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finance/回测/backtrader.ipynb

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{
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"end_time": "2025-02-17T10:16:26.170873Z",
"start_time": "2025-02-17T10:16:26.058931Z"
}
},
"cell_type": "code",
"source": [
"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 = 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\", \"macd\", \"macd_dif\", \"macd_dea\"]]\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 > 2023]\n",
"df.sort_values(by='datetime', inplace=True)\n",
"df.set_index('datetime', inplace=True)\n",
"df"
],
"id": "157a87ab33037c6e",
"outputs": [
{
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" 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",
"2024-12-25 20241225 1475282.94 11.86000 11.92000 12.02000 11.84000 \n",
"2024-12-26 20241226 1000074.70 11.92000 11.86000 11.93000 11.78000 \n",
"2024-12-27 20241227 1290012.28 11.87000 11.83000 11.90000 11.66000 \n",
"2024-12-30 20241230 1351846.36 11.78000 11.95000 11.97000 11.78000 \n",
"2024-12-31 20241231 1475367.33 11.93000 11.70000 11.99000 11.70000 \n",
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"2024-01-08 0.076 -0.135 -0.173 20240108 \n",
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" <th>volume</th>\n",
" <th>open</th>\n",
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" <th>high</th>\n",
" <th>low</th>\n",
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" <td>11.86000</td>\n",
" <td>11.92000</td>\n",
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"cell_type": "code",
"source": [
"def run_backtrader(data, strategy):\n",
" cerebro = bt.Cerebro()\n",
" cerebro.adddata(data)\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",
" cerebro.broker.set_slippage_perc(perc=0.0001)\n",
" cerebro.addanalyzer(bt.analyzers.TimeReturn, _name='pnl') # 返回收益率时序数据\n",
" cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='annual_return') # 年化收益率\n",
" cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe_ratio') # 夏普比率\n",
" cerebro.addanalyzer(bt.analyzers.DrawDown, _name='draw_down') # 回撤\n",
"\n",
" # 将编写的策略添加给大脑，别忘了 ！\n",
" cerebro.addstrategy(strategy)\n",
"\n",
" print('初始资金: %.2f' % cerebro.broker.getvalue())\n",
" results = cerebro.run()\n",
" print('最终资金: %.2f' % cerebro.broker.getvalue())\n",
" # 获取分析结果\n",
" strat = results[0]\n",
" print('年化收益率')\n",
" pairs = strat.analyzers.annual_return.get_analysis()\n",
" for key in pairs.keys():\n",
" print(key, pairs[key])\n",
" 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",
" cerebro.plot(style='candlestick', width=20, height=10)"
],
"id": "d25f6e4d51cdfb20",
"outputs": [],
"execution_count": 73
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-17T10:16:26.920783Z",
"start_time": "2025-02-17T10:16:26.900580Z"
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},
"cell_type": "code",
"source": [
"import talib\n",
"\n",
"# 指标计算\n",
"close_prices = 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",
"df['dif'] = dif\n",
"df['dif'] = df['dif'].fillna(0)\n",
"df['dea'] = dea\n",
"df['dea'] = df['dea'].fillna(0)\n",
"df['hist'] = hist\n",
"df['hist'] = df['hist'].fillna(0)\n",
"\n",
"df"
],
"id": "5774f2437186c503",
"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",
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"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",
"2024-12-25 20241225 1475282.94 11.86000 11.92000 12.02000 11.84000 \n",
"2024-12-26 20241226 1000074.70 11.92000 11.86000 11.93000 11.78000 \n",
"2024-12-27 20241227 1290012.28 11.87000 11.83000 11.90000 11.66000 \n",
"2024-12-30 20241230 1351846.36 11.78000 11.95000 11.97000 11.78000 \n",
"2024-12-31 20241231 1475367.33 11.93000 11.70000 11.99000 11.70000 \n",
"\n",
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" </tr>\n",
" <tr>\n",
" <th>2024-12-26</th>\n",
" <td>20241226</td>\n",
" <td>1000074.70</td>\n",
" <td>11.92000</td>\n",
" <td>11.86000</td>\n",
" <td>11.93000</td>\n",
" <td>11.78000</td>\n",
" <td>0.051</td>\n",
" <td>0.087</td>\n",
" <td>0.062</td>\n",
" <td>20241226</td>\n",
" <td>0.087385</td>\n",
" <td>0.061712</td>\n",
" <td>0.025674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-27</th>\n",
" <td>20241227</td>\n",
" <td>1290012.28</td>\n",
" <td>11.87000</td>\n",
" <td>11.83000</td>\n",
" <td>11.90000</td>\n",
" <td>11.66000</td>\n",
" <td>0.044</td>\n",
" <td>0.089</td>\n",
" <td>0.067</td>\n",
" <td>20241227</td>\n",
" <td>0.089411</td>\n",
" <td>0.067251</td>\n",
" <td>0.022159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-30</th>\n",
" <td>20241230</td>\n",
" <td>1351846.36</td>\n",
" <td>11.78000</td>\n",
" <td>11.95000</td>\n",
" <td>11.97000</td>\n",
" <td>11.78000</td>\n",
" <td>0.052</td>\n",
" <td>0.100</td>\n",
" <td>0.074</td>\n",
" <td>20241230</td>\n",
" <td>0.099551</td>\n",
" <td>0.073711</td>\n",
" <td>0.025840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2024-12-31</th>\n",
" <td>20241231</td>\n",
" <td>1475367.33</td>\n",
" <td>11.93000</td>\n",
" <td>11.70000</td>\n",
" <td>11.99000</td>\n",
" <td>11.70000</td>\n",
" <td>0.020</td>\n",
" <td>0.086</td>\n",
" <td>0.076</td>\n",
" <td>20241231</td>\n",
" <td>0.086419</td>\n",
" <td>0.076253</td>\n",
" <td>0.010166</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>242 rows × 13 columns</p>\n",
"</div>"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 74
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-17T10:23:20.037206Z",
"start_time": "2025-02-17T10:23:19.913993Z"
}
},
"cell_type": "code",
"source": [
"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",
"# 创建策略类\n",
"class MACDStrategy(bt.Strategy):\n",
" def __init__(self):\n",
" print(bt.talib.MACD.__doc__)\n",
" print(bt.talib.MACD(self.data.close, fastperiod=12, slowperiod=26, signalperiod=9))\n",
" self.macd, self.signal, self.hist = bt.talib.MACD(self.data.close, fastperiod=12, slowperiod=26, signalperiod=9)\n",
" # print(dif, dea, macd)\n",
" # self.dif = dif\n",
" # self.dea = dea\n",
" # self.macd = macd\n",
"\n",
" def next(self):\n",
" pre_dif = self.dif[-1]\n",
" pre_dea = self.dea[-1]\n",
" pre_hist = self.hist[-1]\n",
"\n",
" hold = self.getposition(self.data).size\n",
"\n",
" if dif > dea and pre_dif <= pre_dea and dif > 0 and dea > 0:\n",
" self.buy(size=100)\n",
" elif dif < dea and pre_dif >= pre_dea and hold > 0:\n",
" self.sell(size=hold)\n",
"\n",
" def log(self, txt, dt=None):\n",
" \"\"\"\n",
" 输出包含时间的日志。\n",
" \"\"\"\n",
" dt = dt or self.datas[0].datetime.date(0)\n",
" print(f'{dt.isoformat()}, {txt}')\n",
"\n",
"\n",
"run_backtrader(bt.feeds.PandasData(dataname=df), MACDStrategy)"
],
"id": "45f8133af2348b95",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"初始资金: 10000.00\n",
"MACD([input_arrays], [fastperiod=12], [slowperiod=26], [signalperiod=9])\n",
"\n",
"Moving Average Convergence/Divergence (Momentum Indicators)\n",
"\n",
"Inputs:\n",
" price: (any ndarray)\n",
"Parameters:\n",
" fastperiod: 12\n",
" slowperiod: 26\n",
" signalperiod: 9\n",
"Outputs:\n",
" macd\n",
" macdsignal\n",
" macdhist\n",
"<backtrader.talib.MACD object at 0x1251a9ba0>\n"
]
},
{
"ename": "ValueError",
"evalue": "not enough values to unpack (expected 3, got 0)",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[84], line 41\u001B[0m\n\u001B[1;32m 37\u001B[0m dt \u001B[38;5;241m=\u001B[39m dt \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdatas[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mdatetime\u001B[38;5;241m.\u001B[39mdate(\u001B[38;5;241m0\u001B[39m)\n\u001B[1;32m 38\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mdt\u001B[38;5;241m.\u001B[39misoformat()\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtxt\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m---> 41\u001B[0m \u001B[43mrun_backtrader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfeeds\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mPandasData\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdataname\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdf\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mMACDStrategy\u001B[49m\u001B[43m)\u001B[49m\n",
"Cell \u001B[0;32mIn[73], line 18\u001B[0m, in \u001B[0;36mrun_backtrader\u001B[0;34m(data, strategy)\u001B[0m\n\u001B[1;32m 15\u001B[0m cerebro\u001B[38;5;241m.\u001B[39maddstrategy(strategy)\n\u001B[1;32m 17\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m初始资金: \u001B[39m\u001B[38;5;132;01m%.2f\u001B[39;00m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m%\u001B[39m cerebro\u001B[38;5;241m.\u001B[39mbroker\u001B[38;5;241m.\u001B[39mgetvalue())\n\u001B[0;32m---> 18\u001B[0m results \u001B[38;5;241m=\u001B[39m \u001B[43mcerebro\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 19\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m最终资金: \u001B[39m\u001B[38;5;132;01m%.2f\u001B[39;00m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m%\u001B[39m cerebro\u001B[38;5;241m.\u001B[39mbroker\u001B[38;5;241m.\u001B[39mgetvalue())\n\u001B[1;32m 20\u001B[0m \u001B[38;5;66;03m# 获取分析结果\u001B[39;00m\n",
"File \u001B[0;32m~/Library/Caches/pypoetry/virtualenvs/finance-G8vNaf2C-py3.13/lib/python3.13/site-packages/backtrader/cerebro.py:1132\u001B[0m, in \u001B[0;36mCerebro.run\u001B[0;34m(self, **kwargs)\u001B[0m\n\u001B[1;32m 1128\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_dooptimize \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mp\u001B[38;5;241m.\u001B[39mmaxcpus \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m 1129\u001B[0m \u001B[38;5;66;03m# If no optimmization is wished ... or 1 core is to be used\u001B[39;00m\n\u001B[1;32m 1130\u001B[0m \u001B[38;5;66;03m# let's skip process \"spawning\"\u001B[39;00m\n\u001B[1;32m 1131\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m iterstrat \u001B[38;5;129;01min\u001B[39;00m iterstrats:\n\u001B[0;32m-> 1132\u001B[0m runstrat \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrunstrategies\u001B[49m\u001B[43m(\u001B[49m\u001B[43miterstrat\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1133\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrunstrats\u001B[38;5;241m.\u001B[39mappend(runstrat)\n\u001B[1;32m 1134\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_dooptimize:\n",
"File \u001B[0;32m~/Library/Caches/pypoetry/virtualenvs/finance-G8vNaf2C-py3.13/lib/python3.13/site-packages/backtrader/cerebro.py:1222\u001B[0m, in \u001B[0;36mCerebro.runstrategies\u001B[0;34m(self, iterstrat, predata)\u001B[0m\n\u001B[1;32m 1220\u001B[0m sargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdatas \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mlist\u001B[39m(sargs)\n\u001B[1;32m 1221\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 1222\u001B[0m strat \u001B[38;5;241m=\u001B[39m \u001B[43mstratcls\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43msargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mskwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1223\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m bt\u001B[38;5;241m.\u001B[39merrors\u001B[38;5;241m.\u001B[39mStrategySkipError:\n\u001B[1;32m 1224\u001B[0m \u001B[38;5;28;01mcontinue\u001B[39;00m \u001B[38;5;66;03m# do not add strategy to the mix\u001B[39;00m\n",
"File \u001B[0;32m~/Library/Caches/pypoetry/virtualenvs/finance-G8vNaf2C-py3.13/lib/python3.13/site-packages/backtrader/metabase.py:88\u001B[0m, in \u001B[0;36mMetaBase.__call__\u001B[0;34m(cls, *args, **kwargs)\u001B[0m\n\u001B[1;32m 86\u001B[0m _obj, args, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mdonew(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m 87\u001B[0m _obj, args, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mdopreinit(_obj, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m---> 88\u001B[0m _obj, args, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mcls\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdoinit\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_obj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 89\u001B[0m _obj, args, kwargs \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39mdopostinit(_obj, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m 90\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m _obj\n",
"File \u001B[0;32m~/Library/Caches/pypoetry/virtualenvs/finance-G8vNaf2C-py3.13/lib/python3.13/site-packages/backtrader/metabase.py:78\u001B[0m, in \u001B[0;36mMetaBase.doinit\u001B[0;34m(cls, _obj, *args, **kwargs)\u001B[0m\n\u001B[1;32m 77\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mdoinit\u001B[39m(\u001B[38;5;28mcls\u001B[39m, _obj, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m---> 78\u001B[0m \u001B[43m_obj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;21;43m__init__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 79\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m _obj, args, kwargs\n",
"Cell \u001B[0;32mIn[84], line 15\u001B[0m, in \u001B[0;36mMACDStrategy.__init__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 13\u001B[0m \u001B[38;5;28mprint\u001B[39m(bt\u001B[38;5;241m.\u001B[39mtalib\u001B[38;5;241m.\u001B[39mMACD\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__doc__\u001B[39m)\n\u001B[1;32m 14\u001B[0m \u001B[38;5;28mprint\u001B[39m(bt\u001B[38;5;241m.\u001B[39mtalib\u001B[38;5;241m.\u001B[39mMACD(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata\u001B[38;5;241m.\u001B[39mclose, fastperiod\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m12\u001B[39m, slowperiod\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m26\u001B[39m, signalperiod\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m9\u001B[39m))\n\u001B[0;32m---> 15\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmacd, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msignal, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhist \u001B[38;5;241m=\u001B[39m bt\u001B[38;5;241m.\u001B[39mtalib\u001B[38;5;241m.\u001B[39mMACD(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata\u001B[38;5;241m.\u001B[39mclose, fastperiod\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m12\u001B[39m, slowperiod\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m26\u001B[39m, signalperiod\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m9\u001B[39m)\n",
"\u001B[0;31mValueError\u001B[0m: not enough values to unpack (expected 3, got 0)"
]
}
],
"execution_count": 84
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
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"name": "python",
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