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\u73af\u5883\u8981\u6c42\u5c31\u4e00\u6761\uff0cPython 3.10 \u4ee5\u4e0a\u3002Mac\u3001Linux\u3001Windows \u90fd\u884c\uff0c\u6709\u4e00\u53f0\u4e0d\u5173\u673a\u7684\u673a\u5668\u66f4\u597d\u3002\u6574\u4e2a\u8fc7\u7a0b\u4e94\u6b65\u3002<\/p>\n\n\n\n

\u7b2c\u4e00\u6b65\u62ff\u4ee3\u7801\u3002git clone https:\/\/github.com\/sngyai\/Sequoia-X.git<\/code>\uff0c\u6216\u8005\u76f4\u63a5\u5728\u9875\u9762\u4e0a\u4e0b zip \u89e3\u538b\u3002<\/p>\n\n\n\n

\u7b2c\u4e8c\u6b65\u88c5\u4f9d\u8d56\u3002\u63a8\u8350\u7528 uv\uff0c\u4e00\u4e2a Python \u5305\u7ba1\u7406\u5668\uff0c\u6bd4 pip \u5feb\u5f97\u591a\uff0cuv sync<\/code> \u4e00\u6761\u547d\u4ee4\u88c5\u5b8c\u3002\u6ca1\u6709 uv \u5c31\u7528\u8001\u529e\u6cd5\uff0cpip install .<\/code>\u3002<\/p>\n\n\n\n

\u7b2c\u4e09\u6b65\u914d\u98de\u4e66\u3002\u590d\u5236 .env.example<\/code> \u6210 .env<\/code>\uff0c\u91cc\u9762\u53ea\u9700\u8981\u586b\u4e00\u884c\uff0c\u98de\u4e66\u673a\u5668\u4eba\u7684 Webhook \u5730\u5740\u3002\u673a\u5668\u4eba\u4e0d\u7528\u5ba1\u6279\uff0c\u98de\u4e66\u7fa4\u91cc\u70b9\u8bbe\u7f6e\u3001\u6dfb\u52a0\u81ea\u5b9a\u4e49\u673a\u5668\u4eba\uff0c\u5efa\u597d\u5c31\u80fd\u62ff\u5230\u5730\u5740\u3002\u60f3\u7ed9\u4e0d\u540c\u7b56\u7565\u5206\u7fa4\uff0c\u7167\u7740 .env.example<\/code> \u91cc\u7684\u6ce8\u91ca\u52a0 STRATEGY_WEBHOOK_<\/code> \u524d\u7f00\u7684\u53d8\u91cf\u5c31\u884c\u3002<\/p>\n\n\n\n

\u7b2c\u56db\u6b65\u9996\u6b21\u56de\u586b\u3002python main.py --backfill<\/code>\uff0c\u7ea6 12 \u5206\u949f\u62c9\u5b8c\u5168\u5e02\u573a\u5386\u53f2 K \u7ebf\uff0c\u843d\u5728 data\/sequoia_v2.db<\/code>\u3002\u8dd1\u4e00\u6b21\u4ee5\u540e\u5c31\u4e0d\u7528\u518d\u8dd1\u3002<\/p>\n\n\n\n

\u7b2c\u4e94\u6b65\u65e5\u5e38\u8fd0\u884c\u3002python main.py<\/code>\uff0c\u4e24\u4e09\u5206\u949f\u8dd1\u5b8c\uff0c\u54ea\u4e2a\u7b56\u7565\u9009\u51fa\u80a1\u7968\uff0c\u5c31\u63a8\u5230\u5bf9\u5e94\u673a\u5668\u4eba\u3002<\/p>\n\n\n\n

\u60f3\u5168\u81ea\u52a8\uff0c\u52a0\u4e00\u6761 crontab\uff0c\u6bcf\u4e2a\u4ea4\u6613\u65e5 19:15 \u81ea\u52a8\u6267\u884c\u3002<\/p>\n\n\n\n

15 19 * * 1-5 cd \/root\/Sequoia-X && .venv\/bin\/python main.py >> log.txt 2>&1<\/code><\/pre>\n\n\n\n
\u4e00\u53e5\u63d0\u9192\u3002\u514d\u8d39\u7248\u98de\u4e66\u5c31\u6709\u81ea\u5b9a\u4e49\u673a\u5668\u4eba\uff0c\u4e2a\u4eba\u7528\u4e0d\u9700\u8981\u4f01\u4e1a\u7248\u3002\u5168\u5957\u8dd1\u8d77\u6765\u5185\u5b58\u5360\u7528\u4e0d\u5927\uff0c\u6700\u4f4e\u914d\u7684\u4e91\u670d\u52a1\u5668\u6216\u5bb6\u91cc\u65e7\u7b14\u8bb0\u672c\u90fd\u591f\u3002<\/p>\n\n\n\n
\u56db\u3001\u88c5\u597d\u4e4b\u540e\u600e\u4e48\u7528<\/strong><\/h2>\n\n\n\n
\u6211\u7684\u7528\u6cd5\u662f\u628a\u5b83\u653e\u5728\u6d41\u7a0b\u7684\u7b2c\u4e00\u683c\u3002<\/p>\n\n\n\n
\u4ee5\u6700\u8fd1\u4e00\u4e2a\u5b8c\u6574\u4ea4\u6613\u65e5\u7684\u771f\u5b9e\u8f93\u51fa\u4e3a\u4f8b\u3002\u6536\u76d8\u540e\u7cfb\u7edf\u8dd1\u5b8c\uff0c\u6d77\u9f9f\u7fa4 228 \u53ea\uff0cRPS \u7fa4 101 \u53ea\uff0c\u65d7\u5f62\u7fa4 25 \u53ea\uff0c\u5747\u7ebf\u7fa4 19 \u53ea\uff0c\u6da8\u505c\u56de\u8e29\u7fa4 6 \u53ea\uff0c\u53bb\u91cd\u540e\u4e00\u5171 358 \u53ea<\/strong>\u3002\u8fd9 358 \u53ea\u5c31\u662f\u5f53\u5929\u7684\u521d\u7b5b\u6c60\uff0c\u5bf9\u5e94\u7b2c\u4e8c\u8282\u6570\u636e\u91cc\u7684\u542b\u4e49\uff0c\u52a8\u91cf\u7c7b\u4e3a\u4e3b\u3002<\/p>\n\n\n\n
\u7136\u540e\u662f\u5b83\u505a\u4e0d\u4e86\u7684\u90e8\u5206\u3002\u4e8c\u6b21\u9a8c\u8bc1\uff0c\u57fa\u672c\u9762\u3001\u884c\u4e1a\u4f4d\u7f6e\u3001\u4ed3\u4f4d\u7ea6\u675f\uff0c\u90fd\u8fc7\u4e00\u904d\u624d\u8fdb\u81ea\u9009\u3002\u56de\u6d4b\u91cc\u90a3\u4e2a\u5168\u7ebf\u8d1f\u8d85\u989d\u7684\u6da8\u505c\u56de\u8e29\u7fa4\uff0c\u6211\u76f4\u63a5\u9759\u97f3\u4e86\u3002<\/p>\n\n\n\n
\u4e00\u53e5\u8bdd\u5b9a\u4f4d\u3002\u5b83\u66ff\u4f60\u7701\u6389\u7684\u662f\u6bcf\u5929\u7ffb\u6da8\u5e45\u699c\u7684\u90a3\u534a\u5c0f\u65f6\uff0c\u7701\u4e0d\u6389\u7684\uff0c\u662f\u5224\u65ad\u672c\u8eab\u3002<\/p>\n\n\n\n
\u4ee5\u4e0a\u662f\u666e\u901a\u8bfb\u8005\u7684\u90e8\u5206\uff0c\u88c5\u4e0a\u5c31\u80fd\u7528\u3002\u5f80\u4e0b\u662f\u52a8\u624b\u90e8\u5206\uff0c\u5199\u7ed9\u60f3\u81ea\u5df1\u8dd1\u56de\u6d4b\u3001\u60f3\u6539\u7b56\u7565\u7684\u4eba\u3002<\/p>\n\n\n\n
\u4e94\u3001\u8fdb\u9636\uff0c\u6570\u636e\u8fd9\u5173\u6211\u662f\u600e\u4e48\u8fc7\u7684<\/strong><\/h2>\n\n\n\n
\u8981\u590d\u73b0\u56de\u6d4b\uff0c\u5f97\u5148\u5907\u9f50\u6570\u636e\uff0c\u5168\u5e02\u573a 5558 \u53ea\u30012024 \u5e74\u81f3\u4eca\u7684\u540e\u590d\u6743\u65e5 K\u3002\u8fd9\u4e00\u6b65\u6211\u539f\u4ee5\u4e3a\u6700\u7b80\u5355\uff0c\u7ed3\u679c\u82b1\u7684\u65f6\u95f4\u6bd4\u56de\u6d4b\u672c\u8eab\u8fd8\u957f\uff0c\u4e09\u8fde\u5751\u5b9e\u5f55\u3002<\/p>\n\n\n\n
\u7b2c\u4e00\u5751\uff0cbaostock \u9ed1\u540d\u5355\u3002<\/strong>\u6309\u539f\u9879\u76ee\u8def\u5b50 8 \u8fdb\u7a0b\u5e76\u884c\u62c9\u53d6\uff0c\u5934\u51e0\u5206\u949f\u5f88\u987a\uff0c\u4e00\u4e2a\u534a\u5c0f\u65f6\u540e\u5361\u6b7b\u3002\u91cd\u65b0\u767b\u5f55\uff0c\u670d\u52a1\u5668\u56de\u4e86\u516b\u4e2a\u5b57\uff0c\u9ed1\u540d\u5355\u7528\u6237\uff0c\u8bf7\u4e0e\u7ba1\u7406\u5458\u8054\u7cfb<\/strong>\u3002README \u5199\u7684”\u65e0\u9650\u6d41”\uff0c\u5b9e\u6d4b\u542b\u4e49\u662f\u4e0d\u9650\u6b21\u6570\u3001\u9650\u5e76\u53d1\u5f3a\u5ea6\u3002<\/p>\n\n\n\n
\u7b2c\u4e8c\u5751\uff0c\u4e1c\u8d22\u62d2\u8fde\u3002<\/strong>\u6362 akshare\uff0c\u5e95\u5c42\u662f\u4e1c\u65b9\u8d22\u5bcc\u63a5\u53e3\u3002\u8fd9\u53f0\u673a\u5668\u7684\u7f51\u7edc\u73af\u5883\u91cc\uff0c\u4e1c\u8d22 CDN \u76f4\u63a5\u62d2\u7edd\u8fde\u63a5\uff0ccurl \u548c Python \u5168\u6302\u3002<\/p>\n\n\n\n
\u7b2c\u4e09\u5751\uff0c\u817e\u8baf\u9650\u6d41\u3002<\/strong>\u6362\u817e\u8baf\u63a5\u53e3\uff0c\u524d 700 \u53ea\u98de\u5feb\uff0c\u56db\u79d2\u4e00\u767e\u53ea\uff0c\u7136\u540e\u5f00\u59cb\u8fd4\u56de\u7a7a\u5185\u5bb9\u3002<\/p>\n\n\n\n
\u6700\u540e\u8dd1\u901a\u7684\u7ec4\u5408\u662f\u65b0\u6d6a\u7684\u4e24\u4e2a\u63a5\u53e3\uff0c\u4e00\u4e2a\u7ed9\u4e0d\u590d\u6743\u65e5 K\uff0c\u4e00\u4e2a\u7ed9\u590d\u6743\u56e0\u5b50\uff0c\u4e24\u4e2a\u4e00\u4e58\uff0c\u540e\u590d\u6743\u6570\u636e\u5c31\u6709\u4e86\u30025558 \u53ea\u3001344 \u4e07\u884c K \u7ebf\uff0c\u4e5d\u5206\u949f\u62c9\u5b8c<\/strong>\uff0c\u5168\u7a0b\u96f6\u65ad\u6d41\u3002<\/p>\n\n\n\n
\u4e0d\u8fc7\u6570\u636e\u8fc7\u5173\u4e0d\u7b49\u4e8e\u6570\u636e\u5e72\u51c0\u3002\u6211\u540e\u6765\u62ff\u901a\u8fbe\u4fe1\u548c\u817e\u8baf\u4e24\u4e2a\u72ec\u7acb\u6e90\u5bf9\u62cd\uff0c\u53d1\u73b0\u4e00\u4e2a\u85cf\u5f97\u6df1\u7684\u5751\uff1a\u65b0\u6d6a\u7684\u590d\u6743\u56e0\u5b50\u505c\u5728 2022 \u5e74\u5e95\u5c31\u4e0d\u66f4\u65b0\u4e86\u3002\u8fd9\u610f\u5473\u7740 2023 \u5e74\u4ee5\u540e\u5206\u8fc7\u7ea2\u3001\u9001\u8fc7\u8f6c\u7684\u8001\u724c\u80a1\uff0c\u540e\u590d\u6743\u4ef7\u5728\u9664\u6743\u65e5\u9644\u8fd1\u4f1a\u6709\u4e00\u4e24\u4e2a\u70b9\u7684\u5047\u8df3\u7a7a\u3002\u5168\u5e02\u573a\u53d7\u5f71\u54cd\u7684\u5927\u6982 20 \u53ea\uff0c\u5360\u6bd4\u4e0d\u5230 0.4%\uff0c\u5bf9 22 \u4e07\u4e2a\u4fe1\u53f7\u7684\u5927\u76d8\u7edf\u8ba1\u51b2\u4e0d\u6563\uff0c\u4f46\u4f60\u81ea\u5df1\u642d\u7ba1\u7ebf\u65f6\u8981\u7559\u610f\u3002\u514d\u8d39\u6e90\u7684\u590d\u6743\u56e0\u5b50\u4e0d\u662f\u5b9e\u65f6\u7ef4\u62a4\u7684\u3002<\/p>\n\n\n\n
\u8fd9\u6bb5\u5bf9\u4f60\u642d\u81ea\u5df1\u7684\u6570\u636e\u7ba1\u7ebf\u6709\u76f4\u63a5\u53c2\u8003\u4ef7\u503c\u3002\u514d\u8d39\u6570\u636e\u6e90\u7684\u771f\u5b9e\u8fb9\u754c\uff0c\u6587\u6863\u91cc\u4ece\u6765\u4e0d\u5199\u3002<\/p>\n\n\n\n
\u516d\u3001\u52a8\u624b\u533a\uff0c\u6838\u5fc3\u4ee3\u7801<\/strong><\/h2>\n\n\n\n
\u2460 \u6570\u636e\u56de\u586b\uff08\u65b0\u6d6aK\u7ebf\u00d7\u590d\u6743\u56e0\u5b50\uff0c\u591a\u7ebf\u7a0b\uff09<\/p>\n\n\n\n
import requests\nimport re\nimport json\nimport time\nimport bisect\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\n\nHDR = {\"User-Agent\": \"Mozilla\/5.0\", \"Referer\": \"https:\/\/finance.sina.com.cn\"}\nPXY = {\"http\": None, \"https\": None}   # \u5173\u952e\uff1a\u7ed5\u5f00\u7cfb\u7edf\u4ee3\u7406\n\nKLINE = (\"https:\/\/quotes.sina.cn\/cn\/api\/jsonp_v2.php\"\n         \"\/var%20d=\/CN_MarketDataService.getKLineData\")\n\n\ndef fetch_pair(sym, start=\"2024-01-01\", end=\"2026-09-09\"):\n    # 1) \u4e0d\u590d\u6743\u65e5K\n    r = requests.get(KLINE, params={\n        \"symbol\": sym, \"scale\": \"240\", \"ma\": \"no\", \"datalen\": \"640\"},\n        headers=HDR, proxies=PXY, timeout=15)\n    raw = json.loads(re.search(r\"\\((\\[.*\\])\\)\", r.text, re.S).group(1))\n    raw = [k for k in raw if start <= k[\"day\"] <= end]\n\n    # 2) \u540e\u590d\u6743\u56e0\u5b50\n    r2 = requests.get(\n        f\"https:\/\/finance.sina.com.cn\/realstock\/company\/{sym}\/hfq.js\",\n        headers=HDR, proxies=PXY, timeout=12)\n    fd = json.loads(re.search(r\"=(\\{.*\\})\", r2.text, re.S).group(1))\n    fac = sorted((x[\"d\"], float(x[\"f\"])) for x in fd[\"data\"])\n\n    # 3) \u5408\u6210\u540e\u590d\u6743\uff1a\u4ef7\u683c \u00d7 \u5f53\u65e5\u751f\u6548\u56e0\u5b50\n    dates = [d for d, _ in fac]\n    vals = [f for _, f in fac]\n    out = []\n    for k in raw:\n        i = bisect.bisect_right(dates, k[\"day\"]) - 1\n        f = vals[i] if i >= 0 else vals[0]\n        o, h, l, c = (float(k[\"open\"]), float(k[\"high\"]),\n                      float(k[\"low\"]), float(k[\"close\"]))\n        v = float(k[\"volume\"])\n        out.append((sym, k[\"day\"], o*f, h*f, l*f, c*f, v,\n                    v * (o+h+l+c)\/4))   # \u6210\u4ea4\u989d\u4f30\u7b97\n    return out<\/code><\/pre>\n\n\n\n
\u2461 \u516d\u7b56\u7565\u4fe1\u53f7\uff08\u7167\u6284\u9879\u76ee\u53e3\u5f84\uff0c\u5411\u91cf\u5316\uff09<\/p>\n\n\n\n
import pandas as pd\n\n\ndef indicators(g):\n    \"\"\"\u5355\u53ea\u80a1\u7968\u5168\u90e8\u6307\u6807\uff0c\u4e00\u6b21\u7b97\u5b8c\u3002\"\"\"\n    g = g.sort_values(\"date\").reset_index(drop=True)\n    c, h, l, o, v = g[\"close\"], g[\"high\"], g[\"low\"], g[\"open\"], g[\"volume\"]\n    \n    g[\"bars\"] = range(1, len(g) + 1)\n    g[\"ma5\"], g[\"ma20\"] = c.rolling(5).mean(), c.rolling(20).mean()\n    g[\"ma60\"] = c.rolling(60).mean()\n    g[\"vol_ma20\"] = v.rolling(20).mean()\n    g[\"fwd10\"] = c.shift(-10) \/ c - 1          # \u4fe1\u53f7\u540e10\u65e5\u6536\u76ca\n    g[\"high20_prev\"] = h.shift(1).rolling(20).max()   # \u6d77\u9f9f\uff1a\u524d20\u65e5\u6700\u9ad8\n    g[\"prev_close\"] = c.shift(1)\n    g[\"prev_ma5\"] = g[\"ma5\"].shift(1)\n    g[\"prev_ma20\"] = g[\"ma20\"].shift(1)\n    g[\"prev_ma60\"] = g[\"ma60\"].shift(1)\n    g[\"high40\"], g[\"low40\"] = h.rolling(40).max(), l.rolling(40).min()\n    g[\"high10\"], g[\"low10\"] = h.rolling(10).max(), l.rolling(10).min()\n    g[\"vol20_prev\"] = v.shift(1).rolling(20).mean()\n    g[\"prev_close2\"] = c.shift(2)\n    g[\"prev_volume\"] = v.shift(1)\n    g[\"ret120\"] = c \/ c.shift(120) - 1\n    g[\"roll_high120\"] = h.rolling(120, min_periods=60).max()\n    return g\n\n\ndef six_signals(df):\n    \"\"\"\u516d\u4e2a\u7b56\u7565\u7684\u5e03\u5c14\u4fe1\u53f7\uff0c\u9010\u6761\u5bf9\u9f50\u9879\u76ee\u6e90\u7801\u3002\"\"\"\n    b = df[\"bars\"]\n    return {\n        \"turtle\": (b >= 21)\n                  & (df.close > df.high20_prev)\n                  & (df.turnover > 1e8)\n                  & (df.close > df.open)\n                  & (df.close > df.prev_close),\n        \"ma_volume\": (b >= 20)\n                     & (df.prev_ma5 < df.prev_ma20)\n                     & (df.ma5 > df.ma20)\n                     & (df.volume > df.vol_ma20 * 1.5),\n        \"high_tight_flag\": (b >= 40)\n                           & (df.high40 \/ df.low40 > 1.6)\n                           & (df.high10 \/ df.low10 < 1.15)\n                           & (df.low10 >= df.high40 * 0.8)\n                           & (df.volume < df.vol20_prev * 0.6),\n        \"limit_up_shakeout\": (b >= 3)\n                             & (df.prev_close >= df.prev_close2 * 1.095)\n                             & (df.close < df.open)\n                             & (df.volume > df.prev_volume * 2.0)\n                             & (df.low >= df.prev_close),\n        \"uptrend_limit_down\": (b >= 60)\n                              & (df.prev_ma20 > df.prev_ma60)\n                              & (df.close <= df.prev_close * 0.905)\n                              & (df.volume > df.vol_ma20 * 2.0),\n    }<\/code><\/pre>\n\n\n\n
\u2462 \u8d85\u989d\u6536\u76ca\u4f53\u68c0\uff08\u5bf9\u6bd4\u540c\u671f\u5e02\u573a\u4e2d\u4f4d\u6570\uff09<\/p>\n\n\n\n
def excess_report(df, sig):\n    \"\"\"\u4fe1\u53f7\u540e10\u65e5\u6536\u76ca vs \u540c\u65e5\u5168\u5e02\u573a\u4e2d\u4f4d\u6570\u3002\"\"\"\n    bench = (df.dropna(subset=[\"fwd10\"])\n               .groupby(\"date\")[\"fwd10\"].median())   # \u5f53\u65e5\u57fa\u51c6\n    sub = df[sig].dropna(subset=[\"fwd10\"])\n    excess = sub[\"fwd10\"] - sub[\"date\"].map(bench)\n    n = len(excess)\n    t = excess.mean() \/ excess.std(ddof=1) * (n ** 0.5)\n    print(f\"\u4fe1\u53f7 {n} \u4e2a | \u8d85\u989d\u5747\u503c {excess.mean():.2%} \"\n          f\"| \u80dc\u7387 {(excess>0).mean():.1%} | t={t:.1f}\")\n    # RPS \u4fe1\u53f7\uff08\u6a2a\u622a\u9762\u6392\u4f4d\uff0c\u5355\u72ec\u7b97\uff09\n    # df[\"rps\"] = df.groupby(\"date\")[\"ret120\"].rank(pct=True) * 100<\/code><\/pre>\n\n\n\n
\u5b8c\u6574\u53ef\u8fd0\u884c\u7248\u672c\uff08\u542b\u80a1\u7968\u5217\u8868\u62c9\u53d6\u3001SQLite \u843d\u5e93\u3001\u65ad\u70b9\u7eed\u4f20\u3001RPS \u6a2a\u622a\u9762\u8ba1\u7b97\uff09\u5230\u00a0http:\/\/www.laoyulaoyu.com\/code\/sequoia-repro.zip<\/a>\u00a0\u83b7\u53d6\u3002<\/p>\n\n\n\n
\u89c2\u70b9\u603b\u7ed3<\/strong><\/h2>\n\n\n\n
\u628a 22 \u4e07\u4e2a\u4fe1\u53f7\u7ffb\u8bd1\u6210\u4eba\u8bdd\u3002<\/strong>
\u8fd9\u5957\u7cfb\u7edf\u9009\u80a1\u7968\uff0c\u4e94\u4e2a\u7b56\u7565\u9009\u51fa\u6765\u7684\uff0c\u6301\u6709\u4e24\u5468\u5e73\u5747\u80fd\u6bd4\u5e02\u573a\u591a\u8d5a\u4e00\u4e2a\u767e\u5206\u70b9\u4e0a\u4e0b\uff0cRPS \u7c7b\u6700\u5f3a\u3002\u4e00\u4e2a\u7b56\u7565\u9009\u51fa\u6765\u7684\uff0c\u6301\u6709\u8d8a\u4e45\u8d8a\u4e8f\u3002\u5b83\u81ea\u5df1\u4e0d\u77e5\u9053\u8fd9\u4ef6\u4e8b\uff0c\u56e0\u4e3a\u5b83\u7684\u4e16\u754c\u91cc\u53ea\u6709\u4eca\u5929\u3002\u6240\u4ee5\u6b63\u786e\u7684\u6253\u5f00\u65b9\u5f0f\uff0c\u8ba9\u5b83\u5f53\u4f60\u7684\u7c97\u7b5b\u5de5\u4eba\uff0c\u522b\u8ba9\u5b83\u5f53\u4f60\u7684\u51b3\u7b56\u8005\u3002\u4e09\u5206\u949f\u626b\u5b8c\u5168\u5e02\u573a\u7701\u4e0b\u6765\u7684\u65f6\u95f4\uff0c\u6b63\u662f\u4f60\u7528\u6765\u505a\u4e8c\u6b21\u9a8c\u8bc1\u7684\u65f6\u95f4\u3002\u5de5\u5177\u7684\u4ef7\u503c\u4ece\u6765\u4e0d\u662f\u66ff\u4f60\u5224\u65ad\uff0c\u662f\u628a\u5224\u65ad\u9700\u8981\u7684\u6570\u636e\uff0c\u5728\u4f60\u4e0b\u73ed\u4e4b\u524d\u6446\u4e0a\u684c\u3002<\/p>\n\n\n\n
\u4f60\u7684\u4e8c\u6b21\u9a8c\u8bc1\u91cc\u6700\u91cd\u7684\u90a3\u6761\u662f\u4ec0\u4e48\uff0c\u662f\u4f30\u503c\u3001\u884c\u4e1a\uff0c\u8fd8\u662f\u4ed3\u4f4d\u89c4\u5219\uff1f\u8bc4\u8bba\u533a\u804a\u804a\uff0c\u4e0b\u4e00\u7bc7\u6211\u628a”\u4e8c\u6b21\u9a8c\u8bc1”\u62c6\u6210\u53ef\u6267\u884c\u7684\u6e05\u5355\u3002<\/p>\n\n\n\n
\u89c9\u5f97\u6709\u7528\u7684\u8bdd\uff0c\u628a\u8fd9\u7bc7\u8f6c\u7ed9\u540c\u6837\u5728\u641e\u91cf\u5316\u7684\u670b\u53cb\u3002<\/p>\n\n\n\n
\n\n\n\n
#\u91cf\u5316\u9009\u80a1<\/a>\u00a0#\u5f00\u6e90\u9879\u76ee<\/a>\u00a0#Python\u91cf\u5316<\/a>\u00a0#SequoiaX<\/a>\u00a0#A\u80a1<\/a>\u00a0#\u6280\u672f\u5f62\u6001<\/a>\u00a0#\u7a0b\u5e8f\u5316\u9009\u80a1<\/a>\u00a0#\u91cf\u5316\u590d\u76d8<\/a>\u00a0#GitHub\u5f00\u6e90<\/a>\u00a0#\u9009\u80a1\u7cfb\u7edf<\/a>\u00a0#\u91cf\u5316\u5de5\u5177<\/a>\u00a0#\u52a8\u91cf\u7b56\u7565<\/a><\/p>\n\n\n\n
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\u514d\u8d23\u58f0\u660e\uff1a<\/strong>\u672c\u6587\u6240\u6709\u6570\u636e\u5747\u4e3a\u5386\u53f2\u56de\u6d4b\u7ed3\u679c\uff0c\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae\u3002\u5386\u53f2\u8868\u73b0\u4e0d\u4ee3\u8868\u672a\u6765\u6536\u76ca\u3002\u91cf\u5316\u7b56\u7565\u5b58\u5728\u5931\u6548\u98ce\u9669\u3002\u5e02\u573a\u6709\u98ce\u9669\uff0c\u6295\u8d44\u9700\u8c28\u614e\u3002<\/p>\n<\/blockquote>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
\u4f5c \u8005\uff1a\u8001\u4f59\u635e\u9c7c \u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005…<\/p>\n
Continue reading7K\u661f\u7684\u5f00\u6e90\u9009\u80a1\u7cfb\u7edf\u771f\u6b63\u7701\u6389\u7684\uff0c\u662f\u4f60\u6bcf\u5929\u7ffb\u6da8\u5e45\u699c\u7684\u90a3\u534a\u5c0f\u65f6<\/span><\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[17,4,5,6],"class_list":["post-4894","post","type-post","status-publish","format-standard","hentry","category-aiinvest","tag-ai","tag-6","entry"],"_links":{"self":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4894","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/comments?post=4894"}],"version-history":[{"count":1,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4894\/revisions"}],"predecessor-version":[{"id":4902,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/4894\/revisions\/4902"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=4894"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=4894"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=4894"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}