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, 29)
┌─────────┬────────────┬────────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐
│ symbol ┆ long_name ┆ full_excha ┆ currency ┆ … ┆ book_valu ┆ price_to_ ┆ market_ca ┆ shares_ou │
│ --- ┆ --- ┆ nge_name ┆ --- ┆ ┆ e ┆ book ┆ p ┆ tstanding │
│ str ┆ str ┆ --- ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ ┆ ┆ str ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════╪════════════╪════════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡
│ AAPL ┆ Apple Inc. ┆ NasdaqGS ┆ USD ┆ … ┆ 3.767 ┆ 64.427925 ┆ 3.6686e12 ┆ 1.5116e10 │
│ GOOG ┆ Alphabet ┆ NasdaqGS ┆ USD ┆ … ┆ 25.613 ┆ 7.6285477 ┆ 2.3821e12 ┆ 5.5340e9 │
│ ┆ Inc. ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ MSFT ┆ Microsoft ┆ NasdaqGS ┆ USD ┆ … ┆ 38.693 ┆ 10.972527 ┆ 3.1566e12 ┆ 7.4349e9 │
│ ┆ Corporatio ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ ┆ n ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ NVDA ┆ NVIDIA Cor ┆ NasdaqGS ┆ USD ┆ … ┆ 1.744 ┆ 80.3383 ┆ 3.4313e12 ┆ 2.4490e10 │
│ ┆ poration ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ BTC-USD ┆ Bitcoin ┆ CCC ┆ USD ┆ … ┆ 0.0 ┆ 0.0 ┆ 1.8102e12 ┆ 0.0 │
│ ┆ USD ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────┴────────────┴────────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘
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 ┆ 72.796013 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-03 ┆ AAPL ┆ 74.287498 ┆ 75.144997 ┆ 74.125 ┆ 74.357498 ┆ 1.463228e ┆ 72.088318 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-06 ┆ AAPL ┆ 73.447502 ┆ 74.989998 ┆ 73.1875 ┆ 74.949997 ┆ 1.183872e ┆ 72.66272 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ │
│ 2020-01-07 ┆ AAPL ┆ 74.959999 ┆ 75.224998 ┆ 74.370003 ┆ 74.597504 ┆ 1.08872e8 ┆ 72.320969 │
│ 00:00:00 ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ 2020-01-08 ┆ AAPL ┆ 74.290001 ┆ 76.110001 ┆ 74.290001 ┆ 75.797501 ┆ 1.320792e ┆ 73.48436 │
│ 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: (14_176, 17)
┌────────────┬───────────┬──────┬────────┬───┬─────────────┬─────────────┬────────────┬────────────┐
│ expiration ┆ ttm ┆ type ┆ symbol ┆ … ┆ contractSiz ┆ lastTradeDa ┆ impliedVol ┆ inTheMoney │
│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ e ┆ te ┆ atility ┆ --- │
│ str ┆ f64 ┆ str ┆ str ┆ ┆ --- ┆ --- ┆ --- ┆ bool │
│ ┆ ┆ ┆ ┆ ┆ str ┆ datetime[ms ┆ f64 ┆ │
│ ┆ ┆ ┆ ┆ ┆ ┆ ] ┆ ┆ │
╞════════════╪═══════════╪══════╪════════╪═══╪═════════════╪═════════════╪════════════╪════════════╡
│ 2025-01-10 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2024-12-27 ┆ 4.718754 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 16:41:06 ┆ ┆ │
│ 2025-01-10 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2024-11-29 ┆ 10.428226 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:01:29 ┆ ┆ │
│ 2025-01-10 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2025-01-02 ┆ 4.242192 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:37:35 ┆ ┆ │
│ 2025-01-10 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2024-12-27 ┆ 4.589848 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 16:29:00 ┆ ┆ │
│ 2025-01-10 ┆ 0.0 ┆ call ┆ AAPL ┆ … ┆ REGULAR ┆ 2024-12-27 ┆ 4.539067 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:03:06 ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 2027-01-15 ┆ 24.145861 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-01-07 ┆ 0.371283 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 15:32:14 ┆ ┆ │
│ 2027-01-15 ┆ 24.145861 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2024-12-16 ┆ 0.360877 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 14:31:51 ┆ ┆ │
│ 2027-01-15 ┆ 24.145861 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-01-07 ┆ 0.363471 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:21:58 ┆ ┆ │
│ 2027-01-15 ┆ 24.145861 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-01-07 ┆ 0.35987 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 19:21:58 ┆ ┆ │
│ 2027-01-15 ┆ 24.145861 ┆ put ┆ NVDA ┆ … ┆ REGULAR ┆ 2025-01-08 ┆ 0.357428 ┆ true │
│ ┆ ┆ ┆ ┆ ┆ ┆ 17:24:09 ┆ ┆ │
└────────────┴───────────┴──────┴────────┴───┴─────────────┴─────────────┴────────────┴────────────┘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: (52, 7)
┌─────────────────────────────┬────────┬───────────┬───────────┬───────────┬───────────┬───────────┐
│ Items ┆ symbol ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Revenue ┆ AAPL ┆ 8.9498e10 ┆ 1.1958e11 ┆ 9.0753e10 ┆ 8.5777e10 ┆ 9.4930e10 │
│ Cost of Goods Sold ┆ AAPL ┆ 4.9071e10 ┆ 6.4720e10 ┆ 4.8482e10 ┆ 4.6099e10 ┆ 5.1051e10 │
│ Gross Profit ┆ AAPL ┆ 4.0427e10 ┆ 5.4855e10 ┆ 4.2271e10 ┆ 3.9678e10 ┆ 4.3879e10 │
│ Operating Expenses ┆ AAPL ┆ 1.3458e10 ┆ 1.4482e10 ┆ 1.4371e10 ┆ 1.4326e10 ┆ 1.4288e10 │
│ EBITDA ┆ AAPL ┆ 3.0653e10 ┆ 4.3221e10 ┆ 3.0736e10 ┆ 2.8202e10 ┆ 3.2502e10 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Interest Expense ┆ NVDA ┆ 6.3e7 ┆ 6.3e7 ┆ 6.4e7 ┆ 6.1e7 ┆ 6.1e7 │
│ Income Tax Expense ┆ NVDA ┆ 1.2790e9 ┆ 1.8210e9 ┆ 2.3980e9 ┆ 2.6150e9 ┆ 3.0070e9 │
│ Net Income ┆ NVDA ┆ 9.2430e9 ┆ 1.2285e10 ┆ 1.4881e10 ┆ 1.6599e10 ┆ 1.9309e10 │
│ Earnings per Share - Basic ┆ NVDA ┆ 0.25 ┆ 0.37 ┆ 0.604 ┆ 0.68 ┆ 0.79 │
│ Earnings per Share - ┆ NVDA ┆ 0.25 ┆ 0.37 ┆ 0.598 ┆ 0.67 ┆ 0.78 │
│ 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 ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ Cash and Cash Equivalents ┆ AAPL ┆ 2.9965e10 ┆ 4.0760e10 ┆ 3.2695e10 ┆ 2.5565e10 ┆ 2.9943e10 │
│ Accounts Receivable ┆ AAPL ┆ 2.9508e10 ┆ 2.3194e10 ┆ 2.1837e10 ┆ 2.2795e10 ┆ 3.3410e10 │
│ Inventories ┆ AAPL ┆ 6.3310e9 ┆ 6.5110e9 ┆ 6.2320e9 ┆ 6.1650e9 ┆ 7.2860e9 │
│ Other Current Assets ┆ AAPL ┆ 1.4695e10 ┆ 1.3979e10 ┆ 1.3884e10 ┆ 1.4297e10 ┆ 1.4287e10 │
│ Total Current Assets ┆ AAPL ┆ 1.4357e11 ┆ 1.4369e11 ┆ 1.2842e11 ┆ 1.2544e11 ┆ 1.5299e11 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Total Liabilities ┆ NVDA ┆ 2.0883e10 ┆ 2.2750e10 ┆ 2.7930e10 ┆ 2.7070e10 ┆ 3.0114e10 │
│ Common Stock ┆ NVDA ┆ 2e6 ┆ 2e6 ┆ 2e6 ┆ 2.5e7 ┆ 2.5e7 │
│ Retained Earnings ┆ NVDA ┆ 2.0360e10 ┆ 2.9817e10 ┆ 3.6598e10 ┆ 4.5961e10 ┆ 5.3950e10 │
│ Total Equity ┆ NVDA ┆ 3.3265e10 ┆ 4.2978e10 ┆ 4.9142e10 ┆ 5.8157e10 ┆ 6.5899e10 │
│ Total Liabilities and ┆ NVDA ┆ 3.3265e10 ┆ 4.2978e10 ┆ 4.9142e10 ┆ 5.8157e10 ┆ 6.5899e10 │
│ 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 ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═══════════════════════════╪════════╪═══════════╪═══════════╪═══════════╪════════════╪════════════╡
│ Net Income from ┆ AAPL ┆ 2.2956e10 ┆ 3.3916e10 ┆ 2.3636e10 ┆ 2.1448e10 ┆ 1.4736e10 │
│ Continuing Ope… ┆ ┆ ┆ ┆ ┆ ┆ │
│ Depreciation, ┆ AAPL ┆ 2.6530e9 ┆ 2.8480e9 ┆ 2.8360e9 ┆ 2.8500e9 ┆ 2.9110e9 │
│ Amortization, an… ┆ ┆ ┆ ┆ ┆ ┆ │
│ Stock-Based Compensation ┆ AAPL ┆ 2.6250e9 ┆ 2.9970e9 ┆ 2.9640e9 ┆ 2.8690e9 ┆ 2.8580e9 │
│ Changes in Working ┆ AAPL ┆ -6.0600e9 ┆ 1.1230e9 ┆ -5.7640e9 ┆ 1.6840e9 ┆ 6.6080e9 │
│ Capital ┆ ┆ ┆ ┆ ┆ ┆ │
│ Cash Flow from Continuing ┆ AAPL ┆ 2.1598e10 ┆ 3.9895e10 ┆ 2.2690e10 ┆ 2.8858e10 ┆ 2.6811e10 │
│ Oper… ┆ ┆ ┆ ┆ ┆ ┆ │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Operating Cash Flow ┆ NVDA ┆ 7.3320e9 ┆ 1.1499e10 ┆ 1.5345e10 ┆ 1.4488e10 ┆ 1.7627e10 │
│ Investing Cash Flow ┆ NVDA ┆ -3.1700e9 ┆ -6.1090e9 ┆ -5.6930e9 ┆ -3.1840e9 ┆ -4.3460e9 │
│ Financing Cash Flow ┆ NVDA ┆ -4.5250e9 ┆ -3.6290e9 ┆ -9.3450e9 ┆ -1.0320e10 ┆ -1.2745e10 │
│ Ending Cash Position ┆ NVDA ┆ 5.5190e9 ┆ 7.2800e9 ┆ 7.5870e9 ┆ 8.5710e9 ┆ 9.1070e9 │
│ Free Cash Flow ┆ NVDA ┆ 7.0540e9 ┆ 1.1245e10 ┆ 1.4976e10 ┆ 1.3511e10 ┆ 1.6814e10 │
└───────────────────────────┴────────┴───────────┴───────────┴───────────┴────────────┴────────────┘
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 BTC-USD: not found: asOfDateshape: (84, 7)
┌─────────────────────────┬────────┬──────────┬──────────┬──────────┬──────────┬──────────┐
│ Items ┆ symbol ┆ 2023Q3 ┆ 2023Q4 ┆ 2024Q1 ┆ 2024Q2 ┆ 2024Q3 │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞═════════════════════════╪════════╪══════════╪══════════╪══════════╪══════════╪══════════╡
│ Gross Profit Margin ┆ AAPL ┆ 0.451708 ┆ 0.45875 ┆ 0.465781 ┆ 0.462572 ┆ 0.462225 │
│ Operating Profit Margin ┆ AAPL ┆ 0.312856 ┆ 0.337637 ┆ 0.307428 ┆ 0.295557 ┆ 0.311714 │
│ Net Profit Margin ┆ AAPL ┆ 0.256497 ┆ 0.283638 ┆ 0.260443 ┆ 0.250044 ┆ 0.15523 │
│ Return on Assets ┆ AAPL ┆ 0.065108 ┆ 0.09594 ┆ 0.070051 ┆ 0.064678 ┆ 0.040375 │
│ Return on Equity ┆ AAPL ┆ 0.369388 ┆ 0.457706 ┆ 0.31857 ┆ 0.321521 ┆ 0.258753 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ Price to Earnings ┆ NVDA ┆ 4.513902 ┆ 4.186976 ┆ 3.870842 ┆ 4.013374 ┆ 3.851106 │
│ Price to Book ┆ NVDA ┆ 1.254231 ┆ 1.196822 ┆ 1.172154 ┆ 1.145485 ┆ 1.128409 │
│ Price to Sales ┆ NVDA ┆ 2.302539 ┆ 2.32715 ┆ 2.211719 ┆ 2.217643 ┆ 2.119634 │
│ Price to Cashflow ┆ NVDA ┆ 5.690398 ┆ 4.473172 ┆ 3.753796 ┆ 4.59815 ┆ 4.218585 │
│ Price to Free Cashflow ┆ NVDA ┆ 5.914658 ┆ 4.574211 ┆ 3.846287 ┆ 4.930649 ┆ 4.422565 │
└─────────────────────────┴────────┴──────────┴──────────┴──────────┴──────────┴──────────┘
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 ┆ -0.972162 ┆ -0.490721 ┆ -1.24518 ┆ -1.600576 ┆ 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 ┆ 0.051795 ┆ -0.966242 ┆ -0.157471 ┆ 0.280041 ┆ 2.169436 │
│ 2023-12-28 00:00:00 ┆ 0.222624 ┆ -0.113131 ┆ 0.323461 ┆ 0.212483 ┆ -1.876028 │
│ 2023-12-29 00:00:00 ┆ -0.542401 ┆ -0.24773 ┆ 0.202509 ┆ 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 ┆ Cumulative ┆ … ┆ Calmar ┆ Maximum ┆ Value at ┆ Expected │
│ --- ┆ Return ┆ Volatility ┆ Return ┆ ┆ Ratio ┆ Drawdown ┆ Risk ┆ Shortfall │
│ str ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │
│ ┆ str ┆ str ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │
╞═════════╪════════╪═════════════╪═════════════╪═══╪════════╪════════════╪════════════╪════════════╡
│ AAPL ┆ 0.12% ┆ 2.11% ┆ 163.19% ┆ … ┆ 1.04 ┆ 33.53% ┆ -3.24% ┆ -4.71% │
│ GOOG ┆ 0.09% ┆ 2.11% ┆ 106.13% ┆ … ┆ 0.51 ┆ 52.36% ┆ -3.19% ┆ -4.81% │
│ MSFT ┆ 0.11% ┆ 2.05% ┆ 142.95% ┆ … ┆ 0.77 ┆ 41.01% ┆ -2.95% ┆ -4.55% │
│ NVDA ┆ 0.27% ┆ 3.41% ┆ 728.90% ┆ … ┆ 1.06 ┆ 91.15% ┆ -5.13% ┆ -7.00% │
│ BTC-USD ┆ 0.18% ┆ 3.48% ┆ 487.00% ┆ … ┆ 0.48 ┆ 122.82% ┆ -5.10% ┆ -7.98% │
└─────────┴────────┴─────────────┴─────────────┴───┴────────┴────────────┴────────────┴────────────┘
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()