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新增backtesting框架用于回测

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兰缘小妖
2025-02-18 17:00:41 +08:00
parent 5489a0bb4b
commit b8f97f7203
4 changed files with 545 additions and 68 deletions
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420
回测/backtesting.ipynb Normal file
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回测/backtrader.ipynb
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@@ -3,8 +3,8 @@
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-17T14:33:50.205817Z",
"start_time": "2025-02-17T14:33:50.160964Z"
"end_time": "2025-02-18T02:25:15.671867Z",
"start_time": "2025-02-18T02:25:15.090164Z"
}
},
"cell_type": "code",
@@ -12,10 +12,10 @@
"import backtrader as bt\n",
"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 = \\\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/000001.SZ.csv\") \\\n",
" [[\"trade_date\", \"vol\", \"open_qfq\", \"close_qfq\", \"high_qfq\", \"low_qfq\"]]\n",
"# source_df = pd.read_csv(\"/Users/lanyuanxiaoyao/SynologyDrive/data/Tushare/日线行情 1990-2024/分组行情/000001.SZ.csv\") \\\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",
@@ -216,18 +216,18 @@
"</div>"
]
},
"execution_count": 42,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 42
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-17T14:33:50.231103Z",
"start_time": "2025-02-17T14:33:50.225919Z"
"end_time": "2025-02-18T02:29:43.443218Z",
"start_time": "2025-02-18T02:29:43.436601Z"
}
},
"cell_type": "code",
@@ -260,17 +260,17 @@
" print('夏普比率:', strat.analyzers.sharpe_ratio.get_analysis()['sharperatio'])\n",
" print('最大回撤:', strat.analyzers.draw_down.get_analysis()['max']['drawdown'])\n",
" # 绘制结果图表\n",
" return cerebro.plot(style='candlestick', width=20, height=10)"
" return cerebro.plot(style='candlestick')"
],
"id": "d25f6e4d51cdfb20",
"outputs": [],
"execution_count": 43
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-17T14:33:50.274745Z",
"start_time": "2025-02-17T14:33:50.269232Z"
"end_time": "2025-02-18T02:25:16.582436Z",
"start_time": "2025-02-18T02:25:15.803336Z"
}
},
"cell_type": "code",
@@ -291,21 +291,7 @@
"df['hist'] = hist\n",
"df['hist'] = df['hist'].fillna(0)\n",
"\n",
"# df"
],
"id": "5774f2437186c503",
"outputs": [],
"execution_count": 44
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-17T14:33:50.412752Z",
"start_time": "2025-02-17T14:33:50.327691Z"
}
},
"cell_type": "code",
"source": [
"\n",
"class EnhancePandasData(bt.feeds.PandasData):\n",
" lines = (\"dif\", \"dea\", \"hist\")\n",
" params = (\n",
@@ -343,7 +329,7 @@
"\n",
"run_backtrader(EnhancePandasData(dataname=df), MACDStrategy)"
],
"id": "45f8133af2348b95",
"id": "5774f2437186c503",
"outputs": [
{
"name": "stdout",
@@ -373,7 +359,7 @@
"<IPython.core.display.HTML object>"
],
"text/html": [
"<div id='75a19a97-0e96-4034-a26a-d8ac2a51d841'></div>"
"<div id='ba76dac9-3ef2-4dbb-a2b4-b75774d0b531'></div>"
]
},
"metadata": {},
@@ -385,12 +371,12 @@
"[[<Figure size 640x480 with 4 Axes>]]"
]
},
"execution_count": 45,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 45
"execution_count": 4
}
],
"metadata": {