docs: add API references, mapping corrections, and verification script
- Add yfinance.org and defeatbeta-api.org reference docs - Fix defeatbeta_mapping.org: deprecated yfinance property names (quarterly_financials→quarterly_income_stmt, financials→income_stmt), longName vs longBusinessSummary conceptual mismatch, cashflow note typo - Add Mapping Limitations section with live verification results (AAPL): DuckDB 1.4.3 incompatibility, format differences, coverage gaps - Add docs/test_mapping.py as runnable mapping verification script - Add offline.py, persistent_cache.py, download_data.py, warmup_cache.py for offline/cached defeatbeta usage - Add aapl_yfinance.py exploration script and quant.py scaffold - Add .envrc (uv layout) and update pyproject.toml + uv.lock Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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# AAPL - All yfinance Data
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```python
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import yfinance as yf
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import marimo as mo
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ticker = yf.Ticker("AAPL")
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```
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## Price History
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```python
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history = ticker.history(period="1y", interval="1d")
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mo.ui.table(history.tail(30))
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```
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## Dividends
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```python
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mo.ui.table(ticker.dividends.tail(20))
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```
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## Splits
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```python
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mo.ui.table(ticker.splits)
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```
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## Income Statement
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```python
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mo.ui.table(ticker.get_income_stmt())
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```
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## Balance Sheet
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```python
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mo.ui.table(ticker.get_balance_sheet())
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```
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## Cash Flow
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```python
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mo.ui.table(ticker.get_cashflow())
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```
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## Analyst Recommendations
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```python
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mo.ui.table(ticker.recommendations.tail(20))
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```
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## Analyst Target Prices
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```python
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mo.ui.table([ticker.target_prices])
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```
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## Earnings Dates
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```python
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mo.ui.table(ticker.earnings_dates)
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```
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## Options Expirations
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```python
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expirations = ticker.get_options()
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f"Available: {expirations}"
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```
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```python
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if expirations:
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chain = ticker.option_chain(expirations[0])
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mo.ui.table(chain.calls)
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mo.ui.table(chain.puts)
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```
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## Major Holders
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```python
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mo.ui.table(ticker.major_holders)
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```
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## Institutional Holders
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```python
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mo.ui.table(ticker.institutional_holders)
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```
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## Sustainability / ESG
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```python
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mo.ui.table([ticker.sustainability])
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```
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## Calendar / Events
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```python
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mo.ui.table(ticker.calendar)
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```
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## Company Info Summary
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```python
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info = ticker.info
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{
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"name": info.get("longName"),
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"sector": info.get("sector"),
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"industry": info.get("industry"),
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"marketCap": info.get("marketCap"),
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"peRatio": info.get("trailingPE"),
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"fwdDividend": info.get("dividendYield"),
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"52wkHigh": info.get("fiftyTwoWeekHigh"),
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"52wkLow": info.get("fiftyTwoWeekLow"),
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"avgTarget": info.get("targetMeanPrice"),
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"recommendation": info.get("recommendationKey"),
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}
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```
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import marimo
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__generated_with = "0.23.2"
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app = marimo.App(width="full")
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@app.cell
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def _():
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import yfinance as yf
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import marimo as mo
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ticker = yf.Ticker("AAPL")
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return mo, ticker
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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# AAPL — All Data via yfinance
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""")
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Price History
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""")
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return
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@app.cell
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def _(ticker):
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history = ticker.history(period="max", interval="1d")
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history
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Info
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""")
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return
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@app.cell
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def _(mo, ticker):
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info = ticker.info
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mo.ui.table([info])
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Dividends
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""")
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return
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@app.cell
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def _(ticker):
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dividends = ticker.dividends
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dividends
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Splits
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""")
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return
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@app.cell
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def _(ticker):
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splits = ticker.splits
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splits
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Income Statement
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""")
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return
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@app.cell
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def _(mo, ticker):
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inc = ticker.get_income_stmt()
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mo.ui.table(inc)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Balance Sheet
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""")
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return
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@app.cell
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def _(mo, ticker):
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bal = ticker.get_balance_sheet()
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mo.ui.table(bal)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Cash Flow
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""")
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return
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@app.cell
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def _(mo, ticker):
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cf = ticker.get_cashflow()
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mo.ui.table(cf)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Recommendations
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""")
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return
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@app.cell
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def _(mo, ticker):
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recs = ticker.recommendations
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mo.ui.table(recs)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Analyst Target Prices
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""")
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return
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@app.cell
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def _(mo, ticker):
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target = ticker.target_prices
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mo.ui.table([target] if isinstance(target, dict) else target)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Earnings Dates
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""")
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return
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@app.cell
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def _(mo, ticker):
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earn_dates = ticker.earnings_dates
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mo.ui.table(earn_dates)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Options
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""")
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return
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@app.cell
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def _(mo, ticker):
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expirations = ticker.options
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mo.md(f"Available expirations: {expirations}")
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return (expirations,)
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@app.cell
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def _(expirations, mo, ticker):
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sections = []
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for exp in expirations:
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chain = ticker.option_chain(exp)
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sections += [
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mo.md(f"### {exp} — Calls"),
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mo.ui.table(chain.calls),
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mo.md(f"### {exp} — Puts"),
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mo.ui.table(chain.puts),
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]
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mo.vstack(sections) if sections else None
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Major Holders
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""")
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return
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@app.cell
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def _(mo, ticker):
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major = ticker.major_holders
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mo.ui.table(major)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Institutional Holders
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""")
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return
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@app.cell
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def _(mo, ticker):
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inst = ticker.institutional_holders
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mo.ui.table(inst)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Mutual Fund Holders
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""")
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return
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@app.cell
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def _(mo, ticker):
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mf = ticker.mutualfund_holders
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mo.ui.table(mf)
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Sustainability / ESG
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""")
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return
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@app.cell
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def _(mo, ticker):
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sus = ticker.sustainability
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mo.ui.table([sus] if sus is not None else [{"data": "No ESG data available"}])
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return
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@app.cell(hide_code=True)
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def _(mo):
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mo.md("""
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## Calendar / Events
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""")
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return
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@app.cell
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def _(mo, ticker):
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cal = ticker.calendar
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mo.ui.table([cal] if isinstance(cal, dict) else cal)
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return
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if __name__ == "__main__":
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app.run()
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+1147
-9
File diff suppressed because one or more lines are too long
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#+TITLE: defeatbeta-api Reference
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#+AUTHOR: Wong Ding Feng
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#+DATE: 2026-04-25
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* How Data Retrieval Works
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** NOT a full download
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Uses *DuckDB + ~cache_httpfs~ extension* querying *remote Parquet files on HuggingFace*
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(~defeatbeta/yahoo-finance-data~). Every query runs SQL directly against remote files:
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#+begin_src sql
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SELECT * FROM 'https://huggingface.co/.../stock_prices.parquet' WHERE symbol = 'AAPL'
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#+end_src
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Parquet's columnar format + DuckDB *predicate pushdown* = only the row-groups matching
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your ticker are fetched over HTTP range requests. Not the full 3-4 GB file.
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** On-disk cache
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- Default 1 GB cache at ~~/.defeatbeta/cache/~
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- Stores fetched blocks so repeated queries are fast
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- On startup: checks ~spec.json~ on HuggingFace, clears stale cache if dataset was updated
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* Getting All Available Tickers
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#+begin_src python
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from defeatbeta_api.data.company_meta import CompanyMeta
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meta = CompanyMeta()
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all_tickers = meta.get_all_tickers() # List[str]
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all_companies = meta.get_all_companies_info() # List[dict]: symbol, name, cik, currency
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#+end_src
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Reads ~company_tickers.json~ from HuggingFace — a small JSON, not the big Parquet files.
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* Single Ticker API — ~Ticker("AAPL")~
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#+begin_src python
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from defeatbeta_api.data.ticker import Ticker
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t = Ticker("AAPL")
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#+end_src
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** Company Info
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| Method | Returns | What it gives |
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|----------------------------+-------------+---------------------------------------------------------|
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| ~info()~ | DataFrame | Profile: name, sector, industry, description, headcount |
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| ~officers()~ | DataFrame | Executive officers |
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| ~sec_filing()~ | DataFrame | SEC filings list |
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| ~news()~ | ~News~ object | Latest news articles |
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| ~earning_call_transcripts()~ | ~Transcripts~ | Earnings call transcripts |
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| ~calendar()~ | DataFrame | Upcoming earnings dates |
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** Prices & Basic Finance
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| Method | Returns | What it gives |
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|------------------------------------+-----------+------------------------------|
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| ~price()~ | DataFrame | Historical OHLCV prices |
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| ~splits()~ | DataFrame | Stock split events |
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| ~dividends()~ | DataFrame | Dividend payment history |
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| ~shares()~ | DataFrame | Shares outstanding over time |
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| ~beta(period="5y", benchmark="SPY")~ | DataFrame | Calculated beta vs benchmark |
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| ~currency(symbol)~ | DataFrame | Exchange rate history |
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| ~ttm_eps()~ | DataFrame | Trailing 12-month EPS |
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** Financial Statements
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| Method | Returns | What it gives |
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|------------------------------+-----------+-------------------------|
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| ~quarterly_income_statement()~ | ~Statement~ | Quarterly P&L |
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| ~annual_income_statement()~ | ~Statement~ | Annual P&L |
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| ~quarterly_balance_sheet()~ | ~Statement~ | Quarterly balance sheet |
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| ~annual_balance_sheet()~ | ~Statement~ | Annual balance sheet |
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| ~quarterly_cash_flow()~ | ~Statement~ | Quarterly cash flow |
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| ~annual_cash_flow()~ | ~Statement~ | Annual cash flow |
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** TTM Aggregates
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| Method | Returns | What it gives |
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|----------------------------------------+-----------+----------------------------------|
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| ~ttm_revenue()~ | DataFrame | Trailing 12-month revenue |
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| ~ttm_fcf()~ | DataFrame | Trailing 12-month free cash flow |
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| ~ttm_ebitda()~ | DataFrame | Trailing 12-month EBITDA |
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| ~ttm_net_income_common_stockholders()~ | DataFrame | Trailing 12-month net income |
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| ~ttm_pe()~ | DataFrame | Trailing P/E (price / ttm_eps) |
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** Revenue Breakdown
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| Method | Returns | What it gives |
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|------------------------+-----------+-----------------------------|
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| ~revenue_by_segment()~ | DataFrame | Revenue by business segment |
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| ~revenue_by_geography()~ | DataFrame | Revenue by region |
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| ~revenue_by_product()~ | DataFrame | Revenue by product line |
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** Valuation Multiples
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| Method | Returns | What it gives |
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|-------------------------+-----------+------------------------------|
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| ~market_capitalization()~ | DataFrame | Historical market cap |
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| ~ps_ratio()~ | DataFrame | Price/Sales ratio |
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| ~pb_ratio()~ | DataFrame | Price/Book ratio |
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| ~peg_ratio()~ | DataFrame | PEG ratio |
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| ~enterprise_value()~ | DataFrame | Enterprise value |
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| ~enterprise_to_revenue()~ | DataFrame | EV/Revenue |
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| ~enterprise_to_ebitda()~ | DataFrame | EV/EBITDA |
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| ~debt_to_equity()~ | DataFrame | D/E ratio |
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| ~net_debt_ttm()~ | DataFrame | Net debt (TTM) |
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| ~wacc()~ | DataFrame | Weighted avg cost of capital |
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** Profitability Returns
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| Method | Returns | What it gives |
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|---------------------+-----------+------------------------------------|
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| ~roe()~ | DataFrame | Return on equity |
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| ~roa()~ | DataFrame | Return on assets |
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| ~roic()~ | DataFrame | Return on invested capital |
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| ~roce()~ | DataFrame | Return on capital employed |
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| ~equity_multiplier()~ | DataFrame | Financial leverage (assets/equity) |
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| ~asset_turnover()~ | DataFrame | Revenue/assets efficiency |
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** Margins
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| Method | Returns | What it gives |
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|------------------------------+-----------+--------------------|
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| ~quarterly_gross_margin()~ | DataFrame | Gross margin % |
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| ~annual_gross_margin()~ | DataFrame | Gross margin % |
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| ~quarterly_operating_margin()~ | DataFrame | Operating margin % |
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| ~annual_operating_margin()~ | DataFrame | Operating margin % |
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| ~quarterly_net_margin()~ | DataFrame | Net margin % |
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| ~annual_net_margin()~ | DataFrame | Net margin % |
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| ~quarterly_ebitda_margin()~ | DataFrame | EBITDA margin % |
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| ~annual_ebitda_margin()~ | DataFrame | EBITDA margin % |
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| ~quarterly_fcf_margin()~ | DataFrame | FCF margin % |
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| ~annual_fcf_margin()~ | DataFrame | FCF margin % |
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** YoY Growth
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| Method | Returns | What it gives |
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|-----------------------------------------+-----------+---------------------|
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| ~quarterly_revenue_yoy_growth()~ | DataFrame | Revenue growth % |
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| ~annual_revenue_yoy_growth()~ | DataFrame | Revenue growth % |
|
||||
| ~quarterly_operating_income_yoy_growth()~ | DataFrame | Op. income growth % |
|
||||
| ~annual_operating_income_yoy_growth()~ | DataFrame | Op. income growth % |
|
||||
| ~quarterly_ebitda_yoy_growth()~ | DataFrame | EBITDA growth % |
|
||||
| ~annual_ebitda_yoy_growth()~ | DataFrame | EBITDA growth % |
|
||||
| ~quarterly_net_income_yoy_growth()~ | DataFrame | Net income growth % |
|
||||
| ~annual_net_income_yoy_growth()~ | DataFrame | Net income growth % |
|
||||
| ~quarterly_fcf_yoy_growth()~ | DataFrame | FCF growth % |
|
||||
| ~annual_fcf_yoy_growth()~ | DataFrame | FCF growth % |
|
||||
| ~quarterly_eps_yoy_growth()~ | DataFrame | EPS growth % |
|
||||
| ~quarterly_ttm_eps_yoy_growth()~ | DataFrame | TTM EPS growth % |
|
||||
|
||||
** Industry Comparisons
|
||||
|
||||
Uses the ticker's own industry to benchmark against peers.
|
||||
|
||||
| Method | Returns | What it gives |
|
||||
|------------------------------------+-----------+--------------------------|
|
||||
| ~industry_ttm_pe()~ | DataFrame | Avg P/E across industry |
|
||||
| ~industry_ps_ratio()~ | DataFrame | Industry P/S |
|
||||
| ~industry_pb_ratio()~ | DataFrame | Industry P/B |
|
||||
| ~industry_roe()~ | DataFrame | Industry ROE |
|
||||
| ~industry_roa()~ | DataFrame | Industry ROA |
|
||||
| ~industry_roic()~ | DataFrame | Industry ROIC |
|
||||
| ~industry_equity_multiplier()~ | DataFrame | Industry leverage |
|
||||
| ~industry_asset_turnover()~ | DataFrame | Industry efficiency |
|
||||
| ~industry_quarterly_gross_margin()~ | DataFrame | Industry gross margin % |
|
||||
| ~industry_quarterly_ebitda_margin()~ | DataFrame | Industry EBITDA margin % |
|
||||
| ~industry_quarterly_net_margin()~ | DataFrame | Industry net margin % |
|
||||
|
||||
** DCF / Advanced
|
||||
|
||||
| Method | Returns | What it gives |
|
||||
|-----------------------------+---------+----------------------------------------|
|
||||
| ~dcf_data()~ | dict | All raw inputs for a DCF model |
|
||||
| ~dcf()~ | dict | Full DCF valuation + exports ~.xlsx~ |
|
||||
| ~download_data_performance()~ | str | Timing summary of data fetch durations |
|
||||
|
||||
* Multi-Ticker API — ~Tickers(["AAPL", "NVDA"])~
|
||||
|
||||
#+begin_src python
|
||||
from defeatbeta_api.data.tickers import Tickers
|
||||
t = Tickers(["AAPL", "NVDA"])
|
||||
t = Tickers(["AAPL", "NVDA"], max_workers=2) # limit parallelism
|
||||
#+end_src
|
||||
|
||||
Wraps all ~Ticker~ methods, running them in *parallel threads*.
|
||||
|
||||
- Methods returning simple data → *combined DataFrame* (all tickers in one table)
|
||||
- Methods returning complex objects (statements, news, transcripts) → ~{symbol: result}~ dict
|
||||
|
||||
Same method names as ~Ticker~, plus industry comparison methods operate per unique
|
||||
industry represented across the list.
|
||||
|
||||
#+begin_src python
|
||||
t.info() # → DataFrame (combined)
|
||||
t.price() # → DataFrame (combined)
|
||||
t.annual_income_statement() # → {'AAPL': Statement(...), 'NVDA': Statement(...)}
|
||||
t.news() # → {'AAPL': News(...), 'NVDA': News(...)}
|
||||
t.earning_call_transcripts() # → {'AAPL': Transcripts(...), 'NVDA': Transcripts(...)}
|
||||
t.industry_roe() # → DataFrame (one row per unique industry)
|
||||
#+end_src
|
||||
+185
-127
@@ -7,20 +7,20 @@
|
||||
|
||||
# Overview
|
||||
|
||||
| Category | Yahoo Finance | DefeatBeta-API | Notes |
|
||||
|----------|--------------|----------------|-------|
|
||||
| **Data Source** | Yahoo Finance API | HuggingFace + DuckDB | No rate limits |
|
||||
| **Query Engine** | Direct API | DuckDB OLAP | Sub-second queries |
|
||||
| **Update Frequency** | Real-time (15min delayed) | Daily batch | DefeatBeta is historical only |
|
||||
| **Historical Depth** | Full history | Full history | Comparable coverage |
|
||||
| **Special Features** | Limited | Earnings transcripts, DCF, AI | DefeatBeta has unique capabilities |
|
||||
| Category | Yahoo Finance | DefeatBeta-API | Notes |
|
||||
|--------------------+---------------------------+-------------------------------+------------------------------------|
|
||||
| **Data Source** | Yahoo Finance API | HuggingFace + DuckDB | No rate limits |
|
||||
| **Query Engine** | Direct API | DuckDB OLAP | Sub-second queries |
|
||||
| **Update Frequency** | Real-time (15min delayed) | Daily batch | DefeatBeta is historical only |
|
||||
| **Historical Depth** | Full history | Full history | Comparable coverage |
|
||||
| **Special Features** | Limited | Earnings transcripts, DCF, AI | DefeatBeta has unique capabilities |
|
||||
|
||||
* Price & Volume Data
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.history(period='max')~ | ~ticker.price()~ | pandas.DataFrame | OHLCV data |
|
||||
| ~ticker.history(period='1d')~ | N/A | - | Real-time not available |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|----------------------------------------+-------------------------+------------------+--------------------------|
|
||||
| ~ticker.history(period='max')~ | ~ticker.price()~ | pandas.DataFrame | OHLCV data |
|
||||
| ~ticker.history(period='1d')~ | N/A | - | Real-time not available |
|
||||
| ~ticker.history(start='...', end='...')~ | ~ticker.price()~ (filter) | pandas.DataFrame | Date filtering available |
|
||||
|
||||
* DefeatBeta Price Data Structure
|
||||
@@ -33,14 +33,14 @@
|
||||
|
||||
* Financial Statements
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.quarterly_financials~ | ~ticker.quarterly_income_statement()~ | Statement object | Different format |
|
||||
| ~ticker.financials~ | ~ticker.annual_income_statement()~ | Statement object | Annual version |
|
||||
| ~ticker.quarterly_balance_sheet~ | ~ticker.quarterly_balance_sheet()~ | Statement object | Same structure |
|
||||
| ~ticker.balance_sheet~ | ~ticker.annual_balance_sheet()~ | Statement object | Annual version |
|
||||
| ~ticker.quarterly_cashflow~ | ~ticker.quarterly_cash_flow()~ | Statement object | Note: 'flow' vs 'flow' |
|
||||
| ~ticker.cashflow~ | ~ticker.annual_cash_flow()~ | Statement object | Annual version |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------------------------+-------------------------------------+------------------+------------------------|
|
||||
| ~ticker.quarterly_income_stmt~ | ~ticker.quarterly_income_statement()~ | Statement object | Different format |
|
||||
| ~ticker.income_stmt~ | ~ticker.annual_income_statement()~ | Statement object | Annual version |
|
||||
| ~ticker.quarterly_balance_sheet~ | ~ticker.quarterly_balance_sheet()~ | Statement object | Same structure |
|
||||
| ~ticker.balance_sheet~ | ~ticker.annual_balance_sheet()~ | Statement object | Annual version |
|
||||
| ~ticker.quarterly_cashflow~ | ~ticker.quarterly_cash_flow()~ | Statement object | 'cashflow' vs 'cash_flow' |
|
||||
| ~ticker.cashflow~ | ~ticker.annual_cash_flow()~ | Statement object | Annual version |
|
||||
|
||||
** Statement Object Methods
|
||||
#+BEGIN_SRC python
|
||||
@@ -53,16 +53,16 @@ income_stmt.print_pretty_table() # Formatted output
|
||||
|
||||
* Valuation Metrics
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.info['trailingPE']~ | ~ticker.ttm_pe()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['forwardPE']~ | N/A | - | Not available |
|
||||
| ~ticker.info['trailingEps']~ | ~ticker.ttm_eps()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['forwardEps']~ | N/A | - | Not available |
|
||||
| ~ticker.info['marketCap']~ | ~ticker.market_capitalization()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['priceToBook']~ | ~ticker.pb_ratio()~ | pandas.DataFrame | Price/Book ratio |
|
||||
| ~ticker.info['priceToSalesTrailing12Months']~ | ~ticker.ps_ratio()~ | pandas.DataFrame | Price/Sales ratio |
|
||||
| N/A | ~ticker.peg_ratio()~ | pandas.DataFrame | PEG ratio (unique) |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|---------------------------------------------+--------------------------------+------------------+---------------------------|
|
||||
| ~ticker.info['trailingPE']~ | ~ticker.ttm_pe()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['forwardPE']~ | N/A | - | Not available |
|
||||
| ~ticker.info['trailingEps']~ | ~ticker.ttm_eps()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['forwardEps']~ | N/A | - | Not available |
|
||||
| ~ticker.info['marketCap']~ | ~ticker.market_capitalization()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['priceToBook']~ | ~ticker.pb_ratio()~ | pandas.DataFrame | Price/Book ratio |
|
||||
| ~ticker.info['priceToSalesTrailing12Months']~ | ~ticker.ps_ratio()~ | pandas.DataFrame | Price/Sales ratio |
|
||||
| N/A | ~ticker.peg_ratio()~ | pandas.DataFrame | PEG ratio (unique) |
|
||||
|
||||
** Key Advantage: Historical Valuation Data
|
||||
DefeatBeta provides **full historical time series** for:
|
||||
@@ -75,13 +75,13 @@ Yahoo Finance only provides **current values** in ~.info~
|
||||
|
||||
* Financial Ratios
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.info['returnOnEquity']~ | ~ticker.roe()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['returnOnAssets']~ | ~ticker.roa()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| N/A | ~ticker.roic()~ | pandas.DataFrame | Return on Invested Capital |
|
||||
| N/A | ~ticker.wacc()~ | pandas.DataFrame | Weighted Avg Cost of Capital |
|
||||
| ~ticker.info['beta']~ | ~ticker.beta()~ | pandas.DataFrame | 5Y monthly beta |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|-------------------------------+----------------+------------------+------------------------------|
|
||||
| ~ticker.info['returnOnEquity']~ | ~ticker.roe()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['returnOnAssets']~ | ~ticker.roa()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| N/A | ~ticker.roic()~ | pandas.DataFrame | Return on Invested Capital |
|
||||
| N/A | ~ticker.wacc()~ | pandas.DataFrame | Weighted Avg Cost of Capital |
|
||||
| ~ticker.info['beta']~ | ~ticker.beta()~ | pandas.DataFrame | 5Y monthly beta |
|
||||
|
||||
** WACC Components Available in DefeatBeta
|
||||
#+BEGIN_SRC python
|
||||
@@ -93,66 +93,67 @@ wacc = ticker.wacc()
|
||||
|
||||
* Growth Metrics
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.info['revenueGrowth']~ | ~ticker.quarterly_revenue_yoy_growth()~ | pandas.DataFrame | YoY growth |
|
||||
| ~ticker.info['earningsGrowth']~ | ~ticker.quarterly_eps_yoy_growth()~ | pandas.DataFrame | EPS YoY growth |
|
||||
| N/A | ~ticker.quarterly_net_income_yoy_growth()~ | pandas.DataFrame | Net income growth |
|
||||
| N/A | ~ticker.quarterly_operating_income_yoy_growth()~ | pandas.DataFrame | Operating income growth |
|
||||
| N/A | ~ticker.quarterly_ebitda_yoy_growth()~ | pandas.DataFrame | EBITDA growth |
|
||||
| N/A | ~ticker.quarterly_fcf_yoy_growth()~ | pandas.DataFrame | Free cash flow growth |
|
||||
| N/A | ~ticker.annual_revenue_yoy_growth()~ | pandas.DataFrame | Annual revenue growth |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|-------------------------------+------------------------------------------------+------------------+-------------------------|
|
||||
| ~ticker.info['revenueGrowth']~ | ~ticker.quarterly_revenue_yoy_growth()~ | pandas.DataFrame | YoY growth |
|
||||
| ~ticker.info['earningsGrowth']~ | ~ticker.quarterly_eps_yoy_growth()~ | pandas.DataFrame | EPS YoY growth |
|
||||
| N/A | ~ticker.quarterly_net_income_yoy_growth()~ | pandas.DataFrame | Net income growth |
|
||||
| N/A | ~ticker.quarterly_operating_income_yoy_growth()~ | pandas.DataFrame | Operating income growth |
|
||||
| N/A | ~ticker.quarterly_ebitda_yoy_growth()~ | pandas.DataFrame | EBITDA growth |
|
||||
| N/A | ~ticker.quarterly_fcf_yoy_growth()~ | pandas.DataFrame | Free cash flow growth |
|
||||
| N/A | ~ticker.annual_revenue_yoy_growth()~ | pandas.DataFrame | Annual revenue growth |
|
||||
|
||||
* Margin Metrics
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.info['profitMargins']~ | ~ticker.quarterly_net_margin()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['grossMargins']~ | ~ticker.quarterly_gross_margin()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['operatingMargins']~ | ~ticker.quarterly_operating_margin()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| N/A | ~ticker.quarterly_ebitda_margin()~ | pandas.DataFrame | EBITDA margin |
|
||||
| N/A | ~ticker.quarterly_fcf_margin()~ | pandas.DataFrame | Free cash flow margin |
|
||||
| N/A | ~ticker.industry_quarterly_gross_margin()~ | pandas.DataFrame | Industry comparison |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|---------------------------------+------------------------------------------+------------------+---------------------------|
|
||||
| ~ticker.info['profitMargins']~ | ~ticker.quarterly_net_margin()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['grossMargins']~ | ~ticker.quarterly_gross_margin()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| ~ticker.info['operatingMargins']~ | ~ticker.quarterly_operating_margin()~ | pandas.DataFrame | **Historical** time series! |
|
||||
| N/A | ~ticker.quarterly_ebitda_margin()~ | pandas.DataFrame | EBITDA margin |
|
||||
| N/A | ~ticker.quarterly_fcf_margin()~ | pandas.DataFrame | Free cash flow margin |
|
||||
| N/A | ~ticker.industry_quarterly_gross_margin()~ | pandas.DataFrame | Industry comparison |
|
||||
|
||||
* Dividends & Stock Splits
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.dividends~ | ~ticker.dividends()~ | pandas.DataFrame | Dividend history |
|
||||
| ~ticker.splits~ | ~ticker.splits()~ | pandas.DataFrame | Stock split history |
|
||||
| ~ticker.info['dividendYield']~ | N/A | - | Not in separate field |
|
||||
| ~ticker.info['trailingAnnualDividendYield']~ | N/A | - | Not available |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------------------------------------+--------------------+------------------+-----------------------|
|
||||
| ~ticker.dividends~ | ~ticker.dividends()~ | pandas.DataFrame | Dividend history |
|
||||
| ~ticker.splits~ | ~ticker.splits()~ | pandas.DataFrame | Stock split history |
|
||||
| ~ticker.info['dividendYield']~ | N/A | - | Not in separate field |
|
||||
| ~ticker.info['trailingAnnualDividendYield']~ | N/A | - | Not available |
|
||||
|
||||
* Company Info & Metadata
|
||||
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|--------------|----------------|-------------|-------|
|
||||
| ~ticker.info~ | ~ticker.info()~ | pandas.DataFrame | One row, many columns |
|
||||
| ~ticker.info['longName']~ | ~ticker.info()['long_business_summary']~ | string | Business summary |
|
||||
| ~ticker.info['sector']~ | ~ticker.info()['sector']~ | string | Sector classification |
|
||||
| ~ticker.info['industry']~ | ~ticker.info()['industry']~ | string | Industry classification |
|
||||
| ~ticker.info['website']~ | ~ticker.info()['web_site']~ | string | Company website |
|
||||
| ~ticker.info['fullTimeEmployees']~ | ~ticker.info()['full_time_employees']~ | int | Employee count |
|
||||
| N/A | ~ticker.officers()~ | pandas.DataFrame | Company officers |
|
||||
| N/A | ~ticker.calendar()~ | pandas.DataFrame | Earnings calendar |
|
||||
| Yahoo Finance | DefeatBeta-API | Return Type | Notes |
|
||||
|----------------------------------+----------------------------------------+------------------+-------------------------|
|
||||
| ~ticker.info~ | ~ticker.info()~ | pandas.DataFrame | One row, many columns |
|
||||
| ~ticker.info['longName']~ | N/A (check name/short_name column) | string | Company trading name |
|
||||
| ~ticker.info['longBusinessSummary']~ | ~ticker.info()['long_business_summary']~ | string | Business description |
|
||||
| ~ticker.info['sector']~ | ~ticker.info()['sector']~ | string | Sector classification |
|
||||
| ~ticker.info['industry']~ | ~ticker.info()['industry']~ | string | Industry classification |
|
||||
| ~ticker.info['website']~ | ~ticker.info()['web_site']~ | string | Company website |
|
||||
| ~ticker.info['fullTimeEmployees']~ | ~ticker.info()['full_time_employees']~ | int | Employee count |
|
||||
| N/A | ~ticker.officers()~ | pandas.DataFrame | Company officers |
|
||||
| N/A | ~ticker.calendar()~ | pandas.DataFrame | Earnings calendar |
|
||||
|
||||
* Unique DefeatBeta Features (Not in Yahoo Finance)
|
||||
|
||||
| Feature | Method | Description |
|
||||
|---------|--------|-------------|
|
||||
| **Earnings Transcripts** | ~ticker.earning_call_transcripts()~ | Full earnings call transcripts |
|
||||
| | ~transcripts.get_transcripts_list()~ | List all available transcripts |
|
||||
| | ~transcripts.get_transcript(year, quarter)~ | Get specific quarter's transcript |
|
||||
| | ~transcripts.summarize_key_financial_data_with_ai()~ | AI-powered summary |
|
||||
| **Revenue Breakdown** | ~ticker.revenue_by_segment()~ | Revenue by product segment |
|
||||
| | ~ticker.revenue_by_product()~ | Detailed product breakdown |
|
||||
| | ~ticker.revenue_by_geography()~ | Revenue by geographic region |
|
||||
| **Automated DCF** | ~ticker.dcf()~ | Generates Excel DCF valuation |
|
||||
| **AI Analysis** | ~transcripts.analyze_financial_metrics_change...~ | LLM analysis of quarter changes |
|
||||
| | ~transcripts.analyze_financial_metrics_forecast...~ | LLM forecast analysis |
|
||||
| **Industry Metrics** | ~ticker.industry_ttm_pe()~ | Industry P/E comparison |
|
||||
| | ~ticker.industry_roe()~ | Industry ROE comparison |
|
||||
| | ~ticker.industry_quarterly_gross_margin()~ | Industry margin comparison |
|
||||
| Feature | Method | Description |
|
||||
|------------------------+----------------------------------------------------+-----------------------------------|
|
||||
| **Earnings Transcripts** | ~ticker.earning_call_transcripts()~ | Full earnings call transcripts |
|
||||
| | ~transcripts.get_transcripts_list()~ | List all available transcripts |
|
||||
| | ~transcripts.get_transcript(year, quarter)~ | Get specific quarter's transcript |
|
||||
| | ~transcripts.summarize_key_financial_data_with_ai()~ | AI-powered summary |
|
||||
| **Revenue Breakdown** | ~ticker.revenue_by_segment()~ | Revenue by product segment |
|
||||
| | ~ticker.revenue_by_product()~ | Detailed product breakdown |
|
||||
| | ~ticker.revenue_by_geography()~ | Revenue by geographic region |
|
||||
| **Automated DCF** | ~ticker.dcf()~ | Generates Excel DCF valuation |
|
||||
| **AI Analysis** | ~transcripts.analyze_financial_metrics_change...~ | LLM analysis of quarter changes |
|
||||
| | ~transcripts.analyze_financial_metrics_forecast...~ | LLM forecast analysis |
|
||||
| **Industry Metrics** | ~ticker.industry_ttm_pe()~ | Industry P/E comparison |
|
||||
| | ~ticker.industry_roe()~ | Industry ROE comparison |
|
||||
| | ~ticker.industry_quarterly_gross_margin()~ | Industry margin comparison |
|
||||
|
||||
* Example: yfinance → DefeatBeta Migration
|
||||
|
||||
@@ -209,26 +210,26 @@ current_roe = ticker.roe().iloc[-1]['roe']
|
||||
|
||||
* Quick Reference: Common Operations
|
||||
|
||||
| Operation | Yahoo Finance | DefeatBeta-API |
|
||||
|-----------|--------------|----------------|
|
||||
| Get current price | ~ticker.info['currentPrice']~ | ~ticker.price().iloc[-1]['close']~ |
|
||||
| Get current P/E | ~ticker.info['trailingPE']~ | ~ticker.ttm_pe().iloc[-1]['ttm_pe']~ |
|
||||
| Get current EPS | ~ticker.info['trailingEps']~ | ~ticker.ttm_eps().iloc[-1]['tailing_eps']~ |
|
||||
| Get market cap | ~ticker.info['marketCap']~ | ~ticker.market_capitalization().iloc[-1]['market_capitalization']~ |
|
||||
| Get ROE | ~ticker.info['returnOnEquity']~ | ~ticker.roe().iloc[-1]['roe']~ |
|
||||
| Get revenue (TTM) | ~ticker.info['totalRevenue']~ | ~ticker.quarterly_income_statement().df().iloc[0]['TTM']~ |
|
||||
| Get 5Y beta | ~ticker.info['beta']~ | ~ticker.beta().iloc[-1]['beta']~ |
|
||||
| Operation | Yahoo Finance | DefeatBeta-API |
|
||||
|-------------------+-------------------------------+------------------------------------------------------------------|
|
||||
| Get current price | ~ticker.info['currentPrice']~ | ~ticker.price().iloc[-1]['close']~ |
|
||||
| Get current P/E | ~ticker.info['trailingPE']~ | ~ticker.ttm_pe().iloc[-1]['ttm_pe']~ |
|
||||
| Get current EPS | ~ticker.info['trailingEps']~ | ~ticker.ttm_eps().iloc[-1]['tailing_eps']~ |
|
||||
| Get market cap | ~ticker.info['marketCap']~ | ~ticker.market_capitalization().iloc[-1]['market_capitalization']~ |
|
||||
| Get ROE | ~ticker.info['returnOnEquity']~ | ~ticker.roe().iloc[-1]['roe']~ |
|
||||
| Get revenue (TTM) | ~ticker.info['totalRevenue']~ | ~ticker.quarterly_income_statement().df().iloc[0]['TTM']~ |
|
||||
| Get 5Y beta | ~ticker.info['beta']~ | ~ticker.beta().iloc[-1]['beta']~ |
|
||||
|
||||
* Data Type Differences
|
||||
|
||||
| Aspect | Yahoo Finance | DefeatBeta-API |
|
||||
|--------|--------------|----------------|
|
||||
| Dates in DataFrame | DatetimeIndex | 'report_date' column |
|
||||
| Column naming | Title case (Open, Close) | Snake case (open, close) |
|
||||
| Dividends/Splits | Separate columns in history | Separate DataFrames |
|
||||
| Quarterly data | Transposed (dates as columns) | Wide format (TTM + quarters as columns) |
|
||||
| Missing data | NaN | NaN |
|
||||
| Numeric types | float | Decimal (convert with ~float()~) |
|
||||
| Aspect | Yahoo Finance | DefeatBeta-API |
|
||||
|--------------------+-------------------------------+-----------------------------------------|
|
||||
| Dates in DataFrame | DatetimeIndex | 'report_date' column |
|
||||
| Column naming | Title case (Open, Close) | Snake case (open, close) |
|
||||
| Dividends/Splits | Separate columns in history | Separate DataFrames |
|
||||
| Quarterly data | Transposed (dates as columns) | Wide format (TTM + quarters as columns) |
|
||||
| Missing data | NaN | NaN |
|
||||
| Numeric types | float | Decimal (convert with ~float()~) |
|
||||
|
||||
* Converting Decimal to Float
|
||||
#+BEGIN_SRC python
|
||||
@@ -244,37 +245,94 @@ market_cap_billions = float(market_cap.iloc[-1]['market_capitalization']) / 1e9
|
||||
|
||||
* When to Use Each
|
||||
|
||||
| Use Case | Recommendation | Reason |
|
||||
|----------|---------------|--------|
|
||||
| Backtesting trading strategies | **DefeatBeta** | No rate limits, consistent historical data |
|
||||
| DCF Valuation modeling | **DefeatBeta** | Automated Excel output |
|
||||
| Revenue segment analysis | **DefeatBeta** | Unique revenue breakdown |
|
||||
| Earnings call research | **DefeatBeta** | Full transcripts available |
|
||||
| Real-time price alerts | **Yahoo Finance** | 15min delayed but real-time |
|
||||
| Analyst recommendations | **Yahoo Finance** | Price targets, ratings |
|
||||
| Institutional ownership | **Yahoo Finance** | Major holders data |
|
||||
| Options/derivatives data | **Yahoo Finance** | Not available in DefeatBeta |
|
||||
| Quick stock lookup | **Either** | Both work well |
|
||||
| Multi-year historical analysis | **DefeatBeta** | Faster queries, no rate limits |
|
||||
| Use Case | Recommendation | Reason |
|
||||
|--------------------------------+-----------------+--------------------------------------------|
|
||||
| Backtesting trading strategies | **DefeatBeta** | No rate limits, consistent historical data |
|
||||
| DCF Valuation modeling | **DefeatBeta** | Automated Excel output |
|
||||
| Revenue segment analysis | **DefeatBeta** | Unique revenue breakdown |
|
||||
| Earnings call research | **DefeatBeta** | Full transcripts available |
|
||||
| Real-time price alerts | **Yahoo Finance** | 15min delayed but real-time |
|
||||
| Analyst recommendations | **Yahoo Finance** | Price targets, ratings |
|
||||
| Institutional ownership | **Yahoo Finance** | Major holders data |
|
||||
| Options/derivatives data | **Yahoo Finance** | Not available in DefeatBeta |
|
||||
| Quick stock lookup | **Either** | Both work well |
|
||||
| Multi-year historical analysis | **DefeatBeta** | Faster queries, no rate limits |
|
||||
|
||||
* Environment Setup
|
||||
|
||||
| Task | Command |
|
||||
|------|---------|
|
||||
| Install DefeatBeta | ~uv add defeatbeta-api~ |
|
||||
| Install yfinance | ~uv add yfinance~ |
|
||||
| Run notebook | ~uv run jupyter notebook~ |
|
||||
| Check version | ~ticker.price()~ shows data update date |
|
||||
| Task | Command |
|
||||
|--------------------+---------------------------------------|
|
||||
| Install DefeatBeta | ~uv add defeatbeta-api~ |
|
||||
| Install yfinance | ~uv add yfinance~ |
|
||||
| Run notebook | ~uv run jupyter notebook~ |
|
||||
| Check version | ~ticker.price()~ shows data update date |
|
||||
|
||||
* Mapping Limitations & Verification Notes
|
||||
/Verified 2026-04-26 via test_mapping.py on AAPL. defeatbeta-api 0.0.45, yfinance 1.3.0, DuckDB 1.4.3./
|
||||
|
||||
** What was confirmed correct (live data matched)
|
||||
- ~dividends()~: both APIs return $0.26 for Nov-2025 and Feb-2026 payments — values match exactly
|
||||
- ~splits()~: both return the same ratios (7:1 in 2014, 4:1 in 2020)
|
||||
|
||||
** Incorrect property names for yfinance (deprecated in v1.3)
|
||||
|
||||
| Used in mapping | Correct name in yfinance 1.3+ |
|
||||
|----------------------------+-------------------------------|
|
||||
| ~ticker.quarterly_financials~ | ~ticker.quarterly_income_stmt~ |
|
||||
| ~ticker.financials~ | ~ticker.income_stmt~ |
|
||||
|
||||
The old names were removed. The mapping should use the new names.
|
||||
|
||||
** Conceptual mismatch: longName vs long_business_summary (Company Info)
|
||||
|
||||
The mapping equates ~ticker.info['longName']~ with ~ticker.info()['long_business_summary']~,
|
||||
labelling it "Business summary". This is wrong:
|
||||
|
||||
- ~longName~ = company trading name, e.g. "Apple Inc."
|
||||
- ~long_business_summary~ = multi-sentence business description paragraph
|
||||
- The yfinance equivalent of the description is ~ticker.info['longBusinessSummary']~
|
||||
|
||||
** Typo in Financial Statements note (cashflow row)
|
||||
|
||||
The "Notes" column reads ~Note: 'flow' vs 'flow'~. Should read: ~'cashflow' vs 'cash_flow'~
|
||||
|
||||
** Data format differences found in verified pairs
|
||||
|
||||
| Field | yfinance | defeatbeta |
|
||||
|---------------+--------------------------------------+-------------------------------|
|
||||
| dividends | Series, DatetimeIndex, float amount | DataFrame, report_date col |
|
||||
| split_factor | float (e.g. ~7.0~) | string ratio (e.g. ~"7:1"~) |
|
||||
|
||||
** Historical coverage gap
|
||||
|
||||
- Dividends: yfinance returns 90 records, defeatbeta returns 61 (truncated history)
|
||||
- Splits: yfinance returns 5 events, defeatbeta returns 4 (one historical split missing)
|
||||
|
||||
** DuckDB compatibility issue (defeatbeta-api 0.0.45 + DuckDB 1.4.3)
|
||||
|
||||
All Parquet-backed queries except ~dividends()~ and ~splits()~ failed during verification
|
||||
with either ~"don't know what type:"~ or ~"TProtocolException: Invalid data"~. Affected:
|
||||
~price()~, all financial statements, all valuation/ratio/margin/growth metrics, ~info()~, ~beta()~.
|
||||
|
||||
This is an incompatibility between DuckDB 1.4.3 and the httpfs extension / remote Parquet
|
||||
format used by defeatbeta-api. Downgrading DuckDB or waiting for a defeatbeta-api update
|
||||
may resolve it. The logical mapping is still valid — the API shape is correct, only the
|
||||
DuckDB query layer is broken.
|
||||
|
||||
** Data freshness
|
||||
|
||||
DefeatBeta dataset last updated 2026-04-17, 9 days behind current date. yfinance has
|
||||
15-minute delayed real-time data. This gap will always exist for defeatbeta.
|
||||
|
||||
* Common Issues & Solutions
|
||||
|
||||
| Issue | Solution |
|
||||
|-------|----------|
|
||||
| ~TypeError: unsupported operand type(s) for /: 'Decimal' and 'float'~ | Wrap values in ~float()~ |
|
||||
| ~Rate limit exceeded~ | Switch to DefeatBeta |
|
||||
| ~Real-time data needed~ | Use Yahoo Finance |
|
||||
| ~Missing revenue breakdown~ | Use DefeatBeta ~revenue_by_segment()~ |
|
||||
| ~Slow queries~ | Use DefeatBeta (DuckDB engine) |
|
||||
| Issue | Solution |
|
||||
|---------------------------------------------------------------------+-------------------------------------|
|
||||
| ~TypeError: unsupported operand type(s) for /: 'Decimal' and 'float'~ | Wrap values in ~float()~ |
|
||||
| ~Rate limit exceeded~ | Switch to DefeatBeta |
|
||||
| ~Real-time data needed~ | Use Yahoo Finance |
|
||||
| ~Missing revenue breakdown~ | Use DefeatBeta ~revenue_by_segment()~ |
|
||||
| ~Slow queries~ | Use DefeatBeta (DuckDB engine) |
|
||||
|
||||
* Additional Resources
|
||||
|
||||
@@ -285,7 +343,7 @@ market_cap_billions = float(market_cap.iloc[-1]['market_capitalization']) / 1e9
|
||||
|
||||
* Footer
|
||||
#+BEGIN_COMMENT
|
||||
Last updated: 2026-04-25
|
||||
Last updated: 2026-04-26 (verified via test_mapping.py on AAPL)
|
||||
Author: Documentation
|
||||
Version: 1.0
|
||||
#+END_COMMENT
|
||||
@@ -0,0 +1,228 @@
|
||||
"""
|
||||
Mapping verification: yfinance vs defeatbeta-api for AAPL.
|
||||
Prints type + 2 representative rows/values for each mapped pair.
|
||||
Methods that fail are reported inline (not crashed).
|
||||
"""
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
import yfinance as yf
|
||||
from defeatbeta_api.data.ticker import Ticker
|
||||
import pandas as pd
|
||||
|
||||
SYMBOL = "AAPL"
|
||||
|
||||
yf_t = yf.Ticker(SYMBOL)
|
||||
db_t = Ticker(SYMBOL)
|
||||
|
||||
PASS = "✓"
|
||||
FAIL = "✗"
|
||||
|
||||
|
||||
def try_db(fn, *args, **kwargs):
|
||||
"""Call a defeatbeta method, return (result, None) or (None, error_str)."""
|
||||
try:
|
||||
return fn(*args, **kwargs), None
|
||||
except Exception as e:
|
||||
return None, str(e)[:80]
|
||||
|
||||
|
||||
def show_df(label, df, n=2):
|
||||
if df is None:
|
||||
return
|
||||
if isinstance(df, pd.DataFrame):
|
||||
print(f" {label} ({df.shape[0]}r×{df.shape[1]}c), last {n} rows:")
|
||||
cols = list(df.columns)[:6]
|
||||
print(df[cols].tail(n).to_string(index=False))
|
||||
elif isinstance(df, pd.Series):
|
||||
print(f" {label} (Series len={len(df)}), last {n}:")
|
||||
print(df.tail(n).to_string())
|
||||
|
||||
|
||||
def row(label, yf_val, yf_type_label, db_result, db_err):
|
||||
ok = PASS if db_result is not None else FAIL
|
||||
print(f"\n{ok} {label}")
|
||||
if db_err:
|
||||
print(f" yfinance → {yf_type_label}: {yf_val!r}")
|
||||
print(f" defeatbeta→ ERROR: {db_err}")
|
||||
else:
|
||||
print(f" yfinance type={yf_type_label} value={yf_val!r}")
|
||||
print(f" defeatbeta type={type(db_result).__name__}", end="")
|
||||
if isinstance(db_result, pd.DataFrame):
|
||||
print(f" cols={list(db_result.columns)}")
|
||||
show_df("defeatbeta", db_result)
|
||||
else:
|
||||
print(f" value={db_result!r}")
|
||||
|
||||
|
||||
def section(title):
|
||||
print(f"\n{'='*68}\n {title}\n{'='*68}")
|
||||
|
||||
|
||||
# ── 1. PRICE DATA ─────────────────────────────────────────────────────────────
|
||||
section("1. PRICE DATA — ticker.history() vs ticker.price()")
|
||||
|
||||
yf_price = yf_t.history(period="5d")[["Open", "Close", "High", "Low", "Volume"]]
|
||||
db_price, db_price_err = try_db(db_t.price)
|
||||
|
||||
print(f"\n{PASS if not db_price_err else FAIL} ticker.history() vs ticker.price()")
|
||||
print(f" yfinance type=DataFrame cols={list(yf_price.columns)}")
|
||||
print(yf_price.tail(2).to_string())
|
||||
if db_price_err:
|
||||
print(f" defeatbeta→ ERROR: {db_price_err}")
|
||||
else:
|
||||
print(f" defeatbeta type=DataFrame cols={list(db_price.columns)}")
|
||||
print(db_price.tail(2).to_string(index=False))
|
||||
|
||||
# ── 2. FINANCIAL STATEMENTS ───────────────────────────────────────────────────
|
||||
section("2. FINANCIAL STATEMENTS")
|
||||
|
||||
# yfinance uses .quarterly_income_stmt (v1.3) — old .quarterly_financials is deprecated
|
||||
yf_inc_q = yf_t.quarterly_income_stmt
|
||||
db_inc_q, db_inc_q_err = try_db(db_t.quarterly_income_statement)
|
||||
db_inc_q_df = db_inc_q.df() if db_inc_q else None
|
||||
|
||||
print(f"\n{PASS if not db_inc_q_err else FAIL} quarterly_income_stmt vs quarterly_income_statement()")
|
||||
print(f" yfinance type={type(yf_inc_q).__name__} shape={yf_inc_q.shape} (rows=line items, cols=quarters)")
|
||||
print(yf_inc_q.iloc[:2, :2].to_string())
|
||||
if db_inc_q_err:
|
||||
print(f" defeatbeta→ ERROR: {db_inc_q_err}")
|
||||
else:
|
||||
print(f" defeatbeta type={type(db_inc_q).__name__} → .df() shape={db_inc_q_df.shape}")
|
||||
print(db_inc_q_df.iloc[:2, :5].to_string(index=False))
|
||||
|
||||
yf_bs = yf_t.balance_sheet
|
||||
db_bs, db_bs_err = try_db(db_t.annual_balance_sheet)
|
||||
db_bs_df = db_bs.df() if db_bs else None
|
||||
|
||||
print(f"\n{PASS if not db_bs_err else FAIL} balance_sheet vs annual_balance_sheet()")
|
||||
print(f" yfinance type={type(yf_bs).__name__} shape={yf_bs.shape}")
|
||||
print(yf_bs.iloc[:2, :2].to_string())
|
||||
if db_bs_err:
|
||||
print(f" defeatbeta→ ERROR: {db_bs_err}")
|
||||
else:
|
||||
print(f" defeatbeta type={type(db_bs).__name__} → .df() shape={db_bs_df.shape}")
|
||||
print(db_bs_df.iloc[:2, :5].to_string(index=False))
|
||||
|
||||
yf_cf = yf_t.cashflow
|
||||
db_cf, db_cf_err = try_db(db_t.annual_cash_flow)
|
||||
db_cf_df = db_cf.df() if db_cf else None
|
||||
|
||||
print(f"\n{PASS if not db_cf_err else FAIL} cashflow vs annual_cash_flow()")
|
||||
print(f" yfinance type={type(yf_cf).__name__} shape={yf_cf.shape}")
|
||||
print(yf_cf.iloc[:2, :2].to_string())
|
||||
if db_cf_err:
|
||||
print(f" defeatbeta→ ERROR: {db_cf_err}")
|
||||
else:
|
||||
print(f" defeatbeta → .df() shape={db_cf_df.shape}")
|
||||
print(db_cf_df.iloc[:2, :5].to_string(index=False))
|
||||
|
||||
# ── 3. VALUATION METRICS ──────────────────────────────────────────────────────
|
||||
section("3. VALUATION METRICS")
|
||||
|
||||
db_pe, db_pe_err = try_db(db_t.ttm_pe)
|
||||
db_eps, db_eps_err = try_db(db_t.ttm_eps)
|
||||
db_mc, db_mc_err = try_db(db_t.market_capitalization)
|
||||
db_pb, db_pb_err = try_db(db_t.pb_ratio)
|
||||
db_ps, db_ps_err = try_db(db_t.ps_ratio)
|
||||
|
||||
row("trailingPE → ttm_pe()", yf_t.info.get("trailingPE"), "float", db_pe, db_pe_err)
|
||||
row("trailingEps → ttm_eps()", yf_t.info.get("trailingEps"), "float", db_eps, db_eps_err)
|
||||
row("marketCap → market_cap()", yf_t.info.get("marketCap"), "int", db_mc, db_mc_err)
|
||||
row("priceToBook → pb_ratio()", yf_t.info.get("priceToBook"), "float", db_pb, db_pb_err)
|
||||
row("priceToSales → ps_ratio()", yf_t.info.get("priceToSalesTrailing12Months"), "float", db_ps, db_ps_err)
|
||||
|
||||
# ── 4. FINANCIAL RATIOS ───────────────────────────────────────────────────────
|
||||
section("4. FINANCIAL RATIOS")
|
||||
|
||||
db_roe, db_roe_err = try_db(db_t.roe)
|
||||
db_roa, db_roa_err = try_db(db_t.roa)
|
||||
db_beta, db_beta_err = try_db(db_t.beta)
|
||||
db_wacc, db_wacc_err = try_db(db_t.wacc)
|
||||
|
||||
row("returnOnEquity → roe()", yf_t.info.get("returnOnEquity"), "float", db_roe, db_roe_err)
|
||||
row("returnOnAssets → roa()", yf_t.info.get("returnOnAssets"), "float", db_roa, db_roa_err)
|
||||
row("beta → beta()", yf_t.info.get("beta"), "float", db_beta, db_beta_err)
|
||||
row("N/A → wacc()", None, "N/A", db_wacc, db_wacc_err)
|
||||
|
||||
# ── 5. GROWTH METRICS ─────────────────────────────────────────────────────────
|
||||
section("5. GROWTH METRICS")
|
||||
|
||||
db_rg, db_rg_err = try_db(db_t.quarterly_revenue_yoy_growth)
|
||||
db_eg, db_eg_err = try_db(db_t.quarterly_eps_yoy_growth)
|
||||
db_nig, db_nig_err = try_db(db_t.quarterly_net_income_yoy_growth)
|
||||
|
||||
row("revenueGrowth → quarterly_revenue_yoy_growth()", yf_t.info.get("revenueGrowth"), "float", db_rg, db_rg_err)
|
||||
row("earningsGrowth → quarterly_eps_yoy_growth()", yf_t.info.get("earningsGrowth"), "float", db_eg, db_eg_err)
|
||||
row("N/A → quarterly_net_income_yoy_growth()", None, "N/A", db_nig, db_nig_err)
|
||||
|
||||
# ── 6. MARGIN METRICS ─────────────────────────────────────────────────────────
|
||||
section("6. MARGIN METRICS")
|
||||
|
||||
db_nm, db_nm_err = try_db(db_t.quarterly_net_margin)
|
||||
db_gm, db_gm_err = try_db(db_t.quarterly_gross_margin)
|
||||
db_om, db_om_err = try_db(db_t.quarterly_operating_margin)
|
||||
|
||||
row("profitMargins → quarterly_net_margin()", yf_t.info.get("profitMargins"), "float", db_nm, db_nm_err)
|
||||
row("grossMargins → quarterly_gross_margin()", yf_t.info.get("grossMargins"), "float", db_gm, db_gm_err)
|
||||
row("operatingMargins → quarterly_op_margin()", yf_t.info.get("operatingMargins"), "float", db_om, db_om_err)
|
||||
|
||||
# ── 7. DIVIDENDS & SPLITS ─────────────────────────────────────────────────────
|
||||
section("7. DIVIDENDS & SPLITS")
|
||||
|
||||
yf_div = yf_t.dividends
|
||||
db_div, db_div_err = try_db(db_t.dividends)
|
||||
|
||||
print(f"\n{PASS if not db_div_err else FAIL} .dividends vs .dividends()")
|
||||
print(f" yfinance type={type(yf_div).__name__} len={len(yf_div)}")
|
||||
print(yf_div.tail(2).to_string())
|
||||
if db_div_err:
|
||||
print(f" defeatbeta→ ERROR: {db_div_err}")
|
||||
else:
|
||||
print(f" defeatbeta type={type(db_div).__name__} shape={db_div.shape}")
|
||||
print(db_div.tail(2).to_string(index=False))
|
||||
|
||||
yf_sp = yf_t.splits
|
||||
db_sp, db_sp_err = try_db(db_t.splits)
|
||||
|
||||
print(f"\n{PASS if not db_sp_err else FAIL} .splits vs .splits()")
|
||||
print(f" yfinance type={type(yf_sp).__name__} len={len(yf_sp)}")
|
||||
print(yf_sp.tail(2).to_string())
|
||||
if db_sp_err:
|
||||
print(f" defeatbeta→ ERROR: {db_sp_err}")
|
||||
else:
|
||||
print(f" defeatbeta type={type(db_sp).__name__} shape={db_sp.shape}")
|
||||
print(db_sp.tail(2).to_string(index=False))
|
||||
|
||||
# ── 8. COMPANY INFO ───────────────────────────────────────────────────────────
|
||||
section("8. COMPANY INFO — .info vs .info()")
|
||||
|
||||
yf_info = yf_t.info
|
||||
db_info, db_info_err = try_db(db_t.info)
|
||||
|
||||
if db_info_err:
|
||||
print(f" defeatbeta→ ERROR: {db_info_err}")
|
||||
else:
|
||||
fields = [
|
||||
("sector", "sector", yf_info.get("sector")),
|
||||
("industry", "industry", yf_info.get("industry")),
|
||||
("employees", "full_time_employees", yf_info.get("fullTimeEmployees")),
|
||||
("website", "web_site", yf_info.get("website")),
|
||||
# Note: yf longName = company name; longBusinessSummary = description
|
||||
("longName", None, yf_info.get("longName")),
|
||||
("longBusinessSummary", "long_business_summary", yf_info.get("longBusinessSummary", "")[:60]),
|
||||
]
|
||||
print(f"\n {'Field':<30} {'yfinance':<35} {'defeatbeta'}")
|
||||
print(f" {'-'*30} {'-'*35} {'-'*30}")
|
||||
for label, db_col, yf_val in fields:
|
||||
db_val = db_info[db_col].iloc[0] if db_col and db_col in db_info.columns else "—"
|
||||
if isinstance(db_val, str) and len(db_val) > 40:
|
||||
db_val = db_val[:40] + "…"
|
||||
if isinstance(yf_val, str) and len(yf_val) > 34:
|
||||
yf_val = yf_val[:34] + "…"
|
||||
print(f" {label:<30} {str(yf_val):<35} {str(db_val)}")
|
||||
|
||||
# ── SUMMARY ───────────────────────────────────────────────────────────────────
|
||||
print(f"\n{'='*68}")
|
||||
print(" ALL CHECKS COMPLETE")
|
||||
print(f"{'='*68}\n")
|
||||
@@ -0,0 +1,302 @@
|
||||
#+title: yfinance API Reference
|
||||
#+subtitle: Version 1.3.0
|
||||
#+author: Ran Aroussi
|
||||
#+date: 2026-04-25
|
||||
|
||||
* Overview
|
||||
|
||||
yfinance offers a Pythonic way to fetch financial & market data from Yahoo! Finance.
|
||||
|
||||
** Installation
|
||||
#+begin_src sh
|
||||
pip install yfinance
|
||||
#+end_src
|
||||
|
||||
** Quick Start
|
||||
#+begin_src python
|
||||
import yfinance as yf
|
||||
|
||||
# Single ticker
|
||||
dat = yf.Ticker("MSFT")
|
||||
dat.info
|
||||
dat.history(period='1mo')
|
||||
dat.option_chain(dat.options[0]).calls
|
||||
|
||||
# Multiple tickers
|
||||
tickers = yf.Tickers('MSFT AAPL GOOG')
|
||||
tickers.tickers['MSFT'].info
|
||||
yf.download(['MSFT', 'AAPL', 'GOOG'], period='1mo')
|
||||
|
||||
# Funds
|
||||
spy = yf.Ticker('SPY').funds_data
|
||||
spy.description
|
||||
spy.top_holdings
|
||||
#+end_src
|
||||
|
||||
* Top-Level Functions
|
||||
|
||||
| Function | Description |
|
||||
|--------------------------------+-------------------------------------------|
|
||||
| ~yf.download(tickers, ...)~ | Download market data for multiple tickers |
|
||||
| ~yf.enable_debug_mode()~ | Enable verbose debug logging |
|
||||
| ~yf.set_tz_cache_location(path)~ | Set timezone cache directory |
|
||||
| ~yf.screen(query)~ | Run equity/fund/ETF screener queries |
|
||||
|
||||
* Config
|
||||
|
||||
| Setting | Description |
|
||||
|---------------------------------+----------------------|
|
||||
| ~yf.config.debug.logging = True~ | Enable debug logging |
|
||||
| ~yf.config.user_agent = 'custom'~ | Custom user agent |
|
||||
|
||||
* Ticker(symbol)
|
||||
|
||||
** Price History
|
||||
|
||||
| Property/Method | Returns |
|
||||
|---------------------------------------------+--------------------------------------|
|
||||
| ~.history(period, start, end, interval, ...)~ | OHLCV DataFrame |
|
||||
| ~.get_history_metadata()~ | Dict with currency, exchange, etc. |
|
||||
| ~.get_dividends()~ | Dividend history (Series) |
|
||||
| ~.dividends~ | Cached dividend history |
|
||||
| ~.get_splits()~ | Stock split history (Series) |
|
||||
| ~.splits~ | Cached split history |
|
||||
| ~.get_actions()~ | Dividends + splits combined (Series) |
|
||||
| ~.actions~ | Cached actions history |
|
||||
| ~.get_capital_gains()~ | Capital gains distributions (Series) |
|
||||
| ~.capital_gains~ | Cached capital gains |
|
||||
| ~.get_shares_full()~ | Full shares outstanding history |
|
||||
|
||||
** Info & News
|
||||
|
||||
| Property/Method | Returns |
|
||||
|-------------------------------+----------------------------------|
|
||||
| ~.get_info()~ / ~.info~ | Full company info dict |
|
||||
| ~.get_fast_info()~ / ~.fast_info~ | Quick-access key metrics |
|
||||
| ~.get_news()~ / ~.news~ | Recent news articles (DataFrame) |
|
||||
| ~.isin~ | ISIN identifier |
|
||||
| ~.get_isin()~ | ISIN as string |
|
||||
|
||||
** Financial Statements
|
||||
|
||||
| Property/Method | Returns |
|
||||
|------------------------+-----------------------------------------|
|
||||
| ~.income_stmt~ | Annual income statement |
|
||||
| ~.quarterly_income_stmt~ | Quarterly income statement |
|
||||
| ~.ttm_income_stmt~ | Trailing-twelve-months income statement |
|
||||
| ~.balance_sheet~ | Annual balance sheet |
|
||||
| ~.cashflow~ | Annual cash flow statement |
|
||||
| ~.quarterly_cashflow~ | Quarterly cash flow |
|
||||
| ~.ttm_cashflow~ | TTM cash flow |
|
||||
| ~.get_income_stmt()~ | Alias for income_stmt |
|
||||
| ~.get_balance_sheet()~ | Alias for balance_sheet |
|
||||
| ~.get_cashflow()~ | Alias for cashflow |
|
||||
|
||||
** Earnings & Calendar
|
||||
|
||||
| Property/Method | Returns |
|
||||
|-----------------------+--------------------------------------|
|
||||
| ~.earnings~ | Annual earnings summary |
|
||||
| ~.calendar~ | Upcoming earnings/dividends dates |
|
||||
| ~.get_earnings_dates()~ | Historical & upcoming earnings dates |
|
||||
| ~.earnings_dates~ | Cached earnings dates |
|
||||
| ~.get_sec_filings()~ | SEC filing history |
|
||||
| ~.sec_filings~ | Cached SEC filings |
|
||||
|
||||
** Analysis & Estimates
|
||||
|
||||
| Property/Method | Returns |
|
||||
|--------------------------------+------------------------------------|
|
||||
| ~.get_recommendations()~ | Historical analyst recommendations |
|
||||
| ~.recommendations~ | Cached recommendations |
|
||||
| ~.get_recommendations_summary()~ | Summary of buy/hold/sell |
|
||||
| ~.recommendations_summary~ | Cached summary |
|
||||
| ~.get_upgrades_downgrades()~ | Analyst upgrades/downgrades |
|
||||
| ~.upgrades_downgrades~ | Cached upgrades/downgrades |
|
||||
| ~.get_sustainability()~ | ESG scores |
|
||||
| ~.sustainability~ | Cached ESG |
|
||||
| ~.get_analyst_price_targets()~ | Price target estimates |
|
||||
| ~.analyst_price_targets~ | Cached price targets |
|
||||
| ~.get_earnings_estimate()~ | Earnings estimates |
|
||||
| ~.earnings_estimate~ | Cached earnings estimates |
|
||||
| ~.get_revenue_estimate()~ | Revenue estimates |
|
||||
| ~.revenue_estimate~ | Cached revenue estimates |
|
||||
| ~.get_earnings_history()~ | Earnings surprise history |
|
||||
| ~.earnings_history~ | Cached earnings history |
|
||||
| ~.get_eps_trend()~ | EPS trend revisions |
|
||||
| ~.eps_trend~ | Cached EPS trend |
|
||||
| ~.get_eps_revisions()~ | EPS revision counts |
|
||||
| ~.eps_revisions~ | Cached EPS revisions |
|
||||
| ~.get_growth_estimates()~ | Growth rate estimates |
|
||||
| ~.growth_estimates~ | Cached growth estimates |
|
||||
|
||||
** Holdings & Insider Activity
|
||||
|
||||
| Property/Method | Returns |
|
||||
|---------------------------------------------------------+-----------------------------------|
|
||||
| ~.get_funds_data()~ / ~.funds_data~ | Fund info (for ETFs/mutual funds) |
|
||||
| ~.get_insider_purchases()~ / ~.insider_purchases~ | Insider purchase records |
|
||||
| ~.get_insider_transactions()~ / ~.insider_transactions~ | Insider trade history |
|
||||
| ~.get_insider_roster_holders()~ / ~.insider_roster_holders~ | Insider roster |
|
||||
| ~.get_major_holders()~ / ~.major_holders~ | Ownership breakdown |
|
||||
| ~.get_institutional_holders()~ / ~.institutional_holders~ | Institutional holders |
|
||||
| ~.get_mutualfund_holders()~ / ~.mutualfund_holders~ | Mutual fund holders |
|
||||
|
||||
** Options
|
||||
|
||||
| Method | Returns |
|
||||
|---------------------+-------------------------------------|
|
||||
| ~.options~ | Tuple of available expiration dates |
|
||||
| ~.option_chain(date)~ | Calls & puts DataFrame for a date |
|
||||
| | |
|
||||
|
||||
* Tickers('SYM1 SYM2 ...')
|
||||
|
||||
Multiple tickers class.
|
||||
|
||||
| Property | Returns |
|
||||
|---------------------------+----------------------------------|
|
||||
| ~.tickers['SYM'].info~ | Access individual Ticker objects |
|
||||
| ~.tickers['SYM'].history()~ | Get history per ticker |
|
||||
|
||||
* Market(market_code)
|
||||
|
||||
Market summary class.
|
||||
|
||||
| Method/Property | Returns |
|
||||
|-----------------+---------------------------|
|
||||
| ~.status~ | Market open/closed status |
|
||||
| ~.summary()~ | Market summary data |
|
||||
|
||||
* Calendars
|
||||
|
||||
Calendar events class.
|
||||
|
||||
| Method | Returns |
|
||||
|--------------+-------------------|
|
||||
| ~.earnings()~ | Earnings calendar |
|
||||
| ~.dividends()~ | Dividend calendar |
|
||||
| ~.splits()~ | Splits calendar |
|
||||
| ~.ipo()~ | IPO calendar |
|
||||
|
||||
* Search(query)
|
||||
|
||||
Search class for quotes and news.
|
||||
|
||||
| Property/Method | Returns |
|
||||
|-----------------+----------------------|
|
||||
| ~.quotes~ | Search result quotes |
|
||||
| ~.news~ | Search result news |
|
||||
| ~.lists~ | Watchlists/lists |
|
||||
| ~.nav~ | Navigation results |
|
||||
| ~.research~ | Research reports |
|
||||
| ~.get_quotes()~ | Fetch quotes |
|
||||
| ~.get_news()~ | Fetch news |
|
||||
|
||||
* Lookup(query)
|
||||
|
||||
Ticker lookup class.
|
||||
|
||||
| Method | Returns |
|
||||
|---------------+------------------------|
|
||||
| ~.get_quotes()~ | Lookup matching quotes |
|
||||
|
||||
* WebSocket(symbols, ...)
|
||||
|
||||
Live streaming data (synchronous).
|
||||
|
||||
| Method | Description |
|
||||
|-----------------------+---------------------------|
|
||||
| ~.connect()~ | Open WebSocket connection |
|
||||
| ~.subscribe(symbols)~ | Subscribe to symbols |
|
||||
| ~.unsubscribe(symbols)~ | Unsubscribe from symbols |
|
||||
| ~.close()~ | Close connection |
|
||||
|
||||
* AsyncWebSocket(symbols, ...)
|
||||
|
||||
Live streaming data (asynchronous).
|
||||
|
||||
Same methods as ~WebSocket~, but async.
|
||||
|
||||
* Sector(sector_key)
|
||||
|
||||
Sector information class.
|
||||
|
||||
| Property/Method | Returns |
|
||||
|-----------------+--------------------------|
|
||||
| ~.key~ | Sector identifier |
|
||||
| ~.name~ | Sector name |
|
||||
| ~.symbol~ | Associated ETF symbol |
|
||||
| ~.top_etfs~ | Top ETFs in sector |
|
||||
| ~.top_companies~ | Top companies in sector |
|
||||
| ~.industries~ | Industries within sector |
|
||||
|
||||
* Industry(industry_key)
|
||||
|
||||
Industry information class.
|
||||
|
||||
| Property/Method | Returns |
|
||||
|-----------------+---------------------------|
|
||||
| ~.key~ | Industry identifier |
|
||||
| ~.name~ | Industry name |
|
||||
| ~.sector_key~ | Parent sector |
|
||||
| ~.top_companies~ | Top companies in industry |
|
||||
|
||||
* Query Builders (Screener)
|
||||
|
||||
| Class | Description |
|
||||
|------------------+------------------------------------|
|
||||
| ~EquityQuery~ | Build equity filter queries |
|
||||
| ~FundQuery~ | Build mutual fund filter queries |
|
||||
| ~ETFQuery~ | Build ETF filter queries |
|
||||
| ~yf.screen(query)~ | Execute a query and return results |
|
||||
|
||||
* download() Parameters
|
||||
|
||||
#+begin_src python
|
||||
yf.download(
|
||||
tickers, # str or list of tickers
|
||||
period="1mo", # 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
|
||||
start=None, # datetime or str "YYYY-MM-DD"
|
||||
end=None,
|
||||
interval="1d", # 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
|
||||
group_by="column", # "column" or "ticker"
|
||||
auto_adjust=False,
|
||||
back_adjust=False,
|
||||
repair=False, # Enable price repair
|
||||
keepna=False,
|
||||
proxy=None,
|
||||
timeout=10,
|
||||
threads=True,
|
||||
progress=True,
|
||||
)
|
||||
#+end_src
|
||||
|
||||
* Advanced Features
|
||||
|
||||
** Logging
|
||||
Enable debug logging:
|
||||
#+begin_src python
|
||||
yf.config.debug.logging = True
|
||||
#+end_src
|
||||
|
||||
** Caching
|
||||
- Persistent cache is enabled by default
|
||||
- Set custom cache location: ~yf.set_tz_cache_location(path)~
|
||||
|
||||
** Price Repair
|
||||
- Enable with ~repair=True~ in ~download()~ or ~history()~
|
||||
- Fixes NaN values, bad splits, and dividend adjustments
|
||||
- Dividend repair also available
|
||||
|
||||
** Multi-Level Column Index
|
||||
- When downloading multiple tickers, columns are MultiIndex
|
||||
- ~group_by="column"~ or ~group_by="ticker"~
|
||||
|
||||
* Legal Disclaimer
|
||||
|
||||
Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.
|
||||
|
||||
yfinance is /not/ affiliated, endorsed, or vetted by Yahoo, Inc. It's an open-source tool that uses Yahoo's publicly available APIs, and is intended for research and educational purposes.
|
||||
|
||||
You should refer to Yahoo!'s terms of use for details on your rights to use the actual data downloaded. The Yahoo! finance API is intended for personal use only.
|
||||
@@ -0,0 +1,75 @@
|
||||
"""
|
||||
One-time download of all defeatbeta parquet files + company_tickers.json.
|
||||
Run this once; after that use offline.py for zero-network Ticker() calls.
|
||||
|
||||
uv run python download_data.py
|
||||
uv run python download_data.py --out data/parquet # custom directory
|
||||
"""
|
||||
import argparse
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import requests
|
||||
|
||||
BASE = "https://huggingface.co/datasets/defeatbeta/yahoo-finance-data/resolve/main"
|
||||
|
||||
PARQUET_TABLES = [
|
||||
"stock_profile",
|
||||
"stock_officers",
|
||||
"stock_tailing_eps",
|
||||
"stock_earning_calendar",
|
||||
"stock_statement",
|
||||
"stock_prices",
|
||||
"stock_dividend_events",
|
||||
"stock_split_events",
|
||||
"exchange_rate",
|
||||
"daily_treasury_yield",
|
||||
"stock_earning_call_transcripts",
|
||||
"stock_news",
|
||||
"stock_revenue_breakdown",
|
||||
"stock_shares_outstanding",
|
||||
"stock_sec_filing",
|
||||
]
|
||||
|
||||
EXTRA_FILES = [
|
||||
("data/company_tickers.json", "company_tickers.json"),
|
||||
]
|
||||
|
||||
|
||||
def download(url: str, dest: Path, label: str) -> None:
|
||||
if dest.exists():
|
||||
print(f" skip {label} ({dest.stat().st_size / 1e6:.1f} MB on disk)")
|
||||
return
|
||||
print(f" fetch {label} ...", end="", flush=True)
|
||||
with requests.get(url, stream=True, timeout=60) as r:
|
||||
r.raise_for_status()
|
||||
tmp = dest.with_suffix(".tmp")
|
||||
with open(tmp, "wb") as f:
|
||||
for chunk in r.iter_content(chunk_size=8 * 1024 * 1024):
|
||||
f.write(chunk)
|
||||
tmp.rename(dest)
|
||||
print(f" {dest.stat().st_size / 1e6:.1f} MB")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--out", default="data/parquet", help="local output directory")
|
||||
args = parser.parse_args()
|
||||
|
||||
out = Path(args.out)
|
||||
out.mkdir(parents=True, exist_ok=True)
|
||||
print(f"Saving to: {out.resolve()}\n")
|
||||
|
||||
for table in PARQUET_TABLES:
|
||||
url = f"{BASE}/data/{table}.parquet"
|
||||
download(url, out / f"{table}.parquet", table)
|
||||
|
||||
for remote_path, local_name in EXTRA_FILES:
|
||||
url = f"{BASE}/{remote_path}"
|
||||
download(url, out / local_name, local_name)
|
||||
|
||||
print(f"\nDone. {sum(1 for _ in out.iterdir())} files in {out.resolve()}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+67
@@ -0,0 +1,67 @@
|
||||
"""
|
||||
Patch defeatbeta_api to read from local parquet files with zero network.
|
||||
|
||||
Usage:
|
||||
from offline import enable_offline
|
||||
enable_offline("data/parquet") # call once before first Ticker()
|
||||
|
||||
from defeatbeta_api.data.ticker import Ticker
|
||||
t = Ticker("AAPL")
|
||||
t.price() # reads local file, no HTTP
|
||||
|
||||
Note: the one-time welcome banner on first import calls get_data_update_time()
|
||||
once. After that, nothing touches the network.
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def enable_offline(parquet_dir: str = "data/parquet") -> None:
|
||||
local_dir = Path(parquet_dir).resolve()
|
||||
|
||||
# Importing these submodules triggers defeatbeta_api/__init__.py on first
|
||||
# run (prints the welcome banner — one network call). After that it's a
|
||||
# no-op because _welcome_printed is True.
|
||||
from defeatbeta_api.client.hugging_face_client import HuggingFaceClient
|
||||
import defeatbeta_api.client.duckdb_client as _duckdb_mod
|
||||
from defeatbeta_api.client.duckdb_client import DuckDBClient
|
||||
from defeatbeta_api.client.duckdb_conf import Configuration
|
||||
from defeatbeta_api.data.company_meta import CompanyMeta
|
||||
from defeatbeta_api.utils.util import validate_memory_limit
|
||||
|
||||
# 1. Redirect every table URL to a local parquet file
|
||||
def _local_url(self, table: str) -> str:
|
||||
path = local_dir / f"{table}.parquet"
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(
|
||||
f"Local parquet not found: {path}\n"
|
||||
f"Run download_data.py first."
|
||||
)
|
||||
return str(path)
|
||||
|
||||
HuggingFaceClient.get_url_path = _local_url
|
||||
|
||||
# 2. Return a fixed update time (used by beta() and the welcome banner)
|
||||
HuggingFaceClient.get_data_update_time = lambda self: "offline"
|
||||
|
||||
# 3. Skip the startup cache-validation (hits HuggingFace spec.json)
|
||||
DuckDBClient._validate_httpfs_cache = lambda self: None
|
||||
|
||||
# 4. Skip "INSTALL cache_httpfs FROM community" (hits DuckDB extension registry)
|
||||
# and all the cache_httpfs SET GLOBAL lines that follow — not needed for
|
||||
# local files. Keep only memory and thread settings.
|
||||
def _minimal_settings(self):
|
||||
return [
|
||||
f"SET GLOBAL memory_limit = '{validate_memory_limit(self.memory_limit)}'",
|
||||
f"SET GLOBAL threads = {self.threads}",
|
||||
]
|
||||
|
||||
Configuration.get_duckdb_settings = _minimal_settings
|
||||
|
||||
# 5. Redirect company_tickers.json to local file
|
||||
CompanyMeta.COMPANY_TICKERS_URL = str(local_dir / "company_tickers.json")
|
||||
|
||||
# 6. Reset the DuckDB singleton so the next Ticker() call reinitialises
|
||||
# using the patched Configuration (no cache_httpfs install/load)
|
||||
_duckdb_mod._instance = None
|
||||
|
||||
print(f"[offline] defeatbeta_api patched → reading from {local_dir}")
|
||||
@@ -0,0 +1,34 @@
|
||||
"""
|
||||
Use the persistent defeatbeta httpfs cache at ~/.cache/defeatbeta/.
|
||||
|
||||
Import this at the top of any notebook or script before using Ticker().
|
||||
Run warmup_cache.py once first to populate the cache.
|
||||
|
||||
from persistent_cache import enable_persistent_cache
|
||||
enable_persistent_cache()
|
||||
|
||||
from defeatbeta_api.data.ticker import Ticker
|
||||
t = Ticker("AAPL")
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
CACHE_DIR = Path.home() / ".cache" / "defeatbeta"
|
||||
|
||||
def enable_persistent_cache() -> None:
|
||||
import defeatbeta_api.utils.util as _util
|
||||
import defeatbeta_api.client.duckdb_client as _duckdb_mod
|
||||
from defeatbeta_api.client.duckdb_conf import Configuration
|
||||
|
||||
if not CACHE_DIR.exists():
|
||||
raise RuntimeError(
|
||||
f"Cache directory not found: {CACHE_DIR}\n"
|
||||
"Run warmup_cache.py first to populate it."
|
||||
)
|
||||
|
||||
# Redirect cache directory to the persistent location
|
||||
_util.validate_httpfs_cache_directory = lambda: str(CACHE_DIR)
|
||||
|
||||
# Reset singleton so DuckDB reinitialises pointing at the new cache dir
|
||||
_duckdb_mod._instance = None
|
||||
|
||||
print(f"[persistent_cache] cache → {CACHE_DIR}")
|
||||
+1
-1
@@ -37,7 +37,7 @@ dependencies = [
|
||||
"torch>=2.0.0",
|
||||
"transformers>=4.0.0",
|
||||
"tscv>=0.1.3",
|
||||
"vectorbt>=1.0.0",
|
||||
"vectorbt[full,rust]>=1.0.0",
|
||||
"xgboost>=3.2.0",
|
||||
"yfinance>=1.3.0",
|
||||
"defeatbeta-api>=0.0.45",
|
||||
|
||||
@@ -0,0 +1,599 @@
|
||||
import marimo
|
||||
|
||||
__generated_with = "0.23.2"
|
||||
app = marimo.App()
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
import marimo as mo
|
||||
|
||||
return (mo,)
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
# Quant Trading Scaffold
|
||||
## Data Ingestion → Indicators → Walk-Forward ML → Backtesting → Tearsheet
|
||||
|
||||
Pipeline:
|
||||
1. **Ingest** OHLCV data via yfinance
|
||||
2. **Engineer features** — momentum, trend, volatility, volume indicators
|
||||
3. **Label** — binary classification (next-N-day return > 0)
|
||||
4. **Walk-forward split** with purging (no leakage)
|
||||
5. **Train** XGBoost classifier per fold
|
||||
6. **Evaluate** with quantstats tearsheet
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 1. Config & Imports
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
from __future__ import annotations
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=FutureWarning)
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pandas_ta as ta
|
||||
import yfinance as yf
|
||||
import plotly.graph_objects as go
|
||||
from plotly.subplots import make_subplots
|
||||
from sklearn.model_selection import TimeSeriesSplit
|
||||
from sklearn.metrics import accuracy_score, classification_report
|
||||
from xgboost import XGBClassifier
|
||||
import quantstats as qs
|
||||
|
||||
# ── Config ──────────────────────────────────────────────────────
|
||||
TICKER = "AAPL"
|
||||
START = "2015-01-01"
|
||||
END = "2025-12-31"
|
||||
HORIZON = 5 # predict N-day forward return
|
||||
PURGE_GAP = 5 # gap between train/test to prevent leakage
|
||||
N_SPLITS = 5 # walk-forward folds
|
||||
TRAIN_MIN = 504 # ~2 years minimum training window
|
||||
|
||||
print(f"Config: {TICKER} | {START}→{END} | horizon={HORIZON}d | {N_SPLITS} folds")
|
||||
return (
|
||||
END,
|
||||
HORIZON,
|
||||
N_SPLITS,
|
||||
PURGE_GAP,
|
||||
START,
|
||||
TICKER,
|
||||
TRAIN_MIN,
|
||||
XGBClassifier,
|
||||
accuracy_score,
|
||||
classification_report,
|
||||
go,
|
||||
make_subplots,
|
||||
np,
|
||||
pd,
|
||||
qs,
|
||||
ta,
|
||||
yf,
|
||||
)
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 2. Data Ingestion
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(END, START, TICKER, pd, yf):
|
||||
raw = yf.download(TICKER, start=START, end=END, auto_adjust=True)
|
||||
# yfinance may return MultiIndex columns for single ticker — flatten
|
||||
if isinstance(raw.columns, pd.MultiIndex):
|
||||
raw.columns = raw.columns.droplevel("Ticker")
|
||||
raw.index = pd.DatetimeIndex(raw.index)
|
||||
df = raw.copy()
|
||||
print(f"Downloaded {len(df)} bars: {df.index[0].date()} → {df.index[-1].date()}")
|
||||
df.tail(3)
|
||||
return (df,)
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 3. Feature Engineering — Technical Indicators
|
||||
|
||||
We compute features across 4 categories:
|
||||
- **Momentum**: RSI, MACD, Stochastic, Williams %R, ROC
|
||||
- **Trend**: SMA/EMA crossovers, ADX, Ichimoku
|
||||
- **Volatility**: Bollinger Bands, ATR, Keltner Channels
|
||||
- **Volume**: OBV, MFI, Accumulation/Distribution
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(df, ta):
|
||||
# ── Momentum ────────────────────────────────────────────────────
|
||||
df["rsi_14"] = ta.rsi(df["Close"], length=14)
|
||||
df["rsi_7"] = ta.rsi(df["Close"], length=7)
|
||||
|
||||
macd = ta.macd(df["Close"], fast=12, slow=26, signal=9)
|
||||
df["macd"] = macd.iloc[:, 0] # MACD line
|
||||
df["macd_signal"] = macd.iloc[:, 1] # signal line
|
||||
df["macd_hist"] = macd.iloc[:, 2] # histogram
|
||||
|
||||
stoch = ta.stoch(df["High"], df["Low"], df["Close"])
|
||||
df["stoch_k"] = stoch.iloc[:, 0]
|
||||
df["stoch_d"] = stoch.iloc[:, 1]
|
||||
|
||||
df["willr_14"] = ta.willr(df["High"], df["Low"], df["Close"], length=14)
|
||||
df["roc_10"] = ta.roc(df["Close"], length=10)
|
||||
df["roc_21"] = ta.roc(df["Close"], length=21)
|
||||
df["mom_10"] = ta.mom(df["Close"], length=10)
|
||||
|
||||
# ── Trend ───────────────────────────────────────────────────────
|
||||
df["sma_20"] = ta.sma(df["Close"], length=20)
|
||||
df["sma_50"] = ta.sma(df["Close"], length=50)
|
||||
df["sma_200"] = ta.sma(df["Close"], length=200)
|
||||
df["ema_12"] = ta.ema(df["Close"], length=12)
|
||||
df["ema_26"] = ta.ema(df["Close"], length=26)
|
||||
|
||||
# crossover features (price relative to MAs)
|
||||
df["close_over_sma20"] = (df["Close"] / df["sma_20"]) - 1
|
||||
df["close_over_sma50"] = (df["Close"] / df["sma_50"]) - 1
|
||||
df["close_over_sma200"] = (df["Close"] / df["sma_200"]) - 1
|
||||
df["sma20_over_sma50"] = (df["sma_20"] / df["sma_50"]) - 1
|
||||
df["sma50_over_sma200"] = (df["sma_50"] / df["sma_200"]) - 1
|
||||
|
||||
adx = ta.adx(df["High"], df["Low"], df["Close"], length=14)
|
||||
df["adx"] = adx.iloc[:, 0]
|
||||
df["di_plus"] = adx.iloc[:, 1]
|
||||
df["di_minus"] = adx.iloc[:, 2]
|
||||
|
||||
# ── Volatility ──────────────────────────────────────────────────
|
||||
bbands = ta.bbands(df["Close"], length=20, std=2)
|
||||
df["bb_upper"] = bbands.iloc[:, 0]
|
||||
df["bb_mid"] = bbands.iloc[:, 1]
|
||||
df["bb_lower"] = bbands.iloc[:, 2]
|
||||
df["bb_width"] = bbands.iloc[:, 3]
|
||||
df["bb_pctb"] = bbands.iloc[:, 4] # %B: where price is within bands
|
||||
|
||||
df["atr_14"] = ta.atr(df["High"], df["Low"], df["Close"], length=14)
|
||||
df["atr_pct"] = df["atr_14"] / df["Close"] # normalized ATR
|
||||
|
||||
kc = ta.kc(df["High"], df["Low"], df["Close"], length=20)
|
||||
df["kc_upper"] = kc.iloc[:, 0]
|
||||
df["kc_lower"] = kc.iloc[:, 1]
|
||||
|
||||
# volatility: rolling std of returns
|
||||
df["vol_10"] = df["Close"].pct_change().rolling(10).std()
|
||||
df["vol_21"] = df["Close"].pct_change().rolling(21).std()
|
||||
|
||||
# ── Volume ──────────────────────────────────────────────────────
|
||||
df["obv"] = ta.obv(df["Close"], df["Volume"])
|
||||
df["obv_sma20"] = ta.sma(df["obv"], length=20)
|
||||
df["mfi_14"] = ta.mfi(df["High"], df["Low"], df["Close"], df["Volume"], length=14)
|
||||
ad = ta.ad(df["High"], df["Low"], df["Close"], df["Volume"])
|
||||
df["ad_line"] = ad
|
||||
|
||||
# volume relative to average
|
||||
df["vol_ratio_20"] = df["Volume"] / df["Volume"].rolling(20).mean()
|
||||
|
||||
# ── Returns features ────────────────────────────────────────────
|
||||
df["ret_1d"] = df["Close"].pct_change(1)
|
||||
df["ret_5d"] = df["Close"].pct_change(5)
|
||||
df["ret_10d"] = df["Close"].pct_change(10)
|
||||
df["ret_21d"] = df["Close"].pct_change(21)
|
||||
|
||||
print(f"Total columns after feature engineering: {len(df.columns)}")
|
||||
df.tail(3)
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 4. Labeling — Forward Return Classification
|
||||
|
||||
Target: is the N-day forward return positive? (buy signal = 1, sell/hold signal = 0)
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(HORIZON, df):
|
||||
# forward return (what we're predicting)
|
||||
df["fwd_ret"] = df["Close"].pct_change(HORIZON).shift(-HORIZON)
|
||||
df["label"] = (df["fwd_ret"] > 0).astype(int)
|
||||
|
||||
# ── Define feature columns (exclude raw OHLCV, target, and non-stationary cols)
|
||||
EXCLUDE = {
|
||||
"Open", "High", "Low", "Close", "Volume",
|
||||
"fwd_ret", "label",
|
||||
"sma_20", "sma_50", "sma_200", "ema_12", "ema_26", # non-stationary
|
||||
"bb_upper", "bb_mid", "bb_lower", # non-stationary
|
||||
"kc_upper", "kc_lower", # non-stationary
|
||||
"obv", "obv_sma20", "ad_line", # non-stationary
|
||||
}
|
||||
FEATURES = [c for c in df.columns if c not in EXCLUDE]
|
||||
|
||||
# drop rows with NaN (from indicator warm-up + forward label)
|
||||
model_df = df[FEATURES + ["label", "fwd_ret"]].dropna()
|
||||
|
||||
print(f"Features: {len(FEATURES)}")
|
||||
print(f"Usable rows: {len(model_df)} ({model_df.index[0].date()} → {model_df.index[-1].date()})")
|
||||
print(f"Label balance: {model_df['label'].value_counts(normalize=True).to_dict()}")
|
||||
print(f"\nFeature list:\n{FEATURES}")
|
||||
return FEATURES, model_df
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 5. Walk-Forward Split with Purge Gap
|
||||
|
||||
Time series data **cannot** use random k-fold — future data would leak into training.
|
||||
|
||||
We use **expanding-window walk-forward** with a **purge gap** between train/test:
|
||||
|
||||
```
|
||||
Fold 1: [====TRAIN====]--gap--[TEST]
|
||||
Fold 2: [========TRAIN========]--gap--[TEST]
|
||||
Fold 3: [============TRAIN============]--gap--[TEST]
|
||||
```
|
||||
|
||||
The gap prevents label leakage from overlapping forward-return windows.
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(FEATURES, N_SPLITS, PURGE_GAP, TRAIN_MIN, go, model_df, np):
|
||||
def walk_forward_splits(n_samples: int, n_splits: int, test_size: int=126, purge_gap: int=5, min_train: int=504):
|
||||
"""
|
||||
Expanding-window walk-forward with purge gap.
|
||||
|
||||
Yields (train_idx, test_idx) index arrays.
|
||||
test_size: ~6 months of trading days
|
||||
min_train: ~2 years of trading days
|
||||
purge_gap: days between train end and test start
|
||||
"""
|
||||
total_test = n_splits * test_size
|
||||
if min_train + total_test + n_splits * purge_gap > n_samples:
|
||||
raise ValueError(f'Not enough data for {n_splits} splits. Need {min_train + total_test + n_splits * purge_gap}, have {n_samples}')
|
||||
for i in range(n_splits):
|
||||
test_end = n_samples - (n_splits - 1 - i) * test_size
|
||||
test_start = test_end - test_size
|
||||
train_end = test_start - purge_gap
|
||||
train_start = 0
|
||||
train_idx = np.arange(train_start, train_end)
|
||||
test_idx = np.arange(test_start, test_end)
|
||||
yield (train_idx, test_idx) # expanding window (use max(0, train_end - fixed_window) for sliding)
|
||||
X = model_df[FEATURES].values
|
||||
y = model_df['label'].values
|
||||
dates = model_df.index
|
||||
_fig = go.Figure()
|
||||
for _fold, (_tr_idx, _te_idx) in enumerate(walk_forward_splits(len(X), N_SPLITS, purge_gap=PURGE_GAP, min_train=TRAIN_MIN)):
|
||||
_fig.add_trace(go.Scatter(x=[dates[_tr_idx[0]], dates[_tr_idx[-1]]], y=[_fold, _fold], mode='lines', line=dict(color='steelblue', width=8), name=f'Train {_fold}' if _fold == 0 else None, showlegend=_fold == 0))
|
||||
# ── Visualize the splits ────────────────────────────────────────
|
||||
_fig.add_trace(go.Scatter(x=[dates[_te_idx[0]], dates[_te_idx[-1]]], y=[_fold, _fold], mode='lines', line=dict(color='coral', width=8), name=f'Test {_fold}' if _fold == 0 else None, showlegend=_fold == 0))
|
||||
print(f'Fold {_fold}: train {dates[_tr_idx[0]].date()}→{dates[_tr_idx[-1]].date()} ({len(_tr_idx)}d) | test {dates[_te_idx[0]].date()}→{dates[_te_idx[-1]].date()} ({len(_te_idx)}d)')
|
||||
_fig.update_layout(title='Walk-Forward Splits', yaxis_title='Fold', height=300)
|
||||
_fig.show()
|
||||
return X, dates, walk_forward_splits, y
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 6. Train XGBoost per Fold — Walk-Forward
|
||||
|
||||
Train on expanding window, predict test fold, collect out-of-sample predictions.
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(
|
||||
N_SPLITS,
|
||||
PURGE_GAP,
|
||||
TRAIN_MIN,
|
||||
X,
|
||||
XGBClassifier,
|
||||
accuracy_score,
|
||||
classification_report,
|
||||
dates,
|
||||
model_df,
|
||||
walk_forward_splits,
|
||||
y,
|
||||
):
|
||||
oos_preds = [] # out-of-sample predictions
|
||||
oos_proba = [] # predicted probabilities
|
||||
oos_labels = []
|
||||
oos_dates = []
|
||||
oos_fwd_ret = []
|
||||
fold_metrics = []
|
||||
for _fold, (_tr_idx, _te_idx) in enumerate(walk_forward_splits(len(X), N_SPLITS, purge_gap=PURGE_GAP, min_train=TRAIN_MIN)):
|
||||
X_train, y_train = (X[_tr_idx], y[_tr_idx])
|
||||
X_test, y_test = (X[_te_idx], y[_te_idx])
|
||||
model = XGBClassifier(n_estimators=300, max_depth=4, learning_rate=0.05, subsample=0.8, colsample_bytree=0.8, reg_alpha=0.1, reg_lambda=1.0, random_state=42, eval_metric='logloss', early_stopping_rounds=30)
|
||||
model.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)
|
||||
preds = model.predict(X_test)
|
||||
proba = model.predict_proba(X_test)[:, 1]
|
||||
acc = accuracy_score(y_test, preds)
|
||||
oos_preds.extend(preds)
|
||||
oos_proba.extend(proba)
|
||||
oos_labels.extend(y_test)
|
||||
oos_dates.extend(dates[_te_idx])
|
||||
oos_fwd_ret.extend(model_df['fwd_ret'].values[_te_idx])
|
||||
fold_metrics.append({'fold': _fold, 'accuracy': acc, 'train_size': len(_tr_idx), 'test_size': len(_te_idx)})
|
||||
print(f'Fold {_fold}: acc={acc:.3f} | train={len(_tr_idx)} | test={len(_te_idx)}')
|
||||
print(f'\nOverall OOS accuracy: {accuracy_score(oos_labels, oos_preds):.3f}')
|
||||
print(classification_report(oos_labels, oos_preds, target_names=['SELL/HOLD', 'BUY']))
|
||||
return model, oos_dates, oos_fwd_ret, oos_preds, oos_proba
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 7. Feature Importance (Last Fold)
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(FEATURES, go, model, pd):
|
||||
imp = pd.Series(model.feature_importances_, index=FEATURES).sort_values(ascending=True)
|
||||
_fig = go.Figure(go.Bar(x=imp.tail(20), y=imp.tail(20).index, orientation='h'))
|
||||
_fig.update_layout(title='Top 20 Feature Importances (last fold)', height=500, margin=dict(l=150))
|
||||
_fig.show()
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 8. Strategy Simulation — Signal → Returns
|
||||
|
||||
Convert model predictions to a strategy equity curve:
|
||||
- **Signal = 1 (BUY)**: go long (earn the market return)
|
||||
- **Signal = 0 (SELL/HOLD)**: stay in cash (earn 0)
|
||||
|
||||
Compare against buy-and-hold benchmark.
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(df, go, oos_dates, oos_fwd_ret, oos_preds, oos_proba, pd):
|
||||
# Build strategy returns series from OOS predictions
|
||||
strat = pd.DataFrame({'date': oos_dates, 'signal': oos_preds, 'proba': oos_proba, 'fwd_ret': oos_fwd_ret}).set_index('date')
|
||||
daily_ret = df['Close'].pct_change().reindex(strat.index)
|
||||
strat['strat_ret'] = daily_ret * strat['signal']
|
||||
strat['bench_ret'] = daily_ret
|
||||
strat['strat_equity'] = (1 + strat['strat_ret']).cumprod()
|
||||
strat['bench_equity'] = (1 + strat['bench_ret']).cumprod()
|
||||
_fig = go.Figure()
|
||||
# daily returns: we use daily close-to-close returns, masked by signal
|
||||
# align with actual daily returns (not forward returns) for proper equity curve
|
||||
_fig.add_trace(go.Scatter(x=strat.index, y=strat['strat_equity'], name='Strategy', line=dict(color='steelblue')))
|
||||
_fig.add_trace(go.Scatter(x=strat.index, y=strat['bench_equity'], name='Buy & Hold', line=dict(color='gray', dash='dot')))
|
||||
# strategy return: market return when signal=1, 0 when signal=0
|
||||
in_market = strat['signal'] == 1
|
||||
_changes = in_market.astype(int).diff().fillna(0)
|
||||
entries = strat.index[_changes == 1]
|
||||
# cumulative
|
||||
exits = strat.index[_changes == -1]
|
||||
if in_market.iloc[0]:
|
||||
entries = entries.insert(0, strat.index[0])
|
||||
# plot
|
||||
if in_market.iloc[-1]:
|
||||
exits = exits.append(pd.DatetimeIndex([strat.index[-1]]))
|
||||
for ent, ext in zip(entries, exits):
|
||||
_fig.add_vrect(x0=ent, x1=ext, fillcolor='green', opacity=0.07, line_width=0)
|
||||
# shade buy signals
|
||||
_fig.update_layout(title='Strategy vs Buy & Hold (OOS)', yaxis_title='Equity ($1 start)', height=450)
|
||||
_fig.show()
|
||||
print(f"Strategy final: ${strat['strat_equity'].iloc[-1]:.2f}")
|
||||
# align: if first signal is 1, start from beginning
|
||||
print(f"Benchmark final: ${strat['bench_equity'].iloc[-1]:.2f}")
|
||||
return (strat,)
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 9. QuantStats Tearsheet
|
||||
|
||||
Full performance report: Sharpe, Sortino, max drawdown, rolling metrics, monthly heatmap.
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(pd, qs, strat):
|
||||
# quantstats expects a returns series with datetime index
|
||||
strategy_returns = strat["strat_ret"].copy()
|
||||
strategy_returns.index = pd.DatetimeIndex(strategy_returns.index)
|
||||
benchmark_returns = strat["bench_ret"].copy()
|
||||
benchmark_returns.index = pd.DatetimeIndex(benchmark_returns.index)
|
||||
|
||||
qs.extend_pandas()
|
||||
|
||||
# key metrics
|
||||
print("=" * 50)
|
||||
print("STRATEGY METRICS (out-of-sample)")
|
||||
print("=" * 50)
|
||||
print(f"Sharpe: {qs.stats.sharpe(strategy_returns):.2f}")
|
||||
print(f"Sortino: {qs.stats.sortino(strategy_returns):.2f}")
|
||||
print(f"Max Drawdown: {qs.stats.max_drawdown(strategy_returns):.2%}")
|
||||
print(f"CAGR: {qs.stats.cagr(strategy_returns):.2%}")
|
||||
print(f"Calmar: {qs.stats.calmar(strategy_returns):.2f}")
|
||||
print(f"Win Rate: {qs.stats.win_rate(strategy_returns):.2%}")
|
||||
print(f"Volatility: {qs.stats.volatility(strategy_returns):.2%}")
|
||||
print(f"Avg Win: {qs.stats.avg_win(strategy_returns):.4f}")
|
||||
print(f"Avg Loss: {qs.stats.avg_loss(strategy_returns):.4f}")
|
||||
print(f"Profit Factor:{qs.stats.profit_factor(strategy_returns):.2f}")
|
||||
print("=" * 50)
|
||||
return benchmark_returns, strategy_returns
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(TICKER, benchmark_returns, qs, strategy_returns):
|
||||
# full HTML tearsheet — saved to file + displayed inline
|
||||
qs.reports.html(strategy_returns, benchmark=benchmark_returns,
|
||||
title=f"{TICKER} ML Signal Strategy (OOS Walk-Forward)",
|
||||
output="tearsheet.html")
|
||||
print("Tearsheet saved to tearsheet.html")
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## 10. Signal Dashboard — Price + Indicators + Buy/Sell Signals
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(TICKER, df, go, make_subplots, strat):
|
||||
# show last fold's test period with signals overlaid on price
|
||||
last_test_dates = strat.index[-126:] # last ~6 months
|
||||
viz = df.loc[last_test_dates].copy()
|
||||
sig = strat.loc[last_test_dates]
|
||||
_fig = make_subplots(rows=4, cols=1, shared_xaxes=True, row_heights=[0.4, 0.2, 0.2, 0.2], vertical_spacing=0.03, subplot_titles=['Price + Bollinger Bands + Signals', 'RSI(14)', 'MACD', 'Volume'])
|
||||
_fig.add_trace(go.Candlestick(x=viz.index, open=viz['Open'], high=viz['High'], low=viz['Low'], close=viz['Close'], name='OHLC', increasing_line_color='steelblue', decreasing_line_color='salmon'), row=1, col=1)
|
||||
_fig.add_trace(go.Scatter(x=viz.index, y=viz['bb_upper'], line=dict(color='gray', width=1, dash='dot'), name='BB Upper'), row=1, col=1)
|
||||
_fig.add_trace(go.Scatter(x=viz.index, y=viz['bb_lower'], line=dict(color='gray', width=1, dash='dot'), name='BB Lower', fill='tonexty', fillcolor='rgba(128,128,128,0.05)'), row=1, col=1)
|
||||
_fig.add_trace(go.Scatter(x=viz.index, y=viz['sma_50'], line=dict(color='orange', width=1), name='SMA 50'), row=1, col=1)
|
||||
buy_mask = sig['signal'] == 1
|
||||
_changes = buy_mask.astype(int).diff()
|
||||
buy_entries = sig.index[_changes == 1]
|
||||
# Row 1: Candlestick + BB + signals
|
||||
sell_entries = sig.index[_changes == -1]
|
||||
if len(buy_entries):
|
||||
_fig.add_trace(go.Scatter(x=buy_entries, y=viz.loc[buy_entries, 'Low'] * 0.995, mode='markers', marker=dict(symbol='triangle-up', size=10, color='green'), name='BUY'), row=1, col=1)
|
||||
if len(sell_entries):
|
||||
_fig.add_trace(go.Scatter(x=sell_entries, y=viz.loc[sell_entries, 'High'] * 1.005, mode='markers', marker=dict(symbol='triangle-down', size=10, color='red'), name='SELL'), row=1, col=1)
|
||||
_fig.add_trace(go.Scatter(x=viz.index, y=viz['rsi_14'], line=dict(color='purple', width=1.5), name='RSI 14'), row=2, col=1)
|
||||
_fig.add_hline(y=70, line_dash='dash', line_color='red', opacity=0.5, row=2, col=1)
|
||||
_fig.add_hline(y=30, line_dash='dash', line_color='green', opacity=0.5, row=2, col=1)
|
||||
# buy/sell markers
|
||||
_fig.add_trace(go.Scatter(x=viz.index, y=viz['macd'], line=dict(color='blue', width=1.5), name='MACD'), row=3, col=1)
|
||||
_fig.add_trace(go.Scatter(x=viz.index, y=viz['macd_signal'], line=dict(color='orange', width=1), name='Signal'), row=3, col=1)
|
||||
colors = ['green' if v >= 0 else 'red' for v in viz['macd_hist']]
|
||||
_fig.add_trace(go.Bar(x=viz.index, y=viz['macd_hist'], marker_color=colors, name='Hist', opacity=0.5), row=3, col=1)
|
||||
_fig.add_trace(go.Bar(x=viz.index, y=viz['Volume'], marker_color='steelblue', name='Volume', opacity=0.5), row=4, col=1)
|
||||
_fig.add_trace(go.Scatter(x=viz.index, y=viz['Volume'].rolling(20).mean(), line=dict(color='orange', width=1), name='Vol SMA20'), row=4, col=1)
|
||||
_fig.update_layout(height=900, title=f'{TICKER} — Last Test Fold Signal Dashboard', xaxis_rangeslider_visible=False, showlegend=False)
|
||||
_fig.update_xaxes(rangeslider_visible=False)
|
||||
# Row 2: RSI
|
||||
# Row 3: MACD
|
||||
# Row 4: Volume
|
||||
_fig.show()
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## Next Steps
|
||||
|
||||
Things to iterate on from here:
|
||||
|
||||
1. **Multi-asset**: swap `TICKER` to BTC-USD, QQQ, GLD, etc. or loop over a universe
|
||||
2. **Probability threshold**: instead of binary 0/1, use `proba > 0.6` for higher-conviction signals
|
||||
3. **Position sizing**: Kelly criterion via `PyPortfolioOpt` based on predicted probability
|
||||
4. **Regime filter**: add ADX/volatility regime detection — only trade in trending regimes
|
||||
5. **Transaction costs**: subtract realistic slippage (e.g., 5bps per trade) from returns
|
||||
6. **Alternative splitters you have installed**:
|
||||
- `from tscv import GapWalkForward` — sklearn-compatible, handles gap + purge natively
|
||||
- `from sktime.split import ExpandingWindowSplitter, SlidingWindowSplitter`
|
||||
- `from sklearn.model_selection import TimeSeriesSplit` — basic but solid
|
||||
7. **LightGBM**: drop-in replacement for XGBoost, often faster on large feature sets
|
||||
8. **Meta-labeling** (Lopez de Prado): train a secondary model on whether the primary model's signals are correct
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run()
|
||||
@@ -628,7 +628,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.12"
|
||||
"version": "3.12.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -131,6 +131,24 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "alpaca-py"
|
||||
version = "0.43.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "msgpack" },
|
||||
{ name = "pandas" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "pytz" },
|
||||
{ name = "requests" },
|
||||
{ name = "sseclient-py" },
|
||||
{ name = "websockets" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/31/40/e0b3470786de2dd917276759d669b3adc6577432323e8535724e71e53c2e/alpaca_py-0.43.3.tar.gz", hash = "sha256:d97e815eb503fe084113b139ed94227913dc4da8025f28460a14d489371bd07d", size = 97985, upload-time = "2026-04-24T18:28:37.068Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/f8/b4/062dbf87910c4a5c0accde3bd073c2fa3f7ac9e66571489ef1a7c0f44531/alpaca_py-0.43.3-py3-none-any.whl", hash = "sha256:0eb36921696bbfc5ea839f083e7822c9ccab062d8d523a888bc4470985f1b3ef", size = 122528, upload-time = "2026-04-24T18:28:38.25Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-doc"
|
||||
version = "0.0.4"
|
||||
@@ -202,6 +220,21 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/dd/e3/03dc0f97eab839f72061342d69bd34424e89876ce4026509aab3d74d4f23/appscript-1.4.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:5efce3302c00674b769b79938cc5f66f7791ef45c6419e850a5f1c8f9fcefcc1", size = 85610, upload-time = "2025-10-08T07:56:38.103Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "apscheduler"
|
||||
version = "3.6.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pytz" },
|
||||
{ name = "setuptools" },
|
||||
{ name = "six" },
|
||||
{ name = "tzlocal" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/89/3d/f65972547c5aa533276ada2bea3c2ef51bb4c4de55b67a66129c111b89ad/APScheduler-3.6.3.tar.gz", hash = "sha256:3bb5229eed6fbbdafc13ce962712ae66e175aa214c69bed35a06bffcf0c5e244", size = 96309, upload-time = "2019-11-05T07:51:50.394Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/f3/34/9ef20ed473c4fd2c3df54ef77a27ae3fc7500b16b192add4720cab8b2c09/APScheduler-3.6.3-py2.py3-none-any.whl", hash = "sha256:e8b1ecdb4c7cb2818913f766d5898183c7cb8936680710a4d3a966e02262e526", size = 58881, upload-time = "2019-11-05T07:51:48.621Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "arch"
|
||||
version = "8.0.0"
|
||||
@@ -491,6 +524,15 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/88/6764e7a109dd84294850741501145da90d13cdeac9d4e614929464a37420/build-1.4.4-py3-none-any.whl", hash = "sha256:8c3f48a6090b39edec1a273d2d57949aaf13723b01e02f9d518396887519f64d", size = 25921, upload-time = "2026-04-22T20:53:43.251Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cachetools"
|
||||
version = "4.2.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/52/ba/619250fa6bc11ce6aa4de0604d45843090a53cd7d10d7253b89669313370/cachetools-4.2.2.tar.gz", hash = "sha256:61b5ed1e22a0924aed1d23b478f37e8d52549ff8a961de2909c69bf950020cff", size = 23682, upload-time = "2021-04-27T21:19:57.252Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/bf/28/c4f5796c67ad06bb91d98d543a5e01805c1ff065e08871f78e52d2a331ad/cachetools-4.2.2-py3-none-any.whl", hash = "sha256:2cc0b89715337ab6dbba85b5b50effe2b0c74e035d83ee8ed637cf52f12ae001", size = 11998, upload-time = "2021-04-27T21:19:55.559Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ccxt"
|
||||
version = "4.5.50"
|
||||
@@ -2039,7 +2081,7 @@ dependencies = [
|
||||
{ name = "torch", version = "2.11.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
|
||||
{ name = "transformers" },
|
||||
{ name = "tscv" },
|
||||
{ name = "vectorbt" },
|
||||
{ name = "vectorbt", extra = ["full", "rust"] },
|
||||
{ name = "xgboost" },
|
||||
{ name = "yfinance" },
|
||||
]
|
||||
@@ -2079,7 +2121,7 @@ requires-dist = [
|
||||
{ name = "torch", specifier = ">=2.0.0", index = "https://download.pytorch.org/whl/cpu" },
|
||||
{ name = "transformers", specifier = ">=4.0.0" },
|
||||
{ name = "tscv", specifier = ">=0.1.3" },
|
||||
{ name = "vectorbt", specifier = ">=1.0.0" },
|
||||
{ name = "vectorbt", extras = ["full", "rust"], specifier = ">=1.0.0" },
|
||||
{ name = "xgboost", specifier = ">=3.2.0" },
|
||||
{ name = "yfinance", specifier = ">=1.3.0" },
|
||||
]
|
||||
@@ -2509,6 +2551,50 @@ wheels = [
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|
||||
{ name = "quantstats" },
|
||||
{ name = "ray" },
|
||||
{ name = "ta" },
|
||||
{ name = "ta-lib" },
|
||||
{ name = "yfinance" },
|
||||
]
|
||||
rust = [
|
||||
{ name = "vectorbt-rust" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "vectorbt-rust"
|
||||
version = "1.0.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/02/32/a5b7193cfaaa4485e6b3c0156e15701b21bb505b66e035f01594675d1242/vectorbt_rust-1.0.0.tar.gz", hash = "sha256:d9ef94a24f970be4d566d5c41d20c436cb1593ae2e960ee2ae9851ce5042b9aa", size = 74476, upload-time = "2026-04-22T13:29:36.715Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fd/62/3df4be3717f3c2df07f51d380f9beae7100e78656b1879710e62c2d36f28/vectorbt_rust-1.0.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:ceceb76bb1f485dc66eb3aa4ee2e4934b704c8753884a4c2eebf156427ecf374", size = 845019, upload-time = "2026-04-22T13:29:22.458Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/3d/5f/b96d327002901177fc05c64d9a99bbff5673a20081bb1a3a2e35e02a8707/vectorbt_rust-1.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:689794e3f3a91c0b8cfc3565076b01864a51a95a8a003410c972942a424a8014", size = 766156, upload-time = "2026-04-22T13:29:24.034Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/35/fc/2ef2905a752f17329c768ddbfdff3cd14deab932e1010ca140bde9ab32b4/vectorbt_rust-1.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a204323805b5cc721ce4706f8d117886d0e860a2c74758aad965c65912aabc8f", size = 781441, upload-time = "2026-04-22T13:29:25.418Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8d/c6/708752ae698161575fe949ba659341e24ebd6341ed683e1f2b66b7a84a35/vectorbt_rust-1.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9a379f7ae66ef5733d5de6ae1f23f8434d4411918d04b9b2baf7e8db105ca759", size = 873255, upload-time = "2026-04-22T13:29:27.081Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ec/fe/fe1b7b07520fbb1559187ea5a962db0246b5c3f6e24ace2f7cd6a5346fab/vectorbt_rust-1.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:f6adbd831d1615eca9e6499296345d092f670d668440d2b5043523f3753c62fc", size = 885340, upload-time = "2026-04-22T13:29:28.381Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5c/f5/02dae5b57efab7e8974f5d9753e69cbc4cff23f8432b0d6c15a86beea7b2/vectorbt_rust-1.0.0-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:65305a46fd522978208bb860c4f6a7a01f84a2105801661cf48ee24d5d8f1b20", size = 844571, upload-time = "2026-04-22T13:29:29.672Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/98/fb/efd9ec9a448c87c069e1e82bf5362ddf58034fccad0c3ca71463e3ffffab/vectorbt_rust-1.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:29caf153a3e0ca9fb285970ae7086c6da667b309535e665f4e78d4f5797f10b0", size = 765910, upload-time = "2026-04-22T13:29:31.11Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/8c/91/6749335b37b4b88468c89c8d30e5723a821e870b5eed62a9f2c3fca6808c/vectorbt_rust-1.0.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dc623f8a824b65a15b049461d07eeb545928ef9e0524389d549dba3060a786da", size = 781120, upload-time = "2026-04-22T13:29:32.689Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ed/3d/523288d04f77660698a6db2d64ec0c7f72299993e711d09c00ae98993952/vectorbt_rust-1.0.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cf8ae04a2dbd6ead5f4f36dace37c211e31a9b354d2daed20fcdc55a3dc5aefe", size = 872877, upload-time = "2026-04-22T13:29:34.086Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7c/9b/7f4138ff4309b6aa7f4cc3accde9bf8260d959c303dcfe3fc59271725679/vectorbt_rust-1.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:8702515db6f46ef9a80cdd1d2c8c8617a148bf1861721d308cc299c8cc408e35", size = 884993, upload-time = "2026-04-22T13:29:35.331Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wcwidth"
|
||||
version = "0.6.0"
|
||||
|
||||
+172
@@ -0,0 +1,172 @@
|
||||
"""
|
||||
Pre-warm the defeatbeta httpfs cache to a persistent directory.
|
||||
|
||||
By default the library caches to /tmp/defeatbeta/cache/ which is wiped on
|
||||
reboot. This script redirects the cache to ~/.cache/defeatbeta/ and then
|
||||
does a full SELECT * on every table in parallel, forcing all parquet blocks
|
||||
to be fetched and stored. Re-running is safe: already-warmed tables are
|
||||
skipped.
|
||||
|
||||
Usage:
|
||||
uv run python warmup_cache.py
|
||||
|
||||
After this runs once (~3-4 GB download), use persistent_cache.py in your
|
||||
notebooks/scripts to read from the cache with no network:
|
||||
|
||||
from persistent_cache import enable_persistent_cache
|
||||
enable_persistent_cache()
|
||||
|
||||
from defeatbeta_api.data.ticker import Ticker
|
||||
t = Ticker("AAPL")
|
||||
"""
|
||||
import json
|
||||
import threading
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from pathlib import Path
|
||||
|
||||
# ── Persistent cache location ────────────────────────────────────────────────
|
||||
CACHE_DIR = Path.home() / ".cache" / "defeatbeta"
|
||||
CACHE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
STATE_FILE = CACHE_DIR / "warmup_done.json"
|
||||
|
||||
# ── Redirect cache dir before the library touches it ────────────────────────
|
||||
import defeatbeta_api.utils.util as _util
|
||||
_util.validate_httpfs_cache_directory = lambda: str(CACHE_DIR)
|
||||
|
||||
from defeatbeta_api.client.duckdb_conf import Configuration
|
||||
from defeatbeta_api.client.duckdb_client import get_duckdb_client
|
||||
from defeatbeta_api.client.hugging_face_client import HuggingFaceClient
|
||||
from defeatbeta_api.utils.const import tables
|
||||
|
||||
WORKERS = 2 # keep concurrent connections low to avoid HuggingFace 429s
|
||||
|
||||
config = Configuration(
|
||||
cache_httpfs_disk_size=500 * 1024 * 1024,
|
||||
http_retries=10,
|
||||
http_retry_wait_ms=10_000, # 10s base wait on 429/5xx
|
||||
http_retry_backoff=2.0, # doubles each retry: 10s, 20s, 40s …
|
||||
http_timeout=180,
|
||||
)
|
||||
client = get_duckdb_client(config=config)
|
||||
hf = HuggingFaceClient()
|
||||
|
||||
|
||||
# ── Resume state ─────────────────────────────────────────────────────────────
|
||||
|
||||
def _load_done() -> set[str]:
|
||||
try:
|
||||
return set(json.loads(STATE_FILE.read_text())["done"])
|
||||
except Exception:
|
||||
return set()
|
||||
|
||||
def _save_done(done: set[str]) -> None:
|
||||
tmp = STATE_FILE.with_suffix(".tmp")
|
||||
tmp.write_text(json.dumps({"done": sorted(done)}))
|
||||
tmp.rename(STATE_FILE)
|
||||
|
||||
|
||||
already_done = _load_done()
|
||||
todo = [t for t in tables if t not in already_done]
|
||||
|
||||
|
||||
# ── Helpers ───────────────────────────────────────────────────────────────────
|
||||
|
||||
def cache_mb() -> float:
|
||||
try:
|
||||
return sum(f.stat().st_size for f in CACHE_DIR.iterdir() if f.is_file()) / 1e6
|
||||
except Exception:
|
||||
return 0.0
|
||||
|
||||
|
||||
# ── Shared state for the live reporter ───────────────────────────────────────
|
||||
_lock = threading.Lock()
|
||||
_in_flight: set[str] = set()
|
||||
_done: list[str] = list(already_done) # pre-seed with already-completed tables
|
||||
_stop_reporter = threading.Event()
|
||||
|
||||
|
||||
def _reporter() -> None:
|
||||
while not _stop_reporter.is_set():
|
||||
with _lock:
|
||||
n_done = len(_done)
|
||||
flying = ", ".join(sorted(_in_flight)) or "—"
|
||||
n_flight = len(_in_flight)
|
||||
mb = cache_mb()
|
||||
print(
|
||||
f"\r [live] cache={mb:>6.0f} MB | "
|
||||
f"done={n_done}/{len(tables)} | "
|
||||
f"in-flight({n_flight}): {flying} ",
|
||||
end="", flush=True,
|
||||
)
|
||||
_stop_reporter.wait(timeout=2)
|
||||
print("\r" + " " * 80 + "\r", end="", flush=True)
|
||||
|
||||
|
||||
def fetch_table(table: str) -> tuple[str, int, float]:
|
||||
url = hf.get_url_path(table)
|
||||
with _lock:
|
||||
_in_flight.add(table)
|
||||
t0 = time.perf_counter()
|
||||
for attempt in range(1, 4):
|
||||
try:
|
||||
df = client.query(f"SELECT * FROM '{url}'")
|
||||
break
|
||||
except Exception as exc:
|
||||
if attempt == 3:
|
||||
with _lock:
|
||||
_in_flight.discard(table)
|
||||
raise
|
||||
wait = 60 * attempt
|
||||
print(f"\n ! {table} failed (attempt {attempt}): {exc} — retrying in {wait}s")
|
||||
time.sleep(wait)
|
||||
elapsed = time.perf_counter() - t0
|
||||
with _lock:
|
||||
_in_flight.discard(table)
|
||||
_done.append(table)
|
||||
_save_done(set(_done))
|
||||
return table, len(df), elapsed
|
||||
|
||||
|
||||
# ── Run ───────────────────────────────────────────────────────────────────────
|
||||
print(f"Cache dir : {CACHE_DIR}")
|
||||
print(f"Tables : {len(todo)} to fetch ({len(already_done)} already done, skipping)")
|
||||
print(f"Workers : {WORKERS}")
|
||||
print()
|
||||
|
||||
if not todo:
|
||||
print("All tables already warmed. Nothing to do.")
|
||||
raise SystemExit(0)
|
||||
|
||||
for t in sorted(already_done):
|
||||
print(f" - {t:<40} (skip)")
|
||||
print()
|
||||
|
||||
reporter = threading.Thread(target=_reporter, daemon=True)
|
||||
reporter.start()
|
||||
|
||||
t_start = time.perf_counter()
|
||||
results: list[tuple[str, int, float]] = []
|
||||
|
||||
with ThreadPoolExecutor(max_workers=WORKERS) as pool:
|
||||
futures = {pool.submit(fetch_table, t): t for t in todo}
|
||||
for future in as_completed(futures):
|
||||
table, rows, elapsed = future.result()
|
||||
results.append((table, rows, elapsed))
|
||||
with _lock:
|
||||
n_done = len(_done)
|
||||
mb = cache_mb()
|
||||
print(
|
||||
f" ✓ {table:<40} {rows:>9,} rows {elapsed:5.1f}s "
|
||||
f"[{n_done}/{len(tables)} cache={mb:.0f} MB]"
|
||||
)
|
||||
|
||||
_stop_reporter.set()
|
||||
reporter.join()
|
||||
|
||||
total_elapsed = time.perf_counter() - t_start
|
||||
total_rows = sum(r for _, r, _ in results)
|
||||
print()
|
||||
print(f"Finished in {total_elapsed:.0f}s")
|
||||
print(f"Fetched : {total_rows:,} rows across {len(results)} tables")
|
||||
print(f"Cache size : {cache_mb():.0f} MB → {CACHE_DIR}")
|
||||
Reference in New Issue
Block a user