fish
403 lines
13 KiB
Python
403 lines
13 KiB
Python
"""
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主力合约 1月/5月/9月 两两价差走势图 + 均值回归交易信号。
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输出 HTML 图表，浏览器打开即可查看。
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用法: python3 spread_chart.py
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"""
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import sqlite3
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import json
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import os
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from collections import defaultdict
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from statistics import mean, stdev
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DB_PATH = os.path.join(os.path.dirname(__file__), "db", "futures.db")
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OUT_PATH = os.path.join(os.path.dirname(__file__), "spread_chart.html")
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WINDOW = 60
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ENTRY_Z = 2.0
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def build_spreads():
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"""构建三组价差序列：01-05, 05-09, 01-09（同年配对）。"""
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conn = sqlite3.connect(DB_PATH)
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daily = conn.execute(
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"SELECT ts_code, trade_date, close FROM daily WHERE close IS NOT NULL AND close != '' ORDER BY trade_date"
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).fetchall()
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conn.close()
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by_date = defaultdict(dict)
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for ts_code, d, close in daily:
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by_date[d][ts_code] = float(close)
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dates = sorted(by_date.keys())
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def build(month_a, month_b, cross_year=False):
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per_date = {}
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for d in dates:
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items = by_date[d]
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best_yr = -1
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best = None
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for ts_code in items:
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if not ts_code.endswith(".ZCE") or not ts_code.startswith("FG"):
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continue
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yr_str, mon = ts_code[2:4], ts_code[4:6]
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if mon == month_a:
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yr = int(yr_str)
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yr_b_str = str(yr + 1) if cross_year else yr_str
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ts_b = f"FG{yr_b_str}{month_b}.ZCE"
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if ts_b in items and yr > best_yr:
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best_yr = yr
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spread = items[ts_b] - items[ts_code]
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best = (d, spread)
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if best:
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per_date[best[0]] = best[1]
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sorted_dates = sorted(per_date.keys())
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return sorted_dates, [per_date[d] for d in sorted_dates]
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return {
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"01-05": build("01", "05"),
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"05-09": build("05", "09"),
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"01-09": build("01", "09"),
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}
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def compute_signals(spread_data):
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"""计算每个价差的滚动 z-score 和交易信号。"""
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month_map = {
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"01-05": ("01", "05"),
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"05-09": ("05", "09"),
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"01-09": ("01", "09"),
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}
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signals = {}
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for label, (dates, values) in spread_data.items():
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if len(values) < WINDOW + 1:
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signals[label] = None
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continue
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ma, mb = month_map[label]
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recent = values[-WINDOW:]
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roll_mean = mean(recent)
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roll_std = stdev(recent)
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latest = values[-1]
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z = (latest - roll_mean) / roll_std if roll_std > 0 else 0
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if z > ENTRY_Z:
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action = f"做空{mb}，做多{ma}"
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elif z < -ENTRY_Z:
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action = f"做多{mb}，做空{ma}"
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else:
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action = "观望"
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z_dates = dates[WINDOW:]
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z_values = []
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for i in range(WINDOW, len(values)):
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h = values[i - WINDOW : i]
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m = mean(h)
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s = stdev(h)
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z_values.append((values[i] - m) / s if s > 0 else 0)
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signals[label] = {
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"latest": latest,
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"latest_date": dates[-1],
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"mean": round(roll_mean, 1),
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"std": round(roll_std, 1),
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"z_score": round(z, 2),
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"action": action,
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"count": len(values),
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"z_dates": z_dates,
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"z_values": z_values,
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}
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return signals
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def build_chart_data(spread_data, signals):
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"""构建前端所需的数据结构。"""
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all_dates_set = set()
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for dates, _ in spread_data.values():
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all_dates_set.update(dates)
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all_dates = sorted(all_dates_set)
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colors = {
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"01-05": {"line": "#06b6d4", "area": "rgba(6,182,212,0.08)"},
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"05-09": {"line": "#22c55e", "area": "rgba(34,197,94,0.08)"},
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"01-09": {"line": "#f59e0b", "area": "rgba(245,158,11,0.08)"},
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}
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# 主图数据集
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datasets = []
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for label, (dates, values) in spread_data.items():
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date_map = dict(zip(dates, values))
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series = [date_map.get(d) for d in all_dates]
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c = colors[label]
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datasets.append({
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"label": label,
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"data": series,
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"borderColor": c["line"],
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"backgroundColor": c["area"],
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"borderWidth": 1.5,
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"pointRadius": 0,
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"fill": True,
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"tension": 0.1,
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})
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# z-score 数据集
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z_datasets = []
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for label, sig in signals.items():
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if sig is None:
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continue
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date_map = dict(zip(sig["z_dates"], sig["z_values"]))
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series = [date_map.get(d) for d in all_dates]
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c = colors[label]
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z_datasets.append({
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"label": label,
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"data": series,
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"borderColor": c["line"],
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"backgroundColor": "transparent",
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"borderWidth": 1,
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"pointRadius": 0,
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"tension": 0.1,
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})
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# 统计卡片
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stat_cards = []
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for label, (dates, values) in spread_data.items():
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if not values:
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continue
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pos = sum(1 for v in values if v > 0)
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neg = sum(1 for v in values if v < 0)
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stat_cards.append({
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"label": label,
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"color": colors[label]["line"],
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"count": len(values),
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"avg": round(sum(values) / len(values), 1),
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"max": max(values),
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"min": min(values),
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"pos": pos,
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"neg": neg,
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})
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# 信号卡片
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sig_cards = []
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for label, sig in signals.items():
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if sig is None:
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continue
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if "做空" in sig["action"]:
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sig_type = "bear"
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sig_color = "#ef4444"
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elif "做多" in sig["action"]:
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sig_type = "bull"
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sig_color = "#22c55e"
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else:
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sig_type = "neutral"
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sig_color = "#94a3b8"
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sig_cards.append({
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"label": label,
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"color": colors[label]["line"],
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"sig_type": sig_type,
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"sig_color": sig_color,
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"action": sig["action"],
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"latest": sig["latest"],
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"mean": sig["mean"],
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"std": sig["std"],
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"z_score": sig["z_score"],
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"latest_date": sig["latest_date"],
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})
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return {
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"all_dates": all_dates,
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"datasets": datasets,
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"z_datasets": z_datasets,
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"stat_cards": stat_cards,
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"sig_cards": sig_cards,
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}
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TEMPLATE = r"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>FG 主力合约价差走势</title>
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<script src="https://cdn.jsdelivr.net/npm/chart.js@4"></script>
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<style>
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body { background: #0f172a; color: #e2e8f0; font-family: system-ui, sans-serif; padding: 24px; }
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h1 { font-size: 20px; margin-bottom: 8px; color: #f1f5f9; }
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.sub { font-size: 13px; color: #64748b; margin-bottom: 24px; }
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.chart-box { background: #1e293b; border-radius: 12px; padding: 20px; margin-bottom: 16px; }
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.chart-box canvas { width: 100% !important; }
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.stats { display: flex; gap: 16px; flex-wrap: wrap; margin-bottom: 24px; }
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.stat-card { background: #1e293b; border-radius: 12px; padding: 16px 20px; min-width: 170px; flex: 1; }
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.stat-title { font-size: 13px; font-weight: 600; margin-bottom: 8px; }
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.stat-row { font-size: 12px; color: #94a3b8; margin: 2px 0; display: flex; justify-content: space-between; }
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.stat-row .val { color: #e2e8f0; font-weight: 500; }
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.val.pos { color: #22c55e; }
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.val.neg { color: #ef4444; }
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.sig-section { display: flex; gap: 16px; flex-wrap: wrap; margin-bottom: 24px; }
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.sig-card { background: #1e293b; border-radius: 12px; padding: 16px 20px; min-width: 210px; flex: 1; border-left: 3px solid #334155; }
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.sig-card.bull { border-left-color: #22c55e; }
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.sig-card.bear { border-left-color: #ef4444; }
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.sig-card.neutral { border-left-color: #64748b; }
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.sig-hdr { font-size: 12px; color: #64748b; margin-bottom: 2px; }
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.sig-action { font-size: 16px; font-weight: 700; margin-bottom: 8px; }
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.sig-row { font-size: 12px; color: #94a3b8; margin: 2px 0; }
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.sig-row b { color: #e2e8f0; }
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</style>
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</head>
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<body>
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<h1>玻璃期货 — 主力合约价差分析</h1>
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<p class="sub">1月 / 5月 / 9月 两两价差（同年配对，远月减近月） | 均值回归信号（60日滚动，2σ 入场）</p>
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<div class="chart-box">
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<canvas id="chart"></canvas>
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</div>
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<div class="stats" id="stats"></div>
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<div class="sig-section" id="signals"></div>
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<div class="chart-box">
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<canvas id="zchart"></canvas>
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</div>
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<script>
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const D = __DATA__;
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// ── 统计卡片 ──
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let statsHtml = '';
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for (const s of D.statCards) {
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const cls = v => v >= 0 ? 'pos' : 'neg';
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statsHtml += `
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<div class="stat-card">
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<div class="stat-title" style="color:${s.color}">${s.label}</div>
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<div class="stat-row"><span>数据天数</span><span class="val">${s.count}</span></div>
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<div class="stat-row"><span>平均价差</span><span class="val ${cls(s.avg)}">${s.avg}</span></div>
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<div class="stat-row"><span>最大升水</span><span class="val pos">+${s.max}</span></div>
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<div class="stat-row"><span>最大贴水</span><span class="val neg">${s.min}</span></div>
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<div class="stat-row"><span>升水/贴水</span><span class="val"><span class="pos">${s.pos}</span>/<span class="neg">${s.neg}</span></span></div>
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</div>`;
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}
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document.getElementById('stats').innerHTML = statsHtml;
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// ── 信号卡片 ──
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let sigHtml = '';
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for (const s of D.sigCards) {
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const cls = v => v >= 0 ? 'pos' : 'neg';
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sigHtml += `
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<div class="sig-card ${s.sigType}">
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<div class="sig-hdr">${s.label}</div>
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<div class="sig-action" style="color:${s.sigColor}">${s.action}</div>
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<div class="sig-row">当前价差: <b class="${cls(s.latest)}">${s.latest}</b> (${s.latestDate})</div>
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<div class="sig-row">近60日均值: <b>${s.mean}</b> σ: <b>${s.std}</b></div>
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<div class="sig-row">z值: <b class="${cls(s.zScore)}">${s.zScore >= 0 ? '+' : ''}${s.zScore}</b> (超 2 触发)</div>
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</div>`;
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}
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document.getElementById('signals').innerHTML = sigHtml;
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// ── 主图：价差走势 ──
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new Chart(document.getElementById('chart'), {
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type: 'line',
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data: { labels: D.allDates, datasets: D.datasets },
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options: {
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responsive: true,
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maintainAspectRatio: false,
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interaction: { mode: 'index', intersect: false },
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plugins: {
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legend: { labels: { color: '#94a3b8', boxWidth: 14, padding: 16, usePointStyle: true } },
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tooltip: {
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backgroundColor: '#0f172a', titleColor: '#f1f5f9', bodyColor: '#e2e8f0',
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borderColor: '#334155', borderWidth: 1,
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callbacks: {
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label: ctx => ctx.parsed.y !== null ? ctx.dataset.label + ': ' + ctx.parsed.y.toFixed(1) : ''
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}
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}
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},
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scales: {
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x: { ticks: { color: '#64748b', maxTicksLimit: 20, font: { size: 10 } }, grid: { color: '#1e293b' } },
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y: {
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ticks: { color: '#94a3b8' }, grid: { color: '#334155' },
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title: { display: true, text: '价差（元/吨）', color: '#94a3b8' }
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}
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}
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}
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});
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// ── 副图：z-score ──
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new Chart(document.getElementById('zchart'), {
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type: 'line',
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data: { labels: D.allDates, datasets: D.zDatasets },
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options: {
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responsive: true,
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maintainAspectRatio: false,
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interaction: { mode: 'index', intersect: false },
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plugins: {
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legend: { labels: { color: '#94a3b8', boxWidth: 14, padding: 16, usePointStyle: true } },
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tooltip: {
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backgroundColor: '#0f172a', titleColor: '#f1f5f9', bodyColor: '#e2e8f0',
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borderColor: '#334155', borderWidth: 1,
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callbacks: {
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label: ctx => ctx.parsed.y !== null ? ctx.dataset.label + ': z=' + ctx.parsed.y.toFixed(2) : ''
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}
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}
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},
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scales: {
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x: { ticks: { color: '#64748b', maxTicksLimit: 20, font: { size: 10 } }, grid: { color: '#1e293b' } },
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y: {
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ticks: { color: '#94a3b8' }, grid: { color: '#334155' },
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title: { display: true, text: 'z-score', color: '#94a3b8' }
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}
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}
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},
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plugins: [{
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id: 'thresholdLines',
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afterDraw(chart) {
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const ys = chart.scales.y;
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const ctx = chart.ctx;
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[{ v: 2, c: '#ef4444', l: '+2σ' }, { v: -2, c: '#22c55e', l: '-2σ' }].forEach(t => {
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const y = ys.getPixelForValue(t.v);
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ctx.save();
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ctx.beginPath();
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ctx.setLineDash([4, 4]);
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ctx.strokeStyle = t.c;
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ctx.lineWidth = 1;
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ctx.moveTo(chart.chartArea.left, y);
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ctx.lineTo(chart.chartArea.right, y);
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ctx.stroke();
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ctx.restore();
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ctx.fillStyle = t.c;
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ctx.font = '11px system-ui';
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ctx.fillText(t.l, chart.chartArea.right - 36, y - 4);
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});
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}
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}]
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});
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</script>
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</body>
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</html>"""
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def generate_html(chart_data):
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"""渲染 HTML。"""
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payload = json.dumps({
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"allDates": chart_data["all_dates"],
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"datasets": chart_data["datasets"],
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"zDatasets": chart_data["z_datasets"],
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"statCards": chart_data["stat_cards"],
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"sigCards": chart_data["sig_cards"],
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})
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html = TEMPLATE.replace("__DATA__", payload)
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with open(OUT_PATH, "w") as f:
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f.write(html)
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print(f"已生成: {OUT_PATH}")
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if __name__ == "__main__":
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data = build_spreads()
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sigs = compute_signals(data)
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chart_data = build_chart_data(data, sigs)
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generate_html(chart_data)
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