added first version of brinson

attribution/010 style attribution.ipynb

@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "c75c33b2-c69e-412c-9151-8f6671c00cec",
    "metadata": {},
    "outputs": [
@@ -339,7 +339,7 @@
        "[1 rows x 39 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -351,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "e06d5836-1e39-47e7-9e1e-3ba77433aa05",
    "metadata": {},
    "outputs": [
@@ -384,17 +384,60 @@
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-06-30</td>\n",
+       "      <td>2010000045</td>\n",
+       "      <td>海信视像</td>\n",
+       "      <td>600060.XSHG</td>\n",
+       "      <td>-1.983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-06-30</td>\n",
+       "      <td>2010000121</td>\n",
+       "      <td>中青旅</td>\n",
+       "      <td>600138.XSHG</td>\n",
+       "      <td>-0.743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-06-30</td>\n",
+       "      <td>2010000171</td>\n",
+       "      <td>复星医药</td>\n",
+       "      <td>600196.XSHG</td>\n",
+       "      <td>-0.642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-06-30</td>\n",
+       "      <td>2010000377</td>\n",
+       "      <td>片仔癀</td>\n",
+       "      <td>600436.XSHG</td>\n",
+       "      <td>-1.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-06-30</td>\n",
+       "      <td>2010000438</td>\n",
+       "      <td>贵州茅台</td>\n",
+       "      <td>600519.XSHG</td>\n",
+       "      <td>-0.835</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [trade_date, security_code, secShortName, symbol, spret]\n",
-       "Index: []"
+       "   trade_date security_code secShortName       symbol  spret\n",
+       "0  2021-06-30    2010000045         海信视像  600060.XSHG -1.983\n",
+       "1  2021-06-30    2010000121          中青旅  600138.XSHG -0.743\n",
+       "2  2021-06-30    2010000171         复星医药  600196.XSHG -0.642\n",
+       "3  2021-06-30    2010000377          片仔癀  600436.XSHG -1.400\n",
+       "4  2021-06-30    2010000438         贵州茅台  600519.XSHG -0.835"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -406,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "48d5bd0f-d33b-4b5e-bc91-b622cba7c0be",
    "metadata": {},
    "outputs": [
@@ -612,7 +655,7 @@
        "[5 rows x 40 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -624,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "id": "88854e5b-ad2e-4629-bb8c-9e58a851a298",
    "metadata": {},
    "outputs": [
@@ -710,7 +753,7 @@
        "4  2021-06-30    2010000438         贵州茅台  600519.XSHG  24.513"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -723,7 +766,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6102c448-5515-4f1d-ab82-91c0ce98a7ee",
+   "id": "017644bc-3053-4170-89fb-3e87d7ee9339",
    "metadata": {},
    "outputs": [],
    "source": []

fof/brinson.py

+import pandas as pd
+
+
+def single_brinson(portfolio, index_component, industry, stk_rtns):
+    total_df = pd.merge(industry[["security_code", "industry_name"]], portfolio[["security_code", "weight"]],
+                        on=["security_code"], how="left").fillna(0.0)
+    total_df = pd.merge(total_df, index_component[["security_code", "weight"]], on=["security_code"], how="left").fillna(0.0)
+    total_df = pd.merge(total_df, stk_rtns[["security_code", "chg_pct"]], on=["security_code"], how="inner")
+    total_df.columns = ["security_code", "industry", "portfolio", "benchmark", "chg_pct"]
+    total_df["p_ret"] = total_df["portfolio"] * total_df["chg_pct"]
+    total_df["b_ret"] = total_df["benchmark"] * total_df["chg_pct"]
+    total_df = total_df.groupby("industry")[["portfolio", "benchmark", "p_ret", "b_ret"]].agg("sum")
+    total_df["p_ret"] = total_df["p_ret"] / (total_df["portfolio"] + 1e-16)
+    total_df["b_ret"] = total_df["b_ret"] / (total_df["benchmark"] + 1e-16)
+
+    total_df["TR"] = total_df["p_ret"] * total_df["portfolio"] - total_df["b_ret"] * total_df["benchmark"]
+    total_df["SR"] = (total_df["p_ret"] - total_df["b_ret"]) * total_df["benchmark"]
+    total_df["AR"] = total_df["b_ret"] * (total_df["portfolio"] - total_df["benchmark"])
+    total_df["IR"] = (total_df["p_ret"] - total_df["b_ret"]) * (total_df["portfolio"] - total_df["benchmark"])
+    return total_df