498 lines
137 KiB
Plaintext
498 lines
137 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"start_time": "2025-02-21T03:15:40.254285Z"
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}
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},
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"source": [
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"import os\n",
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"import platform\n",
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"\n",
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"import pandas as pd\n",
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"\n",
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"root_path = \"C:\\\\Users\\\\lanyuanxiaoyao\\\\SynologyDrive\\\\data\\\\Tushare\" \\\n",
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" if platform.processor().startswith(\"AMD\") \\\n",
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" else \"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare\""
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],
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"outputs": [],
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"execution_count": 83
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},
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{
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"metadata": {
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"ExecuteTime": {
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}
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},
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"cell_type": "code",
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"source": [
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"def load_stock(code):\n",
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" source_df = pd.read_csv(os.path.join(root_path, \"日线行情 1990-2024\", \"分组行情\", f\"{code}.csv\")) \\\n",
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" [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\"]]\n",
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" temp_df = pd.DataFrame()\n",
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" temp_df[[\"date\", \"Volume\", \"Open\", \"Close\", \"High\", \"Low\"]] = \\\n",
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" source_df[[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\"]]\n",
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" temp_df[\"datetime\"] = pd.to_datetime(temp_df[\"date\"], format=\"%Y%m%d\")\n",
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" temp_df[\"datetime_text\"] = temp_df[\"datetime\"].apply(lambda x: x.strftime(\"%Y%m%d\"))\n",
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" temp_df = temp_df[temp_df[\"datetime\"].dt.year > 2023]\n",
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" temp_df.sort_values(by='datetime', inplace=True)\n",
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" temp_df.set_index('datetime', inplace=True)\n",
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" return temp_df"
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],
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"id": "414a7a0a199096a2",
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"outputs": [],
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"execution_count": 84
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},
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{
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"metadata": {
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"ExecuteTime": {
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}
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},
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"cell_type": "code",
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"source": "df = load_stock(\"000001.SZ\")",
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"id": "bdcc7bfcec324577",
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"outputs": [],
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"execution_count": 85
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-21T03:15:40.423569Z",
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}
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},
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"cell_type": "code",
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"source": [
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"def translate_metrics(data):\n",
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" \"\"\"\n",
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" 将 Pandas Series 中的指标名称翻译为中文并打印结果。\n",
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"\n",
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" 参数:\n",
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" data (pd.Series or pd.DataFrame): 包含指标的 Pandas 数据结构。\n",
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" \"\"\"\n",
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" # 翻译字典\n",
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" translation = {\n",
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" \"Start\": \"开始日期\",\n",
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" \"End\": \"结束日期\",\n",
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" \"Duration\": \"持续时间\",\n",
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" \"Exposure Time [%]\": \"有效时间\",\n",
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" \"Equity Final [$]\": \"最终权益\",\n",
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" \"Equity Peak [$]\": \"最高权益\",\n",
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" \"Commissions [$]\": \"佣金\",\n",
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" \"Return [%]\": \"回报率\",\n",
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" \"Buy & Hold Return [%]\": \"买并持有回报率\",\n",
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" \"Return (Ann.) [%]\": \"年化回报率\",\n",
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" \"Volatility (Ann.) [%]\": \"年化波动率\",\n",
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" \"CAGR [%]\": \"年复合增长率\",\n",
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" \"Sharpe Ratio\": \"夏普比率\",\n",
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" \"Sortino Ratio\": \"索提诺比率\",\n",
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" \"Calmar Ratio\": \"卡玛比率\",\n",
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" \"Max. Drawdown [%]\": \"最大回撤\",\n",
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" \"Avg. Drawdown [%]\": \"平均回撤\",\n",
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" \"Max. Drawdown Duration\": \"最长回撤时间\",\n",
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" \"Avg. Drawdown Duration\": \"平均回撤时间\",\n",
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" \"# Trades\": \"交易次数\",\n",
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" \"Win Rate [%]\": \"胜率\",\n",
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" \"Best Trade [%]\": \"最佳交易\",\n",
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" \"Worst Trade [%]\": \"最差交易\",\n",
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" \"Avg. Trade [%]\": \"平均交易\",\n",
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" \"Max. Trade Duration\": \"最长交易时间\",\n",
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" \"Avg. Trade Duration\": \"平均交易时间\",\n",
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" \"Profit Factor\": \"利润因子\",\n",
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" \"Expectancy [%]\": \"期望\",\n",
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" \"SQN\": \"系统质量系数\",\n",
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" \"Kelly Criterion\": \"凯利比率\"\n",
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" }\n",
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"\n",
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" # 如果是 Series\n",
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" if isinstance(data, pd.Series):\n",
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" for idx in range(len(data)):\n",
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" key = data.index[idx]\n",
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" value = data.iloc[idx]\n",
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" translated_key = translation.get(key, key)\n",
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" print(f\"{translated_key}: {value}\")\n",
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" else:\n",
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" raise ValueError(\"输入数据必须是 Pandas Series 或 DataFrame。\")"
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],
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"id": "f6e932667ad314ff",
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"outputs": [],
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"execution_count": 86
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-21T03:23:42.886623Z",
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"start_time": "2025-02-21T03:23:42.881086Z"
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}
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},
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"cell_type": "code",
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"source": [
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"from backtesting import Strategy\n",
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"from backtesting.lib import crossover\n",
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"import talib\n",
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"\n",
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"\n",
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"class MACDStrategy(Strategy):\n",
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" def init(self):\n",
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" self.dif, self.dea, self.macd = self.I(talib.MACD, self.data.Close, 12, 26, 9)\n",
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"\n",
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" def next(self):\n",
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" curr_dif = self.dif[-1]\n",
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" curr_dea = self.dea[-1]\n",
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" curr_macd = self.macd[-1]\n",
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" if crossover(self.dif, self.dea) and curr_macd > 0 and (curr_dif > curr_macd and curr_dea > curr_macd):\n",
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" self.buy(size=100)\n",
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" elif crossover(self.dea, self.dif):\n",
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" if self.position.size > 0:\n",
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" self.position.close()"
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],
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"id": "1b47368c3b916b9b",
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"outputs": [],
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"execution_count": 94
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-21T03:15:40.473207Z",
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"start_time": "2025-02-21T03:15:40.468715Z"
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}
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},
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"cell_type": "code",
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"source": [
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"class SMAStrategy(Strategy):\n",
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" def init(self):\n",
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" self.sma_fast = self.I(talib.SMA, self.data.Close, 14)\n",
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" self.sma_slow = self.I(talib.SMA, self.data.Close, 60)\n",
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"\n",
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" def next(self):\n",
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" if crossover(self.sma_fast, self.sma_slow):\n",
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" self.buy(size=100)\n",
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" elif crossover(self.sma_slow, self.sma_fast):\n",
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" if self.position.size > 0:\n",
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" self.position.close()"
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],
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"id": "85da7018d63adc0",
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"outputs": [],
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"execution_count": 88
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},
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{
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"metadata": {
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"end_time": "2025-02-21T03:23:45.326016Z",
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}
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},
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"cell_type": "code",
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"source": [
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"from backtesting import Backtest\n",
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"\n",
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"bt = Backtest(df, MACDStrategy, cash=10000, commission=.002, trade_on_close=True)\n",
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"result = bt.run()\n",
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"print(result.iloc[4])\n",
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"bt.plot()"
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],
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"id": "ab574b78805925e5",
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"outputs": [
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{
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(max - min) * .03 : 0;\\n range.start = min - pad;\\n range.end = max + pad;\\n } else console.error('backtesting: scale range error:', min, max, range);\\n };\\n}\\n\\nclearTimeout(window._bt_autoscale_timeout);\\n\\nwindow._bt_autoscale_timeout = setTimeout(function () {\\n /**\\n * @variable cb_obj `fig_ohlc.x_range`.\\n * @variable source `ColumnDataSource`\\n * @variable ohlc_range `fig_ohlc.y_range`.\\n * @variable volume_range `fig_volume.y_range`.\\n */\\n \\\"use strict\\\";\\n\\n let i = Math.max(Math.floor(cb_obj.start), 0),\\n j = Math.min(Math.ceil(cb_obj.end), source.data['ohlc_high'].length);\\n\\n let max = Math.max.apply(null, source.data['ohlc_high'].slice(i, j)),\\n min = Math.min.apply(null, source.data['ohlc_low'].slice(i, j));\\n _bt_scale_range(ohlc_range, min, max, true);\\n\\n if (volume_range) {\\n max = Math.max.apply(null, source.data['Volume'].slice(i, j));\\n _bt_scale_range(volume_range, 0, max * 1.03, false);\\n }\\n\\n}, 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"output_type": "display_data"
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{
|
||
"data": {
|
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|
||
"GridPlot(id='p11108', ...)"
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||
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||
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|
||
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table-cell;\"></div><div style=\"display: table-cell;\">cols = None,</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">context_menu = None,</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">css_classes = [],</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">css_variables = {},</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">disabled = False,</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">elements = [],</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">flow_mode = 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class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar = Toolbar(id='p11107', ...),</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">toolbar_location = 'right',</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">visible = True,</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width = None,</div></div><div class=\"p11139\" style=\"display: none;\"><div style=\"display: table-cell;\"></div><div style=\"display: table-cell;\">width_policy = 'auto')</div></div></div>\n",
|
||
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|
||
"(function() {\n",
|
||
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|
||
" ellipsis.addEventListener(\"click\", function() {\n",
|
||
" const rows = document.getElementsByClassName(\"p11139\");\n",
|
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" for (let i = 0; i < rows.length; i++) {\n",
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" const el = rows[i];\n",
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" el.style.display = expanded ? \"none\" : \"table-row\";\n",
|
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" }\n",
|
||
" ellipsis.innerHTML = expanded ? \"…)\" : \"‹‹‹\";\n",
|
||
" expanded = !expanded;\n",
|
||
" });\n",
|
||
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|
||
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|
||
]
|
||
},
|
||
"execution_count": 95,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 95
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-02-21T03:15:24.196237Z",
|
||
"start_time": "2025-02-21T03:15:09.989176Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"count = 0\n",
|
||
"success = 0\n",
|
||
"\n",
|
||
"for name in os.listdir(os.path.join(root_path, \"日线行情 1990-2024\", \"分组行情\")):\n",
|
||
" if name.endswith(\"SZ.csv\"):\n",
|
||
" stock_df = load_stock(name.replace(\".csv\", \"\"))\n",
|
||
" if stock_df.empty or stock_df.size < 1000:\n",
|
||
" continue\n",
|
||
" bt = Backtest(stock_df, MACDStrategy, cash=10000, commission=.002, trade_on_close=True)\n",
|
||
" result = bt.run()\n",
|
||
" count += 1\n",
|
||
" final = result.iloc[4]\n",
|
||
" if final > 10000:\n",
|
||
" success += 1\n",
|
||
" print(f\"{name}: {result.iloc[4]} 胜率：{(success / count) * 100:.2f}%\")"
|
||
],
|
||
"id": "abcba3a2b27082af",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"300602.SZ.csv: 9519.828495999998 胜率：0.00%\n",
|
||
"301335.SZ.csv: 9483.993636 胜率：0.00%\n",
|
||
"300153.SZ.csv: 10945.991414000004 胜率：33.33%\n",
|
||
"002719.SZ.csv: 10160.783446000001 胜率：50.00%\n",
|
||
"002048.SZ.csv: 8827.930520000002 胜率：40.00%\n",
|
||
"301182.SZ.csv: 9085.706827999997 胜率：33.33%\n",
|
||
"300702.SZ.csv: 9187.587144000001 胜率：28.57%\n",
|
||
"301235.SZ.csv: 9865.623893999997 胜率：25.00%\n",
|
||
"300053.SZ.csv: 9265.564532000004 胜率：22.22%\n",
|
||
"301082.SZ.csv: 9390.439036 胜率：20.00%\n",
|
||
"002619.SZ.csv: 10311.905327999993 胜率：27.27%\n",
|
||
"002148.SZ.csv: 9789.179133999996 胜率：25.00%\n",
|
||
"300817.SZ.csv: 10137.570670000001 胜率：30.77%\n",
|
||
"300917.SZ.csv: 8381.743184 胜率：28.57%\n",
|
||
"000533.SZ.csv: 10497.018527999993 胜率：33.33%\n",
|
||
"002282.SZ.csv: 10193.735793999997 胜率：37.50%\n",
|
||
"301348.SZ.csv: 10061.892033999999 胜率：41.18%\n",
|
||
"301419.SZ.csv: 9292.442044000001 胜率：38.89%\n",
|
||
"002035.SZ.csv: 9591.023048000005 胜率：36.84%\n",
|
||
"300399.SZ.csv: 12489.192386000006 胜率：40.00%\n",
|
||
"301248.SZ.csv: 9710.557384000005 胜率：38.10%\n",
|
||
"301519.SZ.csv: 9829.372 胜率：36.36%\n",
|
||
"002382.SZ.csv: 9769.216531999997 胜率：34.78%\n",
|
||
"300299.SZ.csv: 11216.701843999996 胜率：37.50%\n",
|
||
"002664.SZ.csv: 11549.794853999994 胜率：40.00%\n",
|
||
"002135.SZ.csv: 10007.526262000007 胜率：42.31%\n",
|
||
"002971.SZ.csv: 8151.281703999998 胜率：40.74%\n",
|
||
"002871.SZ.csv: 9211.733576000002 胜率：39.29%\n",
|
||
"002912.SZ.csv: 10146.829072000006 胜率：41.38%\n",
|
||
"300809.SZ.csv: 9588.934147999997 胜率：40.00%\n",
|
||
"002812.SZ.csv: 11656.421232000004 胜率：41.94%\n",
|
||
"300909.SZ.csv: 8588.468658000002 胜率：40.62%\n",
|
||
"002707.SZ.csv: 10577.020016000004 胜率：42.42%\n",
|
||
"002056.SZ.csv: 9459.701375999995 胜率：41.18%\n",
|
||
"002607.SZ.csv: 10173.426610000004 胜率：42.86%\n",
|
||
"002156.SZ.csv: 10170.384258000006 胜率：44.44%\n",
|
||
"000301.SZ.csv: 12005.214035999998 胜率：45.95%\n",
|
||
"000550.SZ.csv: 9263.366957999995 胜率：44.74%\n",
|
||
"300387.SZ.csv: 9745.065623999995 胜率：43.59%\n",
|
||
"301356.SZ.csv: 10010.228 胜率：45.00%\n",
|
||
"300661.SZ.csv: 20063.738909999993 胜率：46.34%\n",
|
||
"300130.SZ.csv: 9971.323087999996 胜率：45.24%\n",
|
||
"300287.SZ.csv: 10603.057631999996 胜率：46.51%\n",
|
||
"301256.SZ.csv: 9861.662801999999 胜率：45.45%\n",
|
||
"300761.SZ.csv: 9193.41177 胜率：44.44%\n",
|
||
"300030.SZ.csv: 8977.232825999996 胜率：43.48%\n",
|
||
"301507.SZ.csv: 9837.594000000001 胜率：42.55%\n",
|
||
"301090.SZ.csv: 9703.311370000001 胜率：41.67%\n",
|
||
"300041.SZ.csv: 10303.257277999997 胜率：42.86%\n",
|
||
"300710.SZ.csv: 7537.6221040000055 胜率：42.00%\n",
|
||
"301227.SZ.csv: 9468.773936 胜率：41.18%\n",
|
||
"000983.SZ.csv: 9954.838291999995 胜率：40.38%\n",
|
||
"301190.SZ.csv: 9659.340995999997 胜率：39.62%\n",
|
||
"000883.SZ.csv: 10401.635018000003 胜率：40.74%\n",
|
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]
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||
},
|
||
{
|
||
"ename": "KeyboardInterrupt",
|
||
"evalue": "",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
|
||
"\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
|
||
"\u001B[0;32m/var/folders/7h/w0cmp4zj6mn9br_6nyj310m40000gn/T/ipykernel_73705/2811901891.py\u001B[0m in \u001B[0;36m?\u001B[0;34m()\u001B[0m\n\u001B[1;32m 6\u001B[0m \u001B[0mstock_df\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mload_stock\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mname\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreplace\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\".csv\"\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 7\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mstock_df\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mempty\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0mstock_df\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msize\u001B[0m \u001B[0;34m<\u001B[0m \u001B[0;36m1000\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 8\u001B[0m \u001B[0;32mcontinue\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 9\u001B[0m \u001B[0mbt\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mBacktest\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mstock_df\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mMACDStrategy\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcash\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m10000\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcommission\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m.002\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtrade_on_close\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 10\u001B[0;31m \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mbt\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrun\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 11\u001B[0m \u001B[0mcount\u001B[0m \u001B[0;34m+=\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 12\u001B[0m \u001B[0mfinal\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mresult\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0miloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m4\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 13\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mfinal\u001B[0m \u001B[0;34m>\u001B[0m \u001B[0;36m10000\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
|
||
"\u001B[0;32m~/Library/Caches/pypoetry/virtualenvs/finance-G8vNaf2C-py3.13/lib/python3.13/site-packages/backtesting/backtesting.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(self, **kwargs)\u001B[0m\n\u001B[1;32m 1337\u001B[0m \u001B[0;31m# for future `indicator._opts['data'].index` calls to work\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1338\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_set_length\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_data\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1339\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1340\u001B[0m \u001B[0mequity\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mSeries\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mbroker\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_equity\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbfill\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfillna\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mbroker\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_cash\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1341\u001B[0;31m self._results = compute_stats(\n\u001B[0m\u001B[1;32m 1342\u001B[0m \u001B[0mtrades\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mbroker\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mclosed_trades\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1343\u001B[0m \u001B[0mequity\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mequity\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 1344\u001B[0m \u001B[0mohlc_data\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_data\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
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"\u001B[0;32m~/Library/Caches/pypoetry/virtualenvs/finance-G8vNaf2C-py3.13/lib/python3.13/site-packages/backtesting/_stats.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(***failed resolving arguments***)\u001B[0m\n\u001B[1;32m 148\u001B[0m \u001B[0;31m# and simple standard deviation\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 149\u001B[0m \u001B[0ms\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'Sharpe Ratio'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0ms\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'Return (Ann.) [%]'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mrisk_free_rate\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0;36m100\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0ms\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'Volatility (Ann.) [%]'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnan\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;31m# noqa: E501\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 150\u001B[0m \u001B[0;31m# Our Sortino mismatches `empyrical.sortino_ratio()` because they use arithmetic mean return\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 151\u001B[0m \u001B[0;32mwith\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0merrstate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdivide\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'ignore'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 152\u001B[0;31m \u001B[0ms\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'Sortino Ratio'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mannualized_return\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mrisk_free_rate\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msqrt\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmean\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mday_returns\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mclip\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0minf\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m**\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msqrt\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mannual_trading_days\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;31m# noqa: E501\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 153\u001B[0m \u001B[0mmax_dd\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m-\u001B[0m\u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnan_to_num\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmax\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 154\u001B[0m \u001B[0ms\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'Calmar Ratio'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mannualized_return\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0;34m-\u001B[0m\u001B[0mmax_dd\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnan\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 155\u001B[0m \u001B[0ms\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloc\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m'Max. Drawdown [%]'\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmax_dd\u001B[0m \u001B[0;34m*\u001B[0m \u001B[0;36m100\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
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"\u001B[0;32m~/Library/Caches/pypoetry/virtualenvs/finance-G8vNaf2C-py3.13/lib/python3.13/site-packages/pandas/core/generic.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(self, lower, upper, axis, inplace, **kwargs)\u001B[0m\n\u001B[1;32m 9071\u001B[0m \u001B[0;31m# so ignore\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 9072\u001B[0m \u001B[0;31m# GH 19992\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 9073\u001B[0m \u001B[0;31m# numpy doesn't drop a list-like bound containing NaN\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 9074\u001B[0m \u001B[0misna_lower\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0misna\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlower\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 9075\u001B[0;31m \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mis_list_like\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlower\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m 9076\u001B[0m \u001B[0;32mif\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0many\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0misna_lower\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 9077\u001B[0m \u001B[0mlower\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m 9078\u001B[0m \u001B[0;32melif\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mall\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0misna_lower\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
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"\u001B[0;31mKeyboardInterrupt\u001B[0m: "
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||
]
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||
}
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||
],
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"execution_count": 81
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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||
"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
|
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}
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},
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||
"nbformat": 4,
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||
"nbformat_minor": 5
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}
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