Source | Limit | Used For |
|---|---|---|
| yfinance | None | Price, financials, indices |
| FMP (free) | 250 req/day | Ratios, comps, news |
| Finnhub (free) | 60 req/min | News, sentiment |

Data is cached in-memory per session to minimize API calls (financials: 1h, news: 10min, indices: 5min).