From 5236a0f6f9839d4359a209352734512fed98f612 Mon Sep 17 00:00:00 2001 From: lanyuanxiaoyao Date: Tue, 11 Feb 2025 17:35:00 +0800 Subject: [PATCH] =?UTF-8?q?=E5=B0=9D=E8=AF=95=E6=9E=84=E5=BB=BA=E5=9B=9E?= =?UTF-8?q?=E6=B5=8B=E6=A8=A1=E5=9D=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- poetry.lock | 95 +++++- pyproject.toml | 3 + 回测/backtrader.ipynb | 37 +++ 材料准备/ta-lib.ipynb | 587 ++++++++++++++++++++++++++++++++++++++ 行情监控/Holdle策略.ipynb | 503 ++++++++++++++++++++++---------- 5 files changed, 1076 insertions(+), 149 deletions(-) create mode 100644 回测/backtrader.ipynb create mode 100644 材料准备/ta-lib.ipynb diff --git a/poetry.lock b/poetry.lock index 22fc8bc..1f5cbd4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -15,14 +15,14 @@ files = [ [[package]] name = "akshare" -version = "1.15.84" +version = "1.15.85" description = "AKShare is an elegant and simple financial data interface library for Python, built for human beings!" optional = 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lock-version = "2.1" python-versions = "^3.12" -content-hash = "9fbcdd3a9fba083ae2a2138f3b82492b3559f8dcd3a7fde8ab521f36387327af" +content-hash = "40ca96657ad381e086f3ea182f88c07ff7d44daa425efe1942266d112701c0ba" diff --git a/pyproject.toml b/pyproject.toml index 66f688e..b56db84 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,9 @@ jupyter = "^1.1.1" matplotlib = "^3.10.0" prophet = "^1.1.6" mplfinance = "^0.12.10b0" +scipy = "^1.15.1" +ta-lib = "^0.6.3" +backtrader = "^1.9.78.123" [build-system] diff --git a/回测/backtrader.ipynb b/回测/backtrader.ipynb new file mode 100644 index 0000000..54f657b --- /dev/null +++ b/回测/backtrader.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/材料准备/ta-lib.ipynb b/材料准备/ta-lib.ipynb new file mode 100644 index 0000000..f8a4819 --- /dev/null +++ b/材料准备/ta-lib.ipynb @@ -0,0 +1,587 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-02-11T06:31:19.433336Z", + "start_time": "2025-02-11T06:31:18.884130Z" + } + }, + "source": [ + "import pandas as pd\n", + "\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", + "source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/600519.SH.csv\") \\\n", + " 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" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/行情监控/Holdle策略.ipynb b/行情监控/Holdle策略.ipynb index 10b415f..7bbd880 100644 --- a/行情监控/Holdle策略.ipynb +++ b/行情监控/Holdle策略.ipynb @@ -3,28 +3,28 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T14:37:48.747081Z", - "start_time": "2025-02-10T14:37:48.743083Z" + "end_time": "2025-02-11T02:51:57.789440Z", + "start_time": "2025-02-11T02:51:57.208223Z" } }, "cell_type": "code", "source": "import pandas as pd", "id": "6a10e07f5f498bc6", "outputs": [], - "execution_count": 27 + "execution_count": 1 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T14:54:43.071056Z", - "start_time": "2025-02-10T14:54:43.027108Z" + "end_time": "2025-02-11T03:14:30.972240Z", + "start_time": "2025-02-11T03:14:30.857326Z" } }, "cell_type": "code", "source": [ - "# 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", + "# 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", " [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\", \"macd\", \"macd_dif\", \"macd_dea\"]]\n", "df = pd.DataFrame()\n", "df[[\"date\", \"volume\", \"open\", \"close\", \"high\", \"low\", \"macd\", \"macd_dif\", \"macd_dea\"]] = \\\n", @@ -39,33 +39,33 @@ { "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", + " 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", "\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", + " 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", "\n", - "[8022 rows x 11 columns]" + "[5591 rows x 11 columns]" ], "text/html": [ "
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\n", "
" ] }, - "execution_count": 35, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 35 + "execution_count": 9 }, { "metadata": { @@ -923,13 +923,15 @@ "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 = daily_df.groupby(pd.Grouper(key='datetime', freq='ME')).agg(\n", + " {\n", + " \"volume\": \"sum\",\n", + " \"open\": \"first\",\n", + " \"close\": \"last\",\n", + " \"high\": \"max\",\n", + " \"low\": \"min\",\n", + " }\n", + ")\n", "monthly_df.reset_index(inplace=True)\n", "monthly_df.set_index('datetime', inplace=True)\n", "monthly_df" @@ -1152,6 +1154,217 @@ "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": { + "ExecuteTime": { + "end_time": "2025-02-11T03:14:36.513645Z", + "start_time": "2025-02-11T03:14:36.474163Z" + } + }, + "cell_type": "code", + "source": [ + "daily_df = df.copy()\n", + "import pandas as pd\n", + "import talib\n", + "import numpy as np\n", + "from scipy.signal import argrelextrema\n", + "\n", + "\n", + "# 假设daily_df已经导入，列名为datetime, open, close, high, low\n", + "# 代码中会直接使用daily_df\n", + "\n", + "# 步骤1: 计算月线行情和月线的MACD\n", + "# 首先需要将日线行情转换为月线行情\n", + "def calculate_monthly_data(daily_df):\n", + " \"\"\"\n", + " 将日线行情转换为月线行情\n", + " datetime: 转换为日期的月初\n", + " open: 每个月的第一个交易日的开盘价\n", + " close: 每个月的最后一个交易日的收盘价\n", + " high: 每个月的最高价\n", + " low: 每个月的最低价\n", + " \"\"\"\n", + " # 将datetime列转换为日期格式\n", + " daily_df['datetime'] = pd.to_datetime(daily_df['datetime'])\n", + "\n", + " # 按月份分组\n", + " monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='M')).agg(\n", + " {\n", + " 'open': 'first',\n", + " 'close': 'last',\n", + " 'high': 'max',\n", + " 'low': 'min'\n", + " }\n", + " )\n", + " return monthly_df\n", + "\n", + "\n", + "# 计算月线的MACD指标\n", + "def calculate_monthly_macd(monthly_df):\n", + " \"\"\"\n", + " 计算月线的MACD指标，包括DIF, DEA和MACD柱状图值\n", + " \"\"\"\n", + " # 获取收盘价序列\n", + " close_prices = monthly_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", + " monthly_df['DIF'] = dif\n", + " monthly_df['DEA'] = dea\n", + " monthly_df['MACD'] = hist\n", + "\n", + " return monthly_df\n", + "\n", + "\n", + "# 筛选出符合条件的月份\n", + "def filter_monthly_macd(monthly_df):\n", + " \"\"\"\n", + " 筛选出满足条件的月份：\n", + " DIF和DEA均在0轴上方，且MACD值大于0并且小于DIF和DEA\n", + " \"\"\"\n", + " # 定义筛选条件\n", + " condition = (monthly_df['DIF'] > 0) & \\\n", + " (monthly_df['DEA'] > 0) & \\\n", + " (monthly_df['MACD'] > 0) & \\\n", + " (monthly_df['MACD'] < monthly_df['DIF']) & \\\n", + " (monthly_df['MACD'] < monthly_df['DEA'])\n", + "\n", + " # 筛选符合条件的月份\n", + " filtered_months = monthly_df[condition]\n", + " return filtered_months\n", + "\n", + "\n", + "# 步骤2: 进一步筛选符合条件的月份\n", + "def further_filter_months(filtered_months, monthly_df):\n", + " \"\"\"\n", + " 在步骤1筛选出的月份中，进一步筛选：\n", + " 1. 该月份的前一个月的MACD值小于0\n", + " 2. 该月份前几个月MACD值经历了从下降（至少连续下降超过2个月）再回升的第一个月\n", + " \"\"\"\n", + " # 合并所有月份的数据\n", + " merged_df = monthly_df.merge(\n", + " filtered_months[['DIF', 'DEA', 'MACD']],\n", + " left_index=True, right_index=True,\n", + " how='left', suffixes=('', '_filtered')\n", + " )\n", + "\n", + " # 条件1: 前一个月的MACD值小于0\n", + " condition1 = merged_df['MACD_filtered'].shift(1) < 0\n", + "\n", + " # 条件2: 前几个月MACD经历了从下降超过2个月再回升的第一个月\n", + " # 使用rolling窗口检查是否连续下降超过2个月，然后回升\n", + " # 创建一个标志，表示是否处于连续下降状态\n", + " merged_df['macd_diff'] = merged_df['MACD_filtered'].diff()\n", + " merged_df['descending'] = merged_df['macd_diff'] < 0\n", + "\n", + " # 使用rolling窗口计算连续下降超过2个月\n", + " merged_df['consecutive_desc'] = merged_df['descending'].rolling(window=3).sum() >= 2\n", + "\n", + " # 检查前一个月是否在连续下降，当前月是否回升\n", + " condition2 = (merged_df['consecutive_desc'].shift(1)) & \\\n", + " (merged_df['macd_diff'] > 0)\n", + "\n", + " # 筛选满足条件的月份\n", + " further_filtered = merged_df[condition1 | condition2]\n", + "\n", + " # 返回筛选出的月份的索引（日期）\n", + " return further_filtered.index.tolist()\n", + "\n", + "\n", + "# 步骤3: 在日线行情中判断峰值\n", + "def find_dayline_peaks(further_filtered_months, daily_df):\n", + " \"\"\"\n", + " 在步骤2筛选出的月份中，从下一个月开始，在日线行情中找到前两个峰值\n", + " \"\"\"\n", + " # 确保日线行情已经按日期排序\n", + " daily_df = daily_df.sort_values(by='datetime')\n", + "\n", + " # 将日线行情的索引设置为datetime\n", + " daily_df.set_index('datetime', inplace=True)\n", + "\n", + " # 初始化结果列表\n", + " result_dates = []\n", + "\n", + " for month in further_filtered_months:\n", + " # 获取下一个月的起始日期\n", + " next_month_start = month + pd.offsets.MonthBegin(1)\n", + " next_month_end = month + pd.offsets.MonthEnd(2) # 可能需要调整范围\n", + "\n", + " # 截取日线行情数据\n", + " period_df = daily_df.loc[next_month_start:next_month_end]\n", + "\n", + " # 找到收盘价的峰值\n", + " # 使用scipy的argrelextrema函数找到相对高点\n", + " peak_indices = argrelextrema(period_df['close'].values, np.greater, order=5)[0]\n", + " if len(peak_indices) >= 2:\n", + " # 获取前两个峰值\n", + " first_peak = peak_indices[0]\n", + " second_peak = peak_indices[1]\n", + "\n", + " # 比较两个峰值\n", + " if period_df['close'].iloc[second_peak] > period_df['close'].iloc[first_peak]:\n", + " result_dates.append(period_df.index[second_peak])\n", + "\n", + " return result_dates\n", + "\n", + "\n", + "# 步骤4: 输出结果\n", + "def main(daily_df):\n", + " # 步骤1: 计算月线行情和MACD\n", + " monthly_df = calculate_monthly_data(daily_df.copy())\n", + " monthly_df = calculate_monthly_macd(monthly_df)\n", + "\n", + " # 步骤1: 筛选出符合条件的月份\n", + " filtered_months = filter_monthly_macd(monthly_df)\n", + "\n", + " if filtered_months.empty:\n", + " print(\"没有满足步骤1条件的月份\")\n", + " return\n", + "\n", + " # 步骤2: 进一步筛选符合条件的月份\n", + " further_filtered_months = further_filter_months(filtered_months, monthly_df)\n", + "\n", + " if not further_filtered_months:\n", + " print(\"没有满足步骤2条件的月份\")\n", + " return\n", + "\n", + " # 步骤3: 在日线行情中查找峰值\n", + " result_dates = find_dayline_peaks(further_filtered_months, daily_df)\n", + "\n", + " # 输出结果\n", + " if result_dates:\n", + " print(\"满足条件的日期:\")\n", + " for date in result_dates:\n", + " print(date.date())\n", + " else:\n", + " print(\"没有找到符合条件的日期\")\n", + "\n", + "\n", + "main(daily_df)" + ], + "id": "46bd60b709f637a2", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "满足条件的日期:\n", + "2005-09-16\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", + " monthly_df = daily_df.groupby(pd.Grouper(key='datetime', freq='M')).agg(\n" + ] + } + ], + "execution_count": 10 } ], "metadata": {