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\u6807\u7684<\/th>\u65e5\u7ebf<\/th>\u5468\u7ebf<\/th>\u6708\u7ebf<\/th><\/tr>
\u8d35\u5dde\u8305\u53f0<\/td>38.26 \u4e2d\u6027<\/td>66.47 \u9707\u8361<\/strong><\/td>54.21 \u4e2d\u6027<\/td><\/tr>
\u4e2d\u56fd\u5e73\u5b89<\/td>44.23 \u4e2d\u6027<\/td>52.98 \u4e2d\u6027<\/td>50.86 \u4e2d\u6027<\/td><\/tr>
\u62db\u5546\u94f6\u884c<\/td>48.56 \u4e2d\u6027<\/td>47.09 \u4e2d\u6027<\/td>56.77 \u4e2d\u6027<\/td><\/tr>
\u957f\u6c5f\u7535\u529b<\/td>42.10 \u4e2d\u6027<\/td>64.34 \u9707\u8361<\/strong><\/td>53.74 \u4e2d\u6027<\/td><\/tr>
\u7d2b\u91d1\u77ff\u4e1a<\/td>53.14 \u4e2d\u6027<\/td>59.82 \u4e2d\u6027<\/td>52.22 \u4e2d\u6027<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\"\"
\u540c\u4e00\u4e2a\u6a2a\u8f74\u4e0a\u770b\uff0c\u65e5\u7ebf\u4e94\u6839\u6324\u5728\u7a84\u5e26\u91cc\uff0c\u5468\u7ebf\u624d\u62c9\u5f00\u8ddd\u79bb<\/figcaption><\/figure>\n\n\n\n

\u5148\u770b\u65e5\u7ebf\u90a3\u4e00\u680f\uff0c\u4e94\u53ea\u5168\u90e8\u4e2d\u6027\uff0c38.26 \u5230 53.14\u3002\u6324\u5728\u4e00\u6761 15 \u4e2a\u70b9\u7684\u7a84\u5e26\u91cc\uff0c\u770b\u7740\u50cf\u4e94\u53ea\u7968\u90fd\u6ca1\u6545\u4e8b\u3002<\/p>\n\n\n\n

\u770b\u5230\u8fd9\u5f20\u8868\uff0c\u591a\u534a\u4f1a\u5148\u60f3\u81ea\u5df1\u624b\u4e0a\u90a3\u53ea\u843d\u5728\u54ea\u4e00\u683c\u3002\u5efa\u8bae\u5927\u5bb6\u53ef\u4ee5\u5148\u81ea\u5df1\u7ffb\u7ffb\uff0c\u628a\u4e09\u4e2a\u6570\u6446\u51fa\u6765\uff0c\u8fd9\u4e00\u6b65\u672c\u8eab\u5c31\u6709\u4fe1\u606f\u3002<\/p>\n\n\n\n

\u8305\u53f0 38.26\uff0c\u79bb 38.2 \u53ea\u5dee 0.06\u3002\u6211\u76ef\u7740\u8fd9\u4e2a\u6570\u770b\u4e86\u597d\u51e0\u904d\uff0c\u6700\u540e\u8fd8\u662f\u628a\u5b83\u5f52\u8fdb\u4e2d\u6027\u90a3\u4e00\u6863\u3002\u8fd9\u79cd\u64e6\u8fb9\u6570\u522b\u5f53\u4fe1\u53f7\uff0c\u56db\u820d\u4e94\u5165\u7684\u8fd0\u6c14\u4e0d\u7b97\u7ed3\u6784\u3002<\/p>\n\n\n\n

\u518d\u770b\u5468\u7ebf\u90a3\u4e00\u680f\uff0c\u5206\u6c34\u5cad\u5728\u8fd9\u513f\u3002\u8305\u53f0 66.47\u3001\u957f\u7535 64.34\uff0c\u4e24\u53ea\u90fd\u8fc7\u4e86 61.8\uff0c\u843d\u5728\u9707\u8361\u533a\u3002\u5269\u4e0b\u4e09\u53ea\u5728\u4e2d\u95f4\uff0c\u7d2b\u91d1 59.82 \u79bb\u9707\u8361\u7ebf\u53ea\u6709\u4e24\u4e2a\u70b9\uff0c\u4e5f\u6ca1\u8fc7\u53bb\u3002<\/p>\n\n\n\n

\u6700\u540e\u770b\u6708\u7ebf\u90a3\u4e00\u680f\uff0c\u4e94\u53ea\u6324\u5728 50.86 \u5230 56.77 \u4e4b\u95f4\uff0c\u6ca1\u6709\u4e00\u53ea\u8fdb\u8d8b\u52bf\u533a\uff0c\u4e5f\u6ca1\u6709\u4e00\u53ea\u5230\u9707\u8361\u533a\u3002<\/p>\n\n\n\n

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\u6708\u7ebf\u7684\u4f4d\u7f6e\u6211\u60f3\u5355\u72ec\u8bf4\u4e00\u53e5\u3002\u6708\u7ebf\u7684\u6781\u7aef\u503c\u6211\u6700\u4fe1\uff0c\u56e0\u4e3a\u6700\u96be\u505a\u51fa\u6765\u3002\u6211\u77e5\u9053\u8981\u8ba9\u5b83\u6389\u5230 38.2 \u4ee5\u4e0b\uff0c\u5f97\u8ba9 14 \u4e2a\u6708\u7684 K \u7ebf\u5927\u4f53\u671d\u4e00\u4e2a\u65b9\u5411\u8d70\u300214 \u5929\u505a\u5230\u4e0d\u96be\uff0c14 \u4e2a\u6708\u505a\u5230\uff0c\u90a3\u662f\u771f\u7684\u5728\u8d70\u3002<\/p>\n\n\n\n

\u539f\u672c\u6307\u671b\u6708\u7ebf\u80fd\u7ed9\u51fa\u4e00\u4e2a\u65b9\u5411\uff0c\u7ed3\u679c\u4e94\u53ea\u5168\u5361\u5728\u4e2d\u95f4\u5e26\uff0c\u8bf4\u660e\u5b83\u4eec\u773c\u4e0b\u90fd\u4e0d\u5728\u591a\u5e74\u7684\u5355\u8fb9\u91cc\u3002\u8bf4\u4e0d\u597d\u8fd9\u4e94\u53ea\u91cc\u54ea\u53ea\u4f1a\u5148\u8d70\u51fa 61.8\uff0c\u4f46\u81f3\u5c11\u73b0\u5728\uff0c\u5b83\u4eec\u90fd\u8fd8\u6ca1\u6709\u90a3\u5c42\u515c\u5e95\u3002\u8fd9\u4e2a\u7ed3\u679c\u4e0d\u7b97\u597d\u770b\uff0c\u4f46\u5b83\u6bd4\u4e00\u4e2a\u6f02\u4eae\u7684\u7ed3\u679c\u6709\u7528\uff0c\u56e0\u4e3a\u5b83\u8bf4\u660e\u8fd9\u5957\u9608\u503c\u5728\u8fd9\u6279\u6807\u7684\u4e0a\u4e0d\u5e38\u7ed9\u4fe1\u53f7\u3002<\/p>\n\n\n\n

\u65e5\u7ebf\u4e3a\u4ec0\u4e48\u4f1a\u9a97\u4eba<\/strong><\/h2>\n\n\n\n

\u4e0a\u9762\u90a3\u5f20\u8868\u91cc\uff0c\u65e5\u7ebf\u4e94\u53ea\u5168\u662f\u4e2d\u6027\uff0c\u770b\u7740\u6700\u6ca1\u7528\u3002\u771f\u51fa\u4e8b\uff0c\u5f80\u5f80\u5c31\u51fa\u5728\u8fd9\u4e00\u5c42\u3002<\/p>\n\n\n\n

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\u6211\u60f3\u4e86\u60f3\uff0c\u6700\u96be\u53d7\u7684\u5730\u65b9\u5728\u8fd9\u513f\u3002\u8fdb\u573a\u7684\u65f6\u5019\u6bcf\u4e00\u6b65\u90fd\u5408\u89c4\u77e9\uff0c\u9519\u4e86\u4ee5\u540e\u56de\u5934\u770b\uff0cK \u7ebf\u4e5f\u6ca1\u6709\u54ea\u4e00\u6839\u957f\u5f97\u4e0d\u5bf9\uff0c\u8981\u602a\u53ea\u80fd\u602a\u81ea\u5df1\u5c11\u770b\u4e86\u4e00\u5c42\u3002<\/p>\n\n\n\n

CHOP \u6293\u7684\u6b63\u662f\u8fd9\u4e2a\u9636\u6bb5\uff0c14 \u5929\u91cc\u4ef7\u683c\u53cd\u590d\u6469\u64e6\u540c\u4e00\u4e2a\u9ad8\u5ea6\uff0c\u8def\u7a0b\u8dd1\u4e86\u5f88\u591a\uff0c\u51c0\u4f4d\u79fb\u51e0\u4e4e\u4e3a\u96f6\uff0c\u8bfb\u6570\u5f53\u7136\u9ad8\u3002<\/p>\n\n\n\n

\u6240\u4ee5\u73b0\u5728\u5e94\u8be5\u53ea\u770b\u5468\u7ebf\u5728\u4e0d\u5728\u9707\u8361\u533a\u3002\u5468\u7ebf\u8bfb\u6570\u5728 61.8 \u4ee5\u4e0a\uff0c\u65e5\u7ebf\u6bcf\u4e00\u6b21\u7a81\u7834\u90fd\u53d1\u751f\u5728\u540c\u4e00\u4e2a\u7bb1\u4f53\u91cc\uff0c\u5931\u8d25\u7684\u6982\u7387\u4f1a\u88ab\u62ac\u5f97\u5f88\u9ad8\u3002\u6211\u8bd5\u8fc7\u628a\u4fe1\u53f7\u753b\u5f97\u66f4\u4e25\u683c\uff0c\u6ca1\u7528\uff0c\u5b83\u811a\u4e0b\u7684\u5730\u677f\u672c\u6765\u5c31\u5728\u9707\u3002<\/p>\n\n\n\n

\u53cd\u8fc7\u6765\u8fd8\u6709\u4e2a\u5751\u3002\u6211\u4e00\u5f00\u59cb\u4e5f\u628a 38.2 \u4ee5\u4e0b\u5f53\u6210\u53ef\u4ee5\u8fdb\u7684\u4fe1\u53f7\uff0c\u540e\u6765\u624d\u53d1\u73b0\u4e0d\u662f\u3002\u65e5\u7ebf\u8bfb\u6570\u5728 38.2 \u4ee5\u4e0b\uff0c\u5bb9\u6613\u8ba9\u4eba\u5174\u594b\u8fc7\u5934\uff0c\u4ee5\u4e3a\u8d8b\u52bf\u6765\u4e86\u3002\u53ef\u6211\u7b97\u8fc7\uff0c14 \u5929\u7a97\u53e3\u6491\u51fa\u6765\u7684\u8d8b\u52bf\u53ea\u6709\u4e09\u4e94\u5929\u7684\u5bff\u547d\uff0c\u7b49\u6211\u5728\u65e5\u7ebf\u4e0a\u786e\u8ba4\u5b8c\uff0c\u5b83\u5f80\u5f80\u5df2\u7ecf\u8d70\u5b8c\u4e00\u534a\u3002<\/p>\n\n\n\n

\u4ee3\u7801<\/strong><\/h2>\n\n\n\n

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import math\nimport numpy as np\nimport pandas as pd\n\n\ndef chop(df, window=14):\n\u00a0 \u00a0 \"\"\"df \u9700\u8981 Open\/High\/Low\/Close \u56db\u5217\uff0c\u6309\u65f6\u95f4\u5347\u5e8f\u3002\"\"\n\u00a0 \u00a0 high_low = df[\"High\"] - df[\"Low\"]\n\u00a0 \u00a0 high_close = (df[\"High\"] - df[\"Close\"].shift(1)).abs()\n\u00a0 \u00a0 low_close = (df[\"Low\"] - df[\"Close\"].shift(1)).abs()\n\u00a0 \u00a0 tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)\n\n\u00a0 \u00a0 atr_sum = tr.rolling(window).sum()\n\u00a0 \u00a0 hh = df[\"High\"].rolling(window).max()\n\u00a0 \u00a0 ll = df[\"Low\"].rolling(window).min()\n\u00a0 \u00a0 rng = (hh - ll).replace(0, np.nan) \u00a0 \u00a0 \u00a0 \u00a0 \u00a0# \u4e00\u5b57\u677f\u4f1a\u9664\u96f6\uff0c\u5148\u6321\u6389\n\n\u00a0 \u00a0 return 100 * np.log10(atr_sum \/ rng) \/ math.log10(window)\n\ndef regime(v):\n\u00a0 \u00a0 if v < 38.2:\n\u00a0 \u00a0 \u00a0 \u00a0 return \"\u8d8b\u52bf\"\n\u00a0 \u00a0 if v > 61.8:\n\u00a0 \u00a0 \u00a0 \u00a0 return \"\u9707\u8361\"\n\u00a0 \u00a0 return \"\u4e2d\u6027\"<\/code><\/pre>\n\n\n\n
\u4e09\u5468\u671f\u6211\u5148\u53d6\u4e86\u4e09\u6b21\u6570\u636e\uff0c\u9ebb\u70e6\u8fd8\u5bb9\u6613\u5bf9\u4e0d\u4e0a\u3002\u540e\u6765\u6539\u6210\u62c9\u4e00\u4efd\u65e5\u7ebf\u91cd\u91c7\u6837\u4e24\u6b21\uff0c\u4e09\u4e2a\u5468\u671f\u7684 OHLC \u5929\u7136\u540c\u6e90\u3002<\/p>\n\n\n\n
import akshare as ak\n\ndef resample(d, rule, start):\n    g = d.resample(rule).agg(\n        {\"Open\": \"first\", \"High\": \"max\", \"Low\": \"min\", \"Close\": \"last\"}\n    ).dropna()\n    return g[g.index >= pd.Timestamp(start)]\n\nraw = ak.stock_zh_a_daily(\n    symbol=\"sh600519\", start_date=\"20150101\", end_date=\"20261020\", adjust=\"qfq\"\n)\nd = raw.rename(columns={\"date\": \"Date\", \"open\": \"Open\", \"high\": \"High\",\n                        \"low\": \"Low\", \"close\": \"Close\"})\nd[\"Date\"] = pd.to_datetime(d[\"Date\"])\nd = d.set_index(\"Date\").sort_index().astype(float)\n\nframes = {\n    \"\u65e5\u7ebf\": resample(d, \"B\", \"2024-01-01\"),\n    \"\u5468\u7ebf\": resample(d, \"W-FRI\", \"2022-01-01\"),\n    \"\u6708\u7ebf\": resample(d, \"ME\", \"2015-01-01\"),\n}\n\nfor name, f in frames.items():\n    s = chop(f).dropna()\n    print(name, round(s.iloc[-1], 2), regime(s.iloc[-1]), s.index[-1].date())<\/code><\/pre>\n\n\n\n
\u8dd1\u51fa\u6765\u540e\u5c31\u662f\u4e0a\u9762\u90a3\u5f20\u8868\u3002\u5468\u7ebf\u7528 W-FRI\uff0c\u6708\u7ebf\u7528\u6708\u672b\uff0c\u4e24\u4e2a\u951a\u70b9\u56fa\u5b9a\uff0c\u540c\u6837\u4e00\u4efd\u65e5\u7ebf\u5728\u8c01\u7684\u673a\u5668\u4e0a\u90fd\u91cd\u91c7\u6837\u6210\u4e00\u6837\u7684\u7ed3\u679c\u3002<\/p>\n\n\n\n
\u4e09\u6761\u80fd\u7528\u7684\u89c4\u5219<\/strong><\/h2>\n\n\n\n
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