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': 1753119384, 'Current Price': 213.44, '24H Volume': 33907412.0, '24H Open': 212.05, '24H High': 215.78, '24H Low': 211.6409, '24H Close': 211.18}
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 ┆ 213.44 │
│ Day Range ┆ 215.78 - 211.64 │
│ 52 Week Change ┆ -5.71% │
│ Previous Close ┆ 211.18 │
│ … ┆ … │
│ 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 ┆ 123.470612 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-04 ┆ 126.889999 ┆ 128.660004 ┆ 125.080002 ┆ 126.360001 ┆ 8.91136e7 ┆ 124.744125 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-05 ┆ 127.129997 ┆ 127.769997 ┆ 124.760002 ┆ 125.019997 ┆ 8.09627e7 ┆ 123.421265 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-06 ┆ 126.010002 ┆ 130.289993 ┆ 124.889999 ┆ 129.619995 ┆ 8.77547e7 ┆ 127.96244 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-09 ┆ 130.470001 ┆ 133.410004 ┆ 129.889999 ┆ 130.149994 ┆ 7.07908e7 ┆ 128.485657 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-12-22 ┆ 195.179993 ┆ 195.410004 ┆ 192.970001 ┆ 193.600006 ┆ 3.71496e7 ┆ 192.192551 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-26 ┆ 193.610001 ┆ 193.889999 ┆ 192.830002 ┆ 193.050003 ┆ 2.89193e7 ┆ 191.646561 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-27 ┆ 192.490005 ┆ 193.5 ┆ 191.089996 ┆ 193.149994 ┆ 4.80877e7 ┆ 191.745819 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-28 ┆ 194.139999 ┆ 194.660004 ┆ 193.169998 ┆ 193.580002 ┆ 3.40499e7 ┆ 192.172699 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-29 ┆ 193.899994 ┆ 194.399994 ┆ 191.729996 ┆ 192.529999 ┆ 4.26721e7 ┆ 191.130341 │
│ 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_025, 16)
┌────────────┬───────────┬──────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐
│ expiration ┆ ttm ┆ type ┆ contractSy ┆ … ┆ contractSi ┆ lastTradeD ┆ impliedVo ┆ inTheMone │
│ --- ┆ --- ┆ --- ┆ mbol ┆ ┆ ze ┆ ate ┆ latility ┆ y │
│ str ┆ f64 ┆ str ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ ┆ ┆ ┆ str ┆ ┆ str ┆ datetime[m ┆ f64 ┆ bool │
│ ┆ ┆ ┆ ┆ ┆ ┆ s] ┆ ┆ │
╞════════════╪═══════════╪══════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL250725 ┆ … ┆ REGULAR ┆ 2025-07-21 ┆ 2.44141 ┆ true │
│ ┆ ┆ ┆ C00110000 ┆ ┆ ┆ 13:46:31 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL250725 ┆ … ┆ REGULAR ┆ 2025-06-27 ┆ 2.269536 ┆ true │
│ ┆ ┆ ┆ C00120000 ┆ ┆ ┆ 17:42:44 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL250725 ┆ … ┆ REGULAR ┆ 2025-07-17 ┆ 2.123052 ┆ true │
│ ┆ ┆ ┆ C00125000 ┆ ┆ ┆ 14:34:08 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL250725 ┆ … ┆ REGULAR ┆ 2025-06-23 ┆ 1.783204 ┆ true │
│ ┆ ┆ ┆ C00135000 ┆ ┆ ┆ 17:28:40 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL250725 ┆ … ┆ REGULAR ┆ 2025-07-18 ┆ 1.687502 ┆ true │
│ ┆ ┆ ┆ C00140000 ┆ ┆ ┆ 14:13:48 ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ AAPL271217 ┆ … ┆ REGULAR ┆ 2025-07-10 ┆ 0.20179 ┆ true │
│ ┆ ┆ ┆ P00350000 ┆ ┆ ┆ 19:16:51 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ AAPL271217 ┆ … ┆ REGULAR ┆ 2025-04-15 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ P00370000 ┆ ┆ ┆ 16:23:18 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ AAPL271217 ┆ … ┆ REGULAR ┆ 2025-05-22 ┆ 0.406531 ┆ true │
│ ┆ ┆ ┆ P00400000 ┆ ┆ ┆ 18:39:16 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ AAPL271217 ┆ … ┆ REGULAR ┆ 2025-05-14 ┆ 0.478414 ┆ true │
│ ┆ ┆ ┆ P00435000 ┆ ┆ ┆ 14:15:57 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ AAPL271217 ┆ … ┆ REGULAR ┆ 2025-07-07 ┆ 0.278572 ┆ true │
│ ┆ ┆ ┆ P00440000 ┆ ┆ ┆ 13:33:18 ┆ ┆ │
└────────────┴───────────┴──────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘
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_795, 5)
┌────────────────┬─────────────────────┬────────────────────┬────────────────────┬─────────────────┐
│ Published Date ┆ Source ┆ Title ┆ Link ┆ Sentiment Score │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[ms] ┆ str ┆ str ┆ str ┆ f64 │
╞════════════════╪═════════════════════╪════════════════════╪════════════════════╪═════════════════╡
│ 2023-01-03 ┆ Al Jazeera ┆ Apple’s market ┆ <a href="https://n ┆ 0.33995 │
│ 08:00:00 ┆ ┆ value drops bel… ┆ ews.google.c… ┆ │
│ 2023-01-03 ┆ Reuters ┆ Apple's stock ┆ <a href="https://n ┆ 0.33995 │
│ 08:00:00 ┆ ┆ market value fal… ┆ ews.google.c… ┆ │
│ 2023-01-04 ┆ Bloomberg.com ┆ Apple’s Stock Is ┆ <a href="https://n ┆ -0.70956 │
│ 08:00:00 ┆ ┆ Losing Its Sh… ┆ ews.google.c… ┆ │
│ 2023-01-04 ┆ The Guardian ┆ Death of the ┆ <a href="https://n ┆ -0.622427 │
│ 08:00:00 ┆ ┆ narrator? Apple u… ┆ ews.google.c… ┆ │
│ 2023-01-03 ┆ Seeking Alpha ┆ Apple Vs. ┆ <a href="https://n ┆ 0.473138 │
│ 08:00:00 ┆ ┆ Microsoft Stock: ┆ ews.google.c… ┆ │
│ ┆ ┆ Whi… ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-12-31 ┆ Business Insider ┆ How to use iForgot ┆ <a href="https://n ┆ 0.0 │
│ 08:00:00 ┆ ┆ for Apple I… ┆ ews.google.c… ┆ │
│ 2023-12-30 ┆ VOI.ID ┆ Apple Inc., Blocks ┆ <a href="https://n ┆ -0.318211 │
│ 20:17:40 ┆ ┆ Russian Mir… ┆ ews.google.c… ┆ │
│ 2024-01-01 ┆ Readly | All ┆ Beginner’s guide ┆ <a href="https://n ┆ 0.0 │
│ 08:00:00 ┆ magazines - one m… ┆ to the health… ┆ ews.google.c… ┆ │
│ 2023-12-31 ┆ VOI.ID ┆ These Are Some Of ┆ <a href="https://n ┆ 0.0 │
│ 08:13:17 ┆ ┆ The New Feat… ┆ ews.google.c… ┆ │
│ 2023-12-31 ┆ Analytics Insight ┆ Apple’s Innovation ┆ <a href="https://n ┆ 0.381819 │
│ 08:00:00 ┆ ┆ Journey: Ke… ┆ ews.google.c… ┆ │
└────────────────┴─────────────────────┴────────────────────┴────────────────────┴─────────────────┘
get_income_statement
Get the Income Statement for the ticker.
Parameters:
frequency(str): frequency of the Income Statement (annual, quarterly).
Returns:
DataFrame: Polars DataFrame containing the Income Statement.
Example:
income_statement = ticker.get_income_statement(frequency="quarterly")
print(income_statement)shape: (11, 6)
┌──────────────────────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Revenue ┆ 9.0753e10 ┆ 8.5777e10 ┆ 9.4930e10 ┆ 1.2430e11 ┆ 9.5359e10 │
│ Cost of Goods Sold ┆ 4.8482e10 ┆ 4.6099e10 ┆ 5.1051e10 ┆ 6.6025e10 ┆ 5.0492e10 │
│ Gross Profit ┆ 4.2271e10 ┆ 3.9678e10 ┆ 4.3879e10 ┆ 5.8275e10 ┆ 4.4867e10 │
│ Operating Expenses ┆ 1.4371e10 ┆ 1.4326e10 ┆ 1.4288e10 ┆ 1.5443e10 ┆ 1.5278e10 │
│ EBITDA ┆ 3.0736e10 ┆ 2.8202e10 ┆ 3.2502e10 ┆ 4.5912e10 ┆ 3.2250e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Profit (EBIT) ┆ 2.7900e10 ┆ 2.5352e10 ┆ 2.9591e10 ┆ 4.2832e10 ┆ 2.9589e10 │
│ Income Tax Expense ┆ 4.4220e9 ┆ 4.0460e9 ┆ 1.4874e10 ┆ 6.2540e9 ┆ 4.5300e9 │
│ Net Income ┆ 2.3636e10 ┆ 2.1448e10 ┆ 1.4736e10 ┆ 3.6330e10 ┆ 2.4780e10 │
│ Earnings per Share - Basic ┆ 1.53 ┆ 1.4 ┆ 0.97 ┆ 2.41 ┆ 1.65 │
│ Earnings per Share - Diluted ┆ 1.53 ┆ 1.4 ┆ 0.97 ┆ 2.4 ┆ 1.65 │
└──────────────────────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
get_balance_sheet
Get the Balance Sheet for the ticker.
Parameters:
frequency(str): frequency of the Balance Sheet (annual, quarterly).
Returns:
DataFrame: Polars DataFrame containing the Balance Sheet.
Example:
balance_sheet = ticker.get_balance_sheet(frequency="quarterly")
print(balance_sheet)shape: (21, 6)
┌──────────────────────────────┬───────────┬───────────┬────────────┬────────────┬────────────┐
│ Items ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════════╪═══════════╪═══════════╪════════════╪════════════╪════════════╡
│ Cash and Cash Equivalents ┆ 3.2695e10 ┆ 2.5565e10 ┆ 2.9943e10 ┆ 3.0299e10 ┆ 2.8162e10 │
│ Accounts Receivable ┆ 2.1837e10 ┆ 2.2795e10 ┆ 3.3410e10 ┆ 2.9639e10 ┆ 2.6136e10 │
│ Inventories ┆ 6.2320e9 ┆ 6.1650e9 ┆ 7.2860e9 ┆ 6.9110e9 ┆ 6.2690e9 │
│ Other Current Assets ┆ 1.3884e10 ┆ 1.4297e10 ┆ 1.4287e10 ┆ 1.3248e10 ┆ 1.4109e10 │
│ Total Current Assets ┆ 1.2842e11 ┆ 1.2544e11 ┆ 1.5299e11 ┆ 1.3324e11 ┆ 1.1867e11 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Total Liabilities ┆ 2.6322e11 ┆ 2.6490e11 ┆ 3.0803e11 ┆ 2.7733e11 ┆ 2.6444e11 │
│ Common Stock ┆ 7.8815e10 ┆ 7.9850e10 ┆ 8.3276e10 ┆ 8.4768e10 ┆ 8.8711e10 │
│ Retained Earnings ┆ 4.3390e9 ┆ -4.7260e9 ┆ -1.9154e10 ┆ -1.1221e10 ┆ -1.5552e10 │
│ Total Equity ┆ 7.4194e10 ┆ 6.6708e10 ┆ 5.6950e10 ┆ 6.6758e10 ┆ 6.6796e10 │
│ Total Liabilities and Equity ┆ 7.4194e10 ┆ 6.6708e10 ┆ 5.6950e10 ┆ 6.6758e10 ┆ 6.6796e10 │
└──────────────────────────────┴───────────┴───────────┴────────────┴────────────┴────────────┘
get_cashflow_statement
Get the Cashflow Statement for the ticker.
Parameters:
frequency(str): frequency of the Cashflow Statement (annual, quarterly).
Returns:
DataFrame: Polars DataFrame containing the Cashflow Statement.
Example:
cashflow_statement = ticker.get_cashflow_statement(frequency="quarterly")
print(cashflow_statement)shape: (26, 6)
┌─────────────────────────────────┬────────────┬────────────┬────────────┬────────────┬────────────┐
│ Items ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════════╪════════════╪════════════╪════════════╪════════════╪════════════╡
│ Net Income from Continuing Ope… ┆ 2.3636e10 ┆ 2.1448e10 ┆ 1.4736e10 ┆ 3.6330e10 ┆ 2.4780e10 │
│ Depreciation, Amortization, an… ┆ 2.8360e9 ┆ 2.8500e9 ┆ 2.9110e9 ┆ 3.0800e9 ┆ 2.6610e9 │
│ Stock-Based Compensation ┆ 2.9640e9 ┆ 2.8690e9 ┆ 2.8580e9 ┆ 3.2860e9 ┆ 3.2260e9 │
│ Changes in Working Capital ┆ -5.7640e9 ┆ 1.6840e9 ┆ 6.6080e9 ┆ -1.0752e10 ┆ -6.5070e9 │
│ Cash Flow from Continuing Oper… ┆ 2.2690e10 ┆ 2.8858e10 ┆ 2.6811e10 ┆ 2.9935e10 ┆ 2.3952e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Cash Flow ┆ 2.2690e10 ┆ 2.8858e10 ┆ 2.6811e10 ┆ 2.9935e10 ┆ 2.3952e10 │
│ Investing Cash Flow ┆ -3.1000e8 ┆ -1.2700e8 ┆ 1.4450e9 ┆ 9.7920e9 ┆ 2.9170e9 │
│ Financing Cash Flow ┆ -3.0433e10 ┆ -3.6017e10 ┆ -2.4948e10 ┆ -3.9371e10 ┆ -2.9006e10 │
│ Ending Cash Position ┆ 3.3921e10 ┆ 2.6635e10 ┆ 2.9943e10 ┆ 3.0299e10 ┆ 2.8162e10 │
│ Free Cash Flow ┆ 2.0694e10 ┆ 2.6707e10 ┆ 2.3903e10 ┆ 2.6995e10 ┆ 2.0881e10 │
└─────────────────────────────────┴────────────┴────────────┴────────────┴────────────┴────────────┘
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 ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════╪══════════╪══════════╪══════════╪══════════╪══════════╡
│ Gross Profit Margin ┆ 0.465781 ┆ 0.462572 ┆ 0.462225 ┆ 0.468825 ┆ 0.470506 │
│ Operating Profit Margin ┆ 0.307428 ┆ 0.295557 ┆ 0.311714 ┆ 0.344586 ┆ 0.310291 │
│ Net Profit Margin ┆ 0.260443 ┆ 0.250044 ┆ 0.15523 ┆ 0.292277 ┆ 0.25986 │
│ Return on Assets ┆ 0.070051 ┆ 0.064678 ┆ 0.040375 ┆ 0.105584 ┆ 0.074811 │
│ Return on Equity ┆ 0.31857 ┆ 0.321521 ┆ 0.258753 ┆ 0.544204 ┆ 0.37098 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Price to Earnings ┆ 7.024243 ┆ 7.129056 ┆ 9.683768 ┆ 4.148472 ┆ 5.866102 │
│ Price to Book ┆ 2.237715 ┆ 2.292139 ┆ 2.505707 ┆ 2.257617 ┆ 2.176208 │
│ Price to Sales ┆ 1.829416 ┆ 1.782576 ┆ 1.503213 ┆ 1.212502 ┆ 1.524366 │
│ Price to Cashflow ┆ 7.3171 ┆ 5.298496 ┆ 5.322442 ┆ 5.034709 ┆ 6.068888 │
│ Price to Free Cashflow ┆ 8.022857 ┆ 5.725241 ┆ 5.969962 ┆ 5.583034 ┆ 6.961448 │
└─────────────────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘
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: (50, 13)
┌────────┬──────────┬───────────┬──────────┬───┬──────────┬──────────┬──────────┬──────────┐
│ strike ┆ 4.01M ┆ 4.93M ┆ 5.85M ┆ … ┆ 16.89M ┆ 17.81M ┆ 22.83M ┆ 28.84M │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════╪══════════╪═══════════╪══════════╪═══╪══════════╪══════════╪══════════╪══════════╡
│ 110.0 ┆ 0.514668 ┆ 0.477446 ┆ 0.448926 ┆ … ┆ 0.36554 ┆ 0.370011 ┆ 0.348862 ┆ 0.341188 │
│ 120.0 ┆ 0.481313 ┆ 0.441675 ┆ 0.420647 ┆ … ┆ 0.349829 ┆ 0.344451 ┆ 0.337835 ┆ 0.325146 │
│ 125.0 ┆ 0.446509 ┆ 0.428623 ┆ 0.407837 ┆ … ┆ 0.342578 ┆ 0.337079 ┆ 0.334904 ┆ 0.316384 │
│ 130.0 ┆ 0.434424 ┆ 0.4121875 ┆ 0.392126 ┆ … ┆ 0.334421 ┆ 0.331037 ┆ 0.322275 ┆ 0.313091 │
│ 135.0 ┆ 0.425723 ┆ 0.396719 ┆ 0.37835 ┆ … ┆ 0.325357 ┆ 0.331339 ┆ 0.32433 ┆ 0.306656 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 290.0 ┆ 0.262576 ┆ 0.246986 ┆ 0.240339 ┆ … ┆ 0.249252 ┆ 0.253693 ┆ 0.2483 ┆ 0.25469 │
│ 300.0 ┆ 0.266926 ┆ 0.252666 ┆ 0.243602 ┆ … ┆ 0.251216 ┆ 0.251563 ┆ 0.256609 ┆ 0.258512 │
│ 315.0 ┆ 0.272002 ┆ 0.262576 ┆ 0.252183 ┆ … ┆ 0.250657 ┆ 0.253232 ┆ 0.253248 ┆ 0.252092 │
│ 320.0 ┆ 0.290854 ┆ 0.272485 ┆ 0.252908 ┆ … ┆ 0.250098 ┆ 0.254902 ┆ 0.249886 ┆ 0.252077 │
│ 325.0 ┆ 0.297622 ┆ 0.272485 ┆ 0.252908 ┆ … ┆ 0.250098 ┆ 0.254902 ┆ 0.249886 ┆ 0.258346 │
└────────┴──────────┴───────────┴──────────┴───┴──────────┴──────────┴──────────┴──────────┘
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': 0.182758788004155, 'Daily Volatility': 1.2520196293421366, 'Total Return': 54.79824562487554, 'Annualized Return': 58.56092019248222, 'Annualized Volatility': 19.893261578496194, 'Alpha': 0.004678991442771688, 'Beta': 0.4767558270097878, 'Sharpe Ratio': 2.8432200506337417, 'Sortino Ratio': 4.608914363385, 'Active Return': 25.797422175035756, 'Active Risk': 13.75536060746574, 'Information Ratio': 1.8754449927713397, 'Calmar Ratio': 3.7647448151454346, 'Maximum Drawdown': 15.555083562873563, 'Value at Risk': -1.7253769392749203, 'Expected Shortfall': -2.5631206727986333, 'Security Prices': shape: (250,)
Series: '' [f64]
[
123.470612
124.744125
123.421265
127.96244
128.485657
…
192.192551
191.646561
191.745819
192.172699
191.130341
], 'Security Returns': shape: (250,)
Series: '' [f64]
[
0.0
1.031431
-1.060459
3.679411
0.408883
…
-0.554754
-0.284085
0.051792
0.222628
-0.542407
], 'Benchmark Returns': shape: (250,)
Series: '' [f64]
[
0.0
0.753897
-1.164553
2.284078
-0.076763
…
0.166006
0.423169
0.143046
0.037017
-0.282648
]}
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(str): Type of report to generate (performance, financials, options, news_sentiment).
Example:
ticker.report(report_type="performance")