yahoors 0.1.92

A python wrapper for Yahoo Finance using rust for the backend.

YahooRS

YahooRS is a Python-based utility for fetching and managing Yahoo Finance data, leveraging DuckDB for efficient local storage and Polars for high-performance data manipulation. It provides tools for candle data, option chains (with Greeks), earnings data, and financial statements.

Features

• Historical Price Data (Candles): Fetch and store historical price data with configurable intervals and periods. Includes automated staleness detection and local caching via collected_at timestamps — data is only re-downloaded when genuinely stale, not on every call.
• Options Analysis: Download full option chains with real-time Greeks (Delta, Gamma, Theta, Vega), Black-Scholes pricing, and probability of profit calculations (both BS-derived and historical). Supports filtering by DTE range, bid/ask minimums, option type, and long/short side.
• Options Screener: Ready-to-use strategies including cash_secured_puts and a general options_screener with yield metrics (premium, ROC, annualized ROC, collateral, expected return).
• Earnings Data: Earnings dates, EPS estimates, and history with automatic staleness handling. Gracefully handles tickers with no earnings data (ETFs, etc.).
• Financial Statements: Retrieve annual and quarterly income statements, balance sheets, and cash flow statements.
• Financial Ratios & Margins: Automated calculation of key financial metrics such as P/E, P/S, P/B, EV/EBITDA, ROE, and various profit margins.
• Local Database (DuckDB): Persists all fetched data locally to minimize redundant API calls and enable fast offline analysis.
• CLI & Library: Accessible via a command-line interface or directly as a Python library.

Installation

pip install yahoors

Data Storage

By default, YahooRS stores data in a DuckDB database located in your platform's standard configuration directory (e.g., ~/.config/yahoors/ on Linux). You can override this by setting the YAHOO_FINANCE_DB environment variable.

CLI Usage

The package installs a yahoors command with several subcommands:

Fetch Candle Data

yahoors get-candles AAPL MSFT --interval 1d --range 1y

Options Screener

yahoors options-screener -s AAPL --min-dte 30 --max-dte 60

Financial Statements

yahoors statements AAPL --statement-type income --annual --ratios

Library Usage

Candle Data

from yahoors import Candles

candles = Candles()

# Fetch historical data (cached — only downloads when stale)
df = candles.get_candles(["AAPL", "MSFT"], interval="1d")

# Get the latest closing price without loading full history
prices = candles.get_last_price(["AAPL", "MSFT"])
# {"AAPL": 189.30, "MSFT": 415.20}

Options

from yahoors import Options

options = Options()

# Full option chain with Greeks and probability metrics
df = options.get_options(["AAPL"])

# Filter by DTE range with side-aware probability of profit
df = options.get_options_by_dte_range(
    ["AAPL", "MSFT"],
    min_dte=1,
    max_dte=10,
    option_type="put",   # "call", "put", or "*"
    side="short",        # "long", "short", or "*" — inverts prob_profit for short positions
    min_bid=0.10,        # filter illiquid contracts
)

Options Screener

from yahoors.modules.screener import cash_secured_puts, options_screener

# Ready-to-use cash-secured put screener
# Returns contracts sorted by expected_return, with yield metrics pre-calculated
df = cash_secured_puts(
    ["AAPL", "MSFT", "AMZN"],
    min_dte=1,
    max_dte=10,
    max_collateral=25_000,   # max capital at risk per contract (strike * 100)
    min_premium=0.10,
    min_roc=0.005,
)
# Columns include: strike, premium, collateral, roc, annualized_roc,
#                  prob_profit, hist_prob_profit, expected_return, dtr, ...

# General screener — pass any options DataFrame
df = options_screener(
    options_df,
    min_dte=0,
    max_dte=30,
    long=False,
    min_collateral=0,
    max_collateral=50_000,
    min_premium=0.10,
    min_roc=0.005,
    max_trade_age=dt.timedelta(hours=2),
)

Earnings

from yahoors import Earnings

earnings = Earnings()

# Upcoming and historical earnings dates
dates_df = earnings.get_earnings_dates(["AAPL", "MSFT"])

# EPS estimates
estimates_df = earnings.get_earnings_estimates(["AAPL"])

# Historical EPS actuals vs estimates
history_df = earnings.get_earnings_history(["AAPL"])

Financial Statements

from yahoors import Statements

statements = Statements()
df = statements.get_statements(["AAPL"], statement_type="income", period="A")

Probability of Profit

YahooRS computes two probability metrics for each contract:

• prob_profit — Black-Scholes derived, using the contract's implied volatility and breakeven price.
• hist_prob_profit — Historical, derived from the actual distribution of past returns over the contract's DTE window.

For side="short", both are automatically inverted (1 - p) so they represent the seller's probability of profit. Contracts where IV cannot be computed (no valid bid/ask/last_price) are returned with null probabilities and are excluded from screener results.

Expected Return

The expected_return column in screener output is computed as:

expected_return = (premium - bs_price) / strike

This represents the edge over fair value — the portion of premium collected above the Black-Scholes theoretical price, normalized by the strike. Positive values indicate you are selling overpriced implied volatility relative to the model.

License

This project is licensed under the MIT License - see the LICENSE file for details.