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2026-05-16 22:25:48 +08:00
commit 2b8f902548
88 changed files with 16386 additions and 0 deletions
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+from __future__ import annotations
+
+import math
+from statistics import mean
+
+
+def rows_to_ohlcv(rows: list[list[str]]) -> tuple[list[float], list[float], list[float], list[float], list[float]]:
+    o, h, l, c, v = [], [], [], [], []
+    for item in rows:
+        if len(item) < 6:
+            continue
+        o.append(float(item[1]))
+        h.append(float(item[2]))
+        l.append(float(item[3]))
+        c.append(float(item[4]))
+        v.append(float(item[5]))
+    return o, h, l, c, v
+
+
+def build_daily_programmatic(rows_1d: list[list[str]], est_quote_vol_24h_usdt: float) -> dict:
+    """
+    日线程序化特征：上方空间（距阶段高）、成交活跃度、简单阻力代理（现价与区间高之间局部高点数量）。
+    """
+    _, high, low, close, vol = rows_to_ohlcv(rows_1d)
+    if len(close) < 10:
+        return {"error": "insufficient_daily", "have": len(close)}
+
+    last = close[-1]
+    look = min(60, len(close))
+    hi = max(high[-look:])
+    lo = min(low[-look:])
+    mid = (hi + lo) / 2 if hi > lo else last
+    range_pct = ((hi - lo) / mid) * 100 if mid > 0 else 0.0
+    upside_pct = ((hi - last) / last) * 100 if last > 0 else 0.0
+
+    # 现价上方到区间高：统计「局部高点」数量作为中间阻力代理（越多越密）
+    seg_h = high[-look:]
+    seg_l = low[-look:]
+    local_peaks = 0
+    for i in range(1, len(seg_h) - 1):
+        if seg_h[i] >= seg_h[i - 1] and seg_h[i] >= seg_h[i + 1]:
+            if seg_h[i] > last * 1.002 and seg_h[i] < hi * 0.998:
+                local_peaks += 1
+
+    vol_tail = vol[-20:] if len(vol) >= 20 else vol
+    vol_mean = mean(vol_tail[:-1]) if len(vol_tail) > 1 else (vol_tail[0] if vol_tail else 1.0)
+    vol_ratio = (vol_tail[-1] / vol_mean) if vol_mean > 0 else 0.0
+
+    sma20 = mean(close[-20:]) if len(close) >= 20 else mean(close)
+    structure_hint = "price_above_sma20" if last >= sma20 else "price_below_sma20"
+
+    return {
+        "last_close": round(last, 8),
+        "range_60d_high": round(hi, 8),
+        "range_60d_low": round(lo, 8),
+        "range_pct_lookback": round(range_pct, 4),
+        "upside_to_range_high_pct": round(max(0.0, upside_pct), 4),
+        "mid_resistance_proxy_peaks": local_peaks,
+        "volume_last_vs_20d_mean": round(vol_ratio, 4),
+        "est_quote_vol_24h_usdt": round(est_quote_vol_24h_usdt, 2),
+        "structure_hint": structure_hint,
+        "sma20": round(sma20, 8),
+    }
+
+
+def programmatic_scores(prog: dict) -> dict:
+    """归一化子分数 0–100，供合成 composite。"""
+    if prog.get("error"):
+        return {"vol": 0.0, "upside": 0.0, "liquidity": 0.0, "mid_clear": 0.0}
+
+    est = float(prog.get("est_quote_vol_24h_usdt") or 0.0)
+    # 成交额：10M≈35，100M≈70
+    vol_score = min(100.0, max(0.0, math.log10(est / 1e6 + 1) * 32.0))
+
+    upside = float(prog.get("upside_to_range_high_pct") or 0.0)
+    upside_score = min(100.0, upside * 4.0)
+
+    vr = float(prog.get("volume_last_vs_20d_mean") or 0.0)
+    liquidity_score = min(100.0, max(0.0, (vr - 1.0) * 35.0 + 40.0))
+
+    peaks = int(prog.get("mid_resistance_proxy_peaks") or 0)
+    mid_clear_score = max(0.0, 100.0 - peaks * 12.0)
+
+    return {
+        "vol": round(vol_score, 2),
+        "upside": round(upside_score, 2),
+        "liquidity": round(liquidity_score, 2),
+        "mid_clear": round(mid_clear_score, 2),
+    }
+
+
+def composite_score(gemma_priority: float, sub: dict) -> float:
+    """gemma_priority 1–10；与程序化子分合成 0–100。"""
+    g = max(1.0, min(10.0, gemma_priority)) * 10.0
+    p = 0.35 * g
+    p += 0.2 * sub.get("vol", 0.0)
+    p += 0.2 * sub.get("upside", 0.0)
+    p += 0.15 * sub.get("liquidity", 0.0)
+    p += 0.1 * sub.get("mid_clear", 0.0)
+    return round(min(100.0, max(0.0, p)), 2)
+
+
+def daily_ohlc_text_block(rows_1d: list[list[str]], max_lines: int = 24) -> str:
+    """给 LLM 的紧凑 OHLCV 文本（时间正序：旧→新，最后一行为最新）。"""
+    rows = rows_1d[-max_lines:] if len(rows_1d) > max_lines else rows_1d
+    lines = ["ts,o,h,l,c,vol"]
+    for item in rows:
+        if len(item) < 6:
+            continue
+        ts, o, h, l, c, v = item[0], item[1], item[2], item[3], item[4], item[5]
+        lines.append(f"{ts},{o},{h},{l},{c},{v}")
+    return "\n".join(lines)