{"id":5071,"date":"2026-10-20T07:27:00","date_gmt":"2026-10-19T23:27:00","guid":{"rendered":"https:\/\/laoyulaoyu.com\/?p=5071"},"modified":"2026-10-12T10:30:44","modified_gmt":"2026-10-12T02:30:44","slug":"%e6%88%91%e6%b5%8b%e4%ba%86tradingview%e4%b8%8a%e7%9a%84supertrend%e5%8a%a0vapi%e7%ad%96%e7%95%a5%ef%bc%8c6%e5%8f%aa%e6%a0%87%e7%9a%8458%e6%ac%a1%e8%b6%8b%e5%8a%bf%e7%bf%bb%e5%90%91","status":"publish","type":"post","link":"https:\/\/laoyulaoyu.com\/index.php\/2026\/10\/20\/%e6%88%91%e6%b5%8b%e4%ba%86tradingview%e4%b8%8a%e7%9a%84supertrend%e5%8a%a0vapi%e7%ad%96%e7%95%a5%ef%bc%8c6%e5%8f%aa%e6%a0%87%e7%9a%8458%e6%ac%a1%e8%b6%8b%e5%8a%bf%e7%bf%bb%e5%90%91\/","title":{"rendered":"\u6211\u6d4b\u4e86TradingView\u4e0a\u7684Supertrend\u52a0VAPI\u7b56\u7565\uff0c6\u53ea\u6807\u768458\u6b21\u8d8b\u52bf\u7ffb\u5411"},"content":{"rendered":"\n

\u4f5c \u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

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\"\"<\/figure>\n\n\n\n
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\"\"
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<\/figcaption><\/figure>\n\n\n\n

Supertrend \u5230\u5e95\u5728\u7b97\u4ec0\u4e48<\/h2>\n\n\n\n

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\"\"
VAPI \u662f\u4e00\u6839\u4e00\u6839\u67f1\u5b50\uff0c\u6b63\u6570\u5806\u5728\u96f6\u8f74\u4e0a\u65b9\uff0c\u8d1f\u6570\u5806\u5728\u4e0b\u9762<\/figcaption><\/figure>\n\n\n\n

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VAPI \u662f\u91cf\u80fd\u7248\u7684\u6536\u76d8\u4ef7\u4f4d\u7f6e<\/h2>\n\n\n\n

VAPI \u5168\u79f0 Volume Accumulation Percentage Indicator\uff0cTradingView \u793e\u533a\u91cc LazyBear 2015 \u5e74 2 \u6708\u53d1\u5e03\u7684\u90a3\u4e00\u7248\u3002<\/p>\n\n\n\n

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\"\"
BUY \u7684\u56db\u9053\u6761\u4ef6\u3002\u5de6\u8fb9\u7bad\u5934\u6307\u7740 Supertrend \u7ed9\u7684 buy \u6807\u7b7e\uff0c\u4e0b\u9762\u90a3\u6392\u7eff\u8272\u67f1\u4f53\u5c31\u662f VAPI<\/figcaption><\/figure>\n\n\n\n

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\u56db\u9053\u95e8\u69db\u957f\u4ec0\u4e48\u6837<\/h2>\n\n\n\n
\"\"
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58 \u6b21\u7ffb\u5411\uff0c\u88ab\u5f71\u7ebf\u6761\u4ef6\u6321\u6389 88%<\/h2>\n\n\n\n

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\"\"
58 \u6b21\u8d8b\u52bf\u7ffb\u5411\u91cc\uff0c\u4e0a\u4e0b\u90fd\u5e26\u5f71\u7ebf\u7684\u6709 51 \u6b21\uff0c\u53ea\u6709\u4e00\u6839\u5f71\u7ebf\u7684 6 \u6b21\uff0c\u5149\u811a\u5149\u5934 1 \u6b21<\/figcaption><\/figure>\n\n\n\n

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\u6211\u770b\u5230 88% \u8fd9\u4e2a\u6570\u7684\u65f6\u5019\u6123\u4e86\u4e00\u4e0b\u3002\u8fd9\u8ddf\u6211\u5728\u56fe\u4e0a\u770b\u5230\u7684\u5b8c\u5168\u4e0d\u662f\u4e00\u56de\u4e8b\uff0c\u5c4f\u5e55\u4e0a\u90a3\u4e9b\u4fe1\u53f7 K \u7ebf\u770b\u7740\u633a\u5e72\u51c0\uff0c\u843d\u5230\u4ee3\u7801\u91cc\u4e00\u7b97\uff0c\u516b\u6210\u4e0a\u9762\u4e0b\u9762\u90fd\u5e26\u523a\u3002<\/p>\n\n\n\n

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\"\"
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\"\"
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\"\"
\u67f1\u5b50\u662f\u4fe1\u53f7\u4e4b\u540e\u4e0d\u540c\u65f6\u95f4\u8de8\u5ea6\u7684\u4e0a\u6da8\u5360\u6bd4\uff0c\u865a\u7ebf\u662f 52.3% \u7684\u57fa\u51c6<\/figcaption><\/figure>\n\n\n\n

BUY \u4fe1\u53f7\u4e4b\u540e 3 \u6839 K \u7ebf\u5185\u4e0a\u6da8\u5360 59.3%\uff0c27 \u4e2a\u6837\u672c\uff0c\u6bd4\u57fa\u51c6 52.3% \u9ad8 7 \u4e2a\u767e\u5206\u70b9\u3002\u5f80\u540e\u62c9\u957f\u5c31\u4e0d\u884c\uff0c24 \u6839\u4e4b\u540e\u8dcc\u56de 48.1%\u3002<\/p>\n\n\n\n

SELL \u4fe1\u53f7\u4e4b\u540e 3 \u6839\u4e0a\u6da8\u5360 54.8%\uff0c\u8dcc\u7684\u53ea\u6709 45.2%\uff0c\u800c\u57fa\u51c6\u662f\u8dcc 47.7%\u3002\u8fd9\u4e00\u6761\u5f97\u8bf4\u660e\u767d\uff0cSELL \u4fe1\u53f7\u4e4b\u540e\u4ef7\u683c\u4e0a\u6da8\u7684\u6982\u7387\u6bd4\u4e0b\u8dcc\u8fd8\u9ad8\uff0c\u65b9\u5411\u662f\u53cd\u7684\u3002<\/p>\n\n\n\n

27 \u4e2a\u548c 31 \u4e2a\u6837\u672c\uff0c\u80dc\u7387\u6ce2\u52a8\u51e0\u4e2a\u767e\u5206\u70b9\u5b8c\u5168\u843d\u5728\u566a\u97f3\u8303\u56f4\u5185\u3002\u52a0\u4e0a\u56db\u9053\u6761\u4ef6\u4e4b\u540e BUY \u662f 0 \u4e2a\u3001SELL \u662f 3 \u4e2a\u3002\u6211\u4e0d\u7528 3 \u4e2a\u6837\u672c\u7b97\u51fa\u4e00\u4e2a 66% \u4e4b\u7c7b\u7684\u6570\u5b57\u6302\u4e0a\u53bb\uff0c\u90a3\u662f\u5728\u7ed9\u81ea\u5df1\u627e\u53f0\u9636\u3002<\/p>\n\n\n\n

VAPI \u5355\u72ec\u7528\u4e5f\u4e0d\u884c\u3002\u4e3a\u6b63\u7684 3370 \u4e2a\u6837\u672c\uff0c\u4e4b\u540e 3 \u6839\u4e0a\u6da8\u5360 53.4%\uff0c\u6bd4\u57fa\u51c6\u9ad8 1.1 \u4e2a\u767e\u5206\u70b9\uff0c\u8fd9\u4e2a\u6570\u5b57\u540c\u6837\u843d\u5728\u566a\u97f3\u91cc\u3002<\/p>\n\n\n\n

\u90a3\u5230\u5e95\u8be5\u600e\u4e48\u7528\u8fd9\u5957\u4e1c\u897f<\/h2>\n\n\n\n

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\"\"
\u5de6\u56fe\u662f\u4fe1\u53f7\u6570\uff0c\u53f3\u56fe\u662f\u5bf9\u57fa\u51c6\u7684\u8d85\u989d\u3002\u540c\u4e00\u5957\u7b5b\u9009\u53e3\u5f84\u53ea\u6362 ATR \u53c2\u6570\uff0c\u8d85\u989d\u7684\u6b63\u8d1f\u53f7\u5c31\u7ffb\u4e86\u597d\u51e0\u904d<\/figcaption><\/figure>\n\n\n\n

\u8fd9\u5f20\u56fe\u662f\u8fd9\u6b21\u6700\u60f3\u62ff\u51fa\u6765\u7684\u4e00\u5f20\u3002\u5de6\u8fb9\u4e09\u6761\u7ebf\u662f\u4e09\u79cd\u7b5b\u6cd5\uff0c\u53ea\u8981\u8d8b\u52bf\u7ffb\u5411\u3001\u53ea\u52a0\u4e00\u6839\u5f71\u7ebf\u3001\u6362\u6210\u5b9e\u4f53\u8fc7\u534a\uff0c\u4e09\u6761\u7684\u533a\u522b\u53ea\u5728\u6536\u7d27\u7a0b\u5ea6\u3002\u6761\u4ef6\u8d8a\u4e25\u4fe1\u53f7\u8d8a\u5c11\uff0c\u8fd9\u4e00\u534a\u7b26\u5408\u76f4\u89c9\u3002\u53f3\u8fb9\u624d\u662f\u5173\u952e\u3002\u540c\u4e00\u6761\u7b5b\u9009\u89c4\u5219\uff0c\u628a ATR \u53c2\u6570\u4ece\u6700\u6fc0\u8fdb\u7684 7\/2 \u62e7\u5230\u6700\u4fdd\u5b88\u7684 20\/5\uff0c\u8d85\u989d\u7684\u6b63\u8d1f\u53f7\u6765\u56de\u7ffb\u3002\u300c\u6362\u6210\u5b9e\u4f53\u8fc7\u534a\u300d\u5728 ATR(7,2) \u4e0b\u8d85\u989d -8.4 \u4e2a\u767e\u5206\u70b9\uff0c\u5230 ATR(20,5) \u4e0b\u8fd8\u662f -12.3 \u4e2a\u767e\u5206\u70b9\uff1b\u300c\u53ea\u6709\u4e00\u6839\u5f71\u7ebf\u300d\u5728 ATR(10,3) \u4e0b\u8d85\u989d +14.4\uff0c\u5230 ATR(14,4) \u5c31\u53d8\u6210\u4e86 -2.3\u3002<\/p>\n\n\n\n

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\u6240\u4ee5\u63a5\u4e0b\u6765\u8be5\u505a\u4ec0\u4e48\u5f88\u6e05\u695a\u3002\u522b\u5148\u8c03\u6761\u4ef6\uff0c\u5148\u770b\u6837\u672c\u91cf\u3002<\/p>\n\n\n\n

\u8bf4\u4e0d\u597d\u5728\u4f60\u81ea\u5df1\u7684\u54c1\u79cd\u4e0a\u4f1a\u662f\u4ec0\u4e48\u7ed3\u679c\uff0c\u4f46\u4e00\u4e2a\u7b56\u7565\u4e03\u4e2a\u6708\u53ea\u5269\u4e09\u7b14\u4fe1\u53f7\uff0c\u65e2\u6ca1\u6cd5\u9a8c\u8bc1\u5b83\uff0c\u4e5f\u6ca1\u6cd5\u8bc1\u4f2a\u5b83\uff0c\u8fd9\u79cd\u4e1c\u897f\u653e\u8fdb\u5b9e\u76d8\u4e0d\u53eb\u7b56\u7565\uff0c\u53eb\u8d4c\u3002\u771f\u6b63\u80fd\u62ff\u6765\u505a\u51b3\u7b56\u7684\u4fe1\u53f7\u91cf\u7ea7\uff0c\u81f3\u5c11\u5f97\u51d1\u51fa\u51e0\u5341\u4e0a\u767e\u7b14\uff0c\u8d5a\u4e86\u4e8f\u4e86\u624d\u8bf4\u660e\u5f97\u4e86\u95ee\u9898\u3002<\/p>\n\n\n\n

\u6211\u8dd1\u5b8c\u7684\u4e24\u4e2a\u811a\u672c\u653e\u5728\u6587\u672b\uff0c\u6539\u6210\u624b\u4e0a\u7684\u6807\u7684\u5c31\u80fd\u8dd1\u3002\u4e00\u4e2a\u8dd1\u4e3b\u590d\u73b0\uff0c\u4e00\u4e2a\u8dd1\u8fd9\u5f20\u5bf9\u7167\u8868\u3002\u60f3\u52a8\u624b\u7684\uff0c\u5efa\u8bae\u5148\u53ea\u8dd1\u4fe1\u53f7\u6570\u8fd9\u4e00\u9879\uff0c\u628a\u4e03\u4e2a\u6708\u7684\u6570\u5b57\u770b\u6e05\u695a\u518d\u51b3\u5b9a\u7528\u4e0d\u7528\u3002<\/p>\n\n\n\n

\u53c2\u8003\u8d44\u6599<\/h2>\n\n\n\n

LazyBear \u5728 TradingView \u53d1\u5e03\u7684 VAPI \u6307\u6807\u8bf4\u660e\uff08VAPI_LB \u9ed8\u8ba4 length 10\uff09\u3002Supertrend \u7684 ATR \u5468\u671f 10 \/ \u4e58\u6570 3 \u9ed8\u8ba4\u7ec4\u5408\uff0cTradingView \u591a\u4e2a\u5b9e\u73b0\u7248\u672c\u4e00\u81f4\u3002NYMEX \u5929\u7136\u6c14\u671f\u8d27 1 \u5206\u949f\u56fe\u622a\u56fe\u53d6\u81ea TradingView \u516c\u5f00\u56fe\u5e93\u3002<\/p>\n\n\n\n

\u5b8c\u6574\u811a\u672c<\/h2>\n\n\n\n

\u811a\u672c\u5206\u56db\u6bb5\uff0c\u5206\u522b\u662f Supertrend\u3001VAPI\u3001\u5f71\u7ebf\u5224\u5b9a\u3001\u4fe1\u53f7\u7b5b\u9009\u4e0e\u7edf\u8ba1\u3002\u88c5\u597d\u4f9d\u8d56\u76f4\u63a5\u8dd1\u3002<\/p>\n\n\n\n

pip install pandas<\/code><\/pre>\n\n\n\n
\u7b2c\u4e00\u6bb5 Supertrend<\/p>\n\n\n\n
\u68d8\u8f6e\u903b\u8f91\u5199\u5728\u5faa\u73af\u91cc\uff0c\u591a\u5934\u65f6\u6b62\u635f\u7ebf\u53ea\u4e0a\u62ac\uff0c\u7a7a\u5934\u65f6\u53ea\u4e0b\u538b\u3002<\/p>\n\n\n\n
import pandas as pd\n\ndef true_range(df):\n    prev_close = df[\"close\"].shift(1)\n    return pd.concat([\n        df[\"high\"] - df[\"low\"],\n        (df[\"high\"] - prev_close).abs(),\n        (df[\"low\"] - prev_close).abs(),\n    ], axis=1).max(axis=1)\n\ndef supertrend(df, period=10, mult=3.0):\n    atr = true_range(df).ewm(alpha=1 \/ period, adjust=False,\n                             min_periods=period).mean()\n    hl2 = (df[\"high\"] + df[\"low\"]) \/ 2.0\n    upper = (hl2 + mult * atr).values\n    lower = (hl2 - mult * atr).values\n    close = df[\"close\"].values\n    n = len(df)\n\n    up, dn = upper.copy(), lower.copy()\n    direction = [1] * n\n    stop = lower.copy()\n\n    for i in range(1, n):\n        up[i] = min(up[i], up[i - 1]) if close[i - 1] > up[i - 1] else up[i]\n        dn[i] = max(dn[i], dn[i - 1]) if close[i - 1] < dn[i - 1] else dn[i]\n        if close[i] > up[i - 1]:\n            direction[i] = 1\n        elif close[i] < dn[i - 1]:\n            direction[i] = -1\n        else:\n            direction[i] = direction[i - 1]\n        stop[i] = dn[i] if direction[i] == 1 else up[i]\n\n    return (pd.Series(direction, index=df.index, name=\"direction\"),\n            pd.Series(stop, index=df.index, name=\"stop\"))<\/code><\/pre>\n\n\n\n
\u7b2c\u4e8c\u6bb5 VAPI<\/p>\n\n\n\n
\u4f4d\u7f6e\u503c x \u5728\u4e00\u5b57\u7ebf\u4e0a\u8981\u8bb0 0\uff0c\u5426\u5219\u6ca1\u6709\u533a\u95f4\u7684 K \u7ebf\u4f1a\u9664\u51fa\u65e0\u7a77\u5927\u6c61\u67d3\u6574\u6bb5\u3002<\/p>\n\n\n\n
def vapi(df, length=10):\n    rng = (df[\"high\"] - df[\"low\"]).replace(0, float(\"nan\"))\n    x = (2 * df[\"close\"] - df[\"high\"] - df[\"low\"]) \/ rng\n    x = x.fillna(0.0)\n    num = (df[\"volume\"] * x).rolling(length).sum()\n    den = df[\"volume\"].rolling(length).sum().replace(0, float(\"nan\"))\n    return (100 * num \/ den).fillna(0.0)<\/code><\/pre>\n\n\n\n
\u7b2c\u4e09\u6bb5\u5f71\u7ebf\u5224\u5b9a<\/p>\n\n\n\n
\u9608\u503c\u662f\u53ef\u8c03\u53c2\u6570\u3002\u8fd9\u4e00\u6bb5\u76f4\u63a5\u51b3\u5b9a\u4fe1\u53f7\u7684\u5b58\u4ea1\uff0c\u626b\u4e00\u904d\u5c31\u77e5\u9053\u5b83\u6709\u591a\u8981\u7d27\u3002<\/p>\n\n\n\n
def wick_count(row, eps=0.02):\n    rng = row[\"high\"] - row[\"low\"]\n    if rng <= 0:\n        return 2\n    threshold = rng * eps\n    upper = row[\"high\"] - max(row[\"open\"], row[\"close\"])\n    lower = min(row[\"open\"], row[\"close\"]) - row[\"low\"]\n    n = 0\n    if upper > threshold:\n        n += 1\n    if lower > threshold:\n        n += 1\n    return n<\/code><\/pre>\n\n\n\n
\u7b2c\u56db\u6bb5\u4fe1\u53f7\u7b5b\u9009\u4e0e\u7edf\u8ba1<\/p>\n\n\n\n
\u524d\u5411\u6536\u76ca\u5fc5\u987b\u5728\u5355\u6807\u7684\u5185\u90e8\u7b97\u5b8c\u518d\u62fc\u8868\u3002\u8de8\u6807\u7684\u76f4\u63a5 concat \u4f1a\u8ba9 shift \u4e32\u884c\uff0c\u8fd9\u662f\u4e2a\u5f88\u5bb9\u6613\u8e29\u7684\u5751\u3002<\/p>\n\n\n\n
def annotate(df):\n    d = df.copy()\n    d[\"direction\"], d[\"stop\"] = supertrend(d)\n    d[\"vapi\"] = vapi(d)\n    d[\"wick\"] = d.apply(wick_count, axis=1)\n\n    d[\"is_buy_flip\"] = (d[\"direction\"] == 1) & (d[\"direction\"].shift(1) == -1)\n    d[\"is_sell_flip\"] = (d[\"direction\"] == -1) & (d[\"direction\"].shift(1) == 1)\n    d[\"prev_green\"] = d[\"close\"].shift(1) > d[\"open\"].shift(1)\n    d[\"prev_red\"] = d[\"close\"].shift(1) < d[\"open\"].shift(1)\n\n    d[\"full_buy\"] = (d[\"is_buy_flip\"] & (d[\"wick\"] == 1)\n                     & d[\"prev_green\"] & (d[\"vapi\"] > 0))\n    d[\"full_sell\"] = (d[\"is_sell_flip\"] & (d[\"wick\"] == 1)\n                      & d[\"prev_red\"] & (d[\"vapi\"] < 0))\n    return d\n\ndef win_rate(d, mask, base):\n    r = (d[\"close\"].shift(-3) \/ d[\"close\"] - 1.0)[mask].dropna() * 100\n    if len(r) == 0:\n        return {\"n\": 0}\n    wr = float((r > 0).mean() * 100)\n    return {\"n\": int(len(r)), \"win_rate\": round(wr, 1),\n            \"vs_base_pp\": round(wr - base, 1)}<\/code><\/pre>\n\n\n\n
\u7b2c\u4e94\u6bb5 \u6362\u7b5b\u6cd5\u5bf9\u7167<\/p>\n\n\n\n
\u8fd9\u4e00\u6bb5\u662f\u6211\u90a3\u5f20\u5bf9\u7167\u56fe\u80cc\u540e\u7684\u903b\u8f91\u3002\u628a\u300c\u4e00\u6839\u5f71\u7ebf\u300d\u6362\u6210\u300c\u5b9e\u4f53\u8fc7\u534a\u300d\uff0c\u518d\u770b\u4e24\u7ec4 ATR \u53c2\u6570\u4e0b\u8d85\u989d\u53d8\u4e0d\u53d8\u53f7\u3002\u4e09\u4e2a\u51fd\u6570\u5c31\u591f\u4e86\uff0c\u6570\u636e\u548c\u524d\u9762\u590d\u7528\u3002<\/p>\n\n\n\n
def build(raw, atr_p, atr_m):\n    \"\"\"\u6309\u7ed9\u5b9a ATR \u53c2\u6570\u751f\u6210\u4e00\u5f20\u5168\u91cf\u8868\u3002\"\"\"\n    frames = []\n    for df in raw.values():\n        d = df.copy()\n        d[\"direction\"], _ = supertrend(d, period=atr_p, mult=atr_m)\n        d[\"vapi\"] = vapi(d)\n        d[\"wick\"] = d.apply(wick_count, axis=1)\n        rng = (d[\"high\"] - d[\"low\"]).replace(0, float(\"nan\"))\n        d[\"body_ratio\"] = ((d[\"close\"] - d[\"open\"]).abs() \/ rng).fillna(0.0)\n        d[\"buy_flip\"] = (d[\"direction\"] == 1) & (d[\"direction\"].shift(1) == -1)\n        d[\"sell_flip\"] = (d[\"direction\"] == -1) & (d[\"direction\"].shift(1) == 1)\n        d[\"prev_green\"] = d[\"close\"].shift(1) > d[\"open\"].shift(1)\n        d[\"prev_red\"] = d[\"close\"].shift(1) < d[\"open\"].shift(1)\n        for n in (3, 6):\n            d[f\"fwd{n}\"] = (d[\"close\"].shift(-n) \/ d[\"close\"] - 1) * 100\n        frames.append(d)\n    return pd.concat(frames, ignore_index=True)\n\n\ndef masks(A):\n    \"\"\"\u4e94\u79cd\u7b5b\u9009\u53e3\u5f84\uff0c\u547d\u540d\u4e0e\u6b63\u6587\u4e00\u81f4\u3002\"\"\"\n    up = A[\"prev_green\"] & (A[\"vapi\"] > 0)\n    dn = A[\"prev_red\"] & (A[\"vapi\"] < 0)\n    core = (A[\"buy_flip\"] & up) | (A[\"sell_flip\"] & dn)\n    return {\n        \"\u53ea\u8981\u8d8b\u52bf\u7ffb\u5411\": A[\"buy_flip\"] | A[\"sell_flip\"],\n        \"\u53ea\u6709\u4e00\u6839\u5f71\u7ebf\uff08\u539f\u6761\u4ef6\uff09\": core & (A[\"wick\"] == 1),\n        \"\u6362\u6210\u5b9e\u4f53\u8fc7\u534a\": core & (A[\"body_ratio\"] > 0.5),\n    }<\/code><\/pre>\n\n\n\n
\u4e24\u4e2a\u811a\u672c\u6211\u8dd1\u5b8c\u7684\u7ed3\u679c\u90fd\u653e\u5728 repro_results.json<\/code> \u548c repro_results_alt.json<\/code> \u91cc\uff0c\u6539\u6210\u624b\u4e0a\u7684\u6807\u7684\u91cd\u8dd1\u5c31\u80fd\u5bf9\u7167\u3002<\/p>\n\n\n\n
\u2014 \u672c\u6587\u5b8c \u2014<\/p>\n\n\n\n
#TradingView #Supertrend #VAPI #\u65e5\u5185\u4ea4\u6613 #\u8d8b\u52bf\u8ddf\u8e2a #\u6210\u4ea4\u91cf\u6307\u6807 #\u4fe1\u53f7\u8fc7\u6ee4 #\u91cf\u5316\u4ea4\u6613 #\u671f\u8d27 #K\u7ebf\u5f62\u6001 #\u4ea4\u6613\u7eaa\u5f8b #\u6307\u6807\u7ec4\u5408<\/p>\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 reading\u6211\u6d4b\u4e86TradingView\u4e0a\u7684Supertrend\u52a0VAPI\u7b56\u7565\uff0c6\u53ea\u6807\u768458\u6b21\u8d8b\u52bf\u7ffb\u5411<\/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-5071","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\/5071","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=5071"}],"version-history":[{"count":1,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/5071\/revisions"}],"predecessor-version":[{"id":5082,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/posts\/5071\/revisions\/5082"}],"wp:attachment":[{"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/media?parent=5071"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/categories?post=5071"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/laoyulaoyu.com\/index.php\/wp-json\/wp\/v2\/tags?post=5071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}