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 ┆ 271.35 ┆ 275.94 - ┆ … ┆ 350.00 - ┆ 42 ┆ ┆ │
│ ┆ ┆ ┆ 268.14 ┆ ┆ 215.00 ┆ ┆ ┆ │
│ GOOG ┆ USD ┆ 381.94 ┆ 382.63 - ┆ … ┆ 405.00 - ┆ 18 ┆ ┆ │
│ ┆ ┆ ┆ 363.09 ┆ ┆ 185.00 ┆ ┆ ┆ │
│ MSFT ┆ USD ┆ 407.78 ┆ 414.42 - ┆ … ┆ 730.00 - ┆ 54 ┆ ┆ │
│ ┆ ┆ ┆ 398.01 ┆ ┆ 392.00 ┆ ┆ ┆ │
│ NVDA ┆ USD ┆ 199.57 ┆ 210.30 - ┆ … ┆ 380.00 - ┆ 57 ┆ ┆ │
│ ┆ ┆ ┆ 198.70 ┆ ┆ 140.00 ┆ ┆ ┆ │
│ BTC-USD ┆ USD ┆ ┆ 77,467.06 ┆ … ┆ - ┆ ┆ 20,023,07 ┆ https://c │
│ ┆ ┆ ┆ - ┆ ┆ ┆ ┆ 8.00 ┆ oinmarket │
│ ┆ ┆ ┆ 76,306.55 ┆ ┆ ┆ ┆ ┆ 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 ┆ 72.400543 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-03 ┆ AAPL ┆ 74.287498 ┆ 75.144997 ┆ 74.125 ┆ 74.357498 ┆ 1.463228e ┆ 71.69664 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-06 ┆ AAPL ┆ 73.447502 ┆ 74.989998 ┆ 73.1875 ┆ 74.949997 ┆ 1.183872e ┆ 72.267937 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-07 ┆ AAPL ┆ 74.959999 ┆ 75.224998 ┆ 74.370003 ┆ 74.597504 ┆ 1.08872e8 ┆ 71.928062 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2020-01-08 ┆ AAPL ┆ 74.290001 ┆ 76.110001 ┆ 74.290001 ┆ 75.797501 ┆ 1.320792e ┆ 73.085121 │
│ 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_892, 17)
┌────────────┬───────────┬──────┬────────┬───┬─────────────┬─────────────┬────────────┬────────────┐
│ expiration ┆ ttm ┆ type ┆ symbol ┆ … ┆ contractSiz ┆ lastTradeDa ┆ impliedVol ┆ inTheMoney │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ e ┆ te ┆ atility ┆ --- │
│ str ┆ f64 ┆ str ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ bool │
│ ┆ ┆ ┆ ┆ ┆ str ┆ datetime[ms ┆ f64 ┆ │
│ ┆ ┆ ┆ ┆ ┆ ┆ ] ┆ ┆ │
╞════════════╪═══════════╪══════╪════════╪═══╪═════════════╪═════════════╪════════════╪════════════╡
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2026-04-28 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 15:31:58 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:42:52 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:49:19 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:41:59 ┆ ┆ │
│ 2026-05-01 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:50:13 ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2026-04-28 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:30:50 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 18:11:49 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2026-04-28 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:48:47 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 13:56:11 ┆ ┆ │
│ 2028-12-15 ┆ 31.504599 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2026-04-30 ┆ 0.00001 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 13:56:11 ┆ ┆ │
└────────────┴───────────┴──────┴────────┴───┴─────────────┴─────────────┴────────────┴────────────┘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).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 = tickers.get_income_statement(frequency="quarterly")
print(income_statement)Error Fetching Data for BTC-USD: fetching income statement for ticker 'BTC-USD' (Quarterly): not found: "asOfDate" not found
Unable to stack shape: (13, 7)
┌─────────────────────────────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ symbol ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 ┆ 2026Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Revenue ┆ MSFT ┆ 7.0066e10 ┆ 7.6441e10 ┆ 7.7673e10 ┆ 8.1273e10 ┆ 8.2886e10 │
│ Cost of Goods Sold ┆ MSFT ┆ 2.1919e10 ┆ 2.4014e10 ┆ 2.4043e10 ┆ 2.5978e10 ┆ 2.6828e10 │
│ Gross Profit ┆ MSFT ┆ 4.8147e10 ┆ 5.2427e10 ┆ 5.3630e10 ┆ 5.5295e10 ┆ 5.6058e10 │
│ Operating Expenses ┆ MSFT ┆ 1.6147e10 ┆ 1.8104e10 ┆ 1.5669e10 ┆ 1.7020e10 ┆ 1.7660e10 │
│ EBITDA ┆ MSFT ┆ 3.9705e10 ┆ 4.4434e10 ┆ 4.8060e10 ┆ 5.8180e10 ┆ 5.0285e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Interest Expense ┆ MSFT ┆ 5.94e8 ┆ 6.15e8 ┆ 6.98e8 ┆ 7.36e8 ┆ 7.78e8 │
│ Income Tax Expense ┆ MSFT ┆ 5.5530e9 ┆ 5.3830e9 ┆ 6.5540e9 ┆ 9.7880e9 ┆ 7.5620e9 │
│ Net Income ┆ MSFT ┆ 2.5824e10 ┆ 2.7233e10 ┆ 2.7747e10 ┆ 3.8458e10 ┆ 3.1778e10 │
│ Earnings per Share - Basic ┆ MSFT ┆ 3.47 ┆ 3.66 ┆ 3.73 ┆ 5.18 ┆ 4.28 │
│ Earnings per Share - ┆ MSFT ┆ 3.46 ┆ 3.65 ┆ 3.72 ┆ 5.16 ┆ 4.27 │
│ Diluted ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────────────────────────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘: lengths don't match: unable to vstack, column names don't match: "2024Q4" and "2025Q1"shape: (37, 7)
┌─────────────────────────────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ symbol ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Revenue ┆ AAPL ┆ 1.2430e11 ┆ 9.5359e10 ┆ 9.4036e10 ┆ 1.0247e11 ┆ 1.4376e11 │
│ Cost of Goods Sold ┆ AAPL ┆ 6.6025e10 ┆ 5.0492e10 ┆ 5.0318e10 ┆ 5.4125e10 ┆ 7.4525e10 │
│ Gross Profit ┆ AAPL ┆ 5.8275e10 ┆ 4.4867e10 ┆ 4.3718e10 ┆ 4.8341e10 ┆ 6.9231e10 │
│ Operating Expenses ┆ AAPL ┆ 1.5443e10 ┆ 1.5278e10 ┆ 1.5516e10 ┆ 1.5914e10 ┆ 1.8379e10 │
│ EBITDA ┆ AAPL ┆ 4.5912e10 ┆ 3.2250e10 ┆ 3.1032e10 ┆ 3.5554e10 ┆ 5.4066e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Interest Expense ┆ NVDA ┆ 6.1e7 ┆ 6.3e7 ┆ 6.2e7 ┆ 6.1e7 ┆ 7.3e7 │
│ Income Tax Expense ┆ NVDA ┆ 3.1260e9 ┆ 3.1350e9 ┆ 4.7840e9 ┆ 6.0260e9 ┆ 7.4380e9 │
│ Net Income ┆ NVDA ┆ 2.2091e10 ┆ 1.8775e10 ┆ 2.6422e10 ┆ 3.1910e10 ┆ 4.2960e10 │
│ Earnings per Share - Basic ┆ NVDA ┆ 0.9 ┆ 0.77 ┆ 1.08 ┆ 1.31 ┆ 1.77 │
│ Earnings per Share - ┆ NVDA ┆ 0.89 ┆ 0.76 ┆ 1.08 ┆ 1.3 ┆ 1.76 │
│ Diluted ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────────────────────────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
get_balance_sheet
Fetch the balance sheet for all tickers.
Parameters:
frequency(str): The frequency of the data (annual or 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 = tickers.get_balance_sheet(frequency="quarterly")
print(balance_sheet)shape: (64, 7)
┌─────────────────────────────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ symbol ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Cash and Cash Equivalents ┆ AAPL ┆ 3.0299e10 ┆ 2.8162e10 ┆ 3.6269e10 ┆ 3.5934e10 ┆ 4.5317e10 │
│ Accounts Receivable ┆ AAPL ┆ 2.9639e10 ┆ 2.6136e10 ┆ 2.7557e10 ┆ 3.9777e10 ┆ 3.9921e10 │
│ Inventories ┆ AAPL ┆ 6.9110e9 ┆ 6.2690e9 ┆ 5.9250e9 ┆ 5.7180e9 ┆ 5.8750e9 │
│ Other Current Assets ┆ AAPL ┆ 1.3248e10 ┆ 1.4109e10 ┆ 1.4359e10 ┆ 1.4585e10 ┆ 1.5002e10 │
│ Total Current Assets ┆ AAPL ┆ 1.3324e11 ┆ 1.1867e11 ┆ 1.2249e11 ┆ 1.4796e11 ┆ 1.5810e11 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Total Liabilities ┆ NVDA ┆ 3.2274e10 ┆ 4.1411e10 ┆ 4.0609e10 ┆ 4.2251e10 ┆ 4.9510e10 │
│ Common Stock ┆ NVDA ┆ 2.4e7 ┆ 2.4e7 ┆ 2.4e7 ┆ 2.4e7 ┆ 2.4e7 │
│ Retained Earnings ┆ NVDA ┆ 6.8038e10 ┆ 7.2158e10 ┆ 8.8737e10 ┆ 1.0791e11 ┆ 1.4697e11 │
│ Total Equity ┆ NVDA ┆ 7.9327e10 ┆ 8.3843e10 ┆ 1.0013e11 ┆ 1.1890e11 ┆ 1.5729e11 │
│ Total Liabilities and ┆ NVDA ┆ 7.9327e10 ┆ 8.3843e10 ┆ 1.0013e11 ┆ 1.1890e11 ┆ 1.5729e11 │
│ Equity ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────────────────────────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘Error Fetching Data for BTC-USD: fetching balance sheet for ticker 'BTC-USD' (Quarterly): not found: "asOfDate" not found
Unable to stack shape: (22, 7)
┌─────────────────────────────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ symbol ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 ┆ 2026Q1 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Cash and Cash Equivalents ┆ MSFT ┆ 2.8828e10 ┆ 3.0242e10 ┆ 2.8849e10 ┆ 2.4296e10 ┆ 3.2105e10 │
│ Accounts Receivable ┆ MSFT ┆ 5.1700e10 ┆ 6.9905e10 ┆ 5.2894e10 ┆ 5.6535e10 ┆ 6.0041e10 │
│ Inventories ┆ MSFT ┆ 8.48e8 ┆ 9.38e8 ┆ 1.1300e9 ┆ 1.0590e9 ┆ 1.2190e9 │
│ Other Current Assets ┆ MSFT ┆ 2.4478e10 ┆ 2.5723e10 ┆ 3.3030e10 ┆ 3.3134e10 ┆ 3.5797e10 │
│ Total Current Assets ┆ MSFT ┆ 1.5664e11 ┆ 1.9113e11 ┆ 1.8907e11 ┆ 1.8019e11 ┆ 1.7533e11 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Total Liabilities ┆ MSFT ┆ 2.4073e11 ┆ 2.7552e11 ┆ 2.7328e11 ┆ 2.7443e11 ┆ 2.7986e11 │
│ Common Stock ┆ MSFT ┆ 1.0696e11 ┆ 1.0910e11 ┆ 1.1096e11 ┆ 1.1279e11 ┆ 1.1507e11 │
│ Retained Earnings ┆ MSFT ┆ 2.1976e11 ┆ 2.3773e11 ┆ 2.5487e11 ┆ 2.8079e11 ┆ 3.0253e11 │
│ Total Equity ┆ MSFT ┆ 3.2189e11 ┆ 3.4348e11 ┆ 3.6308e11 ┆ 3.9088e11 ┆ 4.1437e11 │
│ Total Liabilities and ┆ MSFT ┆ 3.2189e11 ┆ 3.4348e11 ┆ 3.6308e11 ┆ 3.9088e11 ┆ 4.1437e11 │
│ Equity ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────────────────────────┴────────┴───────────┴───────────┴───────────┴───────────┴───────────┘: lengths don't match: unable to vstack, column names don't match: "2024Q4" and "2025Q1"
get_cashflow_statement
Fetch the cash flow statement for all tickers.
Parameters:
frequency(str): The frequency of the data (annual or quarterly).formatted(optional[bool]): Whether to return the data in a formatted manner, defaults to True.
Returns:
DataFrame: Polars DataFrame containing the cash flow statement.
Example:
cashflow_statement = tickers.get_cashflow_statement(frequency="quarterly")
print(cashflow_statement)shape: (82, 7)
┌────────────────────────┬────────┬────────────┬────────────┬────────────┬────────────┬────────────┐
│ Items ┆ symbol ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════════════════════╪════════╪════════════╪════════════╪════════════╪════════════╪════════════╡
│ Net Income from ┆ AAPL ┆ 3.6330e10 ┆ 2.4780e10 ┆ 2.3434e10 ┆ 2.7466e10 ┆ 4.2097e10 │
│ Continuing Ope… ┆ ┆ ┆ ┆ ┆ ┆ │
│ Depreciation, ┆ AAPL ┆ 3.0800e9 ┆ 2.6610e9 ┆ 2.8300e9 ┆ 3.1270e9 ┆ 3.2140e9 │
│ Amortization, an… ┆ ┆ ┆ ┆ ┆ ┆ │
│ Stock-Based ┆ AAPL ┆ 3.2860e9 ┆ 3.2260e9 ┆ 3.1680e9 ┆ 3.1830e9 ┆ 3.5940e9 │
│ Compensation ┆ ┆ ┆ ┆ ┆ ┆ │
│ Changes in Working ┆ AAPL ┆ -1.0752e10 ┆ -6.5070e9 ┆ -2.0340e9 ┆ -5.7070e9 ┆ 5.5480e9 │
│ Capital ┆ ┆ ┆ ┆ ┆ ┆ │
│ Cash Flow from ┆ AAPL ┆ 2.9935e10 ┆ 2.3952e10 ┆ 2.7867e10 ┆ 2.9728e10 ┆ 5.3925e10 │
│ Continuing Oper… ┆ ┆ ┆ ┆ ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Cash Flow ┆ NVDA ┆ 1.6629e10 ┆ 2.7414e10 ┆ 1.5365e10 ┆ 2.3751e10 ┆ 3.6188e10 │
│ Investing Cash Flow ┆ NVDA ┆ -7.1980e9 ┆ -5.2160e9 ┆ -7.1270e9 ┆ -9.0240e9 ┆ -3.0861e10 │
│ Financing Cash Flow ┆ NVDA ┆ -9.9490e9 ┆ -1.5553e10 ┆ -1.1833e10 ┆ -1.4880e10 ┆ -6.2080e9 │
│ Ending Cash Position ┆ NVDA ┆ 8.5890e9 ┆ 1.5234e10 ┆ 1.1639e10 ┆ 1.1486e10 ┆ 1.0605e10 │
│ Free Cash Flow ┆ NVDA ┆ 1.5552e10 ┆ 2.6187e10 ┆ 1.3470e10 ┆ 2.2115e10 ┆ 3.4904e10 │
└────────────────────────┴────────┴────────────┴────────────┴────────────┴────────────┴────────────┘Error Fetching Data for BTC-USD: fetching cashflow statement for ticker 'BTC-USD' (Quarterly): not found: "asOfDate" not found
Unable to stack shape: (31, 6)
┌─────────────────────────────────┬────────┬────────┬────────┬────────┬────────┐
│ Items ┆ symbol ┆ 2024Q3 ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════════╪════════╪════════╪════════╪════════╪════════╡
│ Net Income from Continuing Ope… ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Depreciation, Amortization, an… ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Stock-Based Compensation ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Deferred Income Tax ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Changes in Working Capital ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Cash Flow ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Investing Cash Flow ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Financing Cash Flow ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Ending Cash Position ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
│ Free Cash Flow ┆ MSFT ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │
└─────────────────────────────────┴────────┴────────┴────────┴────────┴────────┘: lengths don't match: unable to append to a DataFrame of width 7 with a DataFrame of width 6
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 MSFT: fetching financial ratios for ticker 'MSFT' (Quarterly): lengths don't match: could not create a new DataFrame: height of column 'Quick Ratio' (4) does not match height of column 'date' (5)
Error Fetching Data for BTC-USD: fetching financial ratios for ticker 'BTC-USD' (Quarterly): not found: "asOfDate" not foundshape: (63, 7)
┌─────────────────────────┬────────┬──────────┬──────────┬──────────┬──────────┬──────────┐
│ Items ┆ symbol ┆ 2024Q4 ┆ 2025Q1 ┆ 2025Q2 ┆ 2025Q3 ┆ 2025Q4 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════╪════════╪══════════╪══════════╪══════════╪══════════╪══════════╡
│ Gross Profit Margin ┆ AAPL ┆ 0.468825 ┆ 0.470506 ┆ 0.464907 ┆ 0.471776 ┆ 0.481587 │
│ Operating Profit Margin ┆ AAPL ┆ 0.344586 ┆ 0.310291 ┆ 0.299906 ┆ 0.316466 ┆ 0.353738 │
│ Net Profit Margin ┆ AAPL ┆ 0.292277 ┆ 0.25986 ┆ 0.249202 ┆ 0.26805 ┆ 0.292836 │
│ Return on Assets ┆ AAPL ┆ 0.105584 ┆ 0.074811 ┆ 0.070692 ┆ 0.076456 ┆ 0.110987 │
│ Return on Equity ┆ AAPL ┆ 0.544204 ┆ 0.37098 ┆ 0.355978 ┆ 0.372506 ┆ 0.477344 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Price to Earnings ┆ NVDA ┆ 3.974017 ┆ 4.916485 ┆ 4.110098 ┆ 3.960044 ┆ 3.835242 │
│ Price to Book ┆ NVDA ┆ 1.106685 ┆ 1.100951 ┆ 1.084549 ┆ 1.062811 ┆ 1.047485 │
│ Price to Sales ┆ NVDA ┆ 2.232082 ┆ 2.094934 ┆ 2.323278 ┆ 2.216696 ┆ 2.418454 │
│ Price to Cashflow ┆ NVDA ┆ 5.279331 ┆ 3.367148 ┆ 7.067816 ┆ 5.320408 ┆ 4.552946 │
│ Price to Free Cashflow ┆ NVDA ┆ 5.644933 ┆ 3.524917 ┆ 8.062138 ┆ 5.713995 ┆ 4.720433 │
└─────────────────────────┴────────┴──────────┴──────────┴──────────┴──────────┴──────────┘
returns
Compute the returns for all tickers.
Returns:
DataFrame: Polars DataFrame containing the returns.
Example:
returns = tickers.returns()
print(returns)shape: (1_460, 6)
┌─────────────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ timestamp ┆ AAPL ┆ GOOG ┆ MSFT ┆ NVDA ┆ BTC-USD │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ 2020-01-02 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ -0.029819 │
│ 2020-01-03 00:00:00 ┆ -0.009722 ┆ -0.004907 ┆ -0.012452 ┆ -0.016006 ┆ 0.051452 │
│ 2020-01-04 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.008955 │
│ 2020-01-05 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.000089 │
│ 2020-01-06 00:00:00 ┆ 0.007968 ┆ 0.024657 ┆ 0.002585 ┆ 0.004194 ┆ 0.048291 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2023-12-27 00:00:00 ┆ 0.000518 ┆ -0.009662 ┆ -0.001575 ┆ 0.0028 ┆ 0.021694 │
│ 2023-12-28 00:00:00 ┆ 0.002226 ┆ -0.001131 ┆ 0.003235 ┆ 0.002125 ┆ -0.01876 │
│ 2023-12-29 00:00:00 ┆ -0.005424 ┆ -0.002477 ┆ 0.002025 ┆ 0.0 ┆ -0.012397 │
│ 2023-12-30 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.001366 │
│ 2023-12-31 00:00:00 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.0 ┆ 0.002569 │
└─────────────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘
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 ┆ 0.00118656 ┆ 0.0211459 ┆ 1.6318924 ┆ … ┆ 1.1070598 ┆ 0.3142726 ┆ -0.032405 ┆ -0.046854 │
│ ┆ 6850365219 ┆ 259594542 ┆ 845126603 ┆ ┆ 349836818 ┆ 810924572 ┆ 508478040 ┆ 813670815 │
│ ┆ ┆ 27 ┆ ┆ ┆ ┆ ┆ 36 ┆ 725 │
│ GOOG ┆ 0.00094234 ┆ 0.0210803 ┆ 1.0613296 ┆ … ┆ 0.6000157 ┆ 0.4460184 ┆ -0.031889 ┆ -0.047746 │
│ ┆ 3635566264 ┆ 787569822 ┆ 874606246 ┆ ┆ 747702262 ┆ 350698127 ┆ 474979566 ┆ 540783610 │
│ ┆ 1 ┆ 38 ┆ ┆ ┆ ┆ 6 ┆ 99 ┆ 084 │
│ MSFT ┆ 0.00109462 ┆ 0.0205458 ┆ 1.4295204 ┆ … ┆ 0.8536311 ┆ 0.3714848 ┆ -0.029481 ┆ -0.045188 │
│ ┆ 7430786214 ┆ 031177071 ┆ 571347915 ┆ ┆ 048592007 ┆ 217046439 ┆ 746711654 ┆ 332855100 │
│ ┆ 4 ┆ 2 ┆ ┆ ┆ ┆ ┆ 32 ┆ 787 │
│ NVDA ┆ 0.00268455 ┆ 0.0341607 ┆ 7.2890281 ┆ … ┆ 1.4532078 ┆ 0.6633509 ┆ -0.051182 ┆ -0.069626 │
│ ┆ 057813385 ┆ 050166645 ┆ 08118632 ┆ ┆ 66307739 ┆ 955832535 ┆ 721681628 ┆ 392088001 │
│ ┆ ┆ 6 ┆ ┆ ┆ ┆ ┆ 67 ┆ 73 │
│ BTC-USD ┆ 0.00183248 ┆ 0.0347987 ┆ 4.8700228 ┆ … ┆ 1.2407104 ┆ 0.7663456 ┆ -0.051000 ┆ -0.079813 │
│ ┆ 8513521362 ┆ 535807811 ┆ 13710888 ┆ ┆ 197326326 ┆ 370856909 ┆ 981516212 ┆ 265012581 │
│ ┆ 8 ┆ 9 ┆ ┆ ┆ ┆ ┆ 96 ┆ 07 │
└─────────┴────────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘
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 (Optional[str]): The type of report to generate (“performance”).
- display (Optional[str]): The display mode for the report. set to “notebook” for Jupyter Notebook display, defaults to “browser”.
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:max_sharpe: Maximize return per unit of risk.min_vol: Minimize overall volatility.max_return: Maximize expected return.min_var: Minimize Value-at-Risk (VaR).min_cvar: Minimize Conditional Value-at-Risk (CVaR).min_drawdown: Minimize maximum portfolio drawdown.
asset_constraints(Optional[List[Tuple[float, float]]]): List of tuples with the lower and upper bounds for the ticker weights.categorical_constraints(Optional[List[Tuple[str, List[str], List[Tuple[str, float, float]]]]]): Category-based constraints. Each tuple has the form:category_name(str): Name of the constraint group (e.g.,"AssetClass").category_per_symbol(List[str]): Assigns each ticker to a category, in the same order asticker_symbols.weight_per_category(List[Tuple[str, float, float]]): Tuples of(category_label, min_weight, max_weight)specifying bounds for each category.
weights(Optional[List[float]]): Weights for asset allocation. If provided, overrides the optimization process.
Returns:
Portfolio: A Portfolio object.
Example:
portfolio = tickers.optimize(objective_function="max_sharpe")
portfolio.performance_chart().show()