From e856588b6e56c12206b04f0d47f4d65184b3d3f8 Mon Sep 17 00:00:00 2001
From: lanyuanxiaoyao <lanyuanxiaoyao@gmail.com>
Date: Mon, 10 Feb 2025 23:22:55 +0800
Subject: [PATCH] =?UTF-8?q?=E8=BF=9B=E8=A1=8C=E4=BA=86=E4=B8=80=E4=BA=9B?=
 =?UTF-8?q?=E5=B0=9D=E8=AF=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 poetry.lock               |   6 +-
 行情监控/Holdle策略.ipynb | 591 ++++++++++++++++++++++++++++++++++----
 2 files changed, 537 insertions(+), 60 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 05bf89e..22fc8bc 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -15,14 +15,14 @@ files = [
 
 [[package]]
 name = "akshare"
-version = "1.15.83"
+version = "1.15.84"
 description = "AKShare is an elegant and simple financial data interface library for Python, built for human beings!"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "akshare-1.15.83-py3-none-any.whl", hash = "sha256:e57b18c94a7f36e46f447ce2827f5b5307821dc7e4b1b5504b1c4ef7eab6669a"},
-    {file = "akshare-1.15.83.tar.gz", hash = "sha256:178df3c7e8c9100fbd58b788ef0132fcbf2f2ba9d20155d9e5e30d49794a6af6"},
+    {file = "akshare-1.15.84-py3-none-any.whl", hash = "sha256:437f12ba66236f2863292ae4d253f974de2fd99f7124e560b3cdd48a8be174b9"},
+    {file = "akshare-1.15.84.tar.gz", hash = "sha256:51c025e2e582591f973a5d1fb9dd9a248164005ae547e9142ac771bae25153eb"},
 ]
 
 [package.dependencies]
diff --git a/行情监控/Holdle策略.ipynb b/行情监控/Holdle策略.ipynb
index d181b87..10b415f 100644
--- a/行情监控/Holdle策略.ipynb
+++ b/行情监控/Holdle策略.ipynb
@@ -3,66 +3,303 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-01-22T02:00:48.759512Z",
-     "start_time": "2025-01-22T01:48:26.785997Z"
+     "end_time": "2025-02-10T14:37:48.747081Z",
+     "start_time": "2025-02-10T14:37:48.743083Z"
     }
    },
    "cell_type": "code",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "文件已成功分组并保存为CSV格式。\n"
-     ]
-    }
-   ],
-   "execution_count": 1,
    "source": "import pandas as pd",
-   "id": "6a10e07f5f498bc6"
+   "id": "6a10e07f5f498bc6",
+   "outputs": [],
+   "execution_count": 27
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-01-22T07:54:35.124992Z",
-     "start_time": "2025-01-22T07:54:35.014745Z"
+     "end_time": "2025-02-10T14:54:43.071056Z",
+     "start_time": "2025-02-10T14:54:43.027108Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/Documents/日线行情/000001.SZ.csv\") \\\n",
-    "    [[\"trade_date\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
+    "# source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/Documents/日线行情/000001.SZ.csv\") \\\n",
+    "source_df = \\\n",
+    "    pd.read_csv(\"C:\\\\Users\\\\lanyuanxiaoyao\\\\SynologyDrive\\\\data\\\\Tushare\\\\日线行情 1990-2024\\\\分组行情\\\\000001.SZ.csv\") \\\n",
+    "        [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
     "df = pd.DataFrame()\n",
-    "df[[\"date\", \"open\", \"close\", \"high\", \"low\", \"macd\", \"macd_dif\", \"macd_dea\"]] = \\\n",
-    "    source_df[[\"trade_date\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
+    "df[[\"date\", \"volume\", \"open\", \"close\", \"high\", \"low\", \"macd\", \"macd_dif\", \"macd_dea\"]] = \\\n",
+    "    source_df[[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
     "df[\"datetime\"] = pd.to_datetime(df[\"date\"], format=\"%Y%m%d\")\n",
     "df[\"datetime_text\"] = df[\"datetime\"].apply(lambda x: x.strftime(\"%Y%m%d\"))\n",
-    "df = df[df[\"datetime\"].dt.year >= 2024]\n",
     "df.sort_values(by='datetime', inplace=True)\n",
-    "\n",
-    "close_median = df[\"close\"].median()\n",
-    "df[\"close_to_median\"] = df[\"close\"] - close_median\n",
-    "macd_max = max(abs(df[\"macd\"].max()), abs(df[\"macd\"].min()))\n",
-    "close_max = max(abs(df[\"close_to_median\"].max()), abs(df[\"close_to_median\"].min()))\n",
-    "ratio = close_max / macd_max\n",
-    "df[\"macd_close\"] = df[\"close_to_median\"] / ratio\n",
-    "\n",
-    "# df[\"macd_sign\"] = df[\"macd\"] > 0\n",
-    "# df[\"group\"] = (df[\"macd_sign\"] != df[\"macd_sign\"].shift(1)).cumsum()\n",
-    "\n",
-    "df[\"macd_diff\"] = df[\"macd\"].diff()\n",
-    "df[\"macd_diff\"] = df[\"macd_diff\"].fillna(0)\n",
-    "df['macd_trend'] = df['macd_diff'].apply(lambda x: 1 if x > 0 else -1 if x < 0 else 0)\n",
-    "df[\"macd_trend_group\"] = (df['macd_trend'] != df['macd_trend'].shift(1)).cumsum()\n",
-    "\n",
-    "df[\"pre_macd\"] = df[\"macd\"].shift(1)\n",
-    "df[\"point\"] = (df[\"macd_dif\"] > 0) & (df[\"macd_dea\"] > 0) & (df[\"macd\"] > 0) & \\\n",
-    "              ((df[\"macd_dif\"] > df[\"macd\"]) & (df[\"macd_dea\"] > df[\"macd\"])) & \\\n",
-    "              (df[\"pre_macd\"] < df[\"macd\"])\n",
-    "df.loc[df[\"point\"] & df[\"point\"].shift(1), \"point\"] = False\n",
-    "\n",
     "df"
    ],
+   "id": "5c40c0d27e1dacd9",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "          date      volume      open     close      high       low   macd  \\\n",
+       "0     19910404        3.00   0.38158   0.38158   0.38158   0.38158  0.000   \n",
+       "1     19910405        2.00   0.37970   0.37970   0.37970   0.37970  0.000   \n",
+       "2     19910408        2.00   0.37595   0.37595   0.37595   0.37595 -0.001   \n",
+       "3     19910409        4.00   0.37407   0.37407   0.37407   0.37407 -0.001   \n",
+       "4     19910410       15.00   0.37219   0.37219   0.37219   0.37219 -0.002   \n",
+       "...        ...         ...       ...       ...       ...       ...    ...   \n",
+       "3813  20241225  1475282.94  11.86000  11.92000  12.02000  11.84000  0.050   \n",
+       "3814  20241226  1000074.70  11.92000  11.86000  11.93000  11.78000  0.051   \n",
+       "3815  20241227  1290012.28  11.87000  11.83000  11.90000  11.66000  0.044   \n",
+       "3816  20241230  1351846.36  11.78000  11.95000  11.97000  11.78000  0.052   \n",
+       "3817  20241231  1475367.33  11.93000  11.70000  11.99000  11.70000  0.020   \n",
+       "\n",
+       "      macd_dif  macd_dea   datetime datetime_text  \n",
+       "0        0.000     0.000 1991-04-04      19910404  \n",
+       "1        0.000     0.000 1991-04-05      19910405  \n",
+       "2       -0.001     0.000 1991-04-08      19910408  \n",
+       "3       -0.001     0.000 1991-04-09      19910409  \n",
+       "4       -0.002    -0.001 1991-04-10      19910410  \n",
+       "...        ...       ...        ...           ...  \n",
+       "3813     0.080     0.055 2024-12-25      20241225  \n",
+       "3814     0.087     0.062 2024-12-26      20241226  \n",
+       "3815     0.089     0.067 2024-12-27      20241227  \n",
+       "3816     0.100     0.074 2024-12-30      20241230  \n",
+       "3817     0.086     0.076 2024-12-31      20241231  \n",
+       "\n",
+       "[8022 rows x 11 columns]"
+      ],
+      "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>date</th>\n",
+       "      <th>volume</th>\n",
+       "      <th>open</th>\n",
+       "      <th>close</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "      <th>macd</th>\n",
+       "      <th>macd_dif</th>\n",
+       "      <th>macd_dea</th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>datetime_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>19910404</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>0.38158</td>\n",
+       "      <td>0.38158</td>\n",
+       "      <td>0.38158</td>\n",
+       "      <td>0.38158</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1991-04-04</td>\n",
+       "      <td>19910404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>19910405</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.37970</td>\n",
+       "      <td>0.37970</td>\n",
+       "      <td>0.37970</td>\n",
+       "      <td>0.37970</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1991-04-05</td>\n",
+       "      <td>19910405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19910408</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.37595</td>\n",
+       "      <td>0.37595</td>\n",
+       "      <td>0.37595</td>\n",
+       "      <td>0.37595</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1991-04-08</td>\n",
+       "      <td>19910408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>19910409</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>0.37407</td>\n",
+       "      <td>0.37407</td>\n",
+       "      <td>0.37407</td>\n",
+       "      <td>0.37407</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>1991-04-09</td>\n",
+       "      <td>19910409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>19910410</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>0.37219</td>\n",
+       "      <td>0.37219</td>\n",
+       "      <td>0.37219</td>\n",
+       "      <td>0.37219</td>\n",
+       "      <td>-0.002</td>\n",
+       "      <td>-0.002</td>\n",
+       "      <td>-0.001</td>\n",
+       "      <td>1991-04-10</td>\n",
+       "      <td>19910410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3813</th>\n",
+       "      <td>20241225</td>\n",
+       "      <td>1475282.94</td>\n",
+       "      <td>11.86000</td>\n",
+       "      <td>11.92000</td>\n",
+       "      <td>12.02000</td>\n",
+       "      <td>11.84000</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>2024-12-25</td>\n",
+       "      <td>20241225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3814</th>\n",
+       "      <td>20241226</td>\n",
+       "      <td>1000074.70</td>\n",
+       "      <td>11.92000</td>\n",
+       "      <td>11.86000</td>\n",
+       "      <td>11.93000</td>\n",
+       "      <td>11.78000</td>\n",
+       "      <td>0.051</td>\n",
+       "      <td>0.087</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>2024-12-26</td>\n",
+       "      <td>20241226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3815</th>\n",
+       "      <td>20241227</td>\n",
+       "      <td>1290012.28</td>\n",
+       "      <td>11.87000</td>\n",
+       "      <td>11.83000</td>\n",
+       "      <td>11.90000</td>\n",
+       "      <td>11.66000</td>\n",
+       "      <td>0.044</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>0.067</td>\n",
+       "      <td>2024-12-27</td>\n",
+       "      <td>20241227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3816</th>\n",
+       "      <td>20241230</td>\n",
+       "      <td>1351846.36</td>\n",
+       "      <td>11.78000</td>\n",
+       "      <td>11.95000</td>\n",
+       "      <td>11.97000</td>\n",
+       "      <td>11.78000</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>2024-12-30</td>\n",
+       "      <td>20241230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3817</th>\n",
+       "      <td>20241231</td>\n",
+       "      <td>1475367.33</td>\n",
+       "      <td>11.93000</td>\n",
+       "      <td>11.70000</td>\n",
+       "      <td>11.99000</td>\n",
+       "      <td>11.70000</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>2024-12-31</td>\n",
+       "      <td>20241231</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8022 rows × 11 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 35
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-10T13:23:42.952570Z",
+     "start_time": "2025-02-10T13:23:42.938745Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "daily_df = df.copy()\n",
+    "daily_df = daily_df[daily_df[\"datetime\"].dt.year >= 2024]\n",
+    "close_median = daily_df[\"close\"].median()\n",
+    "daily_df[\"close_to_median\"] = daily_df[\"close\"] - close_median\n",
+    "macd_max = max(abs(daily_df[\"macd\"].max()), abs(daily_df[\"macd\"].min()))\n",
+    "close_max = max(abs(daily_df[\"close_to_median\"].max()), abs(daily_df[\"close_to_median\"].min()))\n",
+    "ratio = close_max / macd_max\n",
+    "daily_df[\"macd_close\"] = daily_df[\"close_to_median\"] / ratio\n",
+    "\n",
+    "# daily_df[\"macd_sign\"] = daily_df[\"macd\"] > 0\n",
+    "# daily_df[\"group\"] = (daily_df[\"macd_sign\"] != daily_df[\"macd_sign\"].shift(1)).cumsum()\n",
+    "\n",
+    "daily_df[\"macd_diff\"] = daily_df[\"macd\"].diff()\n",
+    "daily_df[\"macd_diff\"] = daily_df[\"macd_diff\"].fillna(0)\n",
+    "daily_df['macd_trend'] = daily_df['macd_diff'].apply(lambda x: 1 if x > 0 else -1 if x < 0 else 0)\n",
+    "daily_df[\"macd_trend_group\"] = (daily_df['macd_trend'] != daily_df['macd_trend'].shift(1)).cumsum()\n",
+    "\n",
+    "daily_df[\"pre_macd\"] = daily_df[\"macd\"].shift(1)\n",
+    "daily_df[\"point\"] = (daily_df[\"macd_dif\"] > 0) & (daily_df[\"macd_dea\"] > 0) & (daily_df[\"macd\"] > 0) & \\\n",
+    "                    ((daily_df[\"macd_dif\"] > daily_df[\"macd\"]) & (daily_df[\"macd_dea\"] > daily_df[\"macd\"])) & \\\n",
+    "                    (daily_df[\"pre_macd\"] < daily_df[\"macd\"])\n",
+    "daily_df.loc[daily_df[\"point\"] & daily_df[\"point\"].shift(1), \"point\"] = False\n",
+    "daily_df"
+   ],
    "id": "f6ca89b1047f1eb0",
    "outputs": [
     {
@@ -374,18 +611,18 @@
        "</div>"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 64
+   "execution_count": 16
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-01-22T08:02:09.486300Z",
-     "start_time": "2025-01-22T08:02:08.742146Z"
+     "end_time": "2025-02-10T13:23:46.230728Z",
+     "start_time": "2025-02-10T13:23:46.054326Z"
     }
    },
    "cell_type": "code",
@@ -394,16 +631,16 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "plt.figure(figsize=(20, 6))\n",
-    "plt.plot(df['datetime_text'], df['macd_dif'], label='DIF', color='red')\n",
-    "plt.plot(df['datetime_text'], df['macd_dea'], label='DEA', color='green')\n",
-    "# plt.plot(df['datetime_text'], df['macd_close'], label='Close', color='gray')\n",
-    "plt.bar(df['datetime_text'], df['macd'], label='MACD')\n",
-    "point_df = df[df[\"point\"]]\n",
-    "# plt.scatter(point_df['datetime_text'], point_df[\"point\"], label='Point', color='blue')\n",
-    "for i, row in point_df.iterrows():\n",
+    "plt.plot(daily_df['datetime_text'], daily_df['macd_dif'], label='DIF', color='red')\n",
+    "plt.plot(daily_df['datetime_text'], daily_df['macd_dea'], label='DEA', color='green')\n",
+    "# plt.plot(daily_df['datetime_text'], daily_df['macd_close'], label='Close', color='gray')\n",
+    "plt.bar(daily_df['datetime_text'], daily_df['macd'], label='MACD')\n",
+    "point_daily_df = daily_df[daily_df[\"point\"]]\n",
+    "# plt.scatter(point_daily_df['datetime_text'], point_daily_df[\"point\"], label='Point', color='blue')\n",
+    "for i, row in point_daily_df.iterrows():\n",
     "    plt.axvline(x=row['datetime_text'], color='blue', linestyle='--')\n",
     "\n",
-    "for group_name, group_data in df.groupby(\"macd_trend_group\"):\n",
+    "for group_name, group_data in daily_df.groupby(\"macd_trend_group\"):\n",
     "    first_row = group_data.iloc[0]\n",
     "    last_row = group_data.iloc[-1]\n",
     "    x_start, y_start = first_row['datetime_text'], first_row['macd']\n",
@@ -418,7 +655,7 @@
     "plt.legend()\n",
     "\n",
     "plt.show()\n",
-    "point_df"
+    "point_daily_df"
    ],
    "id": "25cd7f58d7b8c044",
    "outputs": [
@@ -669,12 +906,252 @@
        "</div>"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 72
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-10T15:07:41.915428Z",
+     "start_time": "2025-02-10T15:07:41.902660Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "daily_df = df.copy()[[\"datetime\", \"volume\", \"open\", \"close\", \"high\", \"low\"]]\n",
+    "monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='ME')).agg({\n",
+    "    \"volume\": \"sum\",\n",
+    "    \"open\": \"first\",\n",
+    "    \"close\": \"last\",\n",
+    "    \"high\": \"max\",\n",
+    "    \"low\": \"min\",\n",
+    "})\n",
+    "monthly_df.reset_index(inplace=True)\n",
+    "monthly_df.set_index('datetime', inplace=True)\n",
+    "monthly_df"
+   ],
+   "id": "584f50c57e061148",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                 volume      open     close      high       low\n",
+       "datetime                                                       \n",
+       "1991-04-30       116.00   0.38158   0.34183   0.38158   0.34183\n",
+       "1991-05-31      1760.00   0.47921   0.42275   0.47921   0.42275\n",
+       "1991-06-30       247.00   0.41856   0.37479   0.41856   0.37479\n",
+       "1991-07-31        58.00   0.37104   0.32572   0.37104   0.32572\n",
+       "1991-08-31     27573.00   0.32407   0.30716   0.33267   0.29185\n",
+       "...                 ...       ...       ...       ...       ...\n",
+       "2024-08-31  19711097.70  10.03061   9.94253  10.32419   9.65874\n",
+       "2024-09-30  27848355.05   9.90339  11.94866  12.03673   9.40431\n",
+       "2024-10-31  39329408.65  13.14254  11.38000  13.14254  11.24000\n",
+       "2024-11-30  31330015.78  11.38000  11.38000  12.04000  11.14000\n",
+       "2024-12-31  25547709.02  11.39000  11.70000  12.02000  11.31000\n",
+       "\n",
+       "[405 rows x 5 columns]"
+      ],
+      "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>volume</th>\n",
+       "      <th>open</th>\n",
+       "      <th>close</th>\n",
+       "      <th>high</th>\n",
+       "      <th>low</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1991-04-30</th>\n",
+       "      <td>116.00</td>\n",
+       "      <td>0.38158</td>\n",
+       "      <td>0.34183</td>\n",
+       "      <td>0.38158</td>\n",
+       "      <td>0.34183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-05-31</th>\n",
+       "      <td>1760.00</td>\n",
+       "      <td>0.47921</td>\n",
+       "      <td>0.42275</td>\n",
+       "      <td>0.47921</td>\n",
+       "      <td>0.42275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-06-30</th>\n",
+       "      <td>247.00</td>\n",
+       "      <td>0.41856</td>\n",
+       "      <td>0.37479</td>\n",
+       "      <td>0.41856</td>\n",
+       "      <td>0.37479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-07-31</th>\n",
+       "      <td>58.00</td>\n",
+       "      <td>0.37104</td>\n",
+       "      <td>0.32572</td>\n",
+       "      <td>0.37104</td>\n",
+       "      <td>0.32572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991-08-31</th>\n",
+       "      <td>27573.00</td>\n",
+       "      <td>0.32407</td>\n",
+       "      <td>0.30716</td>\n",
+       "      <td>0.33267</td>\n",
+       "      <td>0.29185</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>2024-08-31</th>\n",
+       "      <td>19711097.70</td>\n",
+       "      <td>10.03061</td>\n",
+       "      <td>9.94253</td>\n",
+       "      <td>10.32419</td>\n",
+       "      <td>9.65874</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-09-30</th>\n",
+       "      <td>27848355.05</td>\n",
+       "      <td>9.90339</td>\n",
+       "      <td>11.94866</td>\n",
+       "      <td>12.03673</td>\n",
+       "      <td>9.40431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-10-31</th>\n",
+       "      <td>39329408.65</td>\n",
+       "      <td>13.14254</td>\n",
+       "      <td>11.38000</td>\n",
+       "      <td>13.14254</td>\n",
+       "      <td>11.24000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-11-30</th>\n",
+       "      <td>31330015.78</td>\n",
+       "      <td>11.38000</td>\n",
+       "      <td>11.38000</td>\n",
+       "      <td>12.04000</td>\n",
+       "      <td>11.14000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024-12-31</th>\n",
+       "      <td>25547709.02</td>\n",
+       "      <td>11.39000</td>\n",
+       "      <td>11.70000</td>\n",
+       "      <td>12.02000</td>\n",
+       "      <td>11.31000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>405 rows × 5 columns</p>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 43
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-10T15:07:44.088406Z",
+     "start_time": "2025-02-10T15:07:43.838889Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import mplfinance as mpf\n",
+    "\n",
+    "mpf.plot(\n",
+    "    monthly_df,\n",
+    "    type='candle',\n",
+    "    volume=True,\n",
+    "    ylabel='Price',\n",
+    "    ylabel_lower='Shares\\nTraded',\n",
+    "    style='charles',\n",
+    "    show_nontrading=True,\n",
+    "    returnfig=True,\n",
+    "    figsize=(20, 6),\n",
+    ")"
+   ],
+   "id": "2a4da021e0d5fc44",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 2000x600 with 4 Axes>,\n",
+       " [<Axes: ylabel='Price'>,\n",
+       "  <Axes: >,\n",
+       "  <Axes: ylabel='Shares\\nTraded\\n  $10^{6}$'>,\n",
+       "  <Axes: >])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2000x600 with 4 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 44
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "现在有一份股票的日线行情数据导入pandas表中，列datetime,open,close,high,low分别表示交易日、开盘价、收盘价、最高价和最低价，现在需要进行如下计算\n",
+    "1.计算行情月线以及月线的MACD，根据月线的MACD，找出当DIF和DEA均在0轴上方，且MACD值大于0并且小于DIF和DEA的月份\n",
+    "2.在上述筛选出的月份中，找出那些该月份的前一个月的MACD值小于0的月份"
+   ],
+   "id": "8a98fd3bbe6cfffb"
   }
  ],
  "metadata": {