from finalytics import Ticker
ticker = Ticker(symbol="AAPL",
start_date="2023-01-01",
end_date="2024-01-01",
interval="1d",
benchmark_symbol="^GSPC",
confidence_level=0.95,
risk_free_rate=0.02)Ticker Module Documentation
Ticker
A class representing a Ticker object.
__new__
Create a new Ticker object.
Parameters:
symbol(str): The ticker symbol of the asset.start_date(Optional[str]): Optional start date for historical data, defaults to None.end_date(Optional[str]): Optional end date for historical data, defaults to None.interval(Optional[str]): Optional data interval (2m, 5m, 15m, 30m, 1h, 1d, 1wk, 1mo, 3mo), defaults to None.benchmark_symbol(Optional[str]): Optional benchmark symbol, defaults to None.confidence_level(Optional[float]): Optional confidence level for statistics, defaults to None.risk_free_rate(Optional[float]): Optional risk-free rate for calculations, defaults to None.
Returns:
Ticker: A Ticker object.
Example:
get_quote
Get the current ticker quote stats.
Returns:
dict: Dictionary containing current ticker quote stats.
Example:
quote = ticker.get_quote()
print(quote){'Symbol': 'AAPL', 'Name': 'Apple Inc.', 'Exchange': 'NasdaqGS', 'Currency': 'USD', 'Timestamp': 1777579201, 'Current Price': 271.35, '24H Volume': 83815040.0, '24H Open': 270.5, '24H High': 275.94, '24H Low': 268.14, '24H Close': 270.17}
get_summary_stats
Get summary technical and fundamental statistics for the ticker.
Returns:
dict: Dictionary containing summary statistics.
Example:
summary_stats = ticker.get_summary_stats()
print(summary_stats)shape: (65, 2)
┌──────────────────────────┬─────────────────┐
│ Metric ┆ Value │
│ --- ┆ --- │
│ str ┆ str │
╞══════════════════════════╪═════════════════╡
│ Currency ┆ USD │
│ Current Price ┆ 271.35 │
│ Day Range ┆ 275.94 - 268.14 │
│ 52 Week Change ┆ 32.14% │
│ Previous Close ┆ 270.17 │
│ … ┆ … │
│ Morningstar Risk Rating ┆ │
│ Annual Holdings Turnover ┆ │
│ Annual Expense Ratio ┆ │
│ Circulating Supply ┆ │
│ CoinMarketCap Link ┆ │
└──────────────────────────┴─────────────────┘
get_price_history
Get the OHLCV data for the ticker for a given time period.
Returns:
DataFrame: Polars DataFrame containing OHLCV data.
Example:
price_history = ticker.get_price_history()
print(price_history)shape: (250, 7)
┌──────────────┬────────────┬────────────┬────────────┬────────────┬────────────┬────────────┐
│ timestamp ┆ open ┆ high ┆ low ┆ close ┆ volume ┆ adjclose │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[ms] ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════╪════════════╪════════════╪════════════╪════════════╪════════════╪════════════╡
│ 2023-01-03 ┆ 130.279999 ┆ 130.899994 ┆ 124.169998 ┆ 125.07 ┆ 1.121175e8 ┆ 123.096024 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-04 ┆ 126.889999 ┆ 128.660004 ┆ 125.080002 ┆ 126.360001 ┆ 8.91136e7 ┆ 124.365677 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-05 ┆ 127.129997 ┆ 127.769997 ┆ 124.760002 ┆ 125.019997 ┆ 8.09627e7 ┆ 123.046822 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-06 ┆ 126.010002 ┆ 130.289993 ┆ 124.889999 ┆ 129.619995 ┆ 8.77547e7 ┆ 127.574203 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-09 ┆ 130.470001 ┆ 133.410004 ┆ 129.889999 ┆ 130.149994 ┆ 7.07908e7 ┆ 128.09584 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-12-22 ┆ 195.179993 ┆ 195.410004 ┆ 192.970001 ┆ 193.600006 ┆ 3.71496e7 ┆ 191.609482 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-26 ┆ 193.610001 ┆ 193.889999 ┆ 192.830002 ┆ 193.050003 ┆ 2.89193e7 ┆ 191.065125 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-27 ┆ 192.490005 ┆ 193.5 ┆ 191.089996 ┆ 193.149994 ┆ 4.80877e7 ┆ 191.164108 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-28 ┆ 194.139999 ┆ 194.660004 ┆ 193.169998 ┆ 193.580002 ┆ 3.40499e7 ┆ 191.589676 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-29 ┆ 193.899994 ┆ 194.399994 ┆ 191.729996 ┆ 192.529999 ┆ 4.26721e7 ┆ 190.550446 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
└──────────────┴────────────┴────────────┴────────────┴────────────┴────────────┴────────────┘
get_options_chain
Get the options chain for the ticker.
Returns:
DataFrame: Polars DataFrame containing the options chain.
Example:
options_chain = ticker.get_options_chain()
print(options_chain)shape: (2_430, 16)
┌────────────┬───────────┬──────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐
│ expiration ┆ ttm ┆ type ┆ contractSy ┆ … ┆ contractSi ┆ lastTradeD ┆ impliedVo ┆ inTheMone │
│ --- ┆ --- ┆ --- ┆ mbol ┆ ┆ ze ┆ ate ┆ latility ┆ y │
│ str ┆ f64 ┆ str ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ ┆ ┆ ┆ str ┆ ┆ str ┆ datetime[m ┆ f64 ┆ bool │
│ ┆ ┆ ┆ ┆ ┆ ┆ s] ┆ ┆ │
╞════════════╪═══════════╪══════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL260501 ┆ … ┆ REGULAR ┆ 2026-04-28 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ C00110000 ┆ ┆ ┆ 15:31:58 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL260501 ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ C00120000 ┆ ┆ ┆ 19:42:52 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL260501 ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ C00145000 ┆ ┆ ┆ 19:49:19 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL260501 ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ C00150000 ┆ ┆ ┆ 19:41:59 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL260501 ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ C00175000 ┆ ┆ ┆ 19:50:13 ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ AAPL281215 ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ P00440000 ┆ ┆ ┆ 19:09:21 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ AAPL281215 ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ P00450000 ┆ ┆ ┆ 19:08:41 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ AAPL281215 ┆ … ┆ REGULAR ┆ 2026-04-16 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ P00460000 ┆ ┆ ┆ 13:32:05 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ AAPL281215 ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ P00500000 ┆ ┆ ┆ 15:05:49 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ AAPL281215 ┆ … ┆ REGULAR ┆ 2026-04-28 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ P00520000 ┆ ┆ ┆ 19:04:08 ┆ ┆ │
└────────────┴───────────┴──────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘
get_news
Get the historical news headlines for the given ticker.
Returns:
DataFrame: Polars DataFrame containing news headlines.
Example:
news = ticker.get_news()
print(news)shape: (1_624, 5)
┌────────────────┬───────────────────┬─────────────────────┬─────────────────────┬─────────────────┐
│ Published Date ┆ Source ┆ Title ┆ Link ┆ Sentiment Score │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[ms] ┆ str ┆ str ┆ str ┆ f64 │
╞════════════════╪═══════════════════╪═════════════════════╪═════════════════════╪═════════════════╡
│ 2023-01-04 ┆ Investopedia ┆ Investing in Apple ┆ <a href="https://ne ┆ 0.0 │
│ 08:00:00 ┆ ┆ Stock (AAPL… ┆ ws.google.c… ┆ │
│ 2023-01-04 ┆ Bloomberg.com ┆ Apple’s (AAPL) ┆ <a href="https://ne ┆ -0.70956 │
│ 08:00:00 ┆ ┆ Stock Is Losing… ┆ ws.google.c… ┆ │
│ 2023-01-03 ┆ Al Jazeera ┆ Apple’s market ┆ <a href="https://ne ┆ 0.33995 │
│ 08:00:00 ┆ ┆ value drops bel… ┆ ws.google.c… ┆ │
│ 2023-01-03 ┆ Reuters ┆ Apple's stock ┆ <a href="https://ne ┆ 0.33995 │
│ 08:00:00 ┆ ┆ market value fal… ┆ ws.google.c… ┆ │
│ 2023-01-04 ┆ The Guardian ┆ Death of the ┆ <a href="https://ne ┆ -0.622427 │
│ 08:00:00 ┆ ┆ narrator? Apple u… ┆ ws.google.c… ┆ │
│ … ┆ … ┆ … ┆ … ┆ … │
│ 2024-01-01 ┆ Laptop Mag ┆ How to make a GIF ┆ <a href="https://ne ┆ 0.440434 │
│ 08:00:00 ┆ ┆ on iPhone — … ┆ ws.google.c… ┆ │
│ 2023-12-30 ┆ Visual Capitalist ┆ Charted: Tesla’s ┆ <a href="https://ne ┆ 0.0 │
│ 08:00:00 ┆ ┆ Global Sales … ┆ ws.google.c… ┆ │
│ 2024-01-01 ┆ Yahoo Finance UK ┆ 12 charts that show ┆ <a href="https://ne ┆ 0.0 │
│ 08:00:00 ┆ ┆ the cost o… ┆ ws.google.c… ┆ │
│ 2024-01-01 ┆ Advocate.com ┆ Republicans freak ┆ <a href="https://ne ┆ -0.624893 │
│ 08:00:00 ┆ ┆ out over a b… ┆ ws.google.c… ┆ │
│ 2023-12-31 ┆ Analytics Insight ┆ Apple’s Innovation ┆ <a href="https://ne ┆ 0.381819 │
│ 08:00:00 ┆ ┆ Journey: Ke… ┆ ws.google.c… ┆ │
└────────────────┴───────────────────┴─────────────────────┴─────────────────────┴─────────────────┘
get_income_statement
Get the Income Statement for the ticker.
Parameters:
frequency(str): frequency of the Income Statement (annual, quarterly).formatted(optional[bool]): Whether to return the data in a formatted manner, defaults to True.
Returns:
DataFrame: Polars DataFrame containing the Income Statement.
Example:
income_statement = ticker.get_income_statement(frequency="quarterly")
print(income_statement)shape: (11, 6)
┌──────────────────────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Revenue ┆ 1.2430e11 ┆ 9.5359e10 ┆ 9.4036e10 ┆ 1.0247e11 ┆ 1.4376e11 │
│ Cost of Goods Sold ┆ 6.6025e10 ┆ 5.0492e10 ┆ 5.0318e10 ┆ 5.4125e10 ┆ 7.4525e10 │
│ Gross Profit ┆ 5.8275e10 ┆ 4.4867e10 ┆ 4.3718e10 ┆ 4.8341e10 ┆ 6.9231e10 │
│ Operating Expenses ┆ 1.5443e10 ┆ 1.5278e10 ┆ 1.5516e10 ┆ 1.5914e10 ┆ 1.8379e10 │
│ EBITDA ┆ 4.5912e10 ┆ 3.2250e10 ┆ 3.1032e10 ┆ 3.5554e10 ┆ 5.4066e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Profit (EBIT) ┆ 4.2832e10 ┆ 2.9589e10 ┆ 2.8202e10 ┆ 3.2427e10 ┆ 5.0852e10 │
│ Income Tax Expense ┆ 6.2540e9 ┆ 4.5300e9 ┆ 4.5970e9 ┆ 5.3380e9 ┆ 8.9050e9 │
│ Net Income ┆ 3.6330e10 ┆ 2.4780e10 ┆ 2.3434e10 ┆ 2.7466e10 ┆ 4.2097e10 │
│ Earnings per Share - Basic ┆ 2.41 ┆ 1.65 ┆ 1.57 ┆ 1.85 ┆ 2.85 │
│ Earnings per Share - Diluted ┆ 2.4 ┆ 1.65 ┆ 1.57 ┆ 1.85 ┆ 2.84 │
└──────────────────────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
get_balance_sheet
Get the Balance Sheet for the ticker.
Parameters:
frequency(str): frequency of the Balance Sheet (annual, quarterly).formatted(optional[bool]): Whether to return the data in a formatted manner, defaults to True.
Returns:
DataFrame: Polars DataFrame containing the Balance Sheet.
Example:
balance_sheet = ticker.get_balance_sheet(frequency="quarterly")
print(balance_sheet)shape: (21, 6)
┌──────────────────────────────┬────────────┬────────────┬────────────┬────────────┬───────────┐
│ Items ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════════╪════════════╪════════════╪════════════╪════════════╪═══════════╡
│ Cash and Cash Equivalents ┆ 3.0299e10 ┆ 2.8162e10 ┆ 3.6269e10 ┆ 3.5934e10 ┆ 4.5317e10 │
│ Accounts Receivable ┆ 2.9639e10 ┆ 2.6136e10 ┆ 2.7557e10 ┆ 3.9777e10 ┆ 3.9921e10 │
│ Inventories ┆ 6.9110e9 ┆ 6.2690e9 ┆ 5.9250e9 ┆ 5.7180e9 ┆ 5.8750e9 │
│ Other Current Assets ┆ 1.3248e10 ┆ 1.4109e10 ┆ 1.4359e10 ┆ 1.4585e10 ┆ 1.5002e10 │
│ Total Current Assets ┆ 1.3324e11 ┆ 1.1867e11 ┆ 1.2249e11 ┆ 1.4796e11 ┆ 1.5810e11 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Total Liabilities ┆ 2.7733e11 ┆ 2.6444e11 ┆ 2.6566e11 ┆ 2.8551e11 ┆ 2.9111e11 │
│ Common Stock ┆ 8.4768e10 ┆ 8.8711e10 ┆ 8.9806e10 ┆ 9.3568e10 ┆ 9.5221e10 │
│ Retained Earnings ┆ -1.1221e10 ┆ -1.5552e10 ┆ -1.7607e10 ┆ -1.4264e10 ┆ -2.1770e9 │
│ Total Equity ┆ 6.6758e10 ┆ 6.6796e10 ┆ 6.5830e10 ┆ 7.3733e10 ┆ 8.8190e10 │
│ Total Liabilities and Equity ┆ 6.6758e10 ┆ 6.6796e10 ┆ 6.5830e10 ┆ 7.3733e10 ┆ 8.8190e10 │
└──────────────────────────────┴────────────┴────────────┴────────────┴────────────┴───────────┘
get_cashflow_statement
Get the Cashflow Statement for the ticker.
Parameters:
frequency(str): frequency of the Cashflow Statement (annual, quarterly).formatted(optional[bool]): Whether to return the data in a formatted manner, defaults to True.
Returns:
DataFrame: Polars DataFrame containing the Cashflow Statement.
Example:
cashflow_statement = ticker.get_cashflow_statement(frequency="quarterly")
print(cashflow_statement)shape: (26, 6)
┌─────────────────────────────────┬────────────┬────────────┬────────────┬────────────┬────────────┐
│ Items ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════════╪════════════╪════════════╪════════════╪════════════╪════════════╡
│ Net Income from Continuing Ope… ┆ 3.6330e10 ┆ 2.4780e10 ┆ 2.3434e10 ┆ 2.7466e10 ┆ 4.2097e10 │
│ Depreciation, Amortization, an… ┆ 3.0800e9 ┆ 2.6610e9 ┆ 2.8300e9 ┆ 3.1270e9 ┆ 3.2140e9 │
│ Stock-Based Compensation ┆ 3.2860e9 ┆ 3.2260e9 ┆ 3.1680e9 ┆ 3.1830e9 ┆ 3.5940e9 │
│ Changes in Working Capital ┆ -1.0752e10 ┆ -6.5070e9 ┆ -2.0340e9 ┆ -5.7070e9 ┆ 5.5480e9 │
│ Cash Flow from Continuing Oper… ┆ 2.9935e10 ┆ 2.3952e10 ┆ 2.7867e10 ┆ 2.9728e10 ┆ 5.3925e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Cash Flow ┆ 2.9935e10 ┆ 2.3952e10 ┆ 2.7867e10 ┆ 2.9728e10 ┆ 5.3925e10 │
│ Investing Cash Flow ┆ 9.7920e9 ┆ 2.9170e9 ┆ 5.0730e9 ┆ -2.5870e9 ┆ -4.8860e9 │
│ Financing Cash Flow ┆ -3.9371e10 ┆ -2.9006e10 ┆ -2.4833e10 ┆ -2.7476e10 ┆ -3.9656e10 │
│ Ending Cash Position ┆ 3.0299e10 ┆ 2.8162e10 ┆ 3.6269e10 ┆ 3.5934e10 ┆ 4.5317e10 │
│ Free Cash Flow ┆ 2.6995e10 ┆ 2.0881e10 ┆ 2.4405e10 ┆ 2.6486e10 ┆ 5.1552e10 │
└─────────────────────────────────┴────────────┴────────────┴────────────┴────────────┴────────────┘
get_financial_ratios
Get the Financial Ratios for the ticker.
Parameters:
frequency(str): frequency of the Financial Ratios (annual, quarterly).
Returns:
DataFrame: Polars DataFrame containing the Financial Ratios.
Example:
financial_ratios = ticker.get_financial_ratios(frequency="quarterly")
print(financial_ratios)shape: (21, 6)
┌─────────────────────────┬──────────┬──────────┬──────────┬──────────┬──────────┐
│ Items ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════╪══════════╪══════════╪══════════╪══════════╪══════════╡
│ Gross Profit Margin ┆ 0.468825 ┆ 0.470506 ┆ 0.464907 ┆ 0.471776 ┆ 0.481587 │
│ Operating Profit Margin ┆ 0.344586 ┆ 0.310291 ┆ 0.299906 ┆ 0.316466 ┆ 0.353738 │
│ Net Profit Margin ┆ 0.292277 ┆ 0.25986 ┆ 0.249202 ┆ 0.26805 ┆ 0.292836 │
│ Return on Assets ┆ 0.105584 ┆ 0.074811 ┆ 0.070692 ┆ 0.076456 ┆ 0.110987 │
│ Return on Equity ┆ 0.544204 ┆ 0.37098 ┆ 0.355978 ┆ 0.372506 ┆ 0.477344 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Price to Earnings ┆ 4.148472 ┆ 5.866102 ┆ 6.326705 ┆ 5.536336 ┆ 3.91655 │
│ Price to Book ┆ 2.257617 ┆ 2.176208 ┆ 2.252165 ┆ 2.062319 ┆ 1.869543 │
│ Price to Sales ┆ 1.212502 ┆ 1.524366 ┆ 1.57663 ┆ 1.484014 ┆ 1.146909 │
│ Price to Cashflow ┆ 5.034709 ┆ 6.068888 ┆ 5.320271 ┆ 5.115077 ┆ 3.057487 │
│ Price to Free Cashflow ┆ 5.583034 ┆ 6.961448 ┆ 6.074985 ┆ 5.741184 ┆ 3.198227 │
└─────────────────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘
volatility_surface
Computes the implied volatility surface for the ticker options chain.
Returns:
DataFrame: Polars DataFrame containing the implied volatility surface.
Example:
volatility_surface = ticker.volatility_surface()
print(volatility_surface)shape: (69, 13)
┌────────┬──────────┬──────────┬──────────┬───┬──────────┬──────────┬──────────┬──────────┐
│ strike ┆ 3.68M ┆ 4.60M ┆ 5.52M ┆ … ┆ 19.55M ┆ 20.70M ┆ 22.54M ┆ 31.50M │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════╪══════════╪══════════╪══════════╪═══╪══════════╪══════════╪══════════╪══════════╡
│ 110.0 ┆ 0.566875 ┆ 0.574609 ┆ 0.45666 ┆ … ┆ 0.412067 ┆ 0.415088 ┆ 0.403365 ┆ 0.367836 │
│ 120.0 ┆ 0.561074 ┆ 0.528203 ┆ 0.45666 ┆ … ┆ 0.403003 ┆ 0.386205 ┆ 0.386567 ┆ 0.366295 │
│ 125.0 ┆ 0.542705 ┆ 0.598779 ┆ 0.45666 ┆ … ┆ 0.390797 ┆ 0.39261 ┆ 0.396175 ┆ 0.35998 │
│ 130.0 ┆ 0.524336 ┆ 0.505967 ┆ 0.45666 ┆ … ┆ 0.389166 ┆ 0.384271 ┆ 0.403547 ┆ 0.360615 │
│ 135.0 ┆ 0.510801 ┆ 0.50645 ┆ 0.45666 ┆ … ┆ 0.380283 ┆ 0.373032 ┆ 0.370917 ┆ 0.351853 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 360.0 ┆ 0.277319 ┆ 0.26463 ┆ 0.267108 ┆ … ┆ 0.285507 ┆ 0.286504 ┆ 0.288951 ┆ 0.289435 │
│ 365.0 ┆ 0.279897 ┆ 0.269504 ┆ 0.262938 ┆ … ┆ 0.266549 ┆ 0.283291 ┆ 0.286287 ┆ 0.286736 │
│ 375.0 ┆ 0.282476 ┆ 0.274379 ┆ 0.26602 ┆ … ┆ 0.267742 ┆ 0.280079 ┆ 0.283624 ┆ 0.284037 │
│ 390.0 ┆ 0.285054 ┆ 0.279253 ┆ 0.269102 ┆ … ┆ 0.264494 ┆ 0.276866 ┆ 0.28096 ┆ 0.281338 │
│ 400.0 ┆ 0.291821 ┆ 0.279736 ┆ 0.265959 ┆ … ┆ 0.276141 ┆ 0.278498 ┆ 0.280265 ┆ 0.288664 │
└────────┴──────────┴──────────┴──────────┴───┴──────────┴──────────┴──────────┴──────────┘
performance_stats
Compute the performance statistics for the ticker.
Returns:
dict: Dictionary containing performance statistics.
Example:
performance_stats = ticker.performance_stats()
print(performance_stats){'Symbol': 'AAPL', 'Benchmark': '^GSPC', 'Start Date': '2023-01-01', 'End Date': '2024-01-01', 'Interval': 1.4457831325301205, 'Confidence Level': 0.95, 'Risk Free Rate': 0.02, 'Daily Return': 0.0018349267265163399, 'Daily Volatility': 0.012570039046460188, 'Total Return': 0.5479821365994539, 'Annualized Return': 0.5885442873100564, 'Annualized Volatility': 0.19972456417039952, 'Alpha': 0.2062073488235633, 'Beta': 1.1045128515765001, 'Sharpe Ratio': 2.8466417722408446, 'Sortino Ratio': 4.532848925534317, 'Active Return': 0.23052891382646085, 'Active Risk': 0.13810401752204624, 'Information Ratio': 1.6692411847443929, 'Calmar Ratio': 3.9414009276818334, 'Maximum Drawdown': 0.1493236283516616, 'Value at Risk': -0.017176494174410775, 'Expected Shortfall': -0.024986757689988533, 'Security Prices': shape: (250,)
Series: 'adjclose' [f64]
[
123.096024
124.365677
123.046822
127.574203
128.09584
…
191.609482
191.065125
191.164108
191.589676
190.550446
], 'Security Returns': shape: (249,)
Series: 'AAPL' [f64]
[
0.010314
-0.010605
0.036794
0.004089
0.004456
…
-0.005547
-0.002841
0.000518
0.002226
-0.005424
], 'Benchmark Returns': shape: (249,)
Series: 'benchmark_returns' [f64]
[
0.007539
-0.011646
0.022841
-0.000768
0.006978
…
0.00166
0.004232
0.00143
0.00037
-0.002826
]}
performance_chart
Display the performance chart for the ticker.
Parameters:
height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the performance chart.
Example:
performance_chart = ticker.performance_chart()
performance_chart.show()
candlestick_chart
Display the candlestick chart for the ticker.
Parameters:
height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the candlestick chart.
Example:
candlestick_chart = ticker.candlestick_chart()
candlestick_chart.show()
news_sentiment_chart
Display the News Sentiment chart for the ticker.
Parameters:
height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the news sentiment chart.
Example:
news_sentiment_chart = ticker.news_sentiment_chart()
news_sentiment_chart.show()
options_chart
Display the options volatility surface, smile and term structure charts for the ticker.
Parameters:
chart_type(str): Type of options chart (surface, smile, term_structure).height(Optional[int]): Optional height of the plot in pixels, defaults to None.width(Optional[int]): Optional width of the plot in pixels, defaults to None.
Returns:
Plot: Plot object containing the options chart.
Example:
options_chart = ticker.options_chart(chart_type="surface")
options_chart.show()
report
Generate a report for the ticker.
Parameters:
report_type(Optional[str]): Type of report to generate (performance, financials, options, news_sentiment).- display (Optional[str]): The display mode for the report. set to “notebook” for Jupyter Notebook display, defaults to “browser”.
Example:
ticker.report("performance")