1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
|
"""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_insider_transactions(ticker: str) -> pd.DataFrame:
"""Return insider transactions from yfinance.
Columns: Shares, URL, Text, Insider, Position, Transaction, Start Date, Ownership, Value
"""
try:
t = yf.Ticker(ticker.upper())
df = t.insider_transactions
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_sec_filings(ticker: str) -> list[dict]:
"""Return SEC filings from yfinance.
Each dict has: date, type, title, edgarUrl, exhibits.
"""
try:
t = yf.Ticker(ticker.upper())
filings = t.sec_filings
return filings if filings else []
except Exception:
return []
@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)
|
