from finalytics import Tickers
tickers = Tickers(symbols=["AAPL", "GOOG", "MSFT", "NVDA", "BTC-USD"],
start_date="2020-01-01",
end_date="2024-01-01",
interval="1d",
benchmark_symbol="^GSPC",
confidence_level=0.95,
risk_free_rate=0.02)Tickers Module Documentation
Tickers
A class representing a Tickers object.
__new__
Create a new Tickers object.
Parameters:
symbols(List[str]): A list of ticker symbols.start_date(str): The start date of the time period in the format YYYY-MM-DD.end_date(str): The end date of the time period in the format YYYY-MM-DD.interval(str): The interval of the data (2m, 5m, 15m, 30m, 1h, 1d, 1wk, 1mo, 3mo).benchmark_symbol(str): The ticker symbol of the benchmark to compare against.confidence_level(float): The confidence level for the VaR and ES calculations.risk_free_rate(float): The risk free rate to use in the calculations.
Returns:
Tickers: A Tickers object.
Example:
get_summary_stats
Fetch the Ticker Summary Stats Data for all tickers.
Returns:
DataFrame: Polars DataFrame containing summary statistics.
Example:
summary_stats = tickers.get_summary_stats()
print(summary_stats)shape: (5, 53)
┌─────────┬──────────┬────────────┬────────────┬───┬───────────┬───────────┬───────────┬───────────┐
│ Symbol ┆ Currency ┆ Current ┆ Day Range ┆ … ┆ High/Low ┆ Analyst ┆ Circulati ┆ CoinMarke │
│ --- ┆ --- ┆ Price ┆ --- ┆ ┆ Target ┆ Count ┆ ng Supply ┆ tCap Link │
│ str ┆ str ┆ --- ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ ┆ ┆ str ┆ ┆ ┆ str ┆ str ┆ str ┆ str │
╞═════════╪══════════╪════════════╪════════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡
│ AAPL ┆ USD ┆ 213.39 ┆ 215.78 - ┆ … ┆ 300.00 - ┆ 40 ┆ ┆ │
│ ┆ ┆ ┆ 211.64 ┆ ┆ 170.62 ┆ ┆ ┆ │
│ GOOG ┆ USD ┆ 190.35 ┆ 190.84 - ┆ … ┆ 237.00 - ┆ 16 ┆ ┆ │
│ ┆ ┆ ┆ 187.05 ┆ ┆ 185.00 ┆ ┆ ┆ │
│ MSFT ┆ USD ┆ 510.52 ┆ 512.07 - ┆ … ┆ 700.00 - ┆ 50 ┆ ┆ │
│ ┆ ┆ ┆ 505.59 ┆ ┆ 432.00 ┆ ┆ ┆ │
│ NVDA ┆ USD ┆ 172.57 ┆ 173.38 - ┆ … ┆ 250.00 - ┆ 57 ┆ ┆ │
│ ┆ ┆ ┆ 171.45 ┆ ┆ 100.00 ┆ ┆ ┆ │
│ BTC-USD ┆ USD ┆ ┆ 119,605.81 ┆ … ┆ - ┆ ┆ 19,895,46 ┆ https://c │
│ ┆ ┆ ┆ - ┆ ┆ ┆ ┆ 0.00 ┆ oinmarket │
│ ┆ ┆ ┆ 116,710.34 ┆ ┆ ┆ ┆ ┆ cap.com/c │
│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ urr… │
└─────────┴──────────┴────────────┴────────────┴───┴───────────┴───────────┴───────────┴───────────┘
get_price_history
Fetch the OHLCV Data for all tickers.
Returns:
DataFrame: Polars DataFrame containing OHLCV data.
Example:
price_history = tickers.get_price_history()
print(price_history)shape: (5_485, 8)
┌────────────┬─────────┬────────────┬────────────┬────────────┬────────────┬───────────┬───────────┐
│ timestamp ┆ symbol ┆ open ┆ high ┆ low ┆ close ┆ volume ┆ adjclose │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ datetime[m ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
│ s] ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
╞════════════╪═════════╪════════════╪════════════╪════════════╪════════════╪═══════════╪═══════════╡
│ 2020-01-02 ┆ AAPL ┆ 74.059998 ┆ 75.150002 ┆ 73.797501 ┆ 75.087502 ┆ 1.354804e ┆ 72.620842 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-03 ┆ AAPL ┆ 74.287498 ┆ 75.144997 ┆ 74.125 ┆ 74.357498 ┆ 1.463228e ┆ 71.91481 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-06 ┆ AAPL ┆ 73.447502 ┆ 74.989998 ┆ 73.1875 ┆ 74.949997 ┆ 1.183872e ┆ 72.487839 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-07 ┆ AAPL ┆ 74.959999 ┆ 75.224998 ┆ 74.370003 ┆ 74.597504 ┆ 1.08872e8 ┆ 72.146935 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2020-01-08 ┆ AAPL ┆ 74.290001 ┆ 76.110001 ┆ 74.290001 ┆ 75.797501 ┆ 1.320792e ┆ 73.307518 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-12-27 ┆ BTC-USD ┆ 42518.4687 ┆ 43683.1601 ┆ 42167.5820 ┆ 43442.8554 ┆ 2.5261e10 ┆ 43442.855 │
│ 00:00:00 ┆ ┆ 5 ┆ 56 ┆ 31 ┆ 69 ┆ ┆ 469 │
│ 2023-12-28 ┆ BTC-USD ┆ 43468.1992 ┆ 43804.7812 ┆ 42318.5507 ┆ 42627.8554 ┆ 2.2992e10 ┆ 42627.855 │
│ 00:00:00 ┆ ┆ 19 ┆ 5 ┆ 81 ┆ 69 ┆ ┆ 469 │
│ 2023-12-29 ┆ BTC-USD ┆ 42614.6445 ┆ 43124.3242 ┆ 41424.0625 ┆ 42099.4023 ┆ 2.6000e10 ┆ 42099.402 │
│ 00:00:00 ┆ ┆ 31 ┆ 19 ┆ ┆ 44 ┆ ┆ 344 │
│ 2023-12-30 ┆ BTC-USD ┆ 42091.7539 ┆ 42584.125 ┆ 41556.2265 ┆ 42156.9023 ┆ 1.6014e10 ┆ 42156.902 │
│ 00:00:00 ┆ ┆ 06 ┆ ┆ 62 ┆ 44 ┆ ┆ 344 │
│ 2023-12-31 ┆ BTC-USD ┆ 42152.0976 ┆ 42860.9375 ┆ 41998.2539 ┆ 42265.1875 ┆ 1.6397e10 ┆ 42265.187 │
│ 00:00:00 ┆ ┆ 56 ┆ ┆ 06 ┆ ┆ ┆ 5 │
└────────────┴─────────┴────────────┴────────────┴────────────┴────────────┴───────────┴───────────┘
get_options_chain
Fetch the Options Chain Data for all tickers.
Returns:
DataFrame: Polars DataFrame containing the options chain.
Example:
options_chain = tickers.get_options_chain()
print(options_chain)shape: (11_310, 17)
┌────────────┬───────────┬──────┬────────┬───┬─────────────┬─────────────┬────────────┬────────────┐
│ expiration ┆ ttm ┆ type ┆ symbol ┆ … ┆ contractSiz ┆ lastTradeDa ┆ impliedVol ┆ inTheMoney │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ e ┆ te ┆ atility ┆ --- │
│ str ┆ f64 ┆ str ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ bool │
│ ┆ ┆ ┆ ┆ ┆ str ┆ datetime[ms ┆ f64 ┆ │
│ ┆ ┆ ┆ ┆ ┆ ┆ ] ┆ ┆ │
╞════════════╪═══════════╪══════╪════════╪═══╪═════════════╪═════════════╪════════════╪════════════╡
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2025-07-21 ┆ 2.550785 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 13:46:31 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2025-06-27 ┆ 2.345707 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:42:44 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2025-07-17 ┆ 2.19727 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 14:34:08 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2025-06-23 ┆ 1.859376 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:28:40 ┆ ┆ │
│ 2025-07-25 ┆ 0.098555 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2025-07-18 ┆ 1.753907 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 14:13:48 ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-07-18 ┆ 0.238777 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 18:33:34 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-07-15 ┆ 0.24427 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:39:49 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-07-18 ┆ 0.246406 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 14:16:18 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-07-17 ┆ 0.277778 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 16:48:25 ┆ ┆ │
│ 2027-12-17 ┆ 28.843627 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-07-17 ┆ 0.313483 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:52:03 ┆ ┆ │
└────────────┴───────────┴──────┴────────┴───┴─────────────┴─────────────┴────────────┴────────────┘No Options Data for BTC-USD
get_news
Fetch the Historical News Headlines for all tickers.
Returns:
DataFrame: Polars DataFrame containing news headlines.
Example:
news = tickers.get_news()
print(news)
get_income_statement
Fetch the income statement for all tickers.
Parameters:
frequency(str): The frequency of the data (annual or quarterly).
Returns:
DataFrame: Polars DataFrame containing the income statement.
Example:
income_statement = tickers.get_income_statement(frequency="quarterly")
print(income_statement)Error Fetching Data for BTC-USD: not found: asOfDateshape: (50, 7)
┌─────────────────────────────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ symbol ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Revenue ┆ AAPL ┆ 9.0753e10 ┆ 8.5777e10 ┆ 9.4930e10 ┆ 1.2430e11 ┆ 9.5359e10 │
│ Cost of Goods Sold ┆ AAPL ┆ 4.8482e10 ┆ 4.6099e10 ┆ 5.1051e10 ┆ 6.6025e10 ┆ 5.0492e10 │
│ Gross Profit ┆ AAPL ┆ 4.2271e10 ┆ 3.9678e10 ┆ 4.3879e10 ┆ 5.8275e10 ┆ 4.4867e10 │
│ Operating Expenses ┆ AAPL ┆ 1.4371e10 ┆ 1.4326e10 ┆ 1.4288e10 ┆ 1.5443e10 ┆ 1.5278e10 │
│ EBITDA ┆ AAPL ┆ 3.0736e10 ┆ 2.8202e10 ┆ 3.2502e10 ┆ 4.5912e10 ┆ 3.2250e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Interest Expense ┆ NVDA ┆ 6.4e7 ┆ 6.1e7 ┆ 6.1e7 ┆ 6.1e7 ┆ 6.3e7 │
│ Income Tax Expense ┆ NVDA ┆ 2.3980e9 ┆ 2.6150e9 ┆ 3.0070e9 ┆ 3.1260e9 ┆ 3.1350e9 │
│ Net Income ┆ NVDA ┆ 1.4881e10 ┆ 1.6599e10 ┆ 1.9309e10 ┆ 2.2091e10 ┆ 1.8775e10 │
│ Earnings per Share - Basic ┆ NVDA ┆ 0.6 ┆ 0.68 ┆ 0.79 ┆ 0.9 ┆ 0.77 │
│ Earnings per Share - ┆ NVDA ┆ 0.6 ┆ 0.67 ┆ 0.78 ┆ 0.89 ┆ 0.76 │
│ Diluted ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────────────────────────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
get_balance_sheet
Fetch the balance sheet for all tickers.
Parameters:
frequency(str): The frequency of the data (annual or quarterly).
Returns:
DataFrame: Polars DataFrame containing the balance sheet.
Example:
balance_sheet = tickers.get_balance_sheet(frequency="quarterly")
print(balance_sheet)Error Fetching Data for BTC-USD: not found: asOfDateshape: (86, 7)
┌─────────────────────────────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ symbol ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Cash and Cash Equivalents ┆ AAPL ┆ 3.2695e10 ┆ 2.5565e10 ┆ 2.9943e10 ┆ 3.0299e10 ┆ 2.8162e10 │
│ Accounts Receivable ┆ AAPL ┆ 2.1837e10 ┆ 2.2795e10 ┆ 3.3410e10 ┆ 2.9639e10 ┆ 2.6136e10 │
│ Inventories ┆ AAPL ┆ 6.2320e9 ┆ 6.1650e9 ┆ 7.2860e9 ┆ 6.9110e9 ┆ 6.2690e9 │
│ Other Current Assets ┆ AAPL ┆ 1.3884e10 ┆ 1.4297e10 ┆ 1.4287e10 ┆ 1.3248e10 ┆ 1.4109e10 │
│ Total Current Assets ┆ AAPL ┆ 1.2842e11 ┆ 1.2544e11 ┆ 1.5299e11 ┆ 1.3324e11 ┆ 1.1867e11 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Total Liabilities ┆ NVDA ┆ 2.7930e10 ┆ 2.7070e10 ┆ 3.0114e10 ┆ 3.2274e10 ┆ 4.1411e10 │
│ Common Stock ┆ NVDA ┆ 2e6 ┆ 2.5e7 ┆ 2.5e7 ┆ 2.4e7 ┆ 2.4e7 │
│ Retained Earnings ┆ NVDA ┆ 3.6598e10 ┆ 4.5961e10 ┆ 5.3950e10 ┆ 6.8038e10 ┆ 7.2158e10 │
│ Total Equity ┆ NVDA ┆ 4.9142e10 ┆ 5.8157e10 ┆ 6.5899e10 ┆ 7.9327e10 ┆ 8.3843e10 │
│ Total Liabilities and ┆ NVDA ┆ 4.9142e10 ┆ 5.8157e10 ┆ 6.5899e10 ┆ 7.9327e10 ┆ 8.3843e10 │
│ Equity ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────────────────────────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
get_cashflow_statement
Fetch the cash flow statement for all tickers.
Parameters:
frequency(str): The frequency of the data (annual or quarterly).
Returns:
DataFrame: Polars DataFrame containing the cash flow statement.
Example:
cashflow_statement = tickers.get_cashflow_statement(frequency="quarterly")
print(cashflow_statement)Error Fetching Data for BTC-USD: not found: asOfDateshape: (114, 7)
┌──────────────────────┬────────┬───────────┬────────────┬────────────┬────────────┬────────────┐
│ Items ┆ symbol ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞══════════════════════╪════════╪═══════════╪════════════╪════════════╪════════════╪════════════╡
│ Net Income from ┆ AAPL ┆ 2.3636e10 ┆ 2.1448e10 ┆ 1.4736e10 ┆ 3.6330e10 ┆ 2.4780e10 │
│ Continuing Ope… ┆ ┆ ┆ ┆ ┆ ┆ │
│ Depreciation, ┆ AAPL ┆ 2.8360e9 ┆ 2.8500e9 ┆ 2.9110e9 ┆ 3.0800e9 ┆ 2.6610e9 │
│ Amortization, an… ┆ ┆ ┆ ┆ ┆ ┆ │
│ Stock-Based ┆ AAPL ┆ 2.9640e9 ┆ 2.8690e9 ┆ 2.8580e9 ┆ 3.2860e9 ┆ 3.2260e9 │
│ Compensation ┆ ┆ ┆ ┆ ┆ ┆ │
│ Changes in Working ┆ AAPL ┆ -5.7640e9 ┆ 1.6840e9 ┆ 6.6080e9 ┆ -1.0752e10 ┆ -6.5070e9 │
│ Capital ┆ ┆ ┆ ┆ ┆ ┆ │
│ Cash Flow from ┆ AAPL ┆ 2.2690e10 ┆ 2.8858e10 ┆ 2.6811e10 ┆ 2.9935e10 ┆ 2.3952e10 │
│ Continuing Oper… ┆ ┆ ┆ ┆ ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Cash Flow ┆ NVDA ┆ 1.5345e10 ┆ 1.4488e10 ┆ 1.7627e10 ┆ 1.6629e10 ┆ 2.7414e10 │
│ Investing Cash Flow ┆ NVDA ┆ -5.6930e9 ┆ -3.1840e9 ┆ -4.3460e9 ┆ -7.1980e9 ┆ -5.2160e9 │
│ Financing Cash Flow ┆ NVDA ┆ -9.3450e9 ┆ -1.0320e10 ┆ -1.2745e10 ┆ -9.9490e9 ┆ -1.5553e10 │
│ Ending Cash Position ┆ NVDA ┆ 7.5870e9 ┆ 8.5710e9 ┆ 9.1070e9 ┆ 8.5890e9 ┆ 1.5234e10 │
│ Free Cash Flow ┆ NVDA ┆ 1.4976e10 ┆ 1.3511e10 ┆ 1.6814e10 ┆ 1.5552e10 ┆ 2.6187e10 │
└──────────────────────┴────────┴───────────┴────────────┴────────────┴────────────┴────────────┘
get_financial_ratios
Fetch the financial ratios for all tickers.
Parameters:
frequency(str): The frequency of the data (annual or quarterly).
Returns:
DataFrame: Polars DataFrame containing the financial ratios.
Example:
financial_ratios = tickers.get_financial_ratios(frequency="quarterly")
print(financial_ratios)Error Fetching Data for GOOG: not found: Inventory
Error Fetching Data for BTC-USD: not found: asOfDateshape: (63, 7)
┌─────────────────────────┬────────┬──────────┬──────────┬──────────┬──────────┬──────────┐
│ Items ┆ symbol ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════╪════════╪══════════╪══════════╪══════════╪══════════╪══════════╡
│ Gross Profit Margin ┆ AAPL ┆ 0.465781 ┆ 0.462572 ┆ 0.462225 ┆ 0.468825 ┆ 0.470506 │
│ Operating Profit Margin ┆ AAPL ┆ 0.307428 ┆ 0.295557 ┆ 0.311714 ┆ 0.344586 ┆ 0.310291 │
│ Net Profit Margin ┆ AAPL ┆ 0.260443 ┆ 0.250044 ┆ 0.15523 ┆ 0.292277 ┆ 0.25986 │
│ Return on Assets ┆ AAPL ┆ 0.070051 ┆ 0.064678 ┆ 0.040375 ┆ 0.105584 ┆ 0.074811 │
│ Return on Equity ┆ AAPL ┆ 0.31857 ┆ 0.321521 ┆ 0.258753 ┆ 0.544204 ┆ 0.37098 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Price to Earnings ┆ NVDA ┆ 3.870842 ┆ 4.013374 ┆ 3.851106 ┆ 3.974017 ┆ 4.916485 │
│ Price to Book ┆ NVDA ┆ 1.172154 ┆ 1.145485 ┆ 1.128409 ┆ 1.106685 ┆ 1.100951 │
│ Price to Sales ┆ NVDA ┆ 2.211719 ┆ 2.217643 ┆ 2.119634 ┆ 2.232082 ┆ 2.094934 │
│ Price to Cashflow ┆ NVDA ┆ 3.753796 ┆ 4.59815 ┆ 4.218585 ┆ 5.279331 ┆ 3.367148 │
│ Price to Free Cashflow ┆ NVDA ┆ 3.846287 ┆ 4.930649 ┆ 4.422565 ┆ 5.644933 ┆ 3.524917 │
└─────────────────────────┴────────┴──────────┴──────────┴──────────┴──────────┴──────────┘
returns
Compute the returns for all tickers.
Returns:
DataFrame: Polars DataFrame containing the returns.
Example:
returns = tickers.returns()
print(returns)shape: (1_461, 6)
┌─────────────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ timestamp ┆ AAPL ┆ GOOG ┆ MSFT ┆ NVDA ┆ BTC-USD │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ 2020-01-01 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ 2020-01-02 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ -2.981929 │
│ 2020-01-03 00:00:00 ┆ -0.972216 ┆ -0.490725 ┆ -1.245212 ┆ -1.600618 ┆ 5.145166 │
│ 2020-01-04 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.895487 │
│ 2020-01-05 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.008915 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-12-27 00:00:00 ┆ 0.051792 ┆ -0.966248 ┆ -0.157475 ┆ 0.280048 ┆ 2.169436 │
│ 2023-12-28 00:00:00 ┆ 0.22262 ┆ -0.113124 ┆ 0.323458 ┆ 0.212478 ┆ -1.876028 │
│ 2023-12-29 00:00:00 ┆ -0.542407 ┆ -0.247745 ┆ 0.202522 ┆ 0.0 ┆ -1.23969 │
│ 2023-12-30 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.136582 │
│ 2023-12-31 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.256862 │
└─────────────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
performance_stats
Compute the performance stats for all tickers.
Returns:
DataFrame: Polars DataFrame containing the performance stats.
Example:
performance_stats = tickers.performance_stats()
print(performance_stats)shape: (5, 17)
┌─────────┬────────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐
│ Symbol ┆ Daily ┆ Daily Vol ┆ Cumulativ ┆ … ┆ Calmar ┆ Maximum ┆ Value at ┆ Expected │
│ --- ┆ Return ┆ atility ┆ e Return ┆ ┆ Ratio ┆ Drawdown ┆ Risk ┆ Shortfall │
│ str ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │
╞═════════╪════════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡
│ AAPL ┆ 0.11853876 ┆ 2.1124922 ┆ 163.18935 ┆ … ┆ 1.0363003 ┆ 33.534583 ┆ -3.243721 ┆ -4.714312 │
│ ┆ 151227855 ┆ 015317398 ┆ 46071407 ┆ ┆ 450067165 ┆ 864929346 ┆ 165860045 ┆ 377066304 │
│ GOOG ┆ 0.09414066 ┆ 2.1059431 ┆ 106.13292 ┆ … ┆ 0.5105191 ┆ 52.362263 ┆ -3.194601 ┆ -4.806254 │
│ ┆ 363591062 ┆ 471108887 ┆ 586058466 ┆ ┆ 327925187 ┆ 79337456 ┆ 700883718 ┆ 014063553 │
│ MSFT ┆ 0.10935392 ┆ 2.0525398 ┆ 142.95202 ┆ … ┆ 0.7723261 ┆ 41.012542 ┆ -2.951148 ┆ -4.550188 │
│ ┆ 647015274 ┆ 859269206 ┆ 885806145 ┆ ┆ 770460207 ┆ 36879963 ┆ 486874590 ┆ 293354171 │
│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 7 ┆ │
│ NVDA ┆ 0.26818817 ┆ 3.4126834 ┆ 728.90259 ┆ … ┆ 1.0561095 ┆ 91.152591 ┆ -5.126355 ┆ -6.999365 │
│ ┆ 30553994 ┆ 731101097 ┆ 82254568 ┆ ┆ 243646528 ┆ 28188838 ┆ 321018897 ┆ 165509773 │
│ BTC-USD ┆ 0.18312342 ┆ 3.4774959 ┆ 487.00228 ┆ … ┆ 0.7734174 ┆ 122.82135 ┆ -5.098586 ┆ -7.981326 │
│ ┆ 434915735 ┆ 999729025 ┆ 137108877 ┆ ┆ 145027587 ┆ 584673281 ┆ 481791351 ┆ 501258104 │
│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 5 │
└─────────┴────────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘
returns_chart
Display the cumulative returns chart for all tickers.
Returns:
Plot: Plot object containing the returns chart.
Example:
returns_chart = tickers.returns_chart()
returns_chart.show()
returns_matrix
Display the returns correlation matrix for all tickers.
Returns:
Plot: Plot object containing the returns chart.
Example:
returns_matrix = tickers.returns_matrix(height=600, width=800)
returns_matrix.show()
report
Generate a report for all tickers.
Parameters:
- report_type (str): The type of report to generate (“performance”).
Example:
tickers.report("performance")
get_ticker
Fetch the Ticker object for a specific ticker symbol.
Parameters:
symbol(str): The ticker symbol.
Returns:
Ticker: A Ticker object.
Example:
ticker = tickers.get_ticker(symbol="AAPL")
ticker.performance_chart().show()
optimize
Optimizes the tickers given the objective function and constraints.
Parameters:
objective_function(Optional[str]): The objective function for optimization.constraints(Optional[List[Tuple[float, float]]]): List of constraints for optimization.
Returns:
Portfolio: A Portfolio object.
Example:
portfolio = tickers.optimize(objective_function="max_sharpe", constraints=[(0, 1), (0, 1), (0, 1), (0, 1), (0,1)])
portfolio.performance_chart().show()