yfinance Cheat Sheet

01Setup & Installget going

pip install yfinance★
Core install. Python 3.9+.
pip install "yfinance[nospam]"
Adds caching + rate-limiting extras (requests_cache, requests_ratelimiter).
import yfinance as yf★
The universal alias. Everything hangs off yf.
yf.__version__
Check your version — the API moves fast (this sheet = 1.5.x).
pip install -U yfinance
Upgrade often; Yahoo breaks things and fixes land quickly.

02The Ticker Objectone handle

t = yf.Ticker("AAPL")★
Your gateway to everything about one symbol. Cheap to make — no network yet.
t = yf.Ticker("RELIANCE.NS")
Non-US symbols need an exchange suffix (see the symbol card).
t.ticker
The symbol string back out.
t.isin
The security's ISIN (where available).
t.history(...) t.info t.dividends …
Each attribute below is a live, lazy fetch off this one object — reuse it.

03download() · bulk pricesthe workhorse

yf.download("AAPL")★
Prices for one symbol (defaults to last 1mo, daily).
yf.download(["AAPL","MSFT"])★
Many symbols at once → MultiIndex columns. Threaded by default.
yf.download(t, start="2024-01-01", end="2024-12-31")★
Explicit window. end is exclusive.
yf.download(t, period="1y", interval="1d")
Or a rolling period instead of start/end.
yf.download(t, group_by="ticker")
Columns become (ticker, field) instead of (field, ticker).
yf.download(t, auto_adjust=False)
Turn OFF adjustment to get a separate Adj Close back.
yf.download(t, progress=False)★
Silence the progress bar (great in scripts/notebooks).
yf.download(t, threads=True, repair=True)
threads parallelises; repair fixes known Yahoo price glitches.
yf.download(t, multi_level_index=False)
Flatten to plain columns for a single ticker.
yf.download(t, actions=True, prepost=True)
Include dividends/splits columns and pre/post-market bars.

04.history() · one symbolrichest prices

t.history(period="1mo")★
OHLCV DataFrame indexed by tz-aware date. Auto-adjusted.
t.history(period="max")★
Everything Yahoo has for the symbol.
t.history(start="2020-01-01", end="2024-01-01")
Fixed window (end exclusive).
t.history(interval="1h", period="1mo")
Intraday bars (mind the lookback limits — see the interval card).
t.history(auto_adjust=False)
Keep raw OHLC + a separate Adj Close.
t.history(prepost=True)
Include pre- and post-market trading.
t.history(repair=True)
Auto-repair suspicious 100x / currency-mixup prices.
t.get_history_metadata()
Exchange, tz, currency, trading periods behind the last fetch.

05period & intervalthe allowed strings

period = 1d 5d 1mo 3mo 6mo★
…continues: 1y 2y 5y 10y ytd max. A rolling lookback.
interval = 1m 2m 5m 15m 30m★
…then 60m 90m 1h 1d 5d 1wk 1mo 3mo. Bar size.
interval="1m" → last 7 days onlylimit
1-minute data has a hard 7-day window.
any intraday (<1d) → last 60 dayslimit
Sub-daily bars can't go back more than ~60 days.
use period or start/end
If both given, start/end wins; don't mix expectations.

06The Price DataFramereading it

df[["Open","High","Low","Close","Volume"]]★
The five core columns (+ Dividends, Stock Splits).
df.index
A tz-aware DatetimeIndex in the exchange's local time.
df["Close"].plot()★
Straight into pandas/matplotlib — it's just a DataFrame.
data["Close"]["AAPL"]
MultiIndex select: field first (default group_by="column").
data["AAPL"]["Close"]
…ticker first when you passed group_by="ticker".
data.xs("Close", axis=1, level=0)
Slice one field across every ticker cleanly.
no "Adj Close" by defaultchanged
auto_adjust=True is the default now — OHLC are already adjusted.

07Corporate Actionsdividends & splits

t.dividends★
Series of dividend payments by date.
t.splits
Series of stock-split ratios by date.
t.actions★
Dividends + splits together in one DataFrame.
t.capital_gains
Capital-gains distributions (mutual funds).
t.get_shares_full(start="2022-01-01")
Historical shares-outstanding time series.

08Optionsthe chain

t.options★
Tuple of available expiry dates (as strings).
chain = t.option_chain(t.options[0])★
One expiry's chain. Omit the date for the nearest expiry.
chain.calls
Calls DataFrame — strike, bid/ask, IV, OI, volume.
chain.puts
Puts DataFrame, same columns.
chain.underlying
Dict of the underlying's live quote at fetch time.

09Company Info & Newsthe profile

t.fast_info★
Fast & reliable: last_price, market_cap, currency, day_high/low, year_high/low, 50/200-day averages, shares…
t.fast_info["last_price"]★
Dict-like access to a single quick field.
t.info
Big profile dict (sector, summary, ratios…). Slow & fragile — prefer fast_info for quick fields.
t.info["marketCap"]
Hundreds of keys; wrap access in .get().
t.news
Recent news items (list of dicts) for the symbol.
t.get_isin()
Look up the ISIN identifier.

10Financial Statementsfundamentals

t.income_stmt★
Annual income statement (DataFrame, periods as columns).
t.quarterly_income_stmt
Quarterly version. Also ttm_income_stmt.
t.balance_sheet★
Annual balance sheet; quarterly_balance_sheet too.
t.cashflow★
Cash-flow statement; quarterly_cashflow, ttm_cashflow.
t.calendar
Next earnings date, ex-dividend date, estimates.
t.get_earnings_dates(limit=12)
Past & upcoming earnings dates with EPS est. vs actual.
t.sec_filings
Recent SEC filings metadata.
t.earningsdeprecated
Gone/unreliable — read net income from income_stmt instead.

11Analyst Views & Estimatesthe street

t.analyst_price_targets★
Dict: current, low, high, mean, median target.
t.recommendations
Buy/hold/sell counts by period (DataFrame).
t.recommendations_summary
Rolling summary of the above.
t.upgrades_downgrades
Ratings-change history by firm.
t.earnings_estimate
EPS estimates for coming periods.
t.revenue_estimate
Revenue estimates for coming periods.
t.earnings_history
Past estimate vs actual EPS + surprise.
t.eps_trend t.eps_revisions
How estimates are trending / being revised.
t.growth_estimates
Projected growth vs sector/index.

12Holders & Insiderswho owns it

t.major_holders
% insiders vs institutions summary.
t.institutional_holders★
Top institutional holders (DataFrame).
t.mutualfund_holders
Top mutual-fund holders.
t.insider_transactions
Recent insider buys/sells.
t.insider_purchases
Insider purchase activity summary.
t.insider_roster_holders
Current insider roster + holdings.
t.sustainability
ESG risk scores (DataFrame).

13Funds & ETFsfunds_data

f = yf.Ticker("SPY").funds_data★
ETF/mutual-fund-specific bundle.
f.description
Prose overview of the fund.
f.top_holdings★
Largest positions with weights (DataFrame).
f.sector_weightings
Allocation across sectors.
f.asset_classes
Equity / bond / cash breakdown.
f.fund_overview f.fund_operations
Category, family, expense-ratio style facts.
f.equity_holdings f.bond_holdings
Valuation/quality metrics; also bond_ratings.

14Multiple Tickersat scale

ts = yf.Tickers("MSFT AAPL GOOG")★
Space-separated string → a bundle of Ticker objects.
ts.tickers["MSFT"].info★
Reach any member and use the full Ticker API.
ts.tickers["AAPL"].history(period="1mo")
Per-symbol calls, one object each.
yf.download(["AAPL","MSFT"], group_by="ticker")
Bulk prices — usually simpler than Tickers for OHLCV.
ts.live()new
Open a streaming feed for the whole bundle.

15Search & Lookupfind symbols

s = yf.Search("apple", max_results=8)★
Free-text search across Yahoo Finance.
s.quotes
Matching securities; also s.news, s.lists, s.research, s.all.
lk = yf.Lookup("AAPL")new
Resolve a query into typed results.
lk.stock lk.etf lk.currency
Also cryptocurrency, future, index, mutualfund, all.

16Sector · Industry · Markettop-down

sec = yf.Sector("technology")new
A whole GICS-style sector as an object.
sec.top_companies sec.top_etfs
Leaders in the sector; also top_mutual_funds, industries.
sec.overview sec.research_reports
Summary stats + linked research.
ind = yf.Industry("software-infrastructure")
top_companies, top_growth_companies, sector_key…
m = yf.Market("US")
m.status (open/closed) & m.summary (indices snapshot).

17Screener & Queriesfilter the market

yf.screen("day_gainers")★new
Run a predefined screen by name.
yf.PREDEFINED_SCREENER_QUERIES.keys()
most_actives, day_losers, undervalued_growth_stocks, top_etfs_us…
q = yf.EquityQuery("gt", ["intradaymarketcap", 1e9])
A single filter clause.
yf.EquityQuery("and", [q1, q2])
Combine with and/or; ops: eq is-in btwn gt lt gte lte.
yf.screen(q, size=50, sortField="ticker")
Custom screens; also FundQuery, ETFQuery.

18Live StreamingWebSocket

t.live()new
Quickest way to stream a Ticker's live prices.
ws = yf.WebSocket()
Explicit real-time client (uses websockets under the hood).
ws.subscribe(["AAPL","BTC-USD"])★
Register symbols for the live feed.
ws.listen(handler)
Blocking loop; handler(msg) gets each tick as a dict.
yf.AsyncWebSocket()
async/await variant for asyncio apps.
ws.unsubscribe(...) ws.close()
Drop symbols / tear the connection down.

19Config · Cache · Debugbe a good citizen

from curl_cffi import requests★
yfinance runs on curl_cffi now — build sessions from it, not requests.
sess = requests.Session(impersonate="chrome")
Reuse one session to look like a browser & cut throttling.
yf.Ticker("AAPL", session=sess)
Pass your session (caching / rate-limiting) into any call.
yf.set_config(proxy="http://…")new
Global proxy — replaces the deprecated per-call proxy=.
yf.enable_debug_mode()
Verbose logging when a fetch misbehaves.
yf.set_tz_cache_location("~/.yf")
Where the timezone cache (peewee/SQLite) lives.
time.sleep(...) between callsthrottle
Yahoo rate-limits — cache, batch, and slow down to avoid 429s.

★Symbol Conventionsdecode any ticker

AAPL MSFT TSLA
US stocks — plain exchange symbol.
^GSPC ^NSEI ^BSESN ^DJI★
^ = index (S&P 500 · NIFTY 50 · SENSEX · Dow).
RELIANCE.NS TCS.NS★
.NS = NSE India · .BO = BSE (e.g. RELIANCE.BO).
EURUSD=X INR=X
=X = forex pair (USD/INR = INR=X).
BTC-USD ETH-USD
-USD = crypto priced in dollars.
GC=F CL=F
=F = futures (gold · crude). London = .L, Toronto = .TO.

yfinance cheat sheet

Four pictures that make it click

One Ticker → every dataset

download() column shapes

auto_adjust — the silent default

How far back each interval reaches

Worth memorizing