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': 1723233601, 'Current Price': 216.24, '24H Volume': 41126391.0, '24H Open': 212.08, '24H High': 216.78, '24H Low': 211.98, '24H Close': 213.31}
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){'Symbol': 'AAPL', 'Name': 'Apple Inc.', 'Exchange': 'NasdaqGS', 'Currency': 'USD', 'Timestamp': 1723233601, 'Current Price': 216.24, '24H Change': 1.3735914, '24H Volume': 41126391.0, '24H Open': 212.08, '24H High': 216.78, '24H Low': 211.98, '24H Close': 213.31, '52 Week High': 237.23, '52 Week Low': 164.08, '52 Week Change': 0.0, '50 Day Average': 214.756, '200 Day Average': 190.42924, 'Trailing EPS': 6.57, 'Current EPS': 6.7, 'Forward EPS': 7.47, 'Trailing P/E': 32.913242, 'Current P/E': 32.274628, 'Forward P/E': 28.947792, 'Dividend Rate': 0.0, 'Dividend Yield': 0.0, 'Book Value': 4.382, 'Price to Book': 49.347332, 'Market Cap': 3287734812672.0, 'Shares Outstanding': 15204100096.0, 'Average Analyst Rating': ''}
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 ┆ 124.04805 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-04 ┆ 126.889999 ┆ 128.660004 ┆ 125.080002 ┆ 126.360001 ┆ 8.91136e7 ┆ 125.327507 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-05 ┆ 127.129997 ┆ 127.769997 ┆ 124.760002 ┆ 125.019997 ┆ 8.09627e7 ┆ 123.998459 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-01-06 ┆ 126.010002 ┆ 130.289993 ┆ 124.889999 ┆ 129.619995 ┆ 8.77547e7 ┆ 128.560883 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-12-26 ┆ 193.610001 ┆ 193.889999 ┆ 192.830002 ┆ 193.050003 ┆ 2.89193e7 ┆ 192.542816 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-27 ┆ 192.490005 ┆ 193.5 ┆ 191.089996 ┆ 193.149994 ┆ 4.80877e7 ┆ 192.642548 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-28 ┆ 194.139999 ┆ 194.660004 ┆ 193.169998 ┆ 193.580002 ┆ 3.40499e7 ┆ 193.071426 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2023-12-29 ┆ 193.899994 ┆ 194.399994 ┆ 191.729996 ┆ 192.529999 ┆ 4.26288e7 ┆ 192.024185 │
│ 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: (1_645, 16)
┌────────────┬───────────┬──────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐
│ expiration ┆ ttm ┆ type ┆ contractSy ┆ … ┆ contractSi ┆ lastTradeD ┆ impliedVo ┆ inTheMone │
│ --- ┆ --- ┆ --- ┆ mbol ┆ ┆ ze ┆ ate ┆ latility ┆ y │
│ str ┆ f64 ┆ str ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ ┆ ┆ ┆ str ┆ ┆ str ┆ datetime[m ┆ f64 ┆ bool │
│ ┆ ┆ ┆ ┆ ┆ ┆ s] ┆ ┆ │
╞════════════╪═══════════╪══════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡
│ 2024-08-16 ┆ 0.164258 ┆ call ┆ AAPL240816 ┆ … ┆ REGULAR ┆ 2024-08-05 ┆ 14.250001 ┆ true │
│ ┆ ┆ ┆ C00005000 ┆ ┆ ┆ 17:41:05 ┆ ┆ │
│ 2024-08-16 ┆ 0.164258 ┆ call ┆ AAPL240816 ┆ … ┆ REGULAR ┆ 2024-07-16 ┆ 12.261721 ┆ true │
│ ┆ ┆ ┆ C00015000 ┆ ┆ ┆ 16:16:43 ┆ ┆ │
│ 2024-08-16 ┆ 0.164258 ┆ call ┆ AAPL240816 ┆ … ┆ REGULAR ┆ 2024-06-13 ┆ 12.944338 ┆ true │
│ ┆ ┆ ┆ C00050000 ┆ ┆ ┆ 19:47:00 ┆ ┆ │
│ 2024-08-16 ┆ 0.164258 ┆ call ┆ AAPL240816 ┆ … ┆ REGULAR ┆ 2024-07-11 ┆ 4.458989 ┆ true │
│ ┆ ┆ ┆ C00080000 ┆ ┆ ┆ 15:06:40 ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2026-12-18 ┆ 28.219448 ┆ put ┆ AAPL261218 ┆ … ┆ REGULAR ┆ 2024-07-12 ┆ 0.164681 ┆ true │
│ ┆ ┆ ┆ P00310000 ┆ ┆ ┆ 15:19:38 ┆ ┆ │
│ 2026-12-18 ┆ 28.219448 ┆ put ┆ AAPL261218 ┆ … ┆ REGULAR ┆ 2024-07-26 ┆ 0.184029 ┆ true │
│ ┆ ┆ ┆ P00330000 ┆ ┆ ┆ 14:51:20 ┆ ┆ │
│ 2026-12-18 ┆ 28.219448 ┆ put ┆ AAPL261218 ┆ … ┆ REGULAR ┆ 2024-08-06 ┆ 0.193459 ┆ true │
│ ┆ ┆ ┆ P00340000 ┆ ┆ ┆ 16:45:16 ┆ ┆ │
│ 2026-12-18 ┆ 28.219448 ┆ put ┆ AAPL261218 ┆ … ┆ REGULAR ┆ 2024-08-05 ┆ 0.202523 ┆ true │
│ ┆ ┆ ┆ P00350000 ┆ ┆ ┆ 16:42:56 ┆ ┆ │
└────────────┴───────────┴──────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘
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: (3_231, 5)
┌────────────────┬─────────────────────┬────────────────────┬────────────────────┬─────────────────┐
│ Published Date ┆ Source ┆ Title ┆ Link ┆ Sentiment Score │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[ms] ┆ str ┆ str ┆ str ┆ f64 │
╞════════════════╪═════════════════════╪════════════════════╪════════════════════╪═════════════════╡
│ 2023-01-03 ┆ Reuters ┆ Apple's stock ┆ https://news.googl ┆ 0.33995 │
│ 08:00:00 ┆ ┆ market value ┆ e.com/rss/arti… ┆ │
│ ┆ ┆ falls… ┆ ┆ │
│ 2023-01-03 ┆ Al Jazeera English ┆ Apple’s market ┆ https://news.googl ┆ 0.33995 │
│ 08:00:00 ┆ ┆ value drops below… ┆ e.com/rss/arti… ┆ │
│ 2023-01-04 ┆ Bloomberg ┆ Apple’s (AAPL) ┆ https://news.googl ┆ -0.70956 │
│ 08:00:00 ┆ ┆ Stock Is Losing I… ┆ e.com/rss/arti… ┆ │
│ 2023-01-04 ┆ The Guardian ┆ Death of the ┆ https://news.googl ┆ -0.622427 │
│ 08:00:00 ┆ ┆ narrator? Apple ┆ e.com/rss/arti… ┆ │
│ ┆ ┆ unv… ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … │
│ 2024-01-01 ┆ Bloomberg ┆ Baidu’s ┆ https://news.googl ┆ 0.421464 │
│ 08:00:00 ┆ ┆ Live-Streaming ┆ e.com/rss/arti… ┆ │
│ ┆ ┆ Hopes Hit… ┆ ┆ │
│ 2023-12-30 ┆ Business Standard ┆ Billionaire ┆ https://news.googl ┆ 0.0 │
│ 08:00:00 ┆ ┆ Gustavo Cisneros, ┆ e.com/rss/arti… ┆ │
│ ┆ ┆ wh… ┆ ┆ │
│ 2023-12-30 ┆ Gulf News ┆ The Apple Watch ┆ https://news.googl ┆ 0.0 │
│ 08:00:00 ┆ ┆ Series 9, Ultra … ┆ e.com/rss/arti… ┆ │
│ 2024-01-01 ┆ Yahoo Finance ┆ Buffett's ┆ https://news.googl ┆ 0.0 │
│ 08:00:00 ┆ ┆ Bullseye: Meet The ┆ e.com/rss/arti… ┆ │
│ ┆ ┆ 4 S… ┆ ┆ │
└────────────────┴─────────────────────┴────────────────────┴────────────────────┴─────────────────┘
get_income_statement
Get the Income Statement for the ticker.
Returns:
DataFrame: Polars DataFrame containing the Income Statement.
Example:
income_statement = ticker.get_income_statement()
print(income_statement)shape: (13, 6)
┌──────────────────────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ 2023Q1 ┆ 2023Q2 ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Revenue ┆ 9.4836e10 ┆ 8.1797e10 ┆ 8.9498e10 ┆ 1.1958e11 ┆ 9.0753e10 │
│ Cost of Goods Sold ┆ 5.2860e10 ┆ 4.5384e10 ┆ 4.9071e10 ┆ 6.4720e10 ┆ 4.8482e10 │
│ Gross Profit ┆ 4.1976e10 ┆ 3.6413e10 ┆ 4.0427e10 ┆ 5.4855e10 ┆ 4.2271e10 │
│ Operating Expenses ┆ 1.3658e10 ┆ 1.3415e10 ┆ 1.3458e10 ┆ 1.4482e10 ┆ 1.4371e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Income Tax Expense ┆ 4.2220e9 ┆ 2.8520e9 ┆ 4.0420e9 ┆ 6.4070e9 ┆ 4.4220e9 │
│ Net Income ┆ 2.4160e10 ┆ 1.9881e10 ┆ 2.2956e10 ┆ 3.3916e10 ┆ 2.3636e10 │
│ Earnings per Share - Basic ┆ 1.53 ┆ 1.27 ┆ 1.47 ┆ 2.19 ┆ 1.53 │
│ Earnings per Share - Diluted ┆ 1.52 ┆ 1.26 ┆ 1.46 ┆ 2.18 ┆ 1.53 │
└──────────────────────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
get_balance_sheet
Get the Balance Sheet for the ticker.
Returns:
DataFrame: Polars DataFrame containing the Balance Sheet.
Example:
balance_sheet = ticker.get_balance_sheet()
print(balance_sheet)shape: (21, 6)
┌──────────────────────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ 2023Q1 ┆ 2023Q2 ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Cash and Cash Equivalents ┆ 2.4687e10 ┆ 2.8408e10 ┆ 2.9965e10 ┆ 4.0760e10 ┆ 3.2695e10 │
│ Accounts Receivable ┆ 1.7936e10 ┆ 1.9549e10 ┆ 2.9508e10 ┆ 2.3194e10 ┆ 2.1837e10 │
│ Inventories ┆ 7.4820e9 ┆ 7.3510e9 ┆ 6.3310e9 ┆ 6.5110e9 ┆ 6.2320e9 │
│ Other Current Assets ┆ 1.3660e10 ┆ 1.3640e10 ┆ 1.4695e10 ┆ 1.3979e10 ┆ 1.3884e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Common Stock ┆ 6.9568e10 ┆ 7.0667e10 ┆ 7.3812e10 ┆ 7.5236e10 ┆ 7.8815e10 │
│ Retained Earnings ┆ 4.3360e9 ┆ 1.4080e9 ┆ -2.1400e8 ┆ 8.2420e9 ┆ 4.3390e9 │
│ Total Equity ┆ 6.2158e10 ┆ 6.0274e10 ┆ 6.2146e10 ┆ 7.4100e10 ┆ 7.4194e10 │
│ Total Liabilities and Equity ┆ 6.2158e10 ┆ 6.0274e10 ┆ 6.2146e10 ┆ 7.4100e10 ┆ 7.4194e10 │
└──────────────────────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
get_cashflow_statement
Get the Cashflow Statement for the ticker.
Returns:
DataFrame: Polars DataFrame containing the Cashflow Statement.
Example:
cashflow_statement = ticker.get_cashflow_statement()
print(cashflow_statement)shape: (26, 6)
┌─────────────────────────────────┬────────────┬────────────┬────────────┬────────────┬────────────┐
│ Items ┆ 2023Q1 ┆ 2023Q2 ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════════╪════════════╪════════════╪════════════╪════════════╪════════════╡
│ Net Income from Continuing ┆ 2.4160e10 ┆ 1.9881e10 ┆ 2.2956e10 ┆ 3.3916e10 ┆ 2.3636e10 │
│ Opera… ┆ ┆ ┆ ┆ ┆ │
│ Depreciation, Amortization, and ┆ 2.8980e9 ┆ 3.0520e9 ┆ 2.6530e9 ┆ 2.8480e9 ┆ 2.8360e9 │
│ … ┆ ┆ ┆ ┆ ┆ │
│ Stock-Based Compensation ┆ 2.6860e9 ┆ 2.6170e9 ┆ 2.6250e9 ┆ 2.9970e9 ┆ 2.9640e9 │
│ Changes in Working Capital ┆ 2.31e8 ┆ 7.49e8 ┆ -6.0600e9 ┆ 1.1230e9 ┆ -5.7640e9 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Investing Cash Flow ┆ 2.3190e9 ┆ 4.37e8 ┆ 2.3940e9 ┆ 1.9270e9 ┆ -3.1000e8 │
│ Financing Cash Flow ┆ -2.5724e10 ┆ -2.4048e10 ┆ -2.3153e10 ┆ -3.0585e10 ┆ -3.0433e10 │
│ Ending Cash Position ┆ 2.7129e10 ┆ 2.9898e10 ┆ 3.0737e10 ┆ 4.1974e10 ┆ 3.3921e10 │
│ Free Cash Flow ┆ 2.5644e10 ┆ 2.4287e10 ┆ 1.9435e10 ┆ 3.7503e10 ┆ 2.0694e10 │
└─────────────────────────────────┴────────────┴────────────┴────────────┴────────────┴────────────┘
get_financial_ratios
Get the Financial Ratios for the ticker.
Returns:
DataFrame: Polars DataFrame containing the Financial Ratios.
Example:
financial_ratios = ticker.get_financial_ratios()
print(financial_ratios)shape: (21, 6)
┌─────────────────────────┬──────────┬──────────┬──────────┬──────────┬──────────┐
│ Items ┆ 2023Q1 ┆ 2023Q2 ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════╪══════════╪══════════╪══════════╪══════════╪══════════╡
│ Gross Profit Margin ┆ 0.442617 ┆ 0.445163 ┆ 0.451708 ┆ 0.45875 ┆ 0.465781 │
│ Operating Profit Margin ┆ 0.2986 ┆ 0.290121 ┆ 0.312856 ┆ 0.337637 ┆ 0.307428 │
│ Net Profit Margin ┆ 0.254756 ┆ 0.243053 ┆ 0.256497 ┆ 0.283638 ┆ 0.260443 │
│ Return on Assets ┆ 0.072736 ┆ 0.05934 ┆ 0.065108 ┆ 0.09594 ┆ 0.070051 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Price to Book ┆ 2.561199 ┆ 2.627086 ┆ 2.53318 ┆ 2.283239 ┆ 2.237715 │
│ Price to Sales ┆ 1.678677 ┆ 1.935829 ┆ 1.759 ┆ 1.414911 ┆ 1.829416 │
│ Price to Cashflow ┆ 5.574195 ┆ 6.002464 ┆ 7.288962 ┆ 4.240832 ┆ 7.3171 │
│ Price to Free Cashflow ┆ 6.208041 ┆ 6.519743 ┆ 8.10018 ┆ 4.511319 ┆ 8.022857 │
└─────────────────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘
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: (62, 11)
┌────────┬──────────┬──────────┬───────────┬───┬──────────┬──────────┬──────────┬──────────┐
│ strike ┆ 3.15M ┆ 4.30M ┆ 5.22M ┆ … ┆ 16.26M ┆ 17.18M ┆ 22.21M ┆ 28.22M │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════╪══════════╪══════════╪═══════════╪═══╪══════════╪══════════╪══════════╪══════════╡
│ 5.0 ┆ 1.0 ┆ 0.822109 ┆ 1.0 ┆ … ┆ 0.475996 ┆ 0.534971 ┆ 0.476963 ┆ 0.814375 │
│ 15.0 ┆ 1.0 ┆ 0.822109 ┆ 1.0 ┆ … ┆ 0.475996 ┆ 0.534971 ┆ 0.476963 ┆ 0.725671 │
│ 20.0 ┆ 1.0 ┆ 0.822109 ┆ 1.0 ┆ … ┆ 0.475996 ┆ 0.534971 ┆ 0.476963 ┆ 0.636968 │
│ 25.0 ┆ 1.0 ┆ 0.822109 ┆ 1.0 ┆ … ┆ 0.475996 ┆ 0.534971 ┆ 0.476963 ┆ 0.548264 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 340.0 ┆ 0.325176 ┆ 0.299072 ┆ 0.27877 ┆ … ┆ 0.254237 ┆ 0.242152 ┆ 0.243844 ┆ 0.246895 │
│ 350.0 ┆ 0.327109 ┆ 0.307773 ┆ 0.2884375 ┆ … ┆ 0.244327 ┆ 0.241457 ┆ 0.243542 ┆ 0.243119 │
│ 360.0 ┆ 0.353213 ┆ 0.307773 ┆ 0.2884375 ┆ … ┆ 0.244327 ┆ 0.241457 ┆ 0.243542 ┆ 0.243119 │
│ 370.0 ┆ 0.35998 ┆ 0.307773 ┆ 0.2884375 ┆ … ┆ 0.244327 ┆ 0.241457 ┆ 0.243542 ┆ 0.243119 │
└────────┴──────────┴──────────┴───────────┴───┴──────────┴──────────┴──────────┴──────────┘
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': '1d', 'Confidence Level': 0.95, 'Risk Free Rate': 0.02, 'Daily Return': 0.18275875540061556, 'Daily Volatility': 1.252020368619977, 'Total Return': 54.7982296328251, 'Annualized Return': 58.42826625594621, 'Annualized Volatility': 19.875207190535257, 'Alpha': 0.00467905225242686, 'Beta': 0.47675557932957147, 'Sharpe Ratio': 2.8391284536051447, 'Sortino Ratio': 4.602294629698171, 'Active Return': 25.745008299619478, 'Active Risk': 13.742876544849127, 'Information Ratio': 1.8733347575088848, 'Calmar Ratio': 6.153782779707574, 'Maximum Drawdown': 9.494691045744503, 'Value at Risk': -1.7253614349626107, 'Expected Shortfall': -2.5631177996522334, 'Security Prices': shape: (250,)
Series: '' [f64]
[
124.04805
125.327507
123.998459
128.560883
129.086548
129.661774
132.399261
132.319916
133.658859
134.829239
134.105209
134.164719
…
194.198471
197.439926
197.589523
197.050949
195.375366
196.422607
194.318146
194.168518
193.091385
192.542816
192.642548
193.071426
192.024185
], 'Security Returns': shape: (250,)
Series: '' [f64]
[
0.0
1.031421
-1.06046
3.67942
0.408884
0.445613
2.111253
-0.059929
1.011899
0.875647
-0.536997
0.044375
…
0.79201
1.669146
0.075768
-0.272572
-0.85033
0.536015
-1.071395
-0.077001
-0.554741
-0.284098
0.051797
0.222629
-0.542411
], 'Benchmark Returns': shape: (250,)
Series: '' [f64]
[
0.0
0.753897
-1.164553
2.284078
-0.076763
0.697823
1.284942
0.341591
0.399686
-0.203049
-1.55626
-0.763835
…
0.459936
1.365068
0.264706
-0.007625
0.452834
0.586641
-1.468427
1.030147
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()