753 lines
26 KiB
Python
753 lines
26 KiB
Python
#!/usr/bin/env python3
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"""technical_analysis.py — 技术面分析模块 v2
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基于多日价格数据计算支撑位/压力位：
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1. 缓存每日 HLC 到 price_history.json
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2. 使用 5 日最高/最低计算枢轴点
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3. 结合振幅自动调整区间宽度
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使用方式：
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from technical_analysis import full_analysis
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result = full_analysis("603259") # 自动识别A股/港股
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"""
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import json
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import os
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import sys
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import urllib.request
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from datetime import datetime, date
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# 确保本文件所在目录可导入（market_config 与之同目录；本模块会被 MoFin 根脚本跨目录 import）
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from market_config import kline_symbol
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# 腾讯API字段索引
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F = {
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"name": 1, "code": 2, "price": 3, "close_yest": 4, "open": 5,
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"volume": 6, "timestamp": 30, "change": 31, "change_pct": 32,
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"high": 33, "low": 34, "amplitude": 43,
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"turnover": 38, "pe": 39, "pb": 46,
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"limit_up": 47, "limit_down": 48,
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"avg_price": 51, "inner_vol": 52, "outer_vol": 53,
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}
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HISTORY_PATH = "/home/hmo/web-dashboard/data/price_history.json"
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HISTORY_DAYS = 60 # 使用最近 N 天的 HLC 数据
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def _load_history():
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"""读取价格历史缓存"""
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try:
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return json.load(open(HISTORY_PATH))
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except (FileNotFoundError, json.JSONDecodeError):
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return {}
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def _save_history(h):
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json.dump(h, open(HISTORY_PATH, "w"), ensure_ascii=False, indent=2)
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def _market_prefix(code):
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"""根据代码确定腾讯API前缀（统一走 market_config.kline_symbol，规则以它为准）"""
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sym = kline_symbol(code)
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if not sym:
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return "sz" # 兼容旧兜底（非5/6位数字等）
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return sym[:2]
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def get_quote(code):
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"""获取行情数据。先拿DB的价格和涨跌幅，再调腾讯API拿HLC全量数据"""
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import time
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_cache = get_quote.__dict__.get("_cache", {})
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now = time.time()
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cached = _cache.get(code)
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if cached and (now - cached["ts"]) < 60:
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return cached["data"]
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# 先从DB拿基础价格（快速，不阻塞）
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db_price = None
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db_chg = None
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try:
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from mofin_db import get_price_from_db
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p, chg = get_price_from_db(code)
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if p:
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db_price, db_chg = p, chg
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except:
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pass
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# 腾讯API获取全量HLC数据
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raw = str(code).split("_")[0]
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prefix = _market_prefix(code)
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url = f"http://qt.gtimg.cn/q={prefix}{raw}"
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try:
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r = urllib.request.urlopen(url, timeout=5)
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fields = r.read().decode("gbk").split('"')[1].split("~")
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except Exception as e:
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if db_price:
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return {"code": code, "price": db_price, "change_pct": db_chg or 0}
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return {"code": code, "error": str(e)}
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def get(i):
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try:
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return float(fields[i]) if fields[i].strip() else None
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except (IndexError, ValueError):
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return None
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today_str = date.today().isoformat()
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q = {
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"code": raw,
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"market": prefix,
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"name": fields[F["name"]] if len(fields) > F["name"] else code,
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"price": get(3),
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"close_yest": get(4),
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"open": get(5),
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"high": get(33),
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"low": get(34),
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"volume": get(6),
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"amount": get(37),
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"change": get(31),
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"change_pct": get(32),
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"amplitude": get(43),
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"turnover_rate": get(38),
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"pe": get(39),
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"pb": get(46),
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"limit_up": get(47),
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"limit_down": get(48),
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"avg_price": get(51),
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"inner_vol": get(52),
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"outer_vol": get(53),
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"timestamp": fields[F["timestamp"]] if len(fields) > F["timestamp"] else "",
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"_date": today_str,
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}
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# 写入价格历史缓存（每日一次）
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h = get(33) # high
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l = get(34) # low
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c = get(3) # price / close
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v = get(6) # volume（手）
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amt = get(37) # 成交额
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if h and l and c:
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history = _load_history()
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if raw not in history:
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history[raw] = []
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days = history[raw]
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# 如果今天已有记录，更新（盘中数据更精确）
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if days and len(days) > 0 and days[-1].get("date") == today_str:
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days[-1]["high"] = max(days[-1]["high"], h)
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days[-1]["low"] = min(days[-1]["low"], l)
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days[-1]["close"] = c # 盘中用最新价，收盘后是收盘价
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if v: days[-1]["volume"] = v
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if amt: days[-1]["amount"] = amt
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else:
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entry = {"date": today_str, "high": h, "low": l, "close": c}
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if v: entry["volume"] = v
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if amt: entry["amount"] = amt
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days.append(entry)
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# 只保留最近 HISTORY_DAYS 天
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history[raw] = days[-HISTORY_DAYS:]
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_save_history(history)
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# 写入60秒缓存
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get_quote.__dict__["_cache"] = {**get_quote.__dict__.get("_cache", {}), code: {"ts": now, "data": q}}
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return q
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def calc_support_resistance(q):
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"""计算技术支撑位和压力位 — 多日枢轴点算法
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使用多个数据源确定有效区间：
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1. 当日波幅（H-L）
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2. 最近 N 日的最高/最低（从 price_history.json 读取）
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3. 价格基数的百分比（对大市值低波动股票有效）
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"""
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h = q.get("high")
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l = q.get("low")
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c = q.get("price")
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yc = q.get("close_yest")
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amplitude = q.get("amplitude") # 当日振幅%
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code = q.get("code", "")
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if not all([h, l, c]):
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return {"error": "数据不足"}
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# 多日最高/最低（从历史缓存读取）
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history = _load_history()
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hist_days = history.get(code, [])
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multi_high = max(d["high"] for d in hist_days) if hist_days else h
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multi_low = min(d["low"] for d in hist_days) if hist_days else l
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# 有效区间 = max(当日波幅, 多日波幅, 价格×5%)
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daily_range = h - l
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multi_range = multi_high - multi_low
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min_range = c * 0.05 # 5%价格基数
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effective_range = max(daily_range, multi_range, min_range)
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# 如果股价接近多日高点（>80%分位），说明在上升趋势中，扩大区间
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trend_position = (c - multi_low) / (multi_high - multi_low) if multi_high > multi_low else 0.5
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if trend_position > 0.8:
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# 高位运行，扩大有效区间到价格的8%确保合理空间
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effective_range = max(effective_range, c * 0.08)
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elif trend_position < 0.2:
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# 低位运行，同样扩大
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effective_range = max(effective_range, c * 0.08)
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# 如果振幅数据可用且振幅较小(<3%)，进一步扩大区间确保有效性
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if amplitude and amplitude > 0 and amplitude < 3:
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# 低波动股票用 振幅×3 作为最小范围
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amp_based = c * amplitude / 100 * 3
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effective_range = max(effective_range, amp_based)
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# 枢轴点 (Pivot Point)
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pp = (h + l + c) / 3
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# 支撑位
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s1 = 2 * pp - h # 弱支撑
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s2 = pp - effective_range # 强支撑
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# 压力位
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r1 = 2 * pp - l # 弱压力
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r2 = pp + effective_range # 强压力
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# 参考昨收调整
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if yc:
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if yc < s1:
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s1 = yc
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if yc > r1:
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r1 = yc
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# A股涨停/跌停价作为极端边界
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limit_up = q.get("limit_up")
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limit_down = q.get("limit_down")
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market = q.get("market", "hk")
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if market != "hk" and limit_up and limit_down:
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# 注意：当现价逼近涨停/跌停时，limit不再是有效边界
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# 用有效区间判断：如果自然计算的r2/s2在合理范围内不截断
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natural_r2 = r2
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natural_s2 = s2
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# 涨停限制只对距离现价超过2%的强压位生效
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if limit_up < r2 and (limit_up - c) / c < 0.02:
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# 涨停价离现价<2%，说明可能封板，不截断
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pass # 使用自然计算的r2
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elif limit_up < r2:
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r2 = limit_up
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if limit_down > s2 and (c - limit_down) / c < 0.02:
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pass # 接近跌停，不截断
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elif limit_down > s2:
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s2 = limit_down
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return {
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"strong_support": round(s2, 2),
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"weak_support": round(s1, 2),
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"pivot": round(pp, 2),
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"weak_resist": round(r1, 2),
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"strong_resist": round(r2, 2),
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"today_high": h,
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"today_low": l,
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"multi_high": multi_high,
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"multi_low": multi_low,
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"effective_range": round(effective_range, 2),
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}
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def analyze_candlestick(q):
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"""判断K线形态"""
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o = q.get("open")
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c = q.get("price")
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h = q.get("high")
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l = q.get("low")
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yc = q.get("close_yest")
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if not all([o, c, h, l]):
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return {"pattern": "unknown", "sentiment": "neutral"}
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if c >= o:
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body = c - o
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upper = h - c
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lower = o - l
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is_green = True
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else:
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body = o - c
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upper = h - o
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lower = c - l
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is_green = False
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total_range = h - l
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if total_range == 0:
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return {"pattern": "平盘", "sentiment": "neutral"}
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body_pct = body / total_range * 100
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upper_pct = upper / total_range * 100
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lower_pct = lower / total_range * 100
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if body_pct < 5:
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if upper_pct > 60:
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pattern = "倒T线/射击之星"
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sentiment = "bearish"
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elif lower_pct > 60:
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pattern = "锤子线/T字线"
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sentiment = "bullish"
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else:
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pattern = "十字星"
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sentiment = "neutral"
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elif body_pct < 30:
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if upper_pct > 40 and lower_pct > 40:
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pattern = "长影星线"
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sentiment = "neutral"
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elif upper_pct > 40:
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pattern = "倒T线/射击之星"
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sentiment = "bearish" if is_green else "bearish"
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elif lower_pct > 40:
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pattern = "锤子线/T字线"
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sentiment = "bullish" if is_green else "bullish"
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else:
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pattern = "小阳线" if is_green else "小阴线"
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sentiment = "bullish" if is_green else "bearish"
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else:
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if upper_pct > 30:
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pattern = "带上影阳线" if is_green else "带上影阴线"
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sentiment = "neutral" if is_green else "bearish"
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elif lower_pct > 30:
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pattern = "带下影阳线" if is_green else "带下影阴线"
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sentiment = "bullish" if is_green else "neutral"
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else:
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pattern = "光头光脚阳线" if is_green else "光头光脚阴线"
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sentiment = "bullish" if is_green else "bearish"
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gap_up = ""
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gap_down = ""
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if yc:
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if o > yc * 1.01:
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gap_up = "跳空高开"
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if not is_green:
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sentiment = "neutral"
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elif o < yc * 0.99:
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gap_down = "跳空低开"
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if is_green:
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sentiment = "neutral"
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return {
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"pattern": pattern,
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"sentiment": sentiment,
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"body_pct": round(body_pct, 1),
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"upper_shadow_pct": round(upper_pct, 1),
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"lower_shadow_pct": round(lower_pct, 1),
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"is_green": is_green,
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"gap": gap_up or gap_down or "无跳空",
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}
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def analyze_volume(q):
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"""量价分析"""
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outer = q.get("outer_vol")
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inner = q.get("inner_vol")
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turnover = q.get("turnover_rate")
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result = {}
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if outer and inner and (outer + inner) > 0:
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ratio = outer / (outer + inner)
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result["buy_sell_ratio"] = round(ratio, 2)
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if ratio > 0.55:
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result["volume_signal"] = "主动买盘占优"
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elif ratio < 0.45:
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result["volume_signal"] = "主动卖盘占优"
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else:
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result["volume_signal"] = "买卖均衡"
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else:
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result["volume_signal"] = "数据不足"
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if turnover:
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result["turnover_rate"] = turnover
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return result
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def analyze_volume_trend(code):
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"""量价趋势分析：对比历史N日平均成交量，检测量价背离模式
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从 price_history.json 读取历史数据，比较今日量价关系。
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"""
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result = {}
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try:
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history = _load_history()
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days = history.get(code, [])
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if len(days) < 3:
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result["trend"] = "数据不足"
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return result
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today = days[-1]
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prev = days[-2] if len(days) >= 2 else None
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today_vol = today.get("volume", 0)
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today_close = today.get("close", 0)
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if not today_vol or not today_close:
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result["trend"] = "数据不足"
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return result
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# 计算N日均量
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vols_5 = [d.get("volume", 0) for d in days[-6:-1] if d.get("volume")]
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vols_20 = [d.get("volume", 0) for d in days[-21:-1] if d.get("volume")]
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avg_5 = sum(vols_5) / len(vols_5) if vols_5 else 0
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avg_20 = sum(vols_20) / len(vols_20) if vols_20 else 0
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vol_ratio_vs_5 = today_vol / avg_5 if avg_5 > 0 else 0
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vol_ratio_vs_20 = today_vol / avg_20 if avg_20 > 0 else 0
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result["avg_volume_5d"] = round(avg_5, 0)
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result["avg_volume_20d"] = round(avg_20, 0)
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result["today_volume"] = int(today_vol)
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result["volume_ratio_vs_5d"] = round(vol_ratio_vs_5, 2)
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result["volume_ratio_vs_20d"] = round(vol_ratio_vs_20, 2)
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# 最近3日的收盘价和成交量趋势
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if len(days) >= 3:
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recent_close = [d.get("close", 0) for d in days[-4:-1]]
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recent_vol = [d.get("volume", 0) for d in days[-4:-1]]
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if all(recent_close) and all(recent_vol):
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price_up = today_close > recent_close[-1]
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vol_up = today_vol > recent_vol[-1]
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# 量价模式判定
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if vol_ratio_vs_5 >= 1.8:
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# 明显放量
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if price_up:
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result["trend"] = "放量上攻"
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result["action"] = "buy_conformation"
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else:
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# 价格下跌但大幅放量 = 恐慌？还是承接收筹？
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# 看今日K线：如果是阳线（低开高走）= 承接
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# 简单判断：如果close > open = 有承接
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result["trend"] = "放量下跌"
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result["action"] = "watch"
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elif vol_ratio_vs_5 <= 0.6:
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# 明显缩量
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if price_up:
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result["trend"] = "缩量上涨"
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result["action"] = "divergence"
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else:
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result["trend"] = "缩量回调"
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result["action"] = "healthy_pullback"
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elif vol_ratio_vs_5 >= 1.3:
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# 温和放量
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if price_up:
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result["trend"] = "温和放量上涨"
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result["action"] = "bullish"
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else:
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result["trend"] = "温和放量下跌"
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result["action"] = "bearish"
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else:
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# 正常量
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if price_up:
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result["trend"] = "正常量上涨"
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result["action"] = "neutral_bullish"
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else:
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result["trend"] = "正常量下跌"
|
||
result["action"] = "neutral_bearish"
|
||
|
||
# 量价背离检测：价格走高但成交量逐日递减
|
||
if len(days) >= 5:
|
||
close_5 = [d.get("close", 0) for d in days[-5:]]
|
||
vol_5 = [d.get("volume", 0) for d in days[-5:]]
|
||
if all(close_5) and all(vol_5):
|
||
close_trend = close_5[-1] - close_5[0]
|
||
vol_trend = vol_5[-1] - vol_5[0]
|
||
# 价格涨但量跌 = 顶背离
|
||
if close_trend > 0 and vol_trend < 0 and abs(vol_trend) > sum(vol_5) * 0.3:
|
||
result["divergence"] = "顶背离(价涨量缩)"
|
||
# 价格跌但量涨 = 底背离
|
||
elif close_trend < 0 and vol_trend > 0 and abs(vol_trend) > sum(vol_5) * 0.3:
|
||
result["divergence"] = "底背离(价跌量增)"
|
||
|
||
except Exception as e:
|
||
result["trend_error"] = str(e)
|
||
|
||
return result
|
||
|
||
|
||
def analyze_volume_deep(code):
|
||
"""深度量价分析：从日K线分析量价配合/背离/建仓/出货
|
||
|
||
使用 mtf_cache 表的日K线数据做历史量价分析。
|
||
"""
|
||
import sqlite3
|
||
from pathlib import Path
|
||
|
||
DATA_DIR = Path(__file__).parent / "scripts" / "data"
|
||
if not (DATA_DIR / "mofin.db").exists():
|
||
DATA_DIR = Path(__file__).parent / "data"
|
||
try:
|
||
conn = sqlite3.connect(str(DATA_DIR / "mofin.db"))
|
||
row = conn.execute("SELECT cache_json FROM mtf_cache WHERE code=?", (code,)).fetchone()
|
||
conn.close()
|
||
if not row:
|
||
return {"volume_signal": "数据不足"}
|
||
data = json.loads(row[0])
|
||
except Exception:
|
||
return {"volume_signal": "数据不足"}
|
||
|
||
daily = data.get("daily", [])
|
||
if len(daily) < 5:
|
||
return {"volume_signal": "数据不足"}
|
||
|
||
closes = [d["close"] for d in daily]
|
||
volume = [d["volume"] for d in daily]
|
||
n = len(daily)
|
||
|
||
# 基准：最近20日均量（不足20日则用全部）
|
||
lookback = min(20, n - 1)
|
||
avg_vol_20d = sum(volume[-lookback-1:-1]) / lookback if lookback > 0 else volume[-1]
|
||
|
||
# 最近N日的量比
|
||
recent = min(5, n)
|
||
recent_vol_ratios = []
|
||
for i in range(recent):
|
||
vol = volume[-i-1] if i+1 <= n else volume[0]
|
||
recent_vol_ratios.append(round(vol / avg_vol_20d, 2) if avg_vol_20d > 0 else 1)
|
||
|
||
today_ratio = recent_vol_ratios[0] if recent_vol_ratios else 1
|
||
recent_max_ratio = max(recent_vol_ratios) if recent_vol_ratios else 1
|
||
|
||
# 量价配合度
|
||
signals = []
|
||
patterns = {}
|
||
|
||
# 1. 放量检测（量比 > 2x）
|
||
if today_ratio > 2.0:
|
||
signals.append(f"量比{today_ratio:.1f}倍放量")
|
||
patterns["volume_surge"] = True
|
||
# 放量方向
|
||
if len(closes) >= 2 and closes[-1] > closes[-2]:
|
||
patterns["surge_direction"] = "放量上涨"
|
||
if today_ratio > 2.5 and closes[-1] > closes[-2] * 1.03:
|
||
signals[-1] += "↑主力买入"
|
||
else:
|
||
signals[-1] += "↑"
|
||
elif len(closes) >= 2 and closes[-1] < closes[-2]:
|
||
patterns["surge_direction"] = "放量下跌"
|
||
if today_ratio > 2.5 and closes[-1] < closes[-2] * 0.97:
|
||
signals[-1] += "↓主力出货"
|
||
else:
|
||
signals[-1] += "↓"
|
||
else:
|
||
patterns["surge_direction"] = "放量平盘"
|
||
elif today_ratio < 0.5:
|
||
signals.append(f"量比{today_ratio:.1f}倍缩量")
|
||
patterns["volume_shrink"] = True
|
||
else:
|
||
signals.append(f"量比{today_ratio:.1f}倍正常")
|
||
patterns["volume_normal"] = True
|
||
|
||
# 2. 量价趋势分析（近5日 vs 前5日）
|
||
if len(daily) >= 10:
|
||
recent5_vol = sum(volume[-5:]) / 5
|
||
prev5_vol = sum(volume[-10:-5]) / 5
|
||
vol_trend = "增" if recent5_vol > prev5_vol * 1.3 else ("减" if recent5_vol < prev5_vol * 0.7 else "稳")
|
||
recent5_price = closes[-5:]
|
||
price_trend = "涨" if recent5_price[-1] > recent5_price[0] else ("跌" if recent5_price[-1] < recent5_price[0] * 0.95 else "平")
|
||
|
||
if vol_trend == "增" and price_trend == "涨":
|
||
patterns["accumulation"] = True # 量价齐升=建仓
|
||
signals.append(f"近5日{vol_trend}量{price_trend}价=建仓型")
|
||
elif vol_trend == "增" and price_trend == "跌":
|
||
patterns["distribution"] = True # 放量下跌=出货
|
||
signals.append(f"近5日{vol_trend}量{price_trend}价=⚠️出货型")
|
||
elif vol_trend == "减" and price_trend == "涨":
|
||
patterns["divergence"] = True # 量缩价涨=背离
|
||
signals.append(f"近5日{vol_trend}量{price_trend}价=⬆量价背离")
|
||
elif vol_trend == "减" and price_trend == "跌":
|
||
patterns["washout"] = True # 缩量下跌=洗盘末端
|
||
signals.append(f"近5日{vol_trend}量{price_trend}价=洗盘特征")
|
||
else:
|
||
signals.append(f"近5日{vol_trend}量{price_trend}价")
|
||
else:
|
||
vol_trend = price_trend = "?"
|
||
|
||
# 3. 寻找历史放量区间（主力活动痕迹）
|
||
surge_days = []
|
||
for i in range(max(0, n - 60), n):
|
||
vol_ratio = volume[i] / avg_vol_20d if avg_vol_20d > 0 else 0
|
||
if vol_ratio > 2.0:
|
||
surge_days.append({
|
||
"date": daily[i].get("date", ""),
|
||
"ratio": round(vol_ratio, 1),
|
||
"close": closes[i],
|
||
"direction": "涨" if (i > 0 and closes[i] > closes[i-1]) else "跌"
|
||
})
|
||
|
||
# 汇总描述
|
||
vol_level = "放量" if today_ratio > 2.0 else ("缩量" if today_ratio < 0.5 else "正常")
|
||
price_vol = f"{vol_level}"
|
||
if patterns.get("accumulation"):
|
||
price_vol = f"量价齐升(建仓特征) | {signals[-1]}"
|
||
elif patterns.get("distribution"):
|
||
price_vol = f"放量下跌⚠️ | {signals[-1]}"
|
||
elif patterns.get("washout"):
|
||
price_vol = f"缩量回踩(洗盘末端) | {signals[-1]}"
|
||
elif patterns.get("divergence"):
|
||
price_vol = f"量价背离 | {signals[-1]}"
|
||
|
||
return {
|
||
"volume_signal": " ; ".join(signals) if signals else "正常",
|
||
"volume_ratio": today_ratio,
|
||
"avg_volume_20d": int(avg_vol_20d),
|
||
"recent_ratios": recent_vol_ratios,
|
||
"surge_count_60d": len(surge_days),
|
||
"price_vol_description": price_vol,
|
||
"patterns": patterns,
|
||
"surge_days": surge_days[-5:] if surge_days else [],
|
||
}
|
||
|
||
|
||
def _calc_scores(code: str) -> dict:
|
||
"""严谨技术指标：RSI/MACD/量能/背离——从stock_daily确定性计算"""
|
||
try:
|
||
import sqlite3
|
||
conn = sqlite3.connect('/home/hmo/MoFin/data/mofin.db')
|
||
rows = conn.execute(
|
||
"SELECT close, high, low, volume FROM stock_daily WHERE code=? ORDER BY date DESC LIMIT 30",
|
||
(code,)).fetchall()
|
||
conn.close()
|
||
if len(rows) < 14:
|
||
return {"rsi": 50, "macd_hist": 0, "macd_hist_prev": 0, "volume_trend": "neutral", "divergence": "none"}
|
||
closes = [float(r[0]) for r in rows]
|
||
highs = [float(r[1]) for r in rows]
|
||
lows = [float(r[2]) for r in rows]
|
||
volumes = [float(r[3]) for r in rows]
|
||
|
||
# RSI (14期)
|
||
gains = []
|
||
losses = []
|
||
for i in range(1, 14):
|
||
chg = closes[i] - closes[i-1]
|
||
if chg > 0: gains.append(chg)
|
||
else: losses.append(abs(chg))
|
||
avg_gain = sum(gains) / 14 if gains else 0
|
||
avg_loss = sum(losses) / 14 if losses else 0
|
||
rs = avg_gain / avg_loss if avg_loss > 0 else 100
|
||
rsi = 100 - (100 / (1 + rs))
|
||
|
||
# MACD (12,26,9)
|
||
def ema(data, period):
|
||
k = 2 / (period + 1)
|
||
result = data[0]
|
||
for price in data[1:]:
|
||
result = price * k + result * (1 - k)
|
||
return result
|
||
ema12 = ema(closes[:12], 12)
|
||
ema26 = ema(closes[:26], 26) if len(closes) >= 26 else ema12
|
||
dif = ema12 - ema26
|
||
dea = ema(closes[:9], 9) if len(closes) >= 9 else dif
|
||
macd_hist = dif - dea
|
||
macd_hist_prev = macd_hist
|
||
|
||
# 量能趋势
|
||
recent_vol = volumes[:5]
|
||
older_vol = volumes[5:10] if len(volumes) >= 10 else recent_vol
|
||
vol_trend = "neutral"
|
||
if sum(recent_vol) > sum(older_vol) * 1.2:
|
||
vol_trend = "accumulation"
|
||
elif sum(recent_vol) < sum(older_vol) * 0.8:
|
||
vol_trend = "distribution"
|
||
|
||
# 背离检测
|
||
divergence = "none"
|
||
if len(closes) >= 5:
|
||
if closes[0] > closes[4] and volumes[0] < volumes[4]:
|
||
divergence = "bearish"
|
||
elif closes[0] < closes[4] and volumes[0] > volumes[4]:
|
||
divergence = "bullish"
|
||
|
||
return {
|
||
"rsi": round(rsi, 1),
|
||
"macd_hist": round(macd_hist, 4),
|
||
"macd_hist_prev": round(macd_hist_prev, 4),
|
||
"volume_trend": vol_trend,
|
||
"divergence": divergence,
|
||
}
|
||
except Exception as _e:
|
||
print(f" [TA-SCORE] {code} 技术分计算失败: {_e}", flush=True)
|
||
return {"rsi": 50, "macd_hist": 0, "macd_hist_prev": 0, "volume_trend": "neutral", "divergence": "none"}
|
||
|
||
|
||
def full_analysis(code):
|
||
"""完整技术分析（带30秒缓存，避免分钟级波动）"""
|
||
import time
|
||
_cache = full_analysis.__dict__.get("_cache", {})
|
||
now = time.time()
|
||
cached = _cache.get(code)
|
||
if cached and (now - cached["ts"]) < 30:
|
||
return cached["data"]
|
||
|
||
q = get_quote(code)
|
||
if not q or "error" in q:
|
||
return q
|
||
|
||
sr = calc_support_resistance(q)
|
||
candle = analyze_candlestick(q)
|
||
vol = analyze_volume(q)
|
||
# 深度量价分析（使用日K线历史数据）
|
||
vol_deep = {}
|
||
try:
|
||
vol_deep = analyze_volume_deep(code)
|
||
except Exception:
|
||
pass # graceful degradation
|
||
|
||
# 多周期+均线分析（整合 multi_timeframe）
|
||
mtf = {}
|
||
try:
|
||
from multi_timeframe import full_multi_tf_analysis as _mtf
|
||
mtf_raw = _mtf(code)
|
||
if mtf_raw and 'daily' in mtf_raw:
|
||
d = mtf_raw['daily']
|
||
mtf = {
|
||
'mas': d.get('mas', {}),
|
||
'multi_tf_sr': d.get('support_resistance', {}),
|
||
'trend': d.get('trend', {}),
|
||
}
|
||
# 周线弱压/弱撑作为中周期参考
|
||
if 'weekly' in mtf_raw:
|
||
w = mtf_raw['weekly']
|
||
ws = w.get('support_resistance', {})
|
||
mtf['weekly_sr'] = {
|
||
'weak_resist': ws.get('weak_resist'),
|
||
'weak_support': ws.get('weak_support'),
|
||
}
|
||
except Exception:
|
||
pass # non-critical, graceful degradation
|
||
|
||
result = {
|
||
"quote": {
|
||
"name": q.get("name", code),
|
||
"price": q["price"],
|
||
"change_pct": q.get("change_pct", 0),
|
||
"open": q.get("open", 0),
|
||
"high": q.get("high", 0),
|
||
"low": q.get("low", 0),
|
||
"close_yest": q.get("close_yest", 0),
|
||
"volume": q.get("volume", 0),
|
||
"amplitude": q.get("amplitude", 0),
|
||
},
|
||
"support_resistance": sr,
|
||
"candlestick": candle,
|
||
"volume": vol,
|
||
"volume_deep": vol_deep,
|
||
"multi_tf": mtf,
|
||
"scores": _calc_scores(code),
|
||
"analyzed_at": datetime.now().strftime("%H:%M"),
|
||
}
|
||
|
||
# 写入缓存
|
||
_cache[code] = {"ts": now, "data": result}
|
||
full_analysis.__dict__["_cache"] = _cache
|
||
return result
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import sys
|
||
codes = sys.argv[1:] or ["603259", "002594", "00700"]
|
||
for c in codes:
|
||
r = full_analysis(c)
|
||
print(json.dumps(r, ensure_ascii=False, indent=2))
|
||
print()
|