Use alpaca instead of ibkr.

notebooks/ibkr_scratch.ipynb

@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cd849b8e",
+   "metadata": {},
+   "source": [
+    "# IBKR scratch notebook\n",
+    "\n",
+    "This notebook is for quick manual testing of the IBKR gateway connection, portfolio lookup, and a simple SPY order flow.\n",
+    "\n",
+    "> Use this only with a paper-trading or test setup unless you explicitly intend to submit a live order.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "543426a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from ib_insync import IB, Stock\n",
+    "\n",
+    "# Allow the notebook to import local source code from the repository.\n",
+    "repo_root = Path.cwd().resolve()\n",
+    "if (repo_root / \"src\").exists():\n",
+    "    repo_root = repo_root\n",
+    "else:\n",
+    "    repo_root = repo_root.parent\n",
+    "\n",
+    "src_path = repo_root / \"src\"\n",
+    "if str(src_path) not in sys.path:\n",
+    "    sys.path.insert(0, str(src_path))\n",
+    "\n",
+    "from trading_bot.data.fetch_ibkr_daily import (\n",
+    "    IBKR_CLIENT_ID,\n",
+    "    IBKR_CONNECT_TIMEOUT_SECONDS,\n",
+    "    IBKR_HOST,\n",
+    "    IBKR_PORT,\n",
+    ")\n",
+    "\n",
+    "ib = IB()\n",
+    "print(f\"Connecting to IBKR Gateway at {IBKR_HOST}:{IBKR_PORT} with client id {IBKR_CLIENT_ID}...\")\n",
+    "ib.connect(IBKR_HOST, IBKR_PORT, clientId=IBKR_CLIENT_ID, timeout=IBKR_CONNECT_TIMEOUT_SECONDS)\n",
+    "print(\"Connection established.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecdc721f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"Connected: {ib.isConnected()}\")\n",
+    "print(f\"Client ID: {ib.clientId}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10ebc240",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "portfolio = ib.portfolio()\n",
+    "if not portfolio:\n",
+    "    print(\"No open portfolio positions were returned.\")\n",
+    "else:\n",
+    "    for item in portfolio:\n",
+    "        print(\n",
+    "            f\"{item.contract.symbol}: position={item.position}, \"\n",
+    "            f\"market_value={item.marketValue}, unrealized_pnl={item.unrealizedPNL}\"\n",
+    "        )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fed654ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ib_insync import MarketOrder\n",
+    "\n",
+    "contract = Stock(\"SPY\", \"SMART\", \"USD\")\n",
+    "ib.qualifyContracts(contract)\n",
+    "\n",
+    "# Adjust the quantity as needed before running this cell.\n",
+    "order = MarketOrder(\"BUY\", 1)\n",
+    "trade = ib.placeOrder(contract, order)\n",
+    "\n",
+    "print(f\"Submitted order for {contract.symbol}: {trade}\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/models/spy_xgb_v1_meta.json

@@ -1,5 +1,5 @@
 {
-  "p_base": 0.5830346475507766,
+  "p_base": 0.5833333333333334,
   "feature_cols": [
     "VIX_rank_20",
     "TLT_ret_10",
@@ -9,5 +9,5 @@
     "SPY_ret_20",
     "SPY_dist_sma50"
   ],
-  "last_trained_date": "2026-07-17 00:00:00"
+  "last_trained_date": "2026-07-24 00:00:00"
 }

notebooks/spy_direction_dataset.ipynb

@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -52,7 +52,8 @@
     "\n",
     "\n",
     "PROJECT_ROOT = find_project_root()\n",
-    "RAW_DATA_DIR = PROJECT_ROOT / \"data\" / \"ibkr\" / \"daily\"\n",
+    "#RAW_DATA_DIR = PROJECT_ROOT / \"data\" / \"ibkr\" / \"daily\"\n",
+    "RAW_DATA_DIR = PROJECT_ROOT / \"data\" / \"alpaca\" / \"daily\"\n",
     "OUTPUT_PATH = PROJECT_ROOT / \"data\" / \"training\" / \"spy_direction_5d.parquet\"\n",
     "\n",
     "SPY_SYMBOL = \"SPY\"\n",
@@ -233,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -383,7 +384,7 @@
     "df[\"TLT_ret_10\"] = df[\"TLT_close\"].pct_change(10)\n",
     "df[\"USO_ret_5\"] = df[\"USO_close\"].pct_change(5)\n",
     "df[\"SPY_TLT_ratio_ret\"] = (df[\"SPY_close\"] / df[\"TLT_close\"]).pct_change(5)\n",
-    "\n",
+    "df_model\n",
     "spy_forward_close = df[\"SPY_close\"].shift(-5)\n",
     "df[\"spy_up_5d\"] = np.nan\n",
     "df.loc[spy_forward_close > df[\"SPY_close\"], \"spy_up_5d\"] = 1.0\n",