diff --git a/.envrc b/.envrc
new file mode 100644
index 0000000..929f81b
--- /dev/null
+++ b/.envrc
@@ -0,0 +1 @@
+layout uv
diff --git a/aapl_yfinance.md b/aapl_yfinance.md
new file mode 100644
index 0000000..9e3974f
--- /dev/null
+++ b/aapl_yfinance.md
@@ -0,0 +1,119 @@
+# AAPL - All yfinance Data
+
+```python
+import yfinance as yf
+import marimo as mo
+
+ticker = yf.Ticker("AAPL")
+```
+
+## Price History
+
+```python
+history = ticker.history(period="1y", interval="1d")
+mo.ui.table(history.tail(30))
+```
+
+## Dividends
+
+```python
+mo.ui.table(ticker.dividends.tail(20))
+```
+
+## Splits
+
+```python
+mo.ui.table(ticker.splits)
+```
+
+## Income Statement
+
+```python
+mo.ui.table(ticker.get_income_stmt())
+```
+
+## Balance Sheet
+
+```python
+mo.ui.table(ticker.get_balance_sheet())
+```
+
+## Cash Flow
+
+```python
+mo.ui.table(ticker.get_cashflow())
+```
+
+## Analyst Recommendations
+
+```python
+mo.ui.table(ticker.recommendations.tail(20))
+```
+
+## Analyst Target Prices
+
+```python
+mo.ui.table([ticker.target_prices])
+```
+
+## Earnings Dates
+
+```python
+mo.ui.table(ticker.earnings_dates)
+```
+
+## Options Expirations
+
+```python
+expirations = ticker.get_options()
+f"Available: {expirations}"
+```
+
+```python
+if expirations:
+    chain = ticker.option_chain(expirations[0])
+    mo.ui.table(chain.calls)
+    mo.ui.table(chain.puts)
+```
+
+## Major Holders
+
+```python
+mo.ui.table(ticker.major_holders)
+```
+
+## Institutional Holders
+
+```python
+mo.ui.table(ticker.institutional_holders)
+```
+
+## Sustainability / ESG
+
+```python
+mo.ui.table([ticker.sustainability])
+```
+
+## Calendar / Events
+
+```python
+mo.ui.table(ticker.calendar)
+```
+
+## Company Info Summary
+
+```python
+info = ticker.info
+{
+    "name": info.get("longName"),
+    "sector": info.get("sector"),
+    "industry": info.get("industry"),
+    "marketCap": info.get("marketCap"),
+    "peRatio": info.get("trailingPE"),
+    "fwdDividend": info.get("dividendYield"),
+    "52wkHigh": info.get("fiftyTwoWeekHigh"),
+    "52wkLow": info.get("fiftyTwoWeekLow"),
+    "avgTarget": info.get("targetMeanPrice"),
+    "recommendation": info.get("recommendationKey"),
+}
+```
diff --git a/aapl_yfinance.py b/aapl_yfinance.py
new file mode 100644
index 0000000..1f54839
--- /dev/null
+++ b/aapl_yfinance.py
@@ -0,0 +1,279 @@
+import marimo
+
+__generated_with = "0.23.2"
+app = marimo.App(width="full")
+
+
+@app.cell
+def _():
+    import yfinance as yf
+    import marimo as mo
+    ticker = yf.Ticker("AAPL")
+    return mo, ticker
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    # AAPL — All Data via yfinance
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Price History
+    """)
+    return
+
+
+@app.cell
+def _(ticker):
+    history = ticker.history(period="max", interval="1d")
+    history
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Info
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    info = ticker.info
+    mo.ui.table([info])
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Dividends
+    """)
+    return
+
+
+@app.cell
+def _(ticker):
+    dividends = ticker.dividends
+    dividends
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Splits
+    """)
+    return
+
+
+@app.cell
+def _(ticker):
+    splits = ticker.splits
+    splits
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Income Statement
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    inc = ticker.get_income_stmt()
+    mo.ui.table(inc)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Balance Sheet
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    bal = ticker.get_balance_sheet()
+    mo.ui.table(bal)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Cash Flow
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    cf = ticker.get_cashflow()
+    mo.ui.table(cf)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Recommendations
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    recs = ticker.recommendations
+    mo.ui.table(recs)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Analyst Target Prices
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    target = ticker.target_prices
+    mo.ui.table([target] if isinstance(target, dict) else target)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Earnings Dates
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    earn_dates = ticker.earnings_dates
+    mo.ui.table(earn_dates)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Options
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    expirations = ticker.options
+    mo.md(f"Available expirations: {expirations}")
+    return (expirations,)
+
+
+@app.cell
+def _(expirations, mo, ticker):
+    sections = []
+    for exp in expirations:
+        chain = ticker.option_chain(exp)
+        sections += [
+            mo.md(f"### {exp} — Calls"),
+            mo.ui.table(chain.calls),
+            mo.md(f"### {exp} — Puts"),
+            mo.ui.table(chain.puts),
+        ]
+    mo.vstack(sections) if sections else None
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Major Holders
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    major = ticker.major_holders
+    mo.ui.table(major)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Institutional Holders
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    inst = ticker.institutional_holders
+    mo.ui.table(inst)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Mutual Fund Holders
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    mf = ticker.mutualfund_holders
+    mo.ui.table(mf)
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Sustainability / ESG
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    sus = ticker.sustainability
+    mo.ui.table([sus] if sus is not None else [{"data": "No ESG data available"}])
+    return
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md("""
+    ## Calendar / Events
+    """)
+    return
+
+
+@app.cell
+def _(mo, ticker):
+    cal = ticker.calendar
+    mo.ui.table([cal] if isinstance(cal, dict) else cal)
+    return
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/defeatbeta_tutorial.ipynb b/defeatbeta_tutorial.ipynb
index 0415d39..d991c2a 100644
--- a/defeatbeta_tutorial.ipynb
+++ b/defeatbeta_tutorial.ipynb
@@ -44,6 +44,7 @@
       "|___/ \\___|_| \\___|\\__,_|\\__| \\____/ \\___|\\__\\__,_|\u001b[0m\n",
       "\u001b[1;38;5;10m📈:: Data Update Time ::\u001b[0m\t2026-04-17 \u001b[1;38;5;10m::\u001b[0m\n",
       "\u001b[1;38;5;10m📈:: Software Version ::\u001b[0m\t0.0.45      \u001b[1;38;5;10m::\u001b[0m\n",
+      "[persistent_cache] cache → /home/df/.cache/defeatbeta\n",
       "✅ yfinance is installed\n",
       "✅ DefeatBeta-API imported successfully\n",
       "Python version: 3.12.12\n"
@@ -59,6 +60,8 @@
     "\n",
     "# DefeatBeta-API\n",
     "from defeatbeta_api.data.ticker import Ticker\n",
+    "from persistent_cache import enable_persistent_cache\n",
+    "enable_persistent_cache()\n",
     "\n",
     "# Yahoo Finance (for comparison)\n",
     "try:\n",
@@ -84,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -94,12 +97,11 @@
       "============================================================\n",
       "PERFORMANCE COMPARISON: Fetching Price Data\n",
       "============================================================\n",
-      "2026-04-25 17:43:11 INFO DuckDBClient MainThread - Cache is up-to-date. Update time: 2026-04-17\n",
       "\n",
-      "✅ DefeatBeta: 0.617s\n",
+      "✅ DefeatBeta: 0.019s\n",
       "   Data shape: (7897, 7)\n",
       "\n",
-      "✅ Yahoo Finance: 0.493s\n",
+      "✅ Yahoo Finance: 0.276s\n",
       "   Data shape: (11433, 7)\n"
      ]
     }
@@ -141,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -531,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -841,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -887,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -956,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -3416,6 +3418,1142 @@
     "dir(yft)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import vectorbt as vbt\n",
+    "data = Ticker(\"AAPL\")\n",
+    "price = data.price().close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fast_ma = vbt.MA.run(price, 10)\n",
+    "slow_ma = vbt.MA.run(price, 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "entries = fast_ma.ma_crossed_above(slow_ma)\n",
+    "exits = slow_ma.ma_crossed_above(fast_ma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pf = vbt.Portfolio.from_signals(price, entries, exits, init_cash=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(24134.667890761524)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pf.total_profit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = data.price()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Move the 'report_date' column into the Index position\n",
+    "df['report_date'] = pd.to_datetime(df['report_date'])\n",
+    "df = df.set_index('report_date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/df/scratch/trading/learn-trading/.venv/lib/python3.12/site-packages/vectorbt/data/base.py:535: UserWarning: Symbols have mismatching index. Dropping missing data points.\n",
+      "  data = cls.align_index(data, missing=missing_index)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "mode": "markers",
+         "name": "BTC-USD",
+         "showlegend": true,
+         "type": "scatter",
+         "uid": "67aa9626-ac1b-42c0-a099-a328b49dec1c",
+         "x": {
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+          "dtype": "f8"
+         }
+        },
+        {
+         "mode": "markers",
+         "name": "ETH-USD",
+         "showlegend": true,
+         "type": "scatter",
+         "uid": "7748b161-109f-4585-95c5-260ab9ff8b64",
+         "x": {
+          "bdata": "CgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZA==",
+          "dtype": "i1"
+         },
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+          "dtype": "f8"
+         }
+        }
+       ],
+       "layout": {
+        "height": 350,
+        "legend": {
+         "orientation": "h",
+         "traceorder": "normal",
+         "x": 1,
+         "xanchor": "right",
+         "y": 1.02,
+         "yanchor": "bottom"
+        },
+        "margin": {
+         "b": 30,
+         "l": 30,
+         "r": 30,
+         "t": 30
+        },
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
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+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             }
+            },
+            "type": "barpolar"
+           }
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+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
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+              0,
+              "#0d0887"
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "symbols = [\"BTC-USD\", \"ETH-USD\"]\n",
+    "data = vbt.YFData.download(symbols, missing_index=\"drop\")\n",
+    "price = data.get(\"Close\")\n",
+    "\n",
+    "n = np.random.randint(10, 101, size=1000).tolist()\n",
+    "pf = vbt.Portfolio.from_random_signals(price, n=n, init_cash=100, seed=42)\n",
+    "\n",
+    "mean_expectancy = pf.trades.expectancy().groupby([\"randnx_n\", \"symbol\"]).mean()\n",
+    "fig = mean_expectancy.unstack().vbt.scatterplot(xaxis_title=\"randnx_n\", yaxis_title=\"mean_expectancy\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idx</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>cik</th>\n",
+       "      <th>name</th>\n",
+       "      <th>financial_currency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NVDA</td>\n",
+       "      <td>1045810.0</td>\n",
+       "      <td>NVIDIA CORP</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GOOGL</td>\n",
+       "      <td>1652044.0</td>\n",
+       "      <td>Alphabet Inc.</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>320193.0</td>\n",
+       "      <td>Apple Inc.</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>MSFT</td>\n",
+       "      <td>789019.0</td>\n",
+       "      <td>MICROSOFT CORP</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>AMZN</td>\n",
+       "      <td>1018724.0</td>\n",
+       "      <td>AMAZON COM INC</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10396</th>\n",
+       "      <td>extra_5</td>\n",
+       "      <td>USMV</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>iShares MSCI USA Min Vol Factor ETF</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10397</th>\n",
+       "      <td>extra_6</td>\n",
+       "      <td>IWM</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>iShares Russell 2000 ETF</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10398</th>\n",
+       "      <td>extra_7</td>\n",
+       "      <td>VTV</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Vanguard Value ETF</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10399</th>\n",
+       "      <td>extra_8</td>\n",
+       "      <td>TLT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>iShares 20+ Year Treasury Bond ETF</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10400</th>\n",
+       "      <td>extra_9</td>\n",
+       "      <td>JNK</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SPDR Bloomberg High Yield Bond ETF</td>\n",
+       "      <td>USD</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10401 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           idx symbol        cik                                 name  \\\n",
+       "0            0   NVDA  1045810.0                          NVIDIA CORP   \n",
+       "1            1  GOOGL  1652044.0                        Alphabet Inc.   \n",
+       "2            2   AAPL   320193.0                           Apple Inc.   \n",
+       "3            3   MSFT   789019.0                       MICROSOFT CORP   \n",
+       "4            4   AMZN  1018724.0                       AMAZON COM INC   \n",
+       "...        ...    ...        ...                                  ...   \n",
+       "10396  extra_5   USMV        NaN  iShares MSCI USA Min Vol Factor ETF   \n",
+       "10397  extra_6    IWM        NaN             iShares Russell 2000 ETF   \n",
+       "10398  extra_7    VTV        NaN                   Vanguard Value ETF   \n",
+       "10399  extra_8    TLT        NaN   iShares 20+ Year Treasury Bond ETF   \n",
+       "10400  extra_9    JNK        NaN   SPDR Bloomberg High Yield Bond ETF   \n",
+       "\n",
+       "      financial_currency  \n",
+       "0                    USD  \n",
+       "1                    USD  \n",
+       "2                    USD  \n",
+       "3                    USD  \n",
+       "4                    USD  \n",
+       "...                  ...  \n",
+       "10396                USD  \n",
+       "10397                USD  \n",
+       "10398                USD  \n",
+       "10399                USD  \n",
+       "10400                USD  \n",
+       "\n",
+       "[10401 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from defeatbeta_api.data.company_meta import CompanyMeta\n",
+    "\n",
+    "meta = CompanyMeta()\n",
+    "pd.DataFrame(meta.get_all_companies_info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (3999457017.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[64], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    dbt.\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "dbt."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/defeatbeta-api.org b/docs/defeatbeta-api.org
new file mode 100644
index 0000000..82de03d
--- /dev/null
+++ b/docs/defeatbeta-api.org
@@ -0,0 +1,203 @@
+#+TITLE: defeatbeta-api Reference
+#+AUTHOR: Wong Ding Feng
+#+DATE: 2026-04-25
+
+* How Data Retrieval Works
+
+** NOT a full download
+
+Uses *DuckDB + ~cache_httpfs~ extension* querying *remote Parquet files on HuggingFace*
+(~defeatbeta/yahoo-finance-data~). Every query runs SQL directly against remote files:
+
+#+begin_src sql
+SELECT * FROM 'https://huggingface.co/.../stock_prices.parquet' WHERE symbol = 'AAPL'
+#+end_src
+
+Parquet's columnar format + DuckDB *predicate pushdown* = only the row-groups matching
+your ticker are fetched over HTTP range requests. Not the full 3-4 GB file.
+
+** On-disk cache
+
+- Default 1 GB cache at ~~/.defeatbeta/cache/~
+- Stores fetched blocks so repeated queries are fast
+- On startup: checks ~spec.json~ on HuggingFace, clears stale cache if dataset was updated
+
+* Getting All Available Tickers
+
+#+begin_src python
+from defeatbeta_api.data.company_meta import CompanyMeta
+
+meta = CompanyMeta()
+all_tickers  = meta.get_all_tickers()         # List[str]
+all_companies = meta.get_all_companies_info()  # List[dict]: symbol, name, cik, currency
+#+end_src
+
+Reads ~company_tickers.json~ from HuggingFace — a small JSON, not the big Parquet files.
+
+* Single Ticker API — ~Ticker("AAPL")~
+
+#+begin_src python
+from defeatbeta_api.data.ticker import Ticker
+t = Ticker("AAPL")
+#+end_src
+
+** Company Info
+
+| Method                     | Returns     | What it gives                                           |
+|----------------------------+-------------+---------------------------------------------------------|
+| ~info()~                     | DataFrame   | Profile: name, sector, industry, description, headcount |
+| ~officers()~                 | DataFrame   | Executive officers                                      |
+| ~sec_filing()~               | DataFrame   | SEC filings list                                        |
+| ~news()~                     | ~News~ object | Latest news articles                                    |
+| ~earning_call_transcripts()~ | ~Transcripts~ | Earnings call transcripts                               |
+| ~calendar()~                 | DataFrame   | Upcoming earnings dates                                 |
+
+** Prices & Basic Finance
+
+| Method                             | Returns   | What it gives                |
+|------------------------------------+-----------+------------------------------|
+| ~price()~                            | DataFrame | Historical OHLCV prices      |
+| ~splits()~                           | DataFrame | Stock split events           |
+| ~dividends()~                        | DataFrame | Dividend payment history     |
+| ~shares()~                           | DataFrame | Shares outstanding over time |
+| ~beta(period="5y", benchmark="SPY")~ | DataFrame | Calculated beta vs benchmark |
+| ~currency(symbol)~                   | DataFrame | Exchange rate history        |
+| ~ttm_eps()~                          | DataFrame | Trailing 12-month EPS        |
+
+** Financial Statements
+
+| Method                       | Returns   | What it gives           |
+|------------------------------+-----------+-------------------------|
+| ~quarterly_income_statement()~ | ~Statement~ | Quarterly P&L           |
+| ~annual_income_statement()~    | ~Statement~ | Annual P&L              |
+| ~quarterly_balance_sheet()~    | ~Statement~ | Quarterly balance sheet |
+| ~annual_balance_sheet()~       | ~Statement~ | Annual balance sheet    |
+| ~quarterly_cash_flow()~        | ~Statement~ | Quarterly cash flow     |
+| ~annual_cash_flow()~           | ~Statement~ | Annual cash flow        |
+
+** TTM Aggregates
+
+| Method                                 | Returns   | What it gives                    |
+|----------------------------------------+-----------+----------------------------------|
+| ~ttm_revenue()~                          | DataFrame | Trailing 12-month revenue        |
+| ~ttm_fcf()~                              | DataFrame | Trailing 12-month free cash flow |
+| ~ttm_ebitda()~                           | DataFrame | Trailing 12-month EBITDA         |
+| ~ttm_net_income_common_stockholders()~ | DataFrame | Trailing 12-month net income     |
+| ~ttm_pe()~                               | DataFrame | Trailing P/E (price / ttm_eps)   |
+
+** Revenue Breakdown
+
+| Method                 | Returns   | What it gives               |
+|------------------------+-----------+-----------------------------|
+| ~revenue_by_segment()~   | DataFrame | Revenue by business segment |
+| ~revenue_by_geography()~ | DataFrame | Revenue by region           |
+| ~revenue_by_product()~   | DataFrame | Revenue by product line     |
+
+** Valuation Multiples
+
+| Method                  | Returns   | What it gives                |
+|-------------------------+-----------+------------------------------|
+| ~market_capitalization()~ | DataFrame | Historical market cap        |
+| ~ps_ratio()~              | DataFrame | Price/Sales ratio            |
+| ~pb_ratio()~              | DataFrame | Price/Book ratio             |
+| ~peg_ratio()~             | DataFrame | PEG ratio                    |
+| ~enterprise_value()~      | DataFrame | Enterprise value             |
+| ~enterprise_to_revenue()~ | DataFrame | EV/Revenue                   |
+| ~enterprise_to_ebitda()~  | DataFrame | EV/EBITDA                    |
+| ~debt_to_equity()~        | DataFrame | D/E ratio                    |
+| ~net_debt_ttm()~          | DataFrame | Net debt (TTM)               |
+| ~wacc()~                  | DataFrame | Weighted avg cost of capital |
+
+** Profitability Returns
+
+| Method              | Returns   | What it gives                      |
+|---------------------+-----------+------------------------------------|
+| ~roe()~               | DataFrame | Return on equity                   |
+| ~roa()~               | DataFrame | Return on assets                   |
+| ~roic()~              | DataFrame | Return on invested capital         |
+| ~roce()~              | DataFrame | Return on capital employed         |
+| ~equity_multiplier()~ | DataFrame | Financial leverage (assets/equity) |
+| ~asset_turnover()~    | DataFrame | Revenue/assets efficiency          |
+
+** Margins
+
+| Method                       | Returns   | What it gives      |
+|------------------------------+-----------+--------------------|
+| ~quarterly_gross_margin()~     | DataFrame | Gross margin %     |
+| ~annual_gross_margin()~        | DataFrame | Gross margin %     |
+| ~quarterly_operating_margin()~ | DataFrame | Operating margin % |
+| ~annual_operating_margin()~    | DataFrame | Operating margin % |
+| ~quarterly_net_margin()~       | DataFrame | Net margin %       |
+| ~annual_net_margin()~          | DataFrame | Net margin %       |
+| ~quarterly_ebitda_margin()~    | DataFrame | EBITDA margin %    |
+| ~annual_ebitda_margin()~       | DataFrame | EBITDA margin %    |
+| ~quarterly_fcf_margin()~       | DataFrame | FCF margin %       |
+| ~annual_fcf_margin()~          | DataFrame | FCF margin %       |
+
+** YoY Growth
+
+| Method                                  | Returns   | What it gives       |
+|-----------------------------------------+-----------+---------------------|
+| ~quarterly_revenue_yoy_growth()~          | DataFrame | Revenue growth %    |
+| ~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
diff --git a/docs/defeatbeta_mapping.org b/docs/defeatbeta_mapping.org
index 261b4d6..3883ee2 100644
--- a/docs/defeatbeta_mapping.org
+++ b/docs/defeatbeta_mapping.org
@@ -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
\ No newline at end of file
+#+END_COMMENT
diff --git a/docs/test_mapping.py b/docs/test_mapping.py
new file mode 100644
index 0000000..9330cd5
--- /dev/null
+++ b/docs/test_mapping.py
@@ -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")
diff --git a/docs/yfinance.org b/docs/yfinance.org
new file mode 100644
index 0000000..709c4ce
--- /dev/null
+++ b/docs/yfinance.org
@@ -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.
diff --git a/download_data.py b/download_data.py
new file mode 100644
index 0000000..11af934
--- /dev/null
+++ b/download_data.py
@@ -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()
diff --git a/offline.py b/offline.py
new file mode 100644
index 0000000..ece4937
--- /dev/null
+++ b/offline.py
@@ -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}")
diff --git a/persistent_cache.py b/persistent_cache.py
new file mode 100644
index 0000000..4c3db2d
--- /dev/null
+++ b/persistent_cache.py
@@ -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}")
diff --git a/pyproject.toml b/pyproject.toml
index 0e9d7ef..cca4c58 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -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",
diff --git a/quant.py b/quant.py
new file mode 100644
index 0000000..43cbd1d
--- /dev/null
+++ b/quant.py
@@ -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()
diff --git a/quant_scaffold.ipynb b/quant_scaffold.ipynb
index 95ee3f3..1c49b42 100644
--- a/quant_scaffold.ipynb
+++ b/quant_scaffold.ipynb
@@ -628,7 +628,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.12"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,
diff --git a/uv.lock b/uv.lock
index 9e12d62..61dd653 100644
--- a/uv.lock
+++ b/uv.lock
@@ -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 = [
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 ]
 
+[package.optional-dependencies]
+full = [
+    { name = "alpaca-py" },
+    { name = "ccxt" },
+    { name = "pandas-ta-classic" },
+    { name = "python-binance" },
+    { name = "python-telegram-bot" },
+    { 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"
diff --git a/warmup_cache.py b/warmup_cache.py
new file mode 100644
index 0000000..42ea9d4
--- /dev/null
+++ b/warmup_cache.py
@@ -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}")