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"""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_analyst_price_targets(ticker: str) -> dict:
"""Return analyst price target summary (keys: current, high, low, mean, median)."""
try:
t = yf.Ticker(ticker.upper())
data = t.analyst_price_targets
return data if isinstance(data, dict) and data else {}
except Exception:
return {}
@st.cache_data(ttl=3600)
def get_recommendations_summary(ticker: str) -> pd.DataFrame:
"""Return analyst recommendation counts by period.
Columns: period, strongBuy, buy, hold, sell, strongSell.
Row with period='0m' is the current month.
"""
try:
t = yf.Ticker(ticker.upper())
df = t.recommendations_summary
return df if df is not None and not df.empty else pd.DataFrame()
except Exception:
return pd.DataFrame()
@st.cache_data(ttl=3600)
def get_earnings_history(ticker: str) -> pd.DataFrame:
"""Return historical EPS actual vs estimate.
Columns: epsActual, epsEstimate, epsDifference, surprisePercent.
"""
try:
t = yf.Ticker(ticker.upper())
df = t.earnings_history
return df if df is not None and not df.empty else pd.DataFrame()
except Exception:
return pd.DataFrame()
@st.cache_data(ttl=3600)
def get_next_earnings_date(ticker: str) -> str | None:
"""Return the next expected earnings date as a string, or None.
Uses t.calendar (no lxml dependency).
"""
try:
t = yf.Ticker(ticker.upper())
cal = t.calendar
dates = cal.get("Earnings Date", [])
if dates:
return str(dates[0])
return None
except Exception:
return None
@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)
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