diff --git a/建材指数监控/.gitignore b/建材指数监控/.gitignore
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-index.html
-data.json
diff --git a/建材指数监控/index_template.html b/建材指数监控/index_template.html
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-建材价格指数 · 趋势监控
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-建材价格指数 · 趋势监控
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-历史全景 2011.12 – 2026.07
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-周期对比
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-均线系统
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-近30日明细
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- | 日期 | 指数 | 日涨跌% | MA5 | MA20 | MA60 |
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diff --git a/建材指数监控/refresh.py b/建材指数监控/refresh.py
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-"""拉取建材价格指数，更新 data.json 并刷新 index.html。"""
-import json
-import pandas as pd
-import akshare as ak
-
-df = ak.macro_china_construction_index()
-df['日期'] = pd.to_datetime(df['日期'])
-df = df.sort_values('日期').reset_index(drop=True)
-
-for w in [5, 10, 20, 60]:
- df[f'MA{w}'] = df['最新值'].rolling(w).mean().round(1)
- df[f'MA{w}_dir'] = df[f'MA{w}'].diff().apply(lambda x: 'up' if x > 0 else ('down' if x < 0 else 'flat'))
-
-latest = df.iloc[-1]
-
-signals = []
-if latest['最新值'] > latest['MA5']:
- signals.append(('短期', '偏多', '价格 > MA5'))
-else:
- signals.append(('短期', '偏空', '价格 < MA5'))
-if latest['MA5'] > latest['MA20']:
- signals.append(('中期', '偏多', 'MA5 > MA20'))
-else:
- signals.append(('中期', '偏空', 'MA5 < MA20'))
-if latest['MA20'] > latest['MA60']:
- signals.append(('长期', '偏多', 'MA20 > MA60'))
-else:
- signals.append(('长期', '偏空', 'MA20 < MA60'))
-
-momentum_5d = latest['最新值'] - df.iloc[-6]['最新值']
-signals.append(('动量(5日)', '偏多' if momentum_5d > 0 else '偏空', f"{momentum_5d:+d}"))
-
-bull_count = sum(1 for _, s, _ in signals if s == '偏多')
-if bull_count >= 3:
- overall = ('偏多', '#16a34a')
-elif bull_count <= 1:
- overall = ('偏空', '#dc2626')
-else:
- overall = ('震荡', '#d97706')
-
-summary = {
- 'date': latest['日期'].strftime('%Y-%m-%d'),
- 'value': int(latest['最新值']),
- 'change': round(float(latest['涨跌幅']), 4),
- 'chg_3m': round(float(latest['近3月涨跌幅']), 4),
- 'chg_6m': round(float(latest['近6月涨跌幅']), 4),
- 'chg_1y': round(float(latest['近1年涨跌幅']), 4),
-}
-
-mas = {}
-for w in [5, 10, 20, 60]:
- mas[f'MA{w}'] = {
- 'value': float(latest[f'MA{w}']),
- 'direction': latest[f'MA{w}_dir'],
- 'price_vs_ma': 'above' if latest['最新值'] > latest[f'MA{w}'] else 'below'
- }
-
-rows = []
-for _, r in df.tail(30).iterrows():
- rows.append({
- 'date': r['日期'].strftime('%m-%d'),
- 'value': int(r['最新值']),
- 'chg': round(float(r['涨跌幅']), 4),
- 'MA5': None if pd.isna(r['MA5']) else round(float(r['MA5']), 1),
- 'MA20': None if pd.isna(r['MA20']) else round(float(r['MA20']), 1),
- 'MA60': None if pd.isna(r['MA60']) else round(float(r['MA60']), 1),
- })
-
-
-def build_history(df):
- """构建历史全景数据：月度采样 + 周期标注 + 统计。"""
-
- # 月度采样（取每月最后一条有效记录）
- monthly = df.set_index('日期').resample('ME')['最新值'].last().dropna().reset_index()
- ma60_monthly = df.set_index('日期').resample('ME')['MA60'].last().dropna().reset_index()
- monthly_vals = []
- for _, r in monthly.iterrows():
- ma_row = ma60_monthly[ma60_monthly['日期'] == r['日期']]
- ma60_val = None if ma_row.empty or pd.isna(ma_row['MA60'].iloc[0]) else round(float(ma_row['MA60'].iloc[0]), 1)
- monthly_vals.append({
- 'date': r['日期'].strftime('%Y-%m'),
- 'value': int(r['最新值']),
- 'ma60': ma60_val,
- })
-
- # 历史统计
- vals = df['最新值']
- latest_val = float(vals.iloc[-1])
- pct_rank = round((vals < latest_val).sum() / len(vals) * 100, 1)
-
- # 年度涨跌（排除不足 200 个交易日的首尾年）
- df['year'] = df['日期'].dt.year
- yearly = df.groupby('year')['最新值'].agg(['first', 'last', 'count'])
- yearly = yearly[yearly['count'] >= 200]
- yearly['chg'] = ((yearly['last'] - yearly['first']) / yearly['first'] * 100).round(1)
- down_years = int((yearly['chg'] < 0).sum())
- ytd = float(yearly['chg'].iloc[-1]) if len(yearly) > 0 else 0
-
- # 识别主要周期：用未取整的 MA60 找极值，间距至少 4 个月
- ma60_raw = df['最新值'].rolling(60).mean().values # 未取整，保留精度
- all_turns = [] # (date, value, type)
- min_gap = 80 # 交易日，约 4 个月
- last_turn_idx = -999
- for i in range(2, len(ma60_raw) - 2):
- if pd.isna(ma60_raw[i]):
- continue
- if ma60_raw[i] > ma60_raw[i - 1] and ma60_raw[i] > ma60_raw[i - 2] and ma60_raw[i] > ma60_raw[i + 1] and ma60_raw[i] > ma60_raw[i + 2]:
- if i - last_turn_idx >= min_gap:
- all_turns.append((df['日期'].iloc[i], float(ma60_raw[i]), 'peak'))
- last_turn_idx = i
- elif ma60_raw[i] < ma60_raw[i - 1] and ma60_raw[i] < ma60_raw[i - 2] and ma60_raw[i] < ma60_raw[i + 1] and ma60_raw[i] < ma60_raw[i + 2]:
- if i - last_turn_idx >= min_gap:
- all_turns.append((df['日期'].iloc[i], float(ma60_raw[i]), 'trough'))
- last_turn_idx = i
-
- # 构建周期（谷 → 下一个峰）
- cycles = []
- for i, (t_date, t_val, t_type) in enumerate(all_turns):
- if t_type != 'trough':
- continue
- # 找下一个峰
- for j in range(i + 1, len(all_turns)):
- if all_turns[j][2] == 'peak':
- p_date, p_val, _ = all_turns[j]
- gain = round((p_val - t_val) / t_val * 100, 1)
- if gain > 10:
- label = ''
- if t_date.year == 2015 or t_date.year == 2016:
- label = '供给侧改革'
- elif t_date.year == 2020:
- label = '疫情后刺激'
- cycles.append({
- 'label': label,
- 'start': t_date.strftime('%Y-%m'),
- 'end': p_date.strftime('%Y-%m'),
- 'from': int(t_val),
- 'to': int(p_val),
- 'pct': gain,
- })
- break
-
- # 当前反弹（用价格实际谷底，不是 MA60 谷底）
- # 找最近 2 年的价格最低点
- recent_2y = df[df['日期'] >= df['日期'].iloc[-1] - pd.DateOffset(years=2)]
- trough_idx = recent_2y['最新值'].idxmin()
- trough_date = df.loc[trough_idx, '日期']
- trough_val = float(df.loc[trough_idx, '最新值'])
- cur_gain = round((latest_val - trough_val) / trough_val * 100, 1)
- current_rebound = {
- 'label': '当前反弹',
- 'start': trough_date.strftime('%Y-%m'),
- 'end': None,
- 'from': int(trough_val),
- 'to': int(latest_val),
- 'pct': cur_gain,
- }
-
- # 均线结构
- last = df.iloc[-1]
- ma20_vs_ma60 = 'above' if last['MA20'] > last['MA60'] else 'below'
-
- return {
- 'monthly': monthly_vals,
- 'stats': {
- 'mean': round(float(vals.mean()), 1),
- 'min': int(vals.min()),
- 'max': int(vals.max()),
- 'min_date': df.loc[vals.idxmin(), '日期'].strftime('%Y-%m'),
- 'max_date': df.loc[vals.idxmax(), '日期'].strftime('%Y-%m'),
- 'percentile': pct_rank,
- 'down_years': down_years,
- 'ytd_chg': round(ytd, 1),
- },
- 'cycles': cycles,
- 'current_rebound': current_rebound,
- 'ma20_vs_ma60': ma20_vs_ma60,
- }
-
-
-output = {
- 'summary': summary,
- 'mas': mas,
- 'signals': [{'period': p, 'signal': s, 'detail': d} for p, s, d in signals],
- 'overall': {'signal': overall[0], 'color': overall[1]},
- 'recent': rows,
- 'history': build_history(df),
-}
-
-with open('data.json', 'w') as f:
- json.dump(output, f, ensure_ascii=False, indent=2)
-
-SENTINEL = '/*__DATA_SENTINEL__*/{}'
-html = open('index_template.html').read()
-html = html.replace(SENTINEL, json.dumps(output, ensure_ascii=False))
-open('index.html', 'w').write(html)
-
-print(f"刷新完成: {summary['date']} → {summary['value']} (日涨跌 {summary['change']:+.2f}%)")
-print(f"综合信号: {overall[0]}")
-print("信号明细:")
-for p, s, d in signals:
- print(f" {p}: {s} ({d})")