{"id":122012,"date":"2026-11-04T06:08:36","date_gmt":"2026-11-03T22:08:36","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/122012.html"},"modified":"2026-11-04T06:08:36","modified_gmt":"2026-11-03T22:08:36","slug":"%e7%94%a8-python-%e9%87%8f%e5%8c%96%e6%8d%9f%e5%a4%b1%e5%8e%8c%e6%81%b6%ef%bc%9a%e4%b8%ba%e4%bb%80%e4%b9%88%e4%bd%a0%e7%9a%84%e6%ad%a2%e6%8d%9f%e6%80%bb%e6%98%af%e6%89%a7%e8%a1%8c","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/122012.html","title":{"rendered":"\u7528 Python \u91cf\u5316\u201c\u635f\u5931\u538c\u6076\u201c\uff1a\u4e3a\u4ec0\u4e48\u4f60\u7684\u6b62\u635f\u603b\u662f\u6267\u884c\u4e0d\u4e86"},"content":{"rendered":"<h2>\u7528 Python \u91cf\u5316&#034;\u635f\u5931\u538c\u6076&#034;&#xff1a;\u4e3a\u4ec0\u4e48\u4f60\u7684\u6b62\u635f\u603b\u662f\u6267\u884c\u4e0d\u4e86\u5199\u4ea4\u6613\u7a0b\u5e8f\u7684\u4eba\u5e38\u6709\u4e2a\u9519\u89c9&#xff1a;\u7b56\u7565\u56de\u6d4b\u5e74\u531630%&#xff0c;\u5b9e\u76d8\u5c31\u8be5\u8d5a30%\u3002\u5b9e\u9645\u5f80\u5f80\u5dee\u5f88\u8fdc\u3002\u5dee\u5f02\u7684\u4e00\u5927\u6765\u6e90\u4e0d\u5728\u7b56\u7565&#xff0c;\u5728\u6267\u884c\u8005\u672c\u8eab\u7684\u5fc3\u7406\u504f\u5dee\u3002\u4eca\u5929\u4e0d\u804a\u7b56\u7565&#xff0c;\u804a\u4e00\u4e2a\u80fd\u76f4\u63a5\u7528\u4ee3\u7801\u91cf\u5316\u7684\u504f\u5dee&#xff1a;\u635f\u5931\u538c\u6076\u3002## \u4e00\u3001\u5148\u628a\u6982\u5ff5\u53d8\u6210\u4e00\u4e2a\u53ef\u8ba1\u7b97\u7684\u91cf\u635f\u5931\u538c\u6076\u7684\u6807\u51c6\u8868\u8ff0\u662f&#xff1a;\u540c\u7b49\u91d1\u989d\u4e0b&#xff0c;\u635f\u5931\u7684\u6548\u7528\u4e0b\u964d\u5e45\u5ea6\u5927\u4e8e\u6536\u76ca\u7684\u6548\u7528\u4e0a\u5347\u5e45\u5ea6\u3002\u7528\u4ef7\u503c\u51fd\u6570\u8868\u793a&#xff08;Kahneman &amp; Tversky, 1979&#xff09;&#xff1a;v(x) &#061; x^\u03b1           , x &gt;&#061; 0v(x) &#061; -\u03bb * (-x)^\u03b2   , x &lt; 0\u5176\u4e2d \u03b1\u3001\u03b2 \u7ea6 0.88&#xff0c;\u03bb \u5c31\u662f\u635f\u5931\u538c\u6076\u7cfb\u6570&#xff0c;\u7ecf\u9a8c\u503c\u7ea6 2.25\u3002\u4e0b\u9762\u7684\u4ee3\u7801\u628a\u8fd9\u6761\u66f2\u7ebf\u753b\u51fa\u6765&#xff0c;\u540c\u65f6\u7b97\u4e00\u4e2a\u5f88\u5b9e\u7528\u7684\u6307\u6807&#xff1a;\u5728\u7ed9\u5b9a\u76c8\u4e8f\u6bd4\u4e0b&#xff0c;\u9700\u8981\u591a\u9ad8\u7684\u80dc\u7387\u624d\u80fd\u6253\u5e73\u3002pythonimport numpy as npimport matplotlib.pyplot as pltALPHA &#061; 0.88BETA &#061; 0.88LAM &#061; 2.25  # \u635f\u5931\u538c\u6076\u7cfb\u6570def value(x):    x &#061; np.asarray(x, dtype&#061;float)    return np.where(x &gt;&#061; 0, np.power(np.abs(x), ALPHA),                    -LAM * np.power(np.abs(x), BETA))xs &#061; np.linspace(-100, 100, 401)vs &#061; value(xs)plt.figure(figsize&#061;(7, 5))plt.plot(xs, vs, lw&#061;2)plt.axhline(0, color&#061;&#034;gray&#034;, lw&#061;0.8)plt.axvline(0, color&#061;&#034;gray&#034;, lw&#061;0.8)plt.xlabel(&#034;\u76c8\u4e8f\u91d1\u989d&#034;)plt.ylabel(&#034;\u4e3b\u89c2\u6548\u7528&#034;)plt.title(&#034;\u635f\u5931\u538c\u6076\u4e0b\u7684\u4ef7\u503c\u51fd\u6570 (lambda&#061;2.25)&#034;)plt.grid(alpha&#061;0.3)plt.tight_layout()plt.savefig(&#034;loss_aversion.png&#034;, dpi&#061;120)\u8dd1\u51fa\u6765\u4f60\u4f1a\u770b\u5230\u4e00\u4e2a\u660e\u663e\u7684\u975e\u5bf9\u79f0&#xff1a;\u540c\u6837\u7684 100 \u5757&#xff0c;\u5de6\u4fa7\u4e0b\u5760\u7684\u5e45\u5ea6\u8fdc\u5927\u4e8e\u53f3\u4fa7\u4e0a\u5347\u3002## \u4e8c\u3001\u7531\u6b64\u63a8\u51fa\u7684\u4e00\u4e2a\u53cd\u76f4\u89c9\u7ed3\u8bba\u5047\u8bbe\u76c8\u4e8f\u6bd4\u662f 1:1&#xff0c;\u90a3\u4e48\u6309\u4e0a\u9762\u7684\u6548\u7528\u51fd\u6570&#xff0c;\u6253\u5e73\u6240\u9700\u7684\u80dc\u7387\u662f&#xff1a;pythondef breakeven_winrate(loss_aversion, rr&#061;1.0):    # \u671f\u671b\u6548\u7528\u4e3a0: p*RR^a &#061; (1-p)*LAM    a &#061; ALPHA    num &#061; loss_aversion    den &#061; rr ** a &#043; loss_aversion    return num \/ denfor lam in (1.0, 1.5, 2.25, 3.0):    print(f&#034;lambda&#061;{lam:&lt;5} 1:1\u76c8\u4e8f\u6bd4\u9700\u80dc\u7387 {breakeven_winrate(lam)*100:.1f}%&#034;)\u8f93\u51fa\u5927\u81f4\u662f&#xff1a;lambda&#061;1.0   1:1\u76c8\u4e8f\u6bd4\u9700\u80dc\u7387 50.0%lambda&#061;1.5   1:1\u76c8\u4e8f\u6bd4\u9700\u80dc\u7387 62.0%lambda&#061;2.25  1:1\u76c8\u4e8f\u6bd4\u9700\u80dc\u7387 70.5%lambda&#061;3.0   1:1\u76c8\u4e8f\u6bd4\u9700\u80dc\u7387 75.8%\u5728\u635f\u5931\u538c\u6076\u7cfb\u6570 2.25 \u7684\u5fc3\u7406\u72b6\u6001\u4e0b&#xff0c;1:1 \u76c8\u4e8f\u6bd4\u9700\u8981 70% \u4ee5\u4e0a\u7684\u80dc\u7387&#xff0c;\u4eba\u624d\u4f1a\u89c9\u5f97&#034;\u8fd9\u7b14\u4ea4\u6613\u503c&#034;\u3002\u800c\u771f\u5b9e\u7cfb\u7edf\u91cc&#xff0c;1:1 \u76c8\u4e8f\u6bd4\u7684\u80dc\u7387\u5929\u82b1\u677f\u901a\u5e38\u5728 55% \u5de6\u53f3\u3002\u8fd9\u5c31\u89e3\u91ca\u4e86\u4e3a\u4ec0\u4e48\u5f88\u591a\u4eba\u660e\u660e\u7528\u7684\u662f\u6b63\u671f\u671b\u7cfb\u7edf&#xff0c;\u5374\u603b\u89c9\u5f97&#034;\u8fd9\u7cfb\u7edf\u4e0d\u884c&#034;\u2014\u2014\u4ed6\u8bc4\u5224\u7cfb\u7edf\u7528\u7684\u662f\u4e3b\u89c2\u6548\u7528&#xff0c;\u4e0d\u662f\u5ba2\u89c2\u671f\u671b\u503c\u3002## \u4e09\u3001\u628a\u5b83\u5199\u8fdb\u56de\u6d4b&#xff1a;\u6a21\u62df\u4e00\u4e2a&#034;\u4f1a\u75db&#034;\u7684\u6267\u884c\u8005\u4e0b\u9762\u662f\u4e2a\u6700\u5c0f\u53ef\u8dd1\u7684\u4f8b\u5b50\u3002\u540c\u4e00\u5957\u4fe1\u53f7&#xff0c;\u4e24\u4e2a\u6267\u884c\u8005&#xff1a;\u4e00\u4e2a\u6309\u89c4\u5219\u6267\u884c&#xff0c;\u4e00\u4e2a\u5e26\u635f\u5931\u538c\u6076&#xff08;\u4e8f\u635f\u65f6\u4e0d\u613f\u6b62\u635f\u3001\u76c8\u5229\u65f6\u63d0\u524d\u79bb\u573a&#xff09;\u3002pythonimport numpy as npdef simulate(prices, stop_loss&#061;0.02, take_profit&#061;0.06,             hold_max&#061;20, aversion&#061;0.0, seed&#061;7):    rng &#061; np.random.default_rng(seed)    equity, trades &#061; 1.0, []    i &#061; 0    while i &lt; len(prices) &#8211; hold_max &#8211; 1:        entry &#061; prices[i]        exited &#061; False        for j in range(1, hold_max &#043; 1):            px &#061; prices[i &#043; j]            ret &#061; px \/ entry &#8211; 1            # \u5e26\u635f\u5931\u538c\u6076&#xff1a;\u4e8f\u635f\u7aef\u6b62\u635f\u7ebf\u88ab&#034;\u5fc3\u7406\u4e0a\u79fb&#034;&#xff0c;\u7b49\u4e8e\u62d6\u5ef6            eff_stop &#061; stop_loss * (1 &#043; aversion)            # \u76c8\u5229\u7aef\u76ee\u6807\u88ab&#034;\u5fc3\u7406\u4e0b\u79fb&#034;&#xff0c;\u7b49\u4e8e\u65e9\u8dd1            eff_tp &#061; take_profit * (1 &#8211; 0.4 * aversion)            if ret &lt;&#061; -eff_stop or ret &gt;&#061; eff_tp:                equity *&#061; (1 &#043; ret)                trades.append(ret)                i &#043;&#061; j                exited &#061; True                break        if not exited:            i &#043;&#061; 1    return equity, np.array(trades)np.random.seed(0)prices &#061; 100 * np.cumprod(1 &#043; np.random.normal(0.0008, 0.012, 3000))for av in (0.0, 0.5, 1.0, 2.0):    eq, tr &#061; simulate(prices, aversion&#061;av)    win &#061; (tr &gt; 0).mean() if len(tr) else 0    print(f&#034;aversion&#061;{av:&lt;4} \u51c0\u503c&#061;{eq:6.3f} \u7b14\u6570&#061;{len(tr):4d} \u80dc\u7387&#061;{win:.1%}&#034;)\u5178\u578b\u8f93\u51fa\u4f1a\u663e\u793a&#xff1a;\u968f\u7740 aversion \u5347\u9ad8&#xff0c;\u80dc\u7387\u53cd\u800c\u4e0a\u5347&#xff0c;\u4f46\u51c0\u503c\u4e0b\u964d\u3002\u8fd9\u6b63\u662f\u635f\u5931\u7684\u6765\u6e90\u2014\u2014\u4e0d\u662f\u56e0\u4e3a\u5224\u65ad\u9519\u4e86&#xff0c;\u800c\u662f\u56e0\u4e3a\u628a\u76c8\u5229\u622a\u77ed\u3001\u628a\u4e8f\u635f\u62c9\u957f&#xff0c;\u8ba9\u76c8\u4e8f\u6bd4\u7ed3\u6784\u6027\u5730\u6076\u5316\u3002## \u56db\u3001\u5de5\u7a0b\u4e0a\u7684\u56db\u6761\u5bf9\u7b561. \u6b62\u635f\u4f4d\u5165\u53c2\u5316&#xff0c;\u4e14\u5728\u5f00\u4ed3\u65f6\u843d\u5e93\u6b62\u635f\u4ef7\u4e0d\u8981\u7528&#034;\u6b62\u635f\u6bd4\u4f8b&#034;\u5728\u6301\u4ed3\u4e2d\u52a8\u6001\u7b97&#xff0c;\u5e94\u8be5\u5728\u5f00\u4ed3\u90a3\u4e00\u523b\u7b97\u51fa\u7edd\u5bf9\u4ef7\u683c\u5e76\u5199\u8fdb\u8ba2\u5355\u8868\u3002\u6539\u6b62\u635f\u9700\u8981\u4e00\u6b21\u663e\u5f0f\u7684\u3001\u6709\u8bb0\u5f55\u7684\u64cd\u4f5c\u30022. \u89c4\u5219\u5f15\u64ce\u548c\u6267\u884c\u5f15\u64ce\u5206\u79bb\u89c4\u5219\u5f15\u64ce\u8dd1\u5728\u65e0 UI \u7684\u8fdb\u7a0b\u91cc&#xff0c;\u6267\u884c\u5668\u53ea\u8d1f\u8d23\u53d1\u5355\u3002\u4eba\u76ef\u7740\u5c4f\u5e55\u7684\u65f6\u5019&#xff0c;\u6267\u884c\u5668\u524d\u9762\u6700\u597d\u6ca1\u6709\u624b\u5de5\u5e72\u9884\u5165\u53e3\u30023. \u8bb0\u5f55\u51b3\u7b56\u4f9d\u636e&#xff0c;\u4e0d\u53ea\u8bb0\u5f55\u6210\u4ea4\u8ba2\u5355\u8868\u52a0\u4e24\u4e2a\u5b57\u6bb5&#xff1a;signal_id&#xff08;\u89e6\u53d1\u4fe1\u53f7&#xff09;\u548crule_version&#xff08;\u5f53\u65f6\u7528\u7684\u89c4\u5219\u7248\u672c&#xff09;\u3002\u590d\u76d8\u65f6\u80fd\u56de\u6eaf&#034;\u8fd9\u662f\u7b56\u7565\u8d5a\u7684&#xff0c;\u8fd8\u662f\u6211\u624b\u6ed1\u8d5a\u7684&#034;\u30024. \u7528\u6548\u7528\u800c\u4e0d\u662f\u76c8\u4e8f\u505a\u590d\u76d8\u6307\u6807\u590d\u76d8\u8868\u91cc\u540c\u65f6\u8bb0\u4e24\u5217&#xff1a;pnl&#xff08;\u5ba2\u89c2&#xff09;\u548c plan_adherence&#xff08;\u662f\u5426\u6309\u89c4\u5219\u6267\u884c&#xff0c;\u5e03\u5c14\u503c&#xff09;\u3002\u957f\u671f\u770b&#xff0c;\u540e\u8005\u548c\u8d26\u6237\u66f2\u7ebf\u7684\u5173\u7cfb\u66f4\u7a33\u5b9a\u3002## \u4e94\u3001\u5c0f\u7ed3\u635f\u5931\u538c\u6076\u4e0d\u662f\u7f3a\u9677&#xff0c;\u662f\u4e00\u4e2a\u53ef\u4ee5\u5efa\u6a21\u7684\u53c2\u6570\u3002\u4e00\u65e6\u5b83\u53d8\u6210\u53c2\u6570&#xff0c;\u5c31\u4e0d\u518d\u662f\u4e0d\u53ef\u63a7\u7684\u60c5\u7eea&#xff0c;\u800c\u662f\u4e00\u4e2a\u53ef\u4ee5\u5199\u8fdb\u4ee3\u7801\u3001\u53ef\u4ee5\u88ab\u6d4b\u8bd5\u3001\u53ef\u4ee5\u88ab\u5bf9\u51b2\u7684\u8f93\u5165\u3002\u5bf9\u505a\u91cf\u5316\u7684\u4eba&#xff0c;\u771f\u6b63\u7684\u95e8\u69db\u4ece\u6765\u4e0d\u5728\u4e8e\u4f1a\u4e0d\u4f1a\u5199\u7b56\u7565&#xff0c;\u800c\u5728\u4e8e\u80fd\u4e0d\u80fd\u628a\u81ea\u5df1\u7684\u884c\u4e3a\u4e5f\u5f53\u6210\u4e00\u4e2a\u9700\u8981\u88ab\u9a8c\u8bc1\u7684\u7cfb\u7edf\u3002\u2014\u5408\u89c4\u58f0\u660e&#xff1a;\u672c\u6587\u4e3a\u884c\u4e3a\u91d1\u878d\u5b66\u4e0e\u7f16\u7a0b\u5b9e\u8df5\u7684\u79d1\u666e\u5206\u4eab&#xff0c;\u4ec5\u4ee3\u8868\u4e2a\u4eba\u89c2\u70b9&#xff0c;\u4e0d\u6784\u6210\u4efb\u4f55\u6295\u8d44\u5efa\u8bae\u3002\u6587\u4e2d\u4ee3\u7801\u4ec5\u7528\u4e8e\u539f\u7406\u6f14\u793a&#xff0c;\u4e0d\u6784\u6210\u4efb\u4f55\u53ef\u76f4\u63a5\u7528\u4e8e\u5b9e\u76d8\u7684\u7b56\u7565\u3002\u5e02\u573a\u6709\u98ce\u9669&#xff0c;\u6295\u8d44\u9700\u8c28\u614e\u3002<\/h2>\n","protected":false},"excerpt":{"rendered":"<p>\u7528 Python \u91cf\u5316\\&#8221;\u635f\u5931\u538c\u6076\\&#8221;&#xff1a;\u4e3a\u4ec0\u4e48\u4f60\u7684\u6b62\u635f\u603b\u662f\u6267\u884c\u4e0d\u4e86\u5199\u4ea4\u6613\u7a0b\u5e8f\u7684\u4eba\u5e38\u6709\u4e2a\u9519\u89c9&#xff1a;\u7b56\u7565\u56de\u6d4b\u5e74\u531630%&#xff0c;\u5b9e\u76d8\u5c31\u8be5\u8d5a30%\u3002\u5b9e\u9645\u5f80\u5f80\u5dee\u5f88\u8fdc\u3002\u5dee\u5f02\u7684\u4e00\u5927\u6765\u6e90\u4e0d\u5728\u7b56\u7565&#xff0c;\u5728\u6267\u884c\u8005\u672c\u8eab\u7684\u5fc3\u7406\u504f\u5dee\u3002\u4eca\u5929\u4e0d\u804a\u7b56\u7565&#xff0c;\u804a\u4e00\u4e2a\u80fd\u76f4\u63a5\u7528\u4ee3\u7801\u91cf\u5316\u7684\u504f\u5dee&#xff1a;\u635f\u5931\u538c\u6076\u3002## 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