update example

notebooks/Example 5 - Style Factor Analysis.ipynb

@@ -118,13 +118,6 @@
     "df[['factor', 'abs t.']].groupby('factor').mean().sort_values('abs t.', ascending=False).head()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/Example 6 - Target Volatility Builder.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,17 +68,6 @@
     "special_risk = factor['srisk'].values"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000\n",
-    "sec_cov_df = pd.DataFrame(sec_cov, index=codes, columns=codes)\n",
-    "sec_cov_df.iloc[:5, :5]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -92,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,16 +92,29 @@
     "cons_mat = np.ones((len(er), 1))\n",
     "risk_targets = (bm.sum(), bm.sum())\n",
     "target_vol = 0.025\n",
+    "risk_model = dict(cov=None, factor_cov=risk_cov/10000, factor_loading=risk_exposure, idsync=special_risk ** 2 / 10000.)\n",
     "\n",
     "status, p_er, p_weight = \\\n",
-    "    target_vol_builder(er, sec_cov, bm, lbound, ubound, cons_mat, risk_targets, target_vol, target_vol)"
+    "    target_vol_builder(er, risk_model, bm, lbound, ubound, cons_mat, risk_targets, target_vol)\n",
+    "    \n",
+    "sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000. + np.diag(special_risk ** 2) / 10000"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total weight is  1.0000\n",
+      "portfolio activate weight forecasting vol is 0.0250\n",
+      "portfolio er: 2.2232 comparing with benchmark er: 1.2359\n"
+     ]
+    }
+   ],
    "source": [
     "# check the result\n",
     "print(f\"total weight is {p_weight.sum(): .4f}\")\n",
@@ -130,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [