评分改为±100分制；库存图y轴倒置；月差序列过滤远月前12月幽灵数据

generate_fundamental_dashboard.py

@@ -198,16 +198,16 @@ def band_score(latest_value: float, history: pd.Series, day: int, inverse: bool
     if not math.isfinite(low) or not math.isfinite(high):
         return None
     if high == low:
-        raw_score = 100.0 if latest_value >= high else 0.0
+        raw_score = 100.0 if latest_value >= high else -100.0
     elif latest_value >= high:
         raw_score = 100.0
     elif latest_value <= low:
-        raw_score = 0.0
+        raw_score = -100.0
     else:
-        raw_score = 100.0 * (latest_value - low) / (high - low)
-    score = 100.0 - raw_score if inverse else raw_score
+        raw_score = 200.0 * (latest_value - low) / (high - low) - 100.0
+    score = -raw_score if inverse else raw_score
     return {
-        "score": max(0.0, min(100.0, score)),
+        "score": max(-100.0, min(100.0, score)),
         "band_low": low,
         "band_high": high,
         "band_mean": mid,
@@ -279,7 +279,8 @@ def score_series_at(series: pd.Series, date: pd.Timestamp, inverse: bool = False
     less = float((sample < value).sum())
     equal = float((sample == value).sum())
     raw = 100.0 * (less + 0.5 * equal) / len(sample)
-    return 100.0 - raw if inverse else raw
+    raw_mapped = raw * 2.0 - 100.0
+    return -raw_mapped if inverse else raw_mapped
 
 
 def build_score_history(dim_series: dict[str, pd.Series]) -> list[dict[str, Any]]:
@@ -351,7 +352,8 @@ def make_records() -> dict[str, Any]:
                     stat.update(band)
                 else:
                     raw = stat["raw_percentile"]
-                    score = 100.0 - raw if dim in INVERSE_DIMS else raw
+                    raw_mapped = raw * 2.0 - 100.0
+                    score = -raw_mapped if dim in INVERSE_DIMS else raw_mapped
                 stat["score"] = round(score, 1)
                 stat["direction"] = "低库存更优，按正常区间反向计分" if dim in INVERSE_DIMS else "高于正常区间更优"
                 stat["expr"] = str(row.get(dim, "")).strip()
@@ -389,7 +391,8 @@ def make_records() -> dict[str, Any]:
                 stat.update(band)
             else:
                 raw = stat["raw_percentile"] if stat.get("raw_percentile") is not None else 50.0
-                score = 100.0 - raw if dim in INVERSE_DIMS else raw
+                raw_mapped = raw * 2.0 - 100.0
+                score = -raw_mapped if dim in INVERSE_DIMS else raw_mapped
             stat["score"] = round(score, 1)
             stat["direction"] = "高于正常区间更优（近月升水=强势）"
             stat["expr"] = "近月(top3 OI) - 远月(最远>1万手)"
@@ -585,7 +588,7 @@ svg {{ width:100%; height:230px; display:block; }}
     <div class="chips" id="weak"></div>
   </div>
 </section>
-<div class="note">维度得分按当前阴影带范围计算（均值±K×标准差）：正向指标高于上沿为 100，低于下沿为 0，区间内线性映射；库存反向。调整顶部「带宽K」可实时改变阴影带宽度和得分。</div>
+<div class="note">维度得分采用 ±100 分制：高于季节性正常区间上沿为 +100，低于下沿为 -100，区间内线性映射；0 分表示处于正常中位水平。正得分偏利多，负得分偏利空；库存反向。调整顶部「带宽K」可实时改变阴影带宽度和得分。</div>
 <main class="rows" id="rows"></main>
 <section class="history-view" id="historyView">
   <div class="history-tools">
@@ -718,9 +721,8 @@ function render() {{
   document.getElementById("avgScore").textContent = scored.length ? fmt(scored.reduce((s,c)=>s+c.total,0)/scored.length) : "--";
   document.getElementById("coverage").textContent = `当前筛选覆盖 ${{scored.length}} 个有可用数据的品种；${{includeDemand.checked ? "需求参与总分" : "需求不参与总分"}}；${{document.getElementById("includeSpread").checked ? "月差参与总分" : "月差不参与总分"}}。`;
   const sortedAll = scored.slice().sort((a,b)=>b.total-a.total);
-  const splitIdx = Math.ceil(sortedAll.length / 2);
-  const strongList = sortedAll.slice(0, splitIdx);
-  const weakList = sortedAll.slice(splitIdx).reverse();
+  const strongList = sortedAll.filter(c => c.total > 0);
+  const weakList = sortedAll.filter(c => c.total < 0).reverse();
   document.getElementById("strong").innerHTML = strongList.length ? topChips(strongList, "red") : `<span class="hint">暂无可用数据</span>`;
   document.getElementById("weak").innerHTML = weakList.length ? topChips(weakList, "good") : `<span class="hint">暂无可用数据</span>`;
   rowsEl.style.display = activeView === "dashboard" ? "block" : "none";
@@ -774,7 +776,7 @@ function renderChartCell(c, dim, v) {{
     <div class="chart-title">${{title}}</div>
     <div class="metric-title">${{esc(v.expr)}}</div>
     <div class="legend">${{Object.keys(v.years).map(y=>`<span style="--c:${{yearColor(y, v.years)}}">${{y}}</span>`).join("")}}</div>
-    <svg data-chart='${{esc(JSON.stringify(v.years))}}' viewBox="0 0 520 230" preserveAspectRatio="none"></svg>
+    <svg data-chart='${{esc(JSON.stringify(v.years))}}' data-dim='${{esc(dim)}}' viewBox="0 0 520 230" preserveAspectRatio="none"></svg>
   </div>`;
 }}
 function renderPairChartCell(c, ps, idx) {{
@@ -917,12 +919,12 @@ function scoreFromCurrentBand(v, inverse=false) {{
   if (!Number.isFinite(low) || !Number.isFinite(high)) return null;
   if (low > high) {{ const tmp = low; low = high; high = tmp; }}
   let rawScore;
-  if (high === low) rawScore = latest.value >= high ? 100 : 0;
+  if (high === low) rawScore = latest.value >= high ? 100 : -100;
   else if (latest.value >= high) rawScore = 100;
-  else if (latest.value <= low) rawScore = 0;
-  else rawScore = 100 * (latest.value - low) / (high - low);
-  const score = inverse ? 100 - rawScore : rawScore;
-  return Math.max(0, Math.min(100, score));
+  else if (latest.value <= low) rawScore = -100;
+  else rawScore = 200 * (latest.value - low) / (high - low) - 100;
+  const score = inverse ? -rawScore : rawScore;
+  return Math.max(-100, Math.min(100, score));
 }}
 function smoothBand(band, radius) {{
   if (!band.length || radius <= 0) return band;
@@ -1003,10 +1005,11 @@ function drawChart(svg, years) {{
   let min=Math.min(...values), max=Math.max(...values);
   if (min===max) {{ min-=1; max+=1; }}
   const pad=(max-min)*0.12; min-=pad; max+=pad;
-  const y=v=> T + (max-v)/(max-min)*(H-T-B);
+  const invertY = svg.dataset.dim === "库存";
+  const y = invertY ? v => T + (v-min)/(max-min)*(H-T-B) : v => T + (max-v)/(max-min)*(H-T-B);
   let out = "";
   for (let i=0;i<=4;i++) {{
-    const yy=T+i*(H-T-B)/4, val=max-i*(max-min)/4;
+    const yy=T+i*(H-T-B)/4, val=invertY ? min+i*(max-min)/4 : max-i*(max-min)/4;
     out += `<line class="grid" x1="${{L}}" y1="${{yy}}" x2="${{W-R}}" y2="${{yy}}"></line><text x="4" y="${{yy+4}}" font-size="12" fill="#667085">${{val.toFixed(1)}}</text>`;
   }}
   // x轴刻度
@@ -1095,11 +1098,13 @@ function drawHistoryChart(list) {{
   const maxDate = new Date(end).getTime();
   const span = Math.max(1, maxDate - minDate);
   const x = d => L + (new Date(d).getTime() - minDate) / span * (W-L-R);
-  const y = v => T + (100 - v) / 100 * (H-T-B);
+  const yMin = -100, yMax = 100;
+  const y = v => T + (yMax - v) / (yMax - yMin) * (H-T-B);
   let out = "";
-  for (let i=0;i<=5;i++) {{
-    const val = i * 20, yy = y(val);
-    out += `<line class="grid" x1="${{L}}" y1="${{yy}}" x2="${{W-R}}" y2="${{yy}}"></line><text x="8" y="${{yy+4}}" font-size="12" fill="#667085">${{val}}</text>`;
+  for (let i=0;i<=4;i++) {{
+    const val = yMin + i * (yMax - yMin) / 4, yy = y(val);
+    const isZero = val === 0;
+    out += `<line class="${{isZero ? 'axis' : 'grid'}}" x1="${{L}}" y1="${{yy}}" x2="${{W-R}}" y2="${{yy}}"></line><text x="8" y="${{yy+4}}" font-size="12" fill="${{isZero ? '#374151' : '#667085'}}" font-weight="${{isZero ? 'bold' : 'normal'}}">${{val}}</text>`;
   }}
   const ticks = 6;
   for (let i=0;i<=ticks;i++) {{
@@ -1158,7 +1163,7 @@ function drawHistoryChart(list) {{
       return p ? {{name:s.name, score:Number(p[1]), color:historyColor(i), date:p[0]}} : null;
     }}).filter(Boolean).sort((a,b)=>b.score-a.score);
     const py = (ev.clientY - rect.top) / rect.height * H;
-    const guideYValue = Math.max(0, Math.min(100, 100 - ((py - T) / (H - T - B)) * 100));
+    const guideYValue = Math.max(-100, Math.min(100, 100 - ((py - T) / (H - T - B)) * 200));
     const nearest = rows.length ? rows.reduce((best, r) => Math.abs(Number(r.score) - guideYValue) < Math.abs(Number(best.score) - guideYValue) ? r : best, rows[0]) : null;
     if (crosshair) {{
       crosshair.style.display = "block";

spread_from_db.py

@@ -172,6 +172,24 @@ def _extract_year_prefix(contract: str) -> int | None:
         return None
 
 
+def _contract_year(contract: str) -> int | None:
+    """从合约代码推断完整年份。
+    上期所/大商所/中金所 4位数字: RB2610 → 2000+26=2026
+    郑商所 3位数字: MA609 → 2020+6=2026（当前十年2020s）
+    """
+    digits = "".join(c for c in contract if c.isdigit())
+    if len(digits) < 3:
+        return None
+    try:
+        prefix = int(digits[:-2])
+        if len(digits) == 3:  # 郑商所3位数字，前缀1位
+            return 2020 + prefix
+        else:  # 4位数字，前缀2位
+            return 2000 + prefix
+    except (ValueError, IndexError):
+        return None
+
+
 def _year_gap(near: str, far: str) -> int | None:
     """计算近月/远月合约的年份间隔，如 (SC2608, SC2609) → 0, (EG2609, EG2701) → 1。"""
     ny = _extract_year_prefix(near)
@@ -254,7 +272,21 @@ def build_historical_spreads(
                 spread_series = spread_series[spread_series.index >= f"{start_year}-01-01"]
                 if spread_series.dropna().empty:
                     continue
-                # 推断年份：取序列中间日期的年份
+                # 只保留远月合约到期前约12个月开始的数据
+                # 远月合约 c2 交割年月 = (Y, far_month)，保留 date(Y-1, far_month, 1) 之后的数据
+                _far_year = _contract_year(c2)
+                if _far_year is not None:
+                    try:
+                        _far_start = pd.Timestamp(year=_far_year - 1, month=far_month, day=1)
+                        spread_series = spread_series[spread_series.index >= _far_start]
+                    except (ValueError, TypeError):
+                        pass
+                    if spread_series.dropna().empty:
+                        continue
+                # 推断年份：从近月合约代码提取完整年份
+                year_val = _contract_year(c1)
+                if year_val is None:
+                    # 降级方案：使用序列中间日期的年份
                     mid_idx = len(spread_series) // 2
                     year_val = int(spread_series.index[mid_idx].year)
                 hist_series_list.append({
@@ -336,16 +368,19 @@ def _builtin_band_score(value: float, history: pd.Series, day: int, inverse: boo
     low = mid - (mid - q_low) * width_scale
     high = mid + (q_high - mid) * width_scale
     if high == low:
-        score = 50.0
-    elif inverse:
-        score = 100.0 if value <= low else (0.0 if value >= high else 100.0 - (value - low) / (high - low) * 100)
+        raw_score = 100.0 if value >= high else -100.0
+    elif value >= high:
+        raw_score = 100.0
+    elif value <= low:
+        raw_score = -100.0
     else:
-        score = 100.0 if value >= high else (0.0 if value <= low else (value - low) / (high - low) * 100)
+        raw_score = 200.0 * (value - low) / (high - low) - 100.0
+    score = -raw_score if inverse else raw_score
     below = float((sample < value).sum())
     equal = float((sample == value).sum())
     raw_pct = 100.0 * (below + 0.5 * equal) / len(sample)
     return {
-        "score": round(max(0.0, min(100.0, score)), 1),
+        "score": round(max(-100.0, min(100.0, score)), 1),
         "raw_percentile": round(raw_pct, 1),
         "band_low": round(low, 4),
         "band_high": round(high, 4),