From 9689625fc199c8fd13b9d50b05a526ee868f082d Mon Sep 17 00:00:00 2001 From: lanyuanxiaoyao Date: Sun, 16 Feb 2025 23:24:34 +0800 Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90macd=E5=9B=9E=E6=B5=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- poetry.lock | 375 +++++++++++------------ 回测/backtrader.ipynb | 629 +++++++++++++++++++++++++++++++++++--- 行情监控/Holdle策略.ipynb | 469 ++++++++++++++-------------- 3 files changed, 1021 insertions(+), 452 deletions(-) diff --git a/poetry.lock b/poetry.lock index e490a9e..3fa4a64 100644 --- a/poetry.lock +++ b/poetry.lock @@ -15,14 +15,14 @@ files = [ [[package]] name = "akshare" -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!" optional = false python-versions = ">=3.8" groups = ["main"] files = [ - {file = "akshare-1.15.87-py3-none-any.whl", hash = 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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", + "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", + "# 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[[\"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 > 2010]\n", + "df = df[df[\"datetime\"].dt.year > 2023]\n", "df.sort_values(by='datetime', inplace=True)\n", "df.set_index('datetime', inplace=True)\n", - "# df" + "df" ], "id": "157a87ab33037c6e", - "outputs": [], - "execution_count": 46 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "12fde7bfaddbdf4" + "outputs": [ + { + "data": { + "text/plain": [ + " date volume open close high low \\\n", + "datetime \n", + "2024-01-02 20240102 1158366.45 8.57646 8.41205 8.60386 8.41205 \n", + "2024-01-03 20240103 733610.31 8.39379 8.40292 8.42119 8.35725 \n", + "2024-01-04 20240104 864193.99 8.39379 8.32072 8.39379 8.29332 \n", + "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", + 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datevolumeopenclosehighlowmacdmacd_difmacd_deadatetime_textdifdeahist
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2024-01-02202401021158366.458.576468.412058.603868.412050.107-0.162-0.216202401020.0000000.0000000.000000
2024-01-0320240103733610.318.393798.402928.421198.357250.098-0.155-0.203202401030.0000000.0000000.000000
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2024-01-08202401081121156.198.430328.357258.494268.320720.076-0.135-0.173202401080.0000000.0000000.000000
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2024-12-25202412251475282.9411.8600011.9200012.0200011.840000.0500.0800.055202412250.0804750.0552930.025182
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2024-12-27202412271290012.2811.8700011.8300011.9000011.660000.0440.0890.067202412270.0894110.0672510.022159
2024-12-30202412301351846.3611.7800011.9500011.9700011.780000.0520.1000.074202412300.0995510.0737110.025840
2024-12-31202412311475367.3311.9300011.7000011.9900011.700000.0200.0860.076202412310.0864190.0762530.010166
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242 rows × 13 columns

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" + ] + }, + "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", "\n", - "data_feed = bt.feeds.PandasData(dataname=df)\n", - "cerebro.adddata(data_feed)\n", + "\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", - "cerebro.broker.setcash(100000000.0)\n", + "cerebro.broker.setcash(10000.0)\n", "# 佣金，双边各 0.0003\n", "cerebro.broker.setcommission(commission=0.0003)\n", "# 滑点：双边各 0.0001\n", @@ -68,12 +615,20 @@ "# 创建策略类\n", "class TestStrategy(bt.Strategy):\n", " def next(self):\n", - " curr_date = self.datetime.date()\n", - " _, last_day_month = calendar.monthrange(curr_date.year, curr_date.month)\n", - " if curr_date.day == 1:\n", - " self.buy()\n", - " elif curr_date.day == last_day_month:\n", - " self.sell()\n", + " now_dif = self.data.dif[0]\n", + " now_dea = self.data.dea[0]\n", + " now_hist = self.data.hist[0]\n", + "\n", + " pre_dif = self.data.dif[-1]\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", " def log(self, txt, dt=None):\n", " \"\"\"\n", @@ -113,10 +668,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "初始资金: 100000000.00\n", - "最终资金: 99982677.31\n", - "夏普比率: -249.45833106775427\n", - "最大回撤: 0.03266399519865984\n" + "初始资金: 10000.00\n", + "最终资金: 10015.80\n", + "夏普比率: None\n", + "最大回撤: 0.48930011171324134\n" ] }, { @@ -135,7 +690,7 @@ "" ], "text/html": [ - "
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" ] }, "metadata": {}, @@ -147,12 +702,12 @@ "[[
]]" ] }, - "execution_count": 45, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 45 + "execution_count": 160 } ], "metadata": { diff --git a/行情监控/Holdle策略.ipynb b/行情监控/Holdle策略.ipynb index 7bbd880..b2b467f 100644 --- a/行情监控/Holdle策略.ipynb +++ b/行情监控/Holdle策略.ipynb @@ -3,29 +3,29 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-11T02:51:57.789440Z", - "start_time": "2025-02-11T02:51:57.208223Z" + "end_time": "2025-02-16T05:33:10.625500Z", + "start_time": "2025-02-16T05:33:10.622501Z" } }, "cell_type": "code", "source": "import pandas as pd", "id": "6a10e07f5f498bc6", "outputs": [], - "execution_count": 1 + "execution_count": 3 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-11T03:14:30.972240Z", - "start_time": "2025-02-11T03:14:30.857326Z" + "end_time": "2025-02-16T05:33:10.692963Z", + "start_time": "2025-02-16T05:33:10.635906Z" } }, "cell_type": "code", "source": [ - "# 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", - "source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/600519.SH.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", + "# 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[[\"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", @@ -39,33 +39,33 @@ { "data": { "text/plain": [ - " date volume open close high low \\\n", - "2187 20010827 406318.00 4.23675 4.36443 4.63820 4.03295 \n", - "2188 20010828 129647.79 4.29568 4.52525 4.54244 4.24902 \n", - "2189 20010829 53252.75 4.53999 4.46632 4.54244 4.43195 \n", - "2190 20010830 48013.06 4.45405 4.55472 4.60505 4.41967 \n", - "2191 20010831 23231.48 4.56086 4.54367 4.61856 4.51789 \n", - "... ... ... ... ... ... ... \n", - "2182 20241225 17123.39 1538.80000 1530.00000 1538.80000 1526.10000 \n", - "2183 20241226 18286.51 1534.00000 1527.79000 1538.78000 1523.00000 \n", - "2184 20241227 20759.32 1528.90000 1528.97000 1536.00000 1519.50000 \n", - "2185 20241230 25129.82 1533.97000 1525.00000 1543.96000 1525.00000 \n", - "2186 20241231 39354.45 1525.40000 1524.00000 1545.00000 1522.01000 \n", + " 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 macd_dif macd_dea datetime datetime_text \n", - "2187 0.000 0.000 0.000 2001-08-27 20010827 \n", - "2188 0.021 0.013 0.003 2001-08-28 20010828 \n", - "2189 0.025 0.018 0.006 2001-08-29 20010829 \n", - "2190 0.037 0.029 0.010 2001-08-30 20010830 \n", - "2191 0.042 0.036 0.016 2001-08-31 20010831 \n", - "... ... ... ... ... ... \n", - "2182 5.430 5.063 2.348 2024-12-25 20241225 \n", - "2183 4.192 4.968 2.872 2024-12-26 20241226 \n", - "2184 3.295 4.931 3.283 2024-12-27 20241227 \n", - "2185 1.993 4.529 3.533 2024-12-30 20241230 \n", - "2186 0.880 4.083 3.643 2024-12-31 20241231 \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", - "[5591 rows x 11 columns]" + "[8022 rows x 11 columns]" ], "text/html": [ "
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" ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 9 + "execution_count": 4 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T13:23:42.952570Z", - "start_time": "2025-02-10T13:23:42.938745Z" + "end_time": "2025-02-16T05:33:10.728Z", + "start_time": "2025-02-16T05:33:10.707648Z" } }, "cell_type": "code", @@ -305,46 +305,46 @@ { "data": { "text/plain": [ - " date open close high low macd macd_dif \\\n", - "3576 20240102 8.57646 8.41205 8.60386 8.41205 0.107 -0.162 \n", - "3577 20240103 8.39379 8.40292 8.42119 8.35725 0.098 -0.155 \n", - "3578 20240104 8.39379 8.32072 8.39379 8.29332 0.080 -0.153 \n", - "3579 20240105 8.31158 8.46686 8.62213 8.28418 0.087 -0.139 \n", - "3580 20240108 8.43032 8.35725 8.49426 8.32072 0.076 -0.135 \n", - "... ... ... ... ... ... ... ... \n", - "3813 20241225 11.86000 11.92000 12.02000 11.84000 0.050 0.080 \n", - "3814 20241226 11.92000 11.86000 11.93000 11.78000 0.051 0.087 \n", - "3815 20241227 11.87000 11.83000 11.90000 11.66000 0.044 0.089 \n", - "3816 20241230 11.78000 11.95000 11.97000 11.78000 0.052 0.100 \n", - "3817 20241231 11.93000 11.70000 11.99000 11.70000 0.020 0.086 \n", + " date volume open close high low macd \\\n", + "3576 20240102 1158366.45 8.57646 8.41205 8.60386 8.41205 0.107 \n", + "3577 20240103 733610.31 8.39379 8.40292 8.42119 8.35725 0.098 \n", + "3578 20240104 864193.99 8.39379 8.32072 8.39379 8.29332 0.080 \n", + "3579 20240105 1991622.16 8.31158 8.46686 8.62213 8.28418 0.087 \n", + "3580 20240108 1121156.19 8.43032 8.35725 8.49426 8.32072 0.076 \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_dea datetime datetime_text close_to_median macd_close \\\n", - "3576 -0.216 2024-01-02 20240102 -1.50113 -0.339145 \n", - "3577 -0.203 2024-01-03 20240103 -1.51026 -0.341208 \n", - "3578 -0.193 2024-01-04 20240104 -1.59246 -0.359779 \n", - "3579 -0.183 2024-01-05 20240105 -1.44632 -0.326762 \n", - "3580 -0.173 2024-01-08 20240108 -1.55593 -0.351526 \n", - "... ... ... ... ... ... \n", - "3813 0.055 2024-12-25 20241225 2.00682 0.453394 \n", - "3814 0.062 2024-12-26 20241226 1.94682 0.439838 \n", - "3815 0.067 2024-12-27 20241227 1.91682 0.433061 \n", - "3816 0.074 2024-12-30 20241230 2.03682 0.460172 \n", - "3817 0.076 2024-12-31 20241231 1.78682 0.403690 \n", + " macd_dif macd_dea datetime datetime_text close_to_median \\\n", + "3576 -0.162 -0.216 2024-01-02 20240102 -1.50113 \n", + "3577 -0.155 -0.203 2024-01-03 20240103 -1.51026 \n", + "3578 -0.153 -0.193 2024-01-04 20240104 -1.59246 \n", + "3579 -0.139 -0.183 2024-01-05 20240105 -1.44632 \n", + "3580 -0.135 -0.173 2024-01-08 20240108 -1.55593 \n", + "... ... ... ... ... ... \n", + "3813 0.080 0.055 2024-12-25 20241225 2.00682 \n", + "3814 0.087 0.062 2024-12-26 20241226 1.94682 \n", + "3815 0.089 0.067 2024-12-27 20241227 1.91682 \n", + "3816 0.100 0.074 2024-12-30 20241230 2.03682 \n", + "3817 0.086 0.076 2024-12-31 20241231 1.78682 \n", "\n", - " macd_diff macd_trend macd_trend_group pre_macd point \n", - "3576 0.000 0 1 NaN False \n", - "3577 -0.009 -1 2 0.107 False \n", - "3578 -0.018 -1 2 0.098 False \n", - "3579 0.007 1 3 0.080 False \n", - "3580 -0.011 -1 4 0.087 False \n", - "... ... ... ... ... ... \n", - "3813 0.019 1 66 0.031 False \n", - "3814 0.001 1 66 0.050 False \n", - "3815 -0.007 -1 67 0.051 False \n", - "3816 0.008 1 68 0.044 True \n", - "3817 -0.032 -1 69 0.052 False \n", + " macd_close macd_diff macd_trend macd_trend_group pre_macd point \n", + "3576 -0.339145 0.000 0 1 NaN False \n", + "3577 -0.341208 -0.009 -1 2 0.107 False \n", + "3578 -0.359779 -0.018 -1 2 0.098 False \n", + "3579 -0.326762 0.007 1 3 0.080 False \n", + "3580 -0.351526 -0.011 -1 4 0.087 False \n", + "... ... ... ... ... ... ... \n", + "3813 0.453394 0.019 1 66 0.031 False \n", + "3814 0.439838 0.001 1 66 0.050 False \n", + "3815 0.433061 -0.007 -1 67 0.051 False \n", + "3816 0.460172 0.008 1 68 0.044 True \n", + "3817 0.403690 -0.032 -1 69 0.052 False \n", "\n", - "[242 rows x 17 columns]" + "[242 rows x 18 columns]" ], "text/html": [ "
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242 rows × 18 columns

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" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 16 + "execution_count": 5 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T13:23:46.230728Z", - "start_time": "2025-02-10T13:23:46.054326Z" + "end_time": "2025-02-16T05:33:11.383991Z", + "start_time": "2025-02-16T05:33:10.747373Z" } }, "cell_type": "code", @@ -672,35 +684,35 @@ { "data": { "text/plain": [ - " date open close high low macd macd_dif \\\n", - "3645 20240418 9.66336 9.86430 10.07437 9.64509 0.017 0.043 \n", - "3650 20240425 9.59029 9.69076 9.69989 9.57202 0.017 0.057 \n", - "3652 20240429 9.64509 9.87343 9.95563 9.60856 0.039 0.075 \n", - "3658 20240510 9.83690 9.99217 10.01044 9.82776 0.040 0.111 \n", - "3662 20240516 9.90997 10.20224 10.23878 9.89170 0.046 0.133 \n", - "3808 20241218 11.58000 11.65000 11.74000 11.57000 0.005 0.048 \n", - "3811 20241223 11.64000 11.73000 11.84000 11.64000 0.008 0.049 \n", - "3816 20241230 11.78000 11.95000 11.97000 11.78000 0.052 0.100 \n", + " date volume open close high low macd \\\n", + "3645 20240418 3165914.26 9.66336 9.86430 10.07437 9.64509 0.017 \n", + "3650 20240425 1113812.24 9.59029 9.69076 9.69989 9.57202 0.017 \n", + "3652 20240429 2169177.37 9.64509 9.87343 9.95563 9.60856 0.039 \n", + "3658 20240510 1767835.61 9.83690 9.99217 10.01044 9.82776 0.040 \n", + "3662 20240516 3076291.65 9.90997 10.20224 10.23878 9.89170 0.046 \n", + "3808 20241218 1016589.66 11.58000 11.65000 11.74000 11.57000 0.005 \n", + "3811 20241223 1659404.76 11.64000 11.73000 11.84000 11.64000 0.008 \n", + "3816 20241230 1351846.36 11.78000 11.95000 11.97000 11.78000 0.052 \n", "\n", - " macd_dea datetime datetime_text close_to_median macd_close \\\n", - "3645 0.034 2024-04-18 20240418 -0.04888 -0.011043 \n", - "3650 0.048 2024-04-25 20240425 -0.22242 -0.050251 \n", - "3652 0.055 2024-04-29 20240429 -0.03975 -0.008981 \n", - "3658 0.091 2024-05-10 20240510 0.07899 0.017846 \n", - "3662 0.110 2024-05-16 20240516 0.28906 0.065306 \n", - "3808 0.045 2024-12-18 20241218 1.73682 0.392394 \n", - "3811 0.045 2024-12-23 20241223 1.81682 0.410468 \n", - "3816 0.074 2024-12-30 20241230 2.03682 0.460172 \n", + " macd_dif macd_dea datetime datetime_text close_to_median \\\n", + "3645 0.043 0.034 2024-04-18 20240418 -0.04888 \n", + "3650 0.057 0.048 2024-04-25 20240425 -0.22242 \n", + "3652 0.075 0.055 2024-04-29 20240429 -0.03975 \n", + "3658 0.111 0.091 2024-05-10 20240510 0.07899 \n", + "3662 0.133 0.110 2024-05-16 20240516 0.28906 \n", + "3808 0.048 0.045 2024-12-18 20241218 1.73682 \n", + "3811 0.049 0.045 2024-12-23 20241223 1.81682 \n", + "3816 0.100 0.074 2024-12-30 20241230 2.03682 \n", "\n", - " macd_diff macd_trend macd_trend_group pre_macd point \n", - "3645 0.055 1 23 -0.038 True \n", - "3650 0.004 1 25 0.013 True \n", - "3652 0.022 1 27 0.017 True \n", - "3658 0.010 1 29 0.030 True \n", - "3662 0.026 1 31 0.020 True \n", - "3808 0.001 1 64 0.004 True \n", - "3811 0.013 1 66 -0.005 True \n", - "3816 0.008 1 68 0.044 True " + " macd_close macd_diff macd_trend macd_trend_group pre_macd point \n", + "3645 -0.011043 0.055 1 23 -0.038 True \n", + "3650 -0.050251 0.004 1 25 0.013 True \n", + "3652 -0.008981 0.022 1 27 0.017 True \n", + "3658 0.017846 0.010 1 29 0.030 True \n", + "3662 0.065306 0.026 1 31 0.020 True \n", + "3808 0.392394 0.001 1 64 0.004 True \n", + "3811 0.410468 0.013 1 66 -0.005 True \n", + "3816 0.460172 0.008 1 68 0.044 True " ], "text/html": [ "
\n", @@ -722,6 +734,7 @@ " \n", " \n", " date\n", + " volume\n", " open\n", " close\n", " high\n", @@ -744,6 +757,7 @@ " \n", " 3645\n", " 20240418\n", + " 3165914.26\n", " 9.66336\n", " 9.86430\n", " 10.07437\n", @@ -764,6 +778,7 @@ " \n", " 3650\n", " 20240425\n", + " 1113812.24\n", " 9.59029\n", " 9.69076\n", " 9.69989\n", @@ -784,6 +799,7 @@ " \n", " 3652\n", " 20240429\n", + " 2169177.37\n", " 9.64509\n", " 9.87343\n", " 9.95563\n", @@ -804,6 +820,7 @@ " \n", " 3658\n", " 20240510\n", + " 1767835.61\n", " 9.83690\n", " 9.99217\n", " 10.01044\n", @@ -824,6 +841,7 @@ " \n", " 3662\n", " 20240516\n", + " 3076291.65\n", " 9.90997\n", " 10.20224\n", " 10.23878\n", @@ -844,6 +862,7 @@ " \n", " 3808\n", " 20241218\n", + " 1016589.66\n", " 11.58000\n", " 11.65000\n", " 11.74000\n", @@ -864,6 +883,7 @@ " \n", " 3811\n", " 20241223\n", + " 1659404.76\n", " 11.64000\n", " 11.73000\n", " 11.84000\n", @@ -884,6 +904,7 @@ " \n", " 3816\n", " 20241230\n", + " 1351846.36\n", " 11.78000\n", " 11.95000\n", " 11.97000\n", @@ -906,18 +927,18 @@ "
" ] }, - "execution_count": 17, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 17 + "execution_count": 6 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T15:07:41.915428Z", - "start_time": "2025-02-10T15:07:41.902660Z" + "end_time": "2025-02-16T05:33:11.418948Z", + "start_time": "2025-02-16T05:33:11.405501Z" } }, "cell_type": "code", @@ -1086,18 +1107,18 @@ "" ] }, - "execution_count": 43, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 43 + "execution_count": 7 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T15:07:44.088406Z", - "start_time": "2025-02-10T15:07:43.838889Z" + "end_time": "2025-02-16T05:33:12.250581Z", + "start_time": "2025-02-16T05:33:11.724099Z" } }, "cell_type": "code", @@ -1128,7 +1149,7 @@ " ])" ] }, - "execution_count": 44, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -1143,7 +1164,7 @@ "output_type": "display_data" } ], - "execution_count": 44 + "execution_count": 8 }, { "metadata": {}, @@ -1158,8 +1179,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-11T03:14:36.513645Z", - "start_time": "2025-02-11T03:14:36.474163Z" + "end_time": "2025-02-16T05:33:14.092062Z", + "start_time": "2025-02-16T05:33:12.314851Z" } }, "cell_type": "code", @@ -1352,19 +1373,19 @@ "output_type": "stream", "text": [ "满足条件的日期:\n", - "2005-09-16\n" + "1997-05-07\n" ] }, { "name": "stderr", "output_type": "stream", "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" ] } ], - "execution_count": 10 + "execution_count": 9 } ], "metadata": {