知微
423 lines
13 KiB
Python
423 lines
13 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 urllib.request
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from datetime import datetime, date
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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"""
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if code.startswith("sh") or code.startswith("sz") or code.startswith("hk"):
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code = code[2:] if code[2:].isdigit() else code
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raw = str(code).split("_")[0]
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if len(raw) == 5 and raw.isdigit():
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return "hk"
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if raw.startswith("6") or raw.startswith("5"):
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return "sh"
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return "sz"
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def get_quote(code):
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"""获取行情数据。DB 优先（price_monitor 维护），腾讯 API fallback"""
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# DB 优先
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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: return {"code": code, "price": p, "change_pct": chg or 0}
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except: pass
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# Fallback: 腾讯 API
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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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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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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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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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else:
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days.append({"date": today_str, "high": h, "low": l, "close": c})
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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 full_analysis(code):
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"""完整技术分析（带30秒缓存，避免分钟级波动）"""
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import time
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_cache = full_analysis.__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"]) < 30:
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return cached["data"]
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q = get_quote(code)
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if not q or "error" in q:
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return q
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sr = calc_support_resistance(q)
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candle = analyze_candlestick(q)
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vol = analyze_volume(q)
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# 多周期+均线分析（整合 multi_timeframe）
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mtf = {}
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try:
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from multi_timeframe import full_multi_tf_analysis as _mtf
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mtf_raw = _mtf(code)
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if mtf_raw and 'daily' in mtf_raw:
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d = mtf_raw['daily']
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mtf = {
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'mas': d.get('mas', {}),
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'multi_tf_sr': d.get('support_resistance', {}),
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'trend': d.get('trend', {}),
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}
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# 周线弱压/弱撑作为中周期参考
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if 'weekly' in mtf_raw:
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w = mtf_raw['weekly']
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ws = w.get('support_resistance', {})
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mtf['weekly_sr'] = {
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'weak_resist': ws.get('weak_resist'),
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'weak_support': ws.get('weak_support'),
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}
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except Exception:
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pass # non-critical, graceful degradation
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result = {
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"quote": {
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"name": q["name"],
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"price": q["price"],
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"change_pct": q["change_pct"],
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"open": q["open"],
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"high": q["high"],
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"low": q["low"],
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"close_yest": q["close_yest"],
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"volume": q["volume"],
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"amplitude": q["amplitude"],
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},
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"support_resistance": sr,
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"candlestick": candle,
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"volume": vol,
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"multi_tf": mtf,
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"analyzed_at": datetime.now().strftime("%H:%M"),
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}
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# 写入缓存
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_cache[code] = {"ts": now, "data": result}
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full_analysis.__dict__["_cache"] = _cache
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return result
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if __name__ == "__main__":
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import sys
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codes = sys.argv[1:] or ["603259", "002594", "00700"]
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for c in codes:
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r = full_analysis(c)
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print(json.dumps(r, ensure_ascii=False, indent=2))
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print()
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