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完成macd回测

This commit is contained in:
2025-02-16 23:24:34 +08:00
parent 46a539cf72
commit 9689625fc1
3 changed files with 1021 additions and 452 deletions

375
poetry.lock generated
View File

@@ -15,14 +15,14 @@ files = [
[[package]] [[package]]
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version = "1.15.87" version = "1.15.93"
description = "AKShare is an elegant and simple financial data interface library for Python, built for human beings!" description = "AKShare is an elegant and simple financial data interface library for Python, built for human beings!"
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[package.dependencies] [package.dependencies]
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[[package]] [[package]]

View File

@@ -3,8 +3,8 @@
{ {
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2025-02-13T09:38:30.408310Z", "end_time": "2025-02-16T08:09:25.176397Z",
"start_time": "2025-02-13T09:38:30.328797Z" "start_time": "2025-02-16T08:09:25.125996Z"
} }
}, },
"cell_type": "code", "cell_type": "code",
@@ -12,49 +12,596 @@
"import backtrader as bt\n", "import backtrader as bt\n",
"import pandas as pd\n", "import pandas as pd\n",
"\n", "\n",
"# source_df = \\\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", " pd.read_csv(\"C:\\\\Users\\\\lanyuanxiaoyao\\\\SynologyDrive\\\\data\\\\Tushare\\\\日线行情 1990-2024\\\\分组行情\\\\000001.SZ.csv\") \\\n",
"source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/600519.SH.csv\") \\\n", " [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
" [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n", "# source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/600519.SH.csv\") \\\n",
"df = pd.DataFrame()\n", "df = pd.DataFrame()\n",
"df[[\"date\", \"volume\", \"open\", \"close\", \"high\", \"low\", \"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", " 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\"] = 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[\"datetime_text\"] = df[\"datetime\"].apply(lambda x: x.strftime(\"%Y%m%d\"))\n",
"df = df[df[\"datetime\"].dt.year > 2010]\n", "df = df[df[\"datetime\"].dt.year > 2023]\n",
"df.sort_values(by='datetime', inplace=True)\n", "df.sort_values(by='datetime', inplace=True)\n",
"df.set_index('datetime', inplace=True)\n", "df.set_index('datetime', inplace=True)\n",
"# df" "df"
], ],
"id": "157a87ab33037c6e", "id": "157a87ab33037c6e",
"outputs": [], "outputs": [
"execution_count": 46 {
}, "data": {
{ "text/plain": [
"metadata": {}, " date volume open close high low \\\n",
"cell_type": "code", "datetime \n",
"outputs": [], "2024-01-02 20240102 1158366.45 8.57646 8.41205 8.60386 8.41205 \n",
"execution_count": null, "2024-01-03 20240103 733610.31 8.39379 8.40292 8.42119 8.35725 \n",
"source": "", "2024-01-04 20240104 864193.99 8.39379 8.32072 8.39379 8.29332 \n",
"id": "12fde7bfaddbdf4" "2024-01-05 20240105 1991622.16 8.31158 8.46686 8.62213 8.28418 \n",
"2024-01-08 20240108 1121156.19 8.43032 8.35725 8.49426 8.32072 \n",
"... ... ... ... ... ... ... \n",
"2024-12-25 20241225 1475282.94 11.86000 11.92000 12.02000 11.84000 \n",
"2024-12-26 20241226 1000074.70 11.92000 11.86000 11.93000 11.78000 \n",
"2024-12-27 20241227 1290012.28 11.87000 11.83000 11.90000 11.66000 \n",
"2024-12-30 20241230 1351846.36 11.78000 11.95000 11.97000 11.78000 \n",
"2024-12-31 20241231 1475367.33 11.93000 11.70000 11.99000 11.70000 \n",
"\n",
" macd macd_dif macd_dea datetime_text \n",
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"2024-01-02 0.107 -0.162 -0.216 20240102 \n",
"2024-01-03 0.098 -0.155 -0.203 20240103 \n",
"2024-01-04 0.080 -0.153 -0.193 20240104 \n",
"2024-01-05 0.087 -0.139 -0.183 20240105 \n",
"2024-01-08 0.076 -0.135 -0.173 20240108 \n",
"... ... ... ... ... \n",
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"2024-12-26 0.051 0.087 0.062 20241226 \n",
"2024-12-27 0.044 0.089 0.067 20241227 \n",
"2024-12-30 0.052 0.100 0.074 20241230 \n",
"2024-12-31 0.020 0.086 0.076 20241231 \n",
"\n",
"[242 rows x 10 columns]"
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},
"execution_count": 158,
"metadata": {},
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}
],
"execution_count": 158
}, },
{ {
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2025-02-13T09:36:52.737860Z", "end_time": "2025-02-16T08:09:25.209704Z",
"start_time": "2025-02-13T09:36:51.459915Z" "start_time": "2025-02-16T08:09:25.199080Z"
} }
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"import calendar\n", "import talib\n",
"\n", "\n",
"# 指标计算\n",
"close_prices = df['close'].values\n",
"\n",
"# 计算MACD\n",
"dif, dea, hist = talib.MACD(close_prices, fastperiod=12, slowperiod=26, signalperiod=9)\n",
"\n",
"# 将结果添加到数据框\n",
"df['dif'] = dif\n",
"df['dif'] = df['dif'].fillna(0)\n",
"df['dea'] = dea\n",
"df['dea'] = df['dea'].fillna(0)\n",
"df['hist'] = hist\n",
"df['hist'] = df['hist'].fillna(0)\n",
"\n",
"df"
],
"id": "12fde7bfaddbdf4",
"outputs": [
{
"data": {
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" date volume open close high low \\\n",
"datetime \n",
"2024-01-02 20240102 1158366.45 8.57646 8.41205 8.60386 8.41205 \n",
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"2024-01-05 20240105 1991622.16 8.31158 8.46686 8.62213 8.28418 \n",
"2024-01-08 20240108 1121156.19 8.43032 8.35725 8.49426 8.32072 \n",
"... ... ... ... ... ... ... \n",
"2024-12-25 20241225 1475282.94 11.86000 11.92000 12.02000 11.84000 \n",
"2024-12-26 20241226 1000074.70 11.92000 11.86000 11.93000 11.78000 \n",
"2024-12-27 20241227 1290012.28 11.87000 11.83000 11.90000 11.66000 \n",
"2024-12-30 20241230 1351846.36 11.78000 11.95000 11.97000 11.78000 \n",
"2024-12-31 20241231 1475367.33 11.93000 11.70000 11.99000 11.70000 \n",
"\n",
" macd macd_dif macd_dea datetime_text dif dea \\\n",
"datetime \n",
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"2024-01-03 0.098 -0.155 -0.203 20240103 0.000000 0.000000 \n",
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"2024-12-31 0.020 0.086 0.076 20241231 0.086419 0.076253 \n",
"\n",
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"2024-12-26 0.025674 \n",
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"2024-12-31 0.010166 \n",
"\n",
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" <th>2024-12-30</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>20241230</td>\n",
" <td>0.099551</td>\n",
" <td>0.073711</td>\n",
" <td>0.025840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-31</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>20241231</td>\n",
" <td>0.086419</td>\n",
" <td>0.076253</td>\n",
" <td>0.010166</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>242 rows × 13 columns</p>\n",
"</div>"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 159
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-16T08:09:25.361947Z",
"start_time": "2025-02-16T08:09:25.270309Z"
}
},
"cell_type": "code",
"source": [
"cerebro = bt.Cerebro()\n", "cerebro = bt.Cerebro()\n",
"\n", "\n",
"data_feed = bt.feeds.PandasData(dataname=df)\n", "\n",
"cerebro.adddata(data_feed)\n", "class EnhancePandasData(bt.feeds.PandasData):\n",
" lines = (\"dif\", \"dea\", \"hist\")\n",
" params = (\n",
" (\"dif\", -1),\n",
" (\"dea\", -1),\n",
" (\"hist\", -1),\n",
" )\n",
"\n",
"\n",
"cerebro.adddata(EnhancePandasData(dataname=df))\n",
"# 初始资金 100,000,000\n", "# 初始资金 100,000,000\n",
"cerebro.broker.setcash(100000000.0)\n", "cerebro.broker.setcash(10000.0)\n",
"# 佣金,双边各 0.0003\n", "# 佣金,双边各 0.0003\n",
"cerebro.broker.setcommission(commission=0.0003)\n", "cerebro.broker.setcommission(commission=0.0003)\n",
"# 滑点:双边各 0.0001\n", "# 滑点:双边各 0.0001\n",
@@ -68,12 +615,20 @@
"# 创建策略类\n", "# 创建策略类\n",
"class TestStrategy(bt.Strategy):\n", "class TestStrategy(bt.Strategy):\n",
" def next(self):\n", " def next(self):\n",
" curr_date = self.datetime.date()\n", " now_dif = self.data.dif[0]\n",
" _, last_day_month = calendar.monthrange(curr_date.year, curr_date.month)\n", " now_dea = self.data.dea[0]\n",
" if curr_date.day == 1:\n", " now_hist = self.data.hist[0]\n",
" self.buy()\n", "\n",
" elif curr_date.day == last_day_month:\n", " pre_dif = self.data.dif[-1]\n",
" self.sell()\n", " pre_dea = self.data.dea[-1]\n",
" pre_hist = self.data.hist[-1]\n",
"\n",
" hold = self.getposition(self.data).size\n",
"\n",
" if now_dif > now_dea and pre_dif <= pre_dea and now_dif > 0 and now_dea > 0:\n",
" self.buy(size=100)\n",
" elif now_dif < now_dea and pre_dif >= pre_dea and hold > 0:\n",
" self.sell(size=hold)\n",
"\n", "\n",
" def log(self, txt, dt=None):\n", " def log(self, txt, dt=None):\n",
" \"\"\"\n", " \"\"\"\n",
@@ -113,10 +668,10 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"初始资金: 100000000.00\n", "初始资金: 10000.00\n",
"最终资金: 99982677.31\n", "最终资金: 10015.80\n",
"夏普比率: -249.45833106775427\n", "夏普比率: None\n",
"最大回撤: 0.03266399519865984\n" "最大回撤: 0.48930011171324134\n"
] ]
}, },
{ {
@@ -135,7 +690,7 @@
"<IPython.core.display.HTML object>" "<IPython.core.display.HTML object>"
], ],
"text/html": [ "text/html": [
"<div id='8a8873d7-5615-4456-bb60-d011d91f07a9'></div>" "<div id='02240639-900b-4c9e-bace-e1c596384da2'></div>"
] ]
}, },
"metadata": {}, "metadata": {},
@@ -147,12 +702,12 @@
"[[<Figure size 640x480 with 4 Axes>]]" "[[<Figure size 640x480 with 4 Axes>]]"
] ]
}, },
"execution_count": 45, "execution_count": 160,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
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} }
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"metadata": { "metadata": {

View File

@@ -3,29 +3,29 @@
{ {
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2025-02-11T02:51:57.789440Z", "end_time": "2025-02-16T05:33:10.625500Z",
"start_time": "2025-02-11T02:51:57.208223Z" "start_time": "2025-02-16T05:33:10.622501Z"
} }
}, },
"cell_type": "code", "cell_type": "code",
"source": "import pandas as pd", "source": "import pandas as pd",
"id": "6a10e07f5f498bc6", "id": "6a10e07f5f498bc6",
"outputs": [], "outputs": [],
"execution_count": 1 "execution_count": 3
}, },
{ {
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2025-02-11T03:14:30.972240Z", "end_time": "2025-02-16T05:33:10.692963Z",
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} }
}, },
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"# source_df = \\\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", " pd.read_csv(\"C:\\\\Users\\\\lanyuanxiaoyao\\\\SynologyDrive\\\\data\\\\Tushare\\\\日线行情 1990-2024\\\\分组行情\\\\000001.SZ.csv\") \\\n",
"source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/600519.SH.csv\") \\\n",
" [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n", " [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
"# source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/600519.SH.csv\") \\\n",
"df = pd.DataFrame()\n", "df = pd.DataFrame()\n",
"df[[\"date\", \"volume\", \"open\", \"close\", \"high\", \"low\", \"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", " source_df[[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n",
@@ -39,33 +39,33 @@
{ {
"data": { "data": {
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" date volume open close high low \\\n", " date volume open close high low macd \\\n",
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"2188 20010828 129647.79 4.29568 4.52525 4.54244 4.24902 \n", "1 19910405 2.00 0.37970 0.37970 0.37970 0.37970 0.000 \n",
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"2190 20010830 48013.06 4.45405 4.55472 4.60505 4.41967 \n", "3 19910409 4.00 0.37407 0.37407 0.37407 0.37407 -0.001 \n",
"2191 20010831 23231.48 4.56086 4.54367 4.61856 4.51789 \n", "4 19910410 15.00 0.37219 0.37219 0.37219 0.37219 -0.002 \n",
"... ... ... ... ... ... ... \n", "... ... ... ... ... ... ... ... \n",
"2182 20241225 17123.39 1538.80000 1530.00000 1538.80000 1526.10000 \n", "3813 20241225 1475282.94 11.86000 11.92000 12.02000 11.84000 0.050 \n",
"2183 20241226 18286.51 1534.00000 1527.79000 1538.78000 1523.00000 \n", "3814 20241226 1000074.70 11.92000 11.86000 11.93000 11.78000 0.051 \n",
"2184 20241227 20759.32 1528.90000 1528.97000 1536.00000 1519.50000 \n", "3815 20241227 1290012.28 11.87000 11.83000 11.90000 11.66000 0.044 \n",
"2185 20241230 25129.82 1533.97000 1525.00000 1543.96000 1525.00000 \n", "3816 20241230 1351846.36 11.78000 11.95000 11.97000 11.78000 0.052 \n",
"2186 20241231 39354.45 1525.40000 1524.00000 1545.00000 1522.01000 \n", "3817 20241231 1475367.33 11.93000 11.70000 11.99000 11.70000 0.020 \n",
"\n", "\n",
" macd macd_dif macd_dea datetime datetime_text \n", " macd_dif macd_dea datetime datetime_text \n",
"2187 0.000 0.000 0.000 2001-08-27 20010827 \n", "0 0.000 0.000 1991-04-04 19910404 \n",
"2188 0.021 0.013 0.003 2001-08-28 20010828 \n", "1 0.000 0.000 1991-04-05 19910405 \n",
"2189 0.025 0.018 0.006 2001-08-29 20010829 \n", "2 -0.001 0.000 1991-04-08 19910408 \n",
"2190 0.037 0.029 0.010 2001-08-30 20010830 \n", "3 -0.001 0.000 1991-04-09 19910409 \n",
"2191 0.042 0.036 0.016 2001-08-31 20010831 \n", "4 -0.002 -0.001 1991-04-10 19910410 \n",
"... ... ... ... ... ... \n", "... ... ... ... ... \n",
"2182 5.430 5.063 2.348 2024-12-25 20241225 \n", "3813 0.080 0.055 2024-12-25 20241225 \n",
"2183 4.192 4.968 2.872 2024-12-26 20241226 \n", "3814 0.087 0.062 2024-12-26 20241226 \n",
"2184 3.295 4.931 3.283 2024-12-27 20241227 \n", "3815 0.089 0.067 2024-12-27 20241227 \n",
"2185 1.993 4.529 3.533 2024-12-30 20241230 \n", "3816 0.100 0.074 2024-12-30 20241230 \n",
"2186 0.880 4.083 3.643 2024-12-31 20241231 \n", "3817 0.086 0.076 2024-12-31 20241231 \n",
"\n", "\n",
"[5591 rows x 11 columns]" "[8022 rows x 11 columns]"
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@@ -101,74 +101,74 @@
" </thead>\n", " </thead>\n",
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" <tr>\n", " <tr>\n",
" <th>2187</th>\n", " <th>0</th>\n",
" <td>20010827</td>\n", " <td>19910404</td>\n",
" <td>406318.00</td>\n", " <td>3.00</td>\n",
" <td>4.23675</td>\n", " <td>0.38158</td>\n",
" <td>4.36443</td>\n", " <td>0.38158</td>\n",
" <td>4.63820</td>\n", " <td>0.38158</td>\n",
" <td>4.03295</td>\n", " <td>0.38158</td>\n",
" <td>0.000</td>\n", " <td>0.000</td>\n",
" <td>0.000</td>\n", " <td>0.000</td>\n",
" <td>0.000</td>\n", " <td>0.000</td>\n",
" <td>2001-08-27</td>\n", " <td>1991-04-04</td>\n",
" <td>20010827</td>\n", " <td>19910404</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2188</th>\n", " <th>1</th>\n",
" <td>20010828</td>\n", " <td>19910405</td>\n",
" <td>129647.79</td>\n", " <td>2.00</td>\n",
" <td>4.29568</td>\n", " <td>0.37970</td>\n",
" <td>4.52525</td>\n", " <td>0.37970</td>\n",
" <td>4.54244</td>\n", " <td>0.37970</td>\n",
" <td>4.24902</td>\n", " <td>0.37970</td>\n",
" <td>0.021</td>\n", " <td>0.000</td>\n",
" <td>0.013</td>\n", " <td>0.000</td>\n",
" <td>0.003</td>\n", " <td>0.000</td>\n",
" <td>2001-08-28</td>\n", " <td>1991-04-05</td>\n",
" <td>20010828</td>\n", " <td>19910405</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2189</th>\n", " <th>2</th>\n",
" <td>20010829</td>\n", " <td>19910408</td>\n",
" <td>53252.75</td>\n", " <td>2.00</td>\n",
" <td>4.53999</td>\n", " <td>0.37595</td>\n",
" <td>4.46632</td>\n", " <td>0.37595</td>\n",
" <td>4.54244</td>\n", " <td>0.37595</td>\n",
" <td>4.43195</td>\n", " <td>0.37595</td>\n",
" <td>0.025</td>\n", " <td>-0.001</td>\n",
" <td>0.018</td>\n", " <td>-0.001</td>\n",
" <td>0.006</td>\n", " <td>0.000</td>\n",
" <td>2001-08-29</td>\n", " <td>1991-04-08</td>\n",
" <td>20010829</td>\n", " <td>19910408</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2190</th>\n", " <th>3</th>\n",
" <td>20010830</td>\n", " <td>19910409</td>\n",
" <td>48013.06</td>\n", " <td>4.00</td>\n",
" <td>4.45405</td>\n", " <td>0.37407</td>\n",
" <td>4.55472</td>\n", " <td>0.37407</td>\n",
" <td>4.60505</td>\n", " <td>0.37407</td>\n",
" <td>4.41967</td>\n", " <td>0.37407</td>\n",
" <td>0.037</td>\n", " <td>-0.001</td>\n",
" <td>0.029</td>\n", " <td>-0.001</td>\n",
" <td>0.010</td>\n", " <td>0.000</td>\n",
" <td>2001-08-30</td>\n", " <td>1991-04-09</td>\n",
" <td>20010830</td>\n", " <td>19910409</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2191</th>\n", " <th>4</th>\n",
" <td>20010831</td>\n", " <td>19910410</td>\n",
" <td>23231.48</td>\n", " <td>15.00</td>\n",
" <td>4.56086</td>\n", " <td>0.37219</td>\n",
" <td>4.54367</td>\n", " <td>0.37219</td>\n",
" <td>4.61856</td>\n", " <td>0.37219</td>\n",
" <td>4.51789</td>\n", " <td>0.37219</td>\n",
" <td>0.042</td>\n", " <td>-0.002</td>\n",
" <td>0.036</td>\n", " <td>-0.002</td>\n",
" <td>0.016</td>\n", " <td>-0.001</td>\n",
" <td>2001-08-31</td>\n", " <td>1991-04-10</td>\n",
" <td>20010831</td>\n", " <td>19910410</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>...</th>\n", " <th>...</th>\n",
@@ -185,93 +185,93 @@
" <td>...</td>\n", " <td>...</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2182</th>\n", " <th>3813</th>\n",
" <td>20241225</td>\n", " <td>20241225</td>\n",
" <td>17123.39</td>\n", " <td>1475282.94</td>\n",
" <td>1538.80000</td>\n", " <td>11.86000</td>\n",
" <td>1530.00000</td>\n", " <td>11.92000</td>\n",
" <td>1538.80000</td>\n", " <td>12.02000</td>\n",
" <td>1526.10000</td>\n", " <td>11.84000</td>\n",
" <td>5.430</td>\n", " <td>0.050</td>\n",
" <td>5.063</td>\n", " <td>0.080</td>\n",
" <td>2.348</td>\n", " <td>0.055</td>\n",
" <td>2024-12-25</td>\n", " <td>2024-12-25</td>\n",
" <td>20241225</td>\n", " <td>20241225</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2183</th>\n", " <th>3814</th>\n",
" <td>20241226</td>\n", " <td>20241226</td>\n",
" <td>18286.51</td>\n", " <td>1000074.70</td>\n",
" <td>1534.00000</td>\n", " <td>11.92000</td>\n",
" <td>1527.79000</td>\n", " <td>11.86000</td>\n",
" <td>1538.78000</td>\n", " <td>11.93000</td>\n",
" <td>1523.00000</td>\n", " <td>11.78000</td>\n",
" <td>4.192</td>\n", " <td>0.051</td>\n",
" <td>4.968</td>\n", " <td>0.087</td>\n",
" <td>2.872</td>\n", " <td>0.062</td>\n",
" <td>2024-12-26</td>\n", " <td>2024-12-26</td>\n",
" <td>20241226</td>\n", " <td>20241226</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2184</th>\n", " <th>3815</th>\n",
" <td>20241227</td>\n", " <td>20241227</td>\n",
" <td>20759.32</td>\n", " <td>1290012.28</td>\n",
" <td>1528.90000</td>\n", " <td>11.87000</td>\n",
" <td>1528.97000</td>\n", " <td>11.83000</td>\n",
" <td>1536.00000</td>\n", " <td>11.90000</td>\n",
" <td>1519.50000</td>\n", " <td>11.66000</td>\n",
" <td>3.295</td>\n", " <td>0.044</td>\n",
" <td>4.931</td>\n", " <td>0.089</td>\n",
" <td>3.283</td>\n", " <td>0.067</td>\n",
" <td>2024-12-27</td>\n", " <td>2024-12-27</td>\n",
" <td>20241227</td>\n", " <td>20241227</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2185</th>\n", " <th>3816</th>\n",
" <td>20241230</td>\n", " <td>20241230</td>\n",
" <td>25129.82</td>\n", " <td>1351846.36</td>\n",
" <td>1533.97000</td>\n", " <td>11.78000</td>\n",
" <td>1525.00000</td>\n", " <td>11.95000</td>\n",
" <td>1543.96000</td>\n", " <td>11.97000</td>\n",
" <td>1525.00000</td>\n", " <td>11.78000</td>\n",
" <td>1.993</td>\n", " <td>0.052</td>\n",
" <td>4.529</td>\n", " <td>0.100</td>\n",
" <td>3.533</td>\n", " <td>0.074</td>\n",
" <td>2024-12-30</td>\n", " <td>2024-12-30</td>\n",
" <td>20241230</td>\n", " <td>20241230</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>2186</th>\n", " <th>3817</th>\n",
" <td>20241231</td>\n", " <td>20241231</td>\n",
" <td>39354.45</td>\n", " <td>1475367.33</td>\n",
" <td>1525.40000</td>\n", " <td>11.93000</td>\n",
" <td>1524.00000</td>\n", " <td>11.70000</td>\n",
" <td>1545.00000</td>\n", " <td>11.99000</td>\n",
" <td>1522.01000</td>\n", " <td>11.70000</td>\n",
" <td>0.880</td>\n", " <td>0.020</td>\n",
" <td>4.083</td>\n", " <td>0.086</td>\n",
" <td>3.643</td>\n", " <td>0.076</td>\n",
" <td>2024-12-31</td>\n", " <td>2024-12-31</td>\n",
" <td>20241231</td>\n", " <td>20241231</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
"<p>5591 rows × 11 columns</p>\n", "<p>8022 rows × 11 columns</p>\n",
"</div>" "</div>"
] ]
}, },
"execution_count": 9, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
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"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
@@ -1143,7 +1164,7 @@
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"execution_count": 44 "execution_count": 8
}, },
{ {
"metadata": {}, "metadata": {},
@@ -1158,8 +1179,8 @@
{ {
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2025-02-11T03:14:36.513645Z", "end_time": "2025-02-16T05:33:14.092062Z",
"start_time": "2025-02-11T03:14:36.474163Z" "start_time": "2025-02-16T05:33:12.314851Z"
} }
}, },
"cell_type": "code", "cell_type": "code",
@@ -1352,19 +1373,19 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"满足条件的日期:\n", "满足条件的日期:\n",
"2005-09-16\n" "1997-05-07\n"
] ]
}, },
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_60768/2739124595.py:26: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "C:\\Users\\lanyuanxiaoyao\\AppData\\Local\\Temp\\ipykernel_15564\\2739124595.py:26: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='M')).agg(\n" " monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='M')).agg(\n"
] ]
} }
], ],
"execution_count": 10 "execution_count": 9
} }
], ],
"metadata": { "metadata": {