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🔷 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

cd ~/Work/prism
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2. Activate the virtual environment

source .venv/bin/activate
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3. Add API keys

cp .env.example .env
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Open .env and fill in your keys:

FMP_API_KEY=your_key_here
-FINNHUB_API_KEY=your_key_here
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Both are free: -- FMP (Financial Modeling Prep) — financialmodelingprep.com — 250 requests/day -- Finnhub — finnhub.io — 60 requests/minute

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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.
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4. Run the app

streamlit run app.py
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Opens at http://localhost:8501

Daily Usage

cd ~/Work/prism
-source .venv/bin/activate
-streamlit run app.py
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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
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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:

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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

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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).

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