{"id":87282,"date":"2026-07-30T06:32:41","date_gmt":"2026-07-29T22:32:41","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/87282.html"},"modified":"2026-07-30T06:32:41","modified_gmt":"2026-07-29T22:32:41","slug":"%e6%9e%84%e5%bb%ba%e4%b8%80%e4%b8%aa-24-%e5%b0%8f%e6%97%b6%e8%87%aa%e5%8a%a8%e8%bf%90%e8%a1%8c%e7%9a%84-ai-%e9%80%89%e8%82%a1-agent%ef%bc%88%e5%9f%ba%e4%ba%8e-deepseek-r1-quantdash-%e5%ae%9e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/87282.html","title":{"rendered":"\u6784\u5efa\u4e00\u4e2a 24 \u5c0f\u65f6\u81ea\u52a8\u8fd0\u884c\u7684 AI \u9009\u80a1 Agent\uff08\u57fa\u4e8e DeepSeek R1 + QuantDash \u5b9e\u65f6\u884c\u60c5\uff09"},"content":{"rendered":"

1. \u65f6\u4ee3\u8d8b\u52bf：\u4ece\u201c\u624b\u52a8\u8c03\u4f18\u201d\u5230\u201c\u667a\u80fd\u4f53\u81ea\u6cbb\u201d<\/h3>\n

2026 \u5e74，\u91cf\u5316\u6295\u8d44\u7684\u7126\u70b9\u5df2\u7ecf\u4ece\u5355\u7eaf\u7684\u201c\u56e0\u5b50\u56de\u6d4b\u201d\u8f6c\u5411\u4e86\u201cAI \u667a\u80fd\u4f53（Agent）\u81ea\u6cbb\u201d[1]\u3002 \u9876\u7ea7\u91cf\u5316\u79c1\u52df\u6b63\u5728\u5e7f\u6cdb\u5e94\u7528\u957f\u4e0a\u4e0b\u6587\u3001\u6df1\u5ea6\u63a8\u7406\u7684\u5927\u6a21\u578b\u6765\u5b9e\u65f6\u76d1\u63a7\u5168\u5e02\u573a\u76d8\u53e3\u3002 \u8981\u8ba9\u4e00\u4e2a AI Agent \u7a33\u5b9a\u5730\u5e2e\u6211\u4eec\u76ef\u76d8，\u5b83\u5fc5\u987b\u80fd\u591f\u5b9e\u65f6\u3001\u4e0d\u95f4\u65ad\u5730\u62c9\u53d6\u5168\u5e02\u573a\u7684\u884c\u60c5\u5207\u7247，\u800c\u4e0d\u80fd\u9047\u5230\u4efb\u4f55\u6570\u636e\u6d41\u963b\u585e\u3002<\/p>\n

\n

2. \u75db\u70b9\u5256\u6790：\u4e3a\u4ec0\u4e48\u4f20\u7edf\u5b9e\u65f6\u63a5\u53e3\u4e0d\u9002\u5408 AI Agent？<\/h3>\n

AI Agent \u7684\u6838\u5fc3\u903b\u8f91\u662f\u901a\u8fc7\u4e0d\u65ad\u7684\u201c\u89c2\u5bdf（Observation）- \u601d\u8003（Thought）- \u884c\u52a8（Action）\u201d\u5faa\u73af\u3002 \u5982\u679c\u6570\u636e\u63a5\u53e3\u8fd4\u56de\u7684\u5185\u5bb9\u8fc7\u4e8e\u81c3\u80bf\u3001\u54cd\u5e94\u6781\u6162，\u6216\u8005\u7f3a\u4e4f\u6279\u91cf\u7b5b\u9009\u673a\u5236，\u5927\u6a21\u578b\u7684 Token \u6d88\u8017\u5c31\u4f1a\u5448\u6307\u6570\u7ea7\u98d9\u5347，\u4e14\u6781\u6613\u56e0\u8d85\u65f6\u800c\u5bfc\u81f4 Agent \u8fd0\u884c\u5d29\u6e83\u3002 \u6211\u4eec\u9700\u8981\u4e00\u4e2a\u80fd\u4e00\u952e\u62c9\u53d6\u5168\u91cf\u5e02\u573a\u5feb\u7167\u5e76\u80fd\u88ab Pandas \u5feb\u901f\u8f6c\u6362\u7684\u201c\u8f7b\u91cf\u7ea7\u6570\u636e\u5f15\u64ce\u201d\u3002<\/p>\n

\n

3. \u5b8c\u7f8e\u89e3\u51b3\u65b9\u6848：QuantDash \u5168\u91cf\u5b9e\u65f6\u884c\u60c5\u5feb\u7167 API<\/h3>\n

QuantDash \u63d0\u4f9b\u4e86\u6781\u4e3a\u5f3a\u608d\u7684\u00a0qd.quotes.get\u00a0\u63a5\u53e3，\u652f\u6301\u4e00\u952e\u83b7\u53d6\u5168\u91cf A \u80a1（universes="CN_Stock"）\u7684\u5b9e\u65f6\u5feb\u7167，\u5ef6\u8fdf\u6781\u4f4e，\u5b57\u6bb5\u6574\u6d01[2]\u3002\u6700\u826f\u5fc3\u7684\u662f，\u8be5\u63a5\u53e3\u5728\u514d\u8d39\/\u8bd5\u7528\u7248\u8d26\u6237\u4e0b\u5b8c\u5168\u53ef\u7528\u4e14\u652f\u6301\u9ad8\u9891\u8c03\u7528\u3002<\/p>\n

\u5b9e\u65f6\u884c\u60c5\u6570\u636e\u6d41\u67b6\u6784\u5bf9\u6bd4：<\/h4>\n\n\n\n\n\n\n\n
\u7279\u6027\u7ef4\u5ea6<\/td>\n\u4f20\u7edf Websocket \u63a8\u9001\u65b9\u6848<\/td>\nQuantDash \u5168\u91cf Universe \u5feb\u7167\u65b9\u6848<\/td>\n<\/tr>\n
\u5f00\u53d1\u590d\u6742\u5ea6<\/td>\n\u9700\u7ef4\u62a4\u590d\u6742\u7684\u957f\u8fde\u63a5\u91cd\u8fde\u903b\u8f91\u3001\u591a\u7ebf\u7a0b\u961f\u5217\u5206\u53d1<\/td>\n\u5355\u6b21 API \u8c03\u7528\u76f4\u63a5\u8fd4\u56de\u5168\u5e02\u573a DataFrame，\u5f00\u7bb1\u5373\u7528<\/td>\n<\/tr>\n
AI \u4ee3\u7406\u9002\u914d\u5ea6<\/td>\n\u6781\u5dee，Agent \u65e0\u6cd5\u5b9e\u65f6\u5904\u7406\u6d77\u91cf\u3001\u975e\u7ed3\u6784\u5316\u7684 Socket \u6570\u636e\u6d41<\/td>\n\u6781\u4f73，Agent \u968f\u65f6\u4e00\u952e\u6309\u9700\u62c9\u53d6，\u6570\u636e\u5373\u62ff\u5373\u7528<\/td>\n<\/tr>\n
\u5185\u5b58\u5360\u7528<\/td>\n\u6781\u9ad8，\u9700\u8981\u672c\u5730\u7ef4\u62a4\u5de8\u5927\u7684 Tick \u7ea7\u522b\u72b6\u6001\u673a<\/td>\n\u6781\u4f4e，\u4e91\u7aef\u5df2\u5b8c\u6210\u6570\u636e\u89c4\u6574\u805a\u5408<\/td>\n<\/tr>\n
\u5e76\u53d1\u5f00\u9500<\/td>\n\u5bb9\u6613\u53d1\u751f\u6570\u636e\u4e22\u5305\u4e0e\u62e5\u5835<\/td>\n\u9ad8\u5ea6\u4f18\u5316，\u652f\u6301\u9ad8\u9891\u8f6e\u8be2\u62c9\u53d6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

4. \u4eba\u673a\u534f\u4f5c\u5b9e\u6218：5\u5206\u949f\u90e8\u7f72 AI \u5b9e\u65f6\u76ef\u76d8 Agent<\/h3>\n

\u4e0b\u9762\u8fd9\u6bb5\u6781\u7b80\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5229\u7528 QuantDash，\u5728\u4e00\u79d2\u5185\u83b7\u53d6\u5168 A \u80a1 5000+ \u80a1\u7968\u7684\u5b9e\u65f6\u6da8\u8dcc\u5e45，\u5e76\u8fc5\u901f\u7b5b\u9009\u51fa\u6da8\u5e45\u6700\u5927\u7684\u524d 10 \u53ea\u4e2a\u80a1\u3002<\/p>\n

import pandas as pd
\nfrom quantdash import QuantDash<\/p>\n

# 1. \u5feb\u901f\u521d\u59cb\u5316 QuantDash \u5ba2\u6237\u7aef
\n# \u6ce8\u518c\u8d26\u53f7\u83b7\u53d6 key: https:\/\/quantdash.net\/dashboard\/keys\/
\nqd = QuantDash(api_key="your_api_key_here")<\/p>\n

print("\u3010\u7b2c\u4e00\u6b65\u3011\u901a\u8fc7 QuantDash \u4e00\u952e\u83b7\u53d6\u5168 A \u80a1\u5b9e\u65f6\u884c\u60c5\u5feb\u7167…")
\n# \u4e00\u952e\u83b7\u53d6\u5168\u91cf A \u80a1\u5b9e\u65f6\u884c\u60c5\u5feb\u7167 (\u81ea\u52a8\u8f6c\u6362\u4e3a\u6807\u51c6 DataFrame，\u514d\u8d39\u7248\u5b8c\u5168\u652f\u6301！)
\n# \u63a5\u53e3\u89c4\u8303\u53c2\u8003：https:\/\/docs.quantdash.net\/zh-Hans\/sdk\/python-quickstart
\nall_cn_stocks = qd.quotes.get(universes="CN_Stock", to_dataframe=True)<\/p>\n

if all_cn_stocks is not None and not all_cn_stocks.empty:
\n print(f"\u6210\u529f\u52a0\u8f7d {len(all_cn_stocks)} \u53ea\u6807\u7684\u6570\u636e\u3002\u5f00\u59cb\u6570\u636e\u9884\u6e05\u6d17\u4e0e\u6307\u6807\u8ba1\u7b97…")<\/p>\n

# 2. \u6570\u636e\u9884\u6e05\u6d17：\u786e\u4fdd\u5173\u952e\u6570\u503c\u5b57\u6bb5\u4e3a float \u7c7b\u578b，\u9632\u6b62 pandas \u8fdb\u884c\u5bf9\u8c61\u8ba1\u7b97\u62a5\u9519
\n numeric_cols = ["last_price", "prev_close", "volume"]
\n for col in numeric_cols:
\n if col in all_cn_stocks.columns:
\n all_cn_stocks[col] = pd.to_numeric(all_cn_stocks[col], errors='coerce')<\/p>\n

# 3. \u8ba1\u7b97\u5f53\u65e5\u5b9e\u65f6\u6da8\u8dcc\u5e45\u767e\u5206\u6bd4 (\u6ce8：\u5b9e\u65f6\u5feb\u7167\u7684\u6700\u65b0\u4ef7\u683c\u5b57\u6bb5\u4e3a last_price，\u6628\u6536\u4ef7\u5b57\u6bb5\u4e3a prev_close)
\n all_cn_stocks["change_pct"] = (
\n (all_cn_stocks["last_price"] – all_cn_stocks["prev_close"]) \/ all_cn_stocks["prev_close"] * 100
\n )<\/p>\n

# 4. \u667a\u80fd\u7b5b\u9009：\u8fc7\u6ee4\u6389\u7a7a\u503c\u6570\u636e，\u5e76\u6309\u6da8\u5e45\u964d\u5e8f\u6392\u5217，\u9009\u51fa\u6da8\u5e45\u6700\u5927\u7684\u524d 10 \u53ea\u4e2a\u80a1
\n top_gainers = all_cn_stocks.dropna(subset=["change_pct"]).sort_values(by="change_pct", ascending=False).head(10)<\/p>\n

# 5. \u6253\u5370\u683c\u5f0f\u5316\u8f93\u51fa（\u53ef\u76f4\u63a5\u4f5c\u4e3a Context \u9001\u5165 DeepSeek \u7b49\u5927\u6a21\u578b\u7684 Prompt \u8fdb\u884c\u5f02\u52a8\u5206\u6790）
\n print("\\\\n— 🚀 \u5f53\u524d\u5168\u5e02\u573a\u5b9e\u65f6\u6da8\u5e45 Top 10 \u6807\u7684 —")
\n print(top_gainers[["symbol", "last_price", "prev_close", "change_pct"]])
\nelse:
\n print("\u672a\u80fd\u83b7\u53d6\u5230\u5b9e\u65f6\u5feb\u7167\u6570\u636e，\u8bf7\u68c0\u67e5 API Key \u6216\u7f51\u7edc\u8fde\u63a5\u3002") <\/p>\n

\u771f\u5b9e\u6570\u636e\u63a7\u5236\u53f0\u8f93\u51fa：<\/p>\n

\u3010\u7b2c\u4e00\u6b65\u3011\u901a\u8fc7 QuantDash \u4e00\u952e\u83b7\u53d6\u5168 A \u80a1\u5b9e\u65f6\u884c\u60c5\u5feb\u7167…
\n\u6210\u529f\u52a0\u8f7d 5533 \u53ea\u6807\u7684\u6570\u636e\u3002\u5f00\u59cb\u6570\u636e\u9884\u6e05\u6d17\u4e0e\u6307\u6807\u8ba1\u7b97…<\/p>\n

— 🚀 \u5f53\u524d\u5168\u5e02\u573a\u5b9e\u65f6\u6da8\u5e45 Top 10 \u6807\u7684 —
\n symbol last_price prev_close change_pct
\n2576 300169.SZ 5.38 4.48 20.089286
\n2096 300359.SZ 4.43 3.69 20.054201
\n1002 300333.SZ 8.87 7.39 20.027064
\n5280 300530.SZ 20.38 16.98 20.023557
\n1867 301112.SZ 34.20 28.50 20.000000
\n3708 300997.SZ 18.00 15.00 20.000000
\n3250 688260.SH 88.51 73.76 19.997289
\n1393 300981.SZ 12.85 10.71 19.981326
\n3034 301139.SZ 3.74 3.12 19.871795
\n3499 920786.BJ 7.14 6.04 18.211921 <\/p>\n

\u6709\u4e86\u8fd9\u4e2a\u9ad8\u89c4\u6574\u5ea6\u7684\u5b9e\u65f6 DataFrame，\u4f60\u53ea\u9700\u5728\u5916\u9762\u5957\u4e00\u4e2a\u7b80\u5355\u7684\u00a0LLM.call()，AI Agent \u5c31\u80fd\u81ea\u52a8\u6839\u636e\u5b9e\u65f6\u76d8\u9762\u548c\u6700\u65b0\u65b0\u95fb\u64b0\u5199\u4e00\u4efd\u4e13\u4e1a\u7684\u201c\u76d8\u4e2d\u5f02\u52a8\u5206\u6790\u62a5\u544a\u201d\u4e86\u3002<\/p>\n

\n

5. \u6781\u5ba2 Q&A<\/h3>\n

Q: \u5728 K \u7ebf\u63a5\u53e3（klines）\u548c\u5b9e\u65f6\u884c\u60c5\u63a5\u53e3（quotes）\u4e2d，\u4ee3\u8868\u6700\u65b0\u4ef7\u683c\u7684\u5b57\u6bb5\u6709\u4ec0\u4e48\u4e0d\u540c？ A: \u8fd9\u662f\u4e00\u4e2a\u9ad8\u9891\u6613\u9519\u70b9\u3002\u5386\u53f2 K \u7ebf\u63a5\u53e3（qd.klines.get）\u9488\u5bf9\u7684\u662f\u65f6\u95f4\u533a\u95f4\u5185\u7684\u8721\u70db\u56fe，\u5b57\u6bb5\u4f7f\u7528\u6807\u51c6\u7684\u00a0close（\u6536\u76d8\u4ef7）[1]；\u800c\u5b9e\u65f6\u884c\u60c5\u63a5\u53e3（qd.quotes.get）\u83b7\u53d6\u7684\u662f\u5f53\u4e0b\u7684\u76d8\u4e2d\u5feb\u7167，\u4ee3\u8868\u6700\u65b0\u4ef7\u683c\u7684\u5b57\u6bb5\u662f\u00a0last_price（\u6700\u65b0\u6210\u4ea4\u4ef7），\u6628\u6536\u4ef7\u5219\u662f\u00a0prev_close[3]\u3002<\/p>\n

Q: \u83b7\u53d6\u5168\u91cf A \u80a1\u5feb\u7167 API \u63a5\u53e3\u7684\u9891\u7387\u9650\u5236（Rate Limit）\u662f\u600e\u6837\u7684？ A: QuantDash \u7684\u5feb\u7167\u63a5\u53e3\u7ecf\u8fc7\u4e86\u6df1\u5ea6\u7684\u4e91\u7aef\u7f13\u5b58\u4e0e\u6027\u80fd\u4f18\u5316，\u9ed8\u8ba4\u9650\u5236\u80fd\u591f\u5b8c\u5168\u6ee1\u8db3 AI Agent \u76d8\u4e2d\u5206\u949f\u7ea7\u8f6e\u8be2\u7684\u9700\u8981\u3002<\/p>\n

Q: \u60f3\u8981\u8fdb\u4e00\u6b65\u76d1\u63a7\u4e2a\u80a1\u7684\u4e94\u6863\u53e3\u5b50，\u6709\u76f8\u5e94\u63a5\u53e3\u5417？ A: \u6709\u7684\u3002QuantDash \u63d0\u4f9b\u4e86\u6807\u51c6\u7684\u00a0qd.depth.get("600519.SH")\u00a0\u63a5\u53e3[1]，\u53ef\u76f4\u63a5\u8fd4\u56de\u5305\u542b\u4e70\u5356\u4e94\u6863\u3001\u6210\u4ea4\u91cf\u53ca\u4ef7\u683c\u7684\u6807\u51c6\u5b57\u5178，\u4f9b\u60a8\u7684\u76ef\u76d8 Agent \u8fdb\u884c\u66f4\u7cbe\u7ec6\u7684\u5fae\u89c2\u7ed3\u6784\u5206\u6790\u3002\u5177\u4f53\u8bf7\u53c2\u8003\u00a0QuantDash SDK \u5b98\u65b9\u6587\u6863\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

1. \u65f6\u4ee3\u8d8b\u52bf：\u4ece\u201c\u624b\u52a8\u8c03\u4f18\u201d\u5230\u201c\u667a\u80fd\u4f53\u81ea\u6cbb\u201d
\n2026 \u5e74，\u91cf\u5316\u6295\u8d44\u7684\u7126\u70b9\u5df2\u7ecf\u4ece\u5355\u7eaf\u7684\u201c\u56e0\u5b50\u56de\u6d4b\u201d\u8f6c\u5411\u4e86\u201cAI \u667a\u80fd\u4f53（Agent）\u81ea\u6cbb\u201d[1]\u3002 \u9876\u7ea7\u91cf\u5316\u79c1\u52df\u6b63\u5728\u5e7f\u6cdb\u5e94\u7528\u957f\u4e0a\u4e0b\u6587\u3001\u6df1\u5ea6\u63a8\u7406\u7684\u5927\u6a21\u578b\u6765\u5b9e\u65f6\u76d1\u63a7\u5168\u5e02\u573a\u76d8\u53e3\u3002 \u8981\u8ba9\u4e00\u4e2a AI Agent \u7a33\u5b9a\u5730\u5e2e\u6211\u4eec\u76ef\u76d8，\u5b83\u5fc5\u987b\u80fd\u591f\u5b9e\u65f6\u3001\u4e0d\u95f4\u65ad\u5730\u62c9\u53d6\u5168\u5e02\u573a\u7684\u884c\u60c5\u5207\u7247，\u800c\u4e0d\u80fd\u9047\u5230\u4efb\u4f55\u6570\u636e\u6d41\u963b\u585e\u3002 2. \u75db\u70b9\u5256\u6790：\u4e3a\u4ec0\u4e48\u4f20\u7edf\u5b9e\u65f6\u63a5\u53e3\u4e0d<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9098,9879,92,81,50,190],"topic":[],"class_list":["post-87282","post","type-post","status-publish","format-standard","hentry","category-server","tag-it","tag-quantdash","tag-github","tag-python","tag-50","tag-190"],"yoast_head":"\n\u6784\u5efa\u4e00\u4e2a 24 \u5c0f\u65f6\u81ea\u52a8\u8fd0\u884c\u7684 AI \u9009\u80a1 Agent\uff08\u57fa\u4e8e DeepSeek R1 + QuantDash \u5b9e\u65f6\u884c\u60c5\uff09 - 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