Initial commit — Prism financial analysis dashboard

Streamlit app with market bar, price chart, financial statements,
DCF valuation engine, comparable companies, and news feed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

1 files changed, 109 insertions, 0 deletions

diff --git a/services/data_service.py b/services/data_service.py
new file mode 100644
index 0000000..fa9b026
--- /dev/null
+++ b/services/data_service.py
@@ -0,0 +1,109 @@
+"""yfinance wrapper — price history, financial statements, company info."""
+import yfinance as yf
+import pandas as pd
+import streamlit as st
+
+
+@st.cache_data(ttl=60)
+def search_tickers(query: str) -> list[dict]:
+    """Search for tickers by company name or symbol. Returns list of {symbol, name, exchange}."""
+    if not query or len(query.strip()) < 2:
+        return []
+    try:
+        results = yf.Search(query.strip(), max_results=8).quotes
+        out = []
+        for r in results:
+            symbol = r.get("symbol", "")
+            name = r.get("longname") or r.get("shortname") or symbol
+            exchange = r.get("exchange") or r.get("exchDisp", "")
+            if symbol:
+                out.append({"symbol": symbol, "name": name, "exchange": exchange})
+        return out
+    except Exception:
+        return []
+
+
+@st.cache_data(ttl=300)
+def get_company_info(ticker: str) -> dict:
+    """Return company info dict from yfinance."""
+    t = yf.Ticker(ticker.upper())
+    info = t.info or {}
+    return info
+
+
+@st.cache_data(ttl=300)
+def get_price_history(ticker: str, period: str = "1y") -> pd.DataFrame:
+    """Return OHLCV price history."""
+    t = yf.Ticker(ticker.upper())
+    df = t.history(period=period)
+    df.index = pd.to_datetime(df.index)
+    return df
+
+
+@st.cache_data(ttl=3600)
+def get_income_statement(ticker: str, quarterly: bool = False) -> pd.DataFrame:
+    t = yf.Ticker(ticker.upper())
+    df = t.quarterly_income_stmt if quarterly else t.income_stmt
+    return df if df is not None else pd.DataFrame()
+
+
+@st.cache_data(ttl=3600)
+def get_balance_sheet(ticker: str, quarterly: bool = False) -> pd.DataFrame:
+    t = yf.Ticker(ticker.upper())
+    df = t.quarterly_balance_sheet if quarterly else t.balance_sheet
+    return df if df is not None else pd.DataFrame()
+
+
+@st.cache_data(ttl=3600)
+def get_cash_flow(ticker: str, quarterly: bool = False) -> pd.DataFrame:
+    t = yf.Ticker(ticker.upper())
+    df = t.quarterly_cashflow if quarterly else t.cashflow
+    return df if df is not None else pd.DataFrame()
+
+
+@st.cache_data(ttl=300)
+def get_market_indices() -> dict:
+    """Return latest price + day change % for major indices."""
+    symbols = {
+        "S&P 500": "^GSPC",
+        "NASDAQ": "^IXIC",
+        "DOW": "^DJI",
+        "VIX": "^VIX",
+    }
+    result = {}
+    for name, sym in symbols.items():
+        try:
+            t = yf.Ticker(sym)
+            hist = t.history(period="2d")
+            if len(hist) >= 2:
+                prev_close = hist["Close"].iloc[-2]
+                last = hist["Close"].iloc[-1]
+                pct_change = (last - prev_close) / prev_close
+            elif len(hist) == 1:
+                last = hist["Close"].iloc[-1]
+                pct_change = 0.0
+            else:
+                result[name] = {"price": None, "change_pct": None}
+                continue
+            result[name] = {"price": float(last), "change_pct": float(pct_change)}
+        except Exception:
+            result[name] = {"price": None, "change_pct": None}
+    return result
+
+
+@st.cache_data(ttl=3600)
+def get_free_cash_flow_series(ticker: str) -> pd.Series:
+    """Return annual Free Cash Flow series (most recent first)."""
+    t = yf.Ticker(ticker.upper())
+    cf = t.cashflow
+    if cf is None or cf.empty:
+        return pd.Series(dtype=float)
+    if "Free Cash Flow" in cf.index:
+        return cf.loc["Free Cash Flow"].dropna()
+    # Compute from operating CF - capex
+    try:
+        op = cf.loc["Operating Cash Flow"]
+        capex = cf.loc["Capital Expenditure"]
+        return (op + capex).dropna()
+    except KeyError:
+        return pd.Series(dtype=float)