{ "cells": [ { "cell_type": "code", "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2025-02-19T08:45:25.218505Z", "start_time": "2025-02-19T08:45:25.105388Z" } }, "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/000010.SZ.csv\") \\\n", " [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\"]]\n", "df = pd.DataFrame()\n", "df[[\"date\", \"Volume\", \"Open\", \"Close\", \"High\", \"Low\"]] = \\\n", " source_df[[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\"]]\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 > 2023]\n", "df.sort_values(by='datetime', inplace=True)\n", "df.set_index('datetime', inplace=True)\n", "# df" ], "outputs": [], "execution_count": 28 }, { "metadata": { "ExecuteTime": { "end_time": "2025-02-19T08:45:25.263720Z", "start_time": "2025-02-19T08:45:25.256349Z" } }, "cell_type": "code", "source": [ "from backtesting import Strategy\n", "from backtesting.lib import crossover\n", "import talib\n", "\n", "\n", "class MACDStrategy(Strategy):\n", " def init(self):\n", " self.dea, self.dif, self.macd = self.I(talib.MACD, self.data.Close, 12, 26, 9)\n", "\n", " def next(self):\n", " # print(self.position.size)\n", " if crossover(self.dif, self.dea) and self.macd > 0 and (self.dif > self.macd and self.dea > self.macd):\n", " print(\"buy\")\n", " self.buy()\n", " elif crossover(self.dea, self.dif):\n", " print(\"sell\")\n", " self.position.close()" ], "id": "1b47368c3b916b9b", "outputs": [], "execution_count": 29 }, { "metadata": { "ExecuteTime": { "end_time": "2025-02-19T08:46:11.803085Z", "start_time": "2025-02-19T08:46:11.797862Z" } }, "cell_type": "code", "source": [ "class SMAStrategy(Strategy):\n", " def init(self):\n", " self.sma5 = self.I(talib.SMA, self.data.Close, 5)\n", " self.sma14 = self.I(talib.SMA, self.data.Close, 14)\n", "\n", " def next(self):\n", " if crossover(self.sma5, self.sma14):\n", " self.buy(size=100)\n", " elif crossover(self.sma14, self.sma5):\n", " self.position.close()" ], "id": "85da7018d63adc0", "outputs": [], "execution_count": 32 }, { "metadata": { "ExecuteTime": { "end_time": "2025-02-19T08:46:14.120709Z", "start_time": "2025-02-19T08:46:13.585524Z" } }, "cell_type": "code", "source": [ "from backtesting import Backtest\n", "\n", "bt = Backtest(df, SMAStrategy, cash=10000, commission=.002)\n", "result = bt.run()\n", "print(result)\n", "bt.plot()" ], "id": "ab574b78805925e5", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start 2024-01-02 00:00:00\n", "End 2024-12-31 00:00:00\n", "Duration 364 days 00:00:00\n", "Exposure Time [%] 53.71901\n", "Equity Final [$] 10011.80502\n", "Equity Peak [$] 10094.55702\n", "Commissions [$] 8.20398\n", "Return [%] 0.11805\n", "Buy & Hold Return [%] 4.46097\n", "Return (Ann.) [%] 0.12293\n", "Volatility (Ann.) [%] 1.03627\n", "CAGR [%] 0.08171\n", "Sharpe Ratio 0.11863\n", "Sortino Ratio 0.17936\n", "Calmar Ratio 0.14996\n", "Max. Drawdown [%] -0.81977\n", "Avg. Drawdown [%] -0.3199\n", "Max. Drawdown Duration 225 days 00:00:00\n", "Avg. Drawdown Duration 50 days 00:00:00\n", "# Trades 10\n", "Win Rate [%] 40.0\n", "Best Trade [%] 26.04651\n", "Worst Trade [%] -13.91353\n", "Avg. Trade [%] 0.66839\n", "Max. Trade Duration 43 days 00:00:00\n", "Avg. Trade Duration 18 days 00:00:00\n", "Profit Factor 1.3721\n", "Expectancy [%] 1.25043\n", "SQN 0.26051\n", "Kelly Criterion 0.087\n", "_strategy SMAStrategy\n", "_equity_curve ...\n", "_trades Size EntryBa...\n", "dtype: object\n" ] }, { "data": { "text/html": [ "\n", "
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width = None,
width_policy = 'auto')
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