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# -*- coding: utf-8 -*-
"""
多维度技术指标联动研判模块
包含12个固定参数指标、5大研判维度和统一信号共振逻辑
固定参数指标清单：
MACD(5,10,3) KDJ(3,3,3) EMA5/10/20/60 BOLL(20,2)
RSI(6,14) CCI(20) 成交量+5/10日均量 OBV DMI(14,6) SAR(0.02,0.2) MFI(14)
5大研判维度：
1. 趋势判定：MACD + 多级EMA + BOLL
2. 拐点判定：KDJ + RSI + CCI
3. 行情验证：成交量 + OBV
4. 强度确认：DMI + MFI
5. 风控配套：SAR
信号生效原则：多指标同向共振优先采信，背离则暂缓观望
"""
import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Optional
import warnings
warnings.filterwarnings('ignore')
# === 兼容层：优先尝试 talib，失败则用自带兼容模块 ===
try:
import talib
_HAS_TALIB = True
except ImportError:
_HAS_TALIB = False
import importlib.util, sys, os
_talib_path = os.path.join(os.path.dirname(__file__), 'talib.py')
if os.path.exists(_talib_path):
_spec = importlib.util.spec_from_file_location('talib_compat', _talib_path)
talib = importlib.util.module_from_spec(_spec)
sys.modules['talib'] = talib
_spec.loader.exec_module(talib)
else:
raise ImportError("talib 或 talib.py 兼容层均不可用")
# ============================================================================
# 第一部分：TechnicalIndicators —— 12个固定参数指标计算
# ============================================================================
class TechnicalIndicators:
"""技术指标计算类 —— 严格按固定参数执行"""
def __init__(self):
self.indicators = {}
def calculate_all_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
"""计算全部12类技术指标"""
df = df.copy()
if 'timestamps' in df.columns:
df = df.sort_values('timestamps')
df = self._calc_ema_system(df) # EMA5/10/20/60
df = self._calc_macd(df) # MACD(5,10,3)
df = self._calc_kdj(df) # KDJ(3,3,3)
df = self._calc_boll(df) # BOLL(20,2)
df = self._calc_rsi(df) # RSI(6,14)
df = self._calc_cci(df) # CCI(20)
df = self._calc_volume(df) # 成交量+5/10日均量
df = self._calc_obv(df) # OBV
df = self._calc_dmi(df) # DMI(14,6)
df = self._calc_sar(df) # SAR(0.02,0.2)
df = self._calc_mfi(df) # MFI(14)
df = self._calc_auxiliary(df) # 辅助指标（ATR/ROC/MOM等）
return df
# ---------- 1. EMA 均线系统 ----------
def _calc_ema_system(self, df):
c = df['close'].values.astype(float)
for p in (5, 10, 20, 60):
df[f'ema_{p}'] = talib.EMA(c, timeperiod=p)
# SMA 作为辅助参考
for p in (5, 10, 20):
df[f'sma_{p}'] = talib.SMA(c, timeperiod=p)
return df
# ---------- 2. MACD(5,10,3) ----------
def _calc_macd(self, df):
c = df['close'].values.astype(float)
df['macd'], df['macd_signal'], df['macd_hist'] = talib.MACD(
c, fastperiod=5, slowperiod=10, signalperiod=3)
return df
# ---------- 3. KDJ(3,3,3) ----------
def _calc_kdj(self, df):
h = df['high'].values.astype(float)
l = df['low'].values.astype(float)
c = df['close'].values.astype(float)
df['k'], df['d'] = talib.STOCH(
h, l, c, fastk_period=3, slowk_period=3, slowd_period=3)
df['j'] = 3 * df['k'] - 2 * df['d']
return df
# ---------- 4. BOLL(20,2) ----------
def _calc_boll(self, df):
c = df['close'].values.astype(float)
df['bb_upper'], df['bb_middle'], df['bb_lower'] = talib.BBANDS(
c, timeperiod=20, nbdevup=2, nbdevdn=2)
df['bb_width'] = (df['bb_upper'] - df['bb_lower']) / df['bb_middle']
df['bb_position'] = (c - df['bb_lower']) / (df['bb_upper'] - df['bb_lower'])
return df
# ---------- 5. RSI(6,14) ----------
def _calc_rsi(self, df):
c = df['close'].values.astype(float)
df['rsi_6'] = talib.RSI(c, timeperiod=6)
df['rsi_14'] = talib.RSI(c, timeperiod=14)
return df
# ---------- 6. CCI(20) ----------
def _calc_cci(self, df):
h = df['high'].values.astype(float)
l = df['low'].values.astype(float)
c = df['close'].values.astype(float)
df['cci'] = talib.CCI(h, l, c, timeperiod=20)
return df
# ---------- 7. 成交量体系 ----------
def _calc_volume(self, df):
v = df['volume'].values.astype(float)
df['vol_ma5'] = talib.SMA(v, timeperiod=5)
df['vol_ma10'] = talib.SMA(v, timeperiod=10)
df['vol_ratio5'] = v / df['vol_ma5']
df['vol_ratio10'] = v / df['vol_ma10']
return df
# ---------- 8. OBV ----------
def _calc_obv(self, df):
c = df['close'].values.astype(float)
v = df['volume'].values.astype(float)
df['obv'] = talib.OBV(c, v)
# OBV 均线
df['obv_ma10'] = talib.EMA(df['obv'].values.astype(float), timeperiod=10)
return df
# ---------- 9. DMI(14,6) ----------
def _calc_dmi(self, df):
h = df['high'].values.astype(float)
l = df['low'].values.astype(float)
c = df['close'].values.astype(float)
df['adx'] = talib.ADX(h, l, c, timeperiod=14)
df['plus_di'] = talib.PLUS_DI(h, l, c, timeperiod=14)
df['minus_di'] = talib.MINUS_DI(h, l, c, timeperiod=14)
# ADX 平滑（6周期EMA）用于趋势确认
df['adx_smooth'] = talib.EMA(df['adx'].fillna(0).values.astype(float), timeperiod=6)
df['di_diff'] = df['plus_di'] - df['minus_di'] # 多空力量差
return df
# ---------- 10. SAR(0.02,0.2) ----------
def _calc_sar(self, df):
h = df['high'].values.astype(float)
l = df['low'].values.astype(float)
df['sar'] = talib.SAR(h, l, acceleration=0.02, maximum=0.2)
df['sar_signal'] = np.where(df['close'] > df['sar'], 1, -1) # 1多头 -1空头
return df
# ---------- 11. MFI(14) ----------
def _calc_mfi(self, df):
h = df['high'].values.astype(float)
l = df['low'].values.astype(float)
c = df['close'].values.astype(float)
v = df['volume'].values.astype(float)
df['mfi'] = talib.MFI(h, l, c, v, timeperiod=14)
return df
# ---------- 辅助指标 ----------
def _calc_auxiliary(self, df):
h = df['high'].values.astype(float)
l = df['low'].values.astype(float)
c = df['close'].values.astype(float)
v = df['volume'].values.astype(float)
df['atr'] = talib.ATR(h, l, c, timeperiod=14)
df['willr'] = talib.WILLR(h, l, c, timeperiod=14)
df['roc_10'] = talib.ROC(c, timeperiod=10)
df['mom_10'] = talib.MOM(c, timeperiod=10)
df['cmo'] = talib.CMO(c, timeperiod=14)
# AD 累积/派发
df['ad'] = talib.AD(h, l, c, v)
# 威廉%R 辅助
df['aroon_up'], df['aroon_down'] = talib.AROON(h, l, timeperiod=14)
df['ultosc'] = talib.ULTOSC(h, l, c, timeperiod1=7, timeperiod2=14, timeperiod3=28)
# 量价关系
df['price_change'] = df['close'].pct_change()
df['volume_change'] = df['volume'].pct_change()
return df
# ============================================================================
# 第二部分：保留原有 VolumePriceAnalyzer / WaveTheoryAnalyzer（向后兼容）
# ============================================================================
class VolumePriceAnalyzer:
"""量价分析器"""
def __init__(self):
self.indicators = TechnicalIndicators()
def analyze_volume_price_relationship(self, df):
analysis = {}
price_change = df['close'].pct_change()
volume_change = df['volume'].pct_change()
corr_val = price_change.corr(volume_change)
analysis['volume_price_correlation'] = 0.0 if (corr_val is None or (isinstance(corr_val, float) and np.isnan(corr_val))) else corr_val
analysis['volume_price_divergence'] = self._detect_divergence(df)
analysis['volume_anomaly_score'] = self._calculate_volume_anomaly_score(df)
analysis['volume_price_sync_score'] = self._calculate_sync_score(df)
return analysis
def _detect_divergence(self, df):
pt = df['close'].rolling(window=10).apply(lambda x: np.polyfit(range(len(x)), x, 1)[0])
vt = df['volume'].rolling(window=10).apply(lambda x: np.polyfit(range(len(x)), x, 1)[0])
d = np.corrcoef(pt.dropna(), vt.dropna())[0, 1]
return d if not np.isnan(d) else 0.0
def _calculate_volume_anomaly_score(self, df):
vm = df['volume'].rolling(20).mean()
vs = df['volume'].rolling(20).std()
a = np.abs((df['volume'] - vm) / vs)
return a.mean() if not a.isna().all() else 0.0
def _calculate_sync_score(self, df):
pd_dir = np.sign(df['close'].diff())
vd_dir = np.sign(df['volume'].diff())
s = (pd_dir == vd_dir).mean()
return s if not np.isnan(s) else 0.0
class WaveTheoryAnalyzer:
"""波浪理论分析器"""
def __init__(self):
self.indicators = TechnicalIndicators()
def analyze_wave_patterns(self, df):
analysis = {}
analysis['wave_count'] = self._count_waves(df)
analysis['wave_strength'] = self._calculate_wave_strength(df)
analysis['fibonacci_levels'] = self._calculate_fibonacci_analysis(df)
analysis['wave_phase'] = self._identify_wave_phase(df)
return analysis
def _count_waves(self, df):
c = df['close'].values
waves, trend = 0, 0
for i in range(1, len(c)):
if c[i] > c[i-1]:
if trend <= 0: waves += 1; trend = 1
else:
if trend >= 0: waves += 1; trend = -1
return waves
def _calculate_wave_strength(self, df):
c = df['close'].values
changes = np.diff(c)
cc, mc = 0, 0
for ch in changes:
if ch > 0:
cc += 1; mc = max(mc, cc)
else:
cc = 0
return mc / len(changes) if len(changes) > 0 else 0.0
def _calculate_fibonacci_analysis(self, df):
c = df['close'].values
rh, rl = np.max(c[-20:]), np.min(c[-20:])
return {k: rl + (rh - rl) * v for k, v in
[('fib_236', 0.236), ('fib_382', 0.382), ('fib_500', 0.5),
('fib_618', 0.618), ('fib_786', 0.786)]}
def _identify_wave_phase(self, df):
c = df['close'].values
if len(c) < 10: return "insufficient_data"
rt = np.polyfit(range(10), c[-10:], 1)[0]
if rt > 0.01: return "impulse_wave"
elif rt < -0.01: return "corrective_wave"
return "consolidation"
# ============================================================================
# 第三部分：MultiDimensionSignalAnalyzer —— 多维度联动研判 + 信号共振
# ============================================================================
class MultiDimensionSignalAnalyzer:
"""
多维度技术指标联动研判体系
5大研判维度：
1. 趋势判定 (Trend) : MACD(5,10,3) + EMA5/10/20/60 + BOLL(20,2)
2. 拐点判定 (Inflection) : KDJ(3,3,3) + RSI(6,14) + CCI(20)
3. 行情验证 (Verification): 成交量+5/10日均量 + OBV
4. 强度确认 (Strength) : DMI(14,6) + MFI(14)
5. 风控配套 (RiskControl) : SAR(0.02,0.2)
信号生效原则：
- 多指标同向共振 → 采信
- 指标相互背离 → 暂缓观望
"""
def __init__(self):
self.calc = TechnicalIndicators()
def analyze(self, df: pd.DataFrame) -> Dict:
"""
执行完整的多维度联动研判
Parameters
----------
df : DataFrame
OHLCV 数据（需包含 open/high/low/close/volume 列）
Returns
-------
dict 包含 dimensions、resonance、overall_signal、indicators_detail
"""
df = self.calc.calculate_all_indicators(df.copy())
last = df.iloc[-1]
prev = df.iloc[-2] if len(df) >= 2 else last
# 提取最新值
iv = self._extract_latest(df, last, prev)
# 5大维度逐一研判
d_trend = self._judge_trend(iv)
d_inflection = self._judge_inflection(iv)
d_verification = self._judge_verification(iv, df)
d_strength = self._judge_strength(iv)
d_risk = self._judge_risk(iv)
dimensions = {
'trend': d_trend,
'inflection': d_inflection,
'verification': d_verification,
'strength': d_strength,
'risk_control': d_risk,
}
# 信号共振
resonance = self._calc_resonance(dimensions)
# 综合研判
overall = self._calc_overall(dimensions, resonance)
# 指标详情（供前端展示）
detail = self._build_detail(iv, dimensions)
return {
'dimensions': dimensions,
'resonance': resonance,
'overall_signal': overall,
'indicators_detail': detail,
}
# ------------------------------------------------------------------
# 提取最新指标值
# ------------------------------------------------------------------
def _extract_latest(self, df, last, prev):
"""从 DataFrame 中提取最新和前一日的指标值"""
def _v(col):
return last.get(col, np.nan)
def _p(col):
return prev.get(col, np.nan)
return {
# 价格
'close': _v('close'), 'open': _v('open'),
'high': _v('high'), 'low': _v('low'),
# EMA
'ema5': _v('ema_5'), 'ema10': _v('ema_10'),
'ema20': _v('ema_20'), 'ema60': _v('ema_60'),
# MACD
'macd': _v('macd'), 'macd_signal': _v('macd_signal'),
'macd_hist': _v('macd_hist'),
'macd_prev': _p('macd'), 'macd_signal_prev': _p('macd_signal'),
# KDJ
'k': _v('k'), 'd': _v('d'), 'j': _v('j'),
'k_prev': _p('k'), 'd_prev': _p('d'),
# BOLL
'bb_upper': _v('bb_upper'), 'bb_middle': _v('bb_middle'),
'bb_lower': _v('bb_lower'), 'bb_width': _v('bb_width'),
'bb_position': _v('bb_position'),
# RSI
'rsi6': _v('rsi_6'), 'rsi14': _v('rsi_14'),
# CCI
'cci': _v('cci'),
# 成交量
'volume': _v('volume'), 'vol_ma5': _v('vol_ma5'), 'vol_ma10': _v('vol_ma10'),
'vol_ratio5': _v('vol_ratio5'), 'vol_ratio10': _v('vol_ratio10'),
'close_prev': _p('close'),
# OBV
'obv': _v('obv'), 'obv_ma10': _v('obv_ma10'),
# DMI
'adx': _v('adx'), 'adx_smooth': _v('adx_smooth'),
'plus_di': _v('plus_di'), 'minus_di': _v('minus_di'),
'di_diff': _v('di_diff'),
# SAR
'sar': _v('sar'), 'sar_signal': _v('sar_signal'),
# MFI
'mfi': _v('mfi'),
# 辅助
'atr': _v('atr'),
}
# ------------------------------------------------------------------
# 维度1：趋势判定 (MACD + EMA + BOLL)
# ------------------------------------------------------------------
def _judge_trend(self, iv):
"""趋势判定：MACD + 多级EMA均线定方向，BOLL确认运行格局"""
signals = []
# --- MACD ---
macd_score = 0
macd_text = []
# MACD柱状图方向
if not np.isnan(iv['macd_hist']):
if iv['macd_hist'] > 0:
macd_score += 1
macd_text.append('MACD柱>0，多头动能')
else:
macd_score -= 1
macd_text.append('MACD柱<0，空头动能')
# MACD金叉/死叉
if not np.isnan(iv['macd']) and not np.isnan(iv['macd_signal']):
if not np.isnan(iv['macd_prev']) and not np.isnan(iv['macd_signal_prev']):
prev_diff = iv['macd_prev'] - iv['macd_signal_prev']
curr_diff = iv['macd'] - iv['macd_signal']
if prev_diff <= 0 and curr_diff > 0:
macd_score += 2
macd_text.append('MACD金叉确认')
elif prev_diff >= 0 and curr_diff < 0:
macd_score -= 2
macd_text.append('MACD死叉确认')
if iv['macd'] > iv['macd_signal']:
macd_score += 0.5
else:
macd_score -= 0.5
# MACD零轴位置
if not np.isnan(iv['macd']):
if iv['macd'] > 0:
macd_score += 1
macd_text.append('MACD运行在零轴上方')
else:
macd_score -= 1
macd_text.append('MACD运行在零轴下方')
signals.append({
'indicator': 'MACD(5,10,3)',
'score': macd_score,
'max_score': 4,
'signal': 'bullish' if macd_score > 0 else 'bearish' if macd_score < 0 else 'neutral',
'details': macd_text
})
# --- EMA均线系统 ---
ema_score = 0
ema_text = []
close = iv['close']
# 多头排列判定：EMA5 > EMA10 > EMA20 > EMA60
emas = [('EMA5', iv['ema5']), ('EMA10', iv['ema10']),
('EMA20', iv['ema20']), ('EMA60', iv['ema60'])]
valid_emas = [(n, v) for n, v in emas if not np.isnan(v)]
if len(valid_emas) >= 2:
is_bull = all(valid_emas[i][1] >= valid_emas[i+1][1]
for i in range(len(valid_emas)-1))
is_bear = all(valid_emas[i][1] <= valid_emas[i+1][1]
for i in range(len(valid_emas)-1))
if is_bull:
ema_score += 2
ema_text.append('均线多头排列')
elif is_bear:
ema_score -= 2
ema_text.append('均线空头排列')
# 价格相对EMA5位置
if not np.isnan(iv['ema5']):
if close > iv['ema5']:
ema_score += 0.5
ema_text.append('价格站上EMA5')
else:
ema_score -= 0.5
ema_text.append('价格跌破EMA5')
# EMA5/EMA10金叉死叉（短期）
if not np.isnan(iv['ema5']) and not np.isnan(iv['ema10']):
if iv['ema5'] > iv['ema10']:
ema_score += 0.5
ema_text.append('EMA5>EMA10短线偏多')
else:
ema_score -= 0.5
ema_text.append('EMA5<EMA10短线偏空')
# EMA20支撑/压力
if not np.isnan(iv['ema20']):
if close > iv['ema20']:
ema_score += 0.5
ema_text.append('价格在EMA20上方运行')
else:
ema_score -= 0.5
ema_text.append('价格在EMA20下方运行')
signals.append({
'indicator': 'EMA均线系统',
'score': ema_score,
'max_score': 3.5,
'signal': 'bullish' if ema_score > 0 else 'bearish' if ema_score < 0 else 'neutral',
'details': ema_text
})
# --- BOLL ---
boll_score = 0
boll_text = []
if not np.isnan(iv['bb_position']):
bp = iv['bb_position']
if bp > 0.8:
boll_score -= 1
boll_text.append('价格接近上轨，注意回调风险')
elif bp > 0.5:
boll_score += 0.5
boll_text.append('价格在中上轨区间，偏强运行')
elif bp < 0.2:
boll_score += 1
boll_text.append('价格接近下轨，关注反弹机会')
elif bp < 0.5:
boll_score -= 0.5
boll_text.append('价格在中下轨区间，偏弱运行')
# 轨道收窄/扩张
if not np.isnan(iv['bb_width']):
# 简化：用bb_width值判断
bw = iv['bb_width']
if bw > 0.15:
boll_text.append('布林带宽较大，波动剧烈')
elif bw < 0.05:
boll_text.append('布林带极度收窄，变盘在即')
signals.append({
'indicator': 'BOLL(20,2)',
'score': boll_score,
'max_score': 1.5,
'signal': 'bullish' if boll_score > 0 else 'bearish' if boll_score < 0 else 'neutral',
'details': boll_text
})
# 维度总分
total = sum(s['score'] for s in signals)
max_total = sum(s['max_score'] for s in signals)
return {
'name': '趋势判定',
'description': 'MACD + 多级EMA均线定方向，BOLL确认运行格局',
'signals': signals,
'score': total,
'max_score': max_total,
'verdict': 'bullish' if total > max_total * 0.3 else 'bearish' if total < -max_total * 0.3 else 'neutral',
'verdict_text': '趋势偏多' if total > max_total * 0.3 else '趋势偏空' if total < -max_total * 0.3 else '趋势不明/震荡',
}
# ------------------------------------------------------------------
# 维度2：拐点判定 (KDJ + RSI + CCI)
# ------------------------------------------------------------------
def _judge_inflection(self, iv):
"""拐点判定：KDJ+RSI+CCI组合捕捉短期高低点反转"""
signals = []
# --- KDJ(3,3,3) ---
kdj_score = 0
kdj_text = []
if not np.isnan(iv['k']) and not np.isnan(iv['d']):
k, d, j = iv['k'], iv['d'], iv['j']
# 超买超卖
if k > 80 and d > 80:
kdj_score -= 1.5
kdj_text.append('KDJ高位超买区(K={:.0f},D={:.0f})'.format(k, d))
elif k < 20 and d < 20:
kdj_score += 1.5
kdj_text.append('KDJ低位超卖区(K={:.0f},D={:.0f})'.format(k, d))
elif k > 50:
kdj_score += 0.5
kdj_text.append('KDJ处于强势区间')
elif k < 50:
kdj_score -= 0.5
kdj_text.append('KDJ处于弱势区间')
# J值极端
if not np.isnan(j):
if j > 100:
kdj_score -= 1
kdj_text.append('J值>100，严重超买')
elif j < 0:
kdj_score += 1
kdj_text.append('J值<0，严重超卖')
# 金叉死叉
if not np.isnan(iv['k_prev']) and not np.isnan(iv['d_prev']):
if iv['k_prev'] <= iv['d_prev'] and k > d:
kdj_score += 1.5
kdj_text.append('KDJ低位金叉，反转信号')
elif iv['k_prev'] >= iv['d_prev'] and k < d:
kdj_score -= 1.5
kdj_text.append('KDJ高位死叉，见顶信号')
signals.append({
'indicator': 'KDJ(3,3,3)',
'score': kdj_score,
'max_score': 4,
'signal': 'bullish' if kdj_score > 0 else 'bearish' if kdj_score < 0 else 'neutral',
'details': kdj_text
})
# --- RSI(6,14) ---
rsi_score = 0
rsi_text = []
# RSI6 短线
if not np.isnan(iv['rsi6']):
if iv['rsi6'] > 80:
rsi_score -= 1
rsi_text.append('RSI6={:.1f}，严重超买'.format(iv['rsi6']))
elif iv['rsi6'] > 70:
rsi_score -= 0.5
rsi_text.append('RSI6={:.1f}，超买区间'.format(iv['rsi6']))
elif iv['rsi6'] < 20:
rsi_score += 1
rsi_text.append('RSI6={:.1f}，严重超卖'.format(iv['rsi6']))
elif iv['rsi6'] < 30:
rsi_score += 0.5
rsi_text.append('RSI6={:.1f}，超卖区间'.format(iv['rsi6']))
else:
rsi_text.append('RSI6={:.1f}，正常区间'.format(iv['rsi6']))
# RSI14 中线
if not np.isnan(iv['rsi14']):
if iv['rsi14'] > 70:
rsi_score -= 0.5
rsi_text.append('RSI14={:.1f}，偏超买'.format(iv['rsi14']))
elif iv['rsi14'] < 30:
rsi_score += 0.5
rsi_text.append('RSI14={:.1f}，偏超卖'.format(iv['rsi14']))
# RSI背离辅助（简化：用RSI6与RSI14的方向差异）
if not np.isnan(iv['rsi6']) and not np.isnan(iv['rsi14']):
if iv['rsi6'] > 70 and iv['rsi14'] < 50:
rsi_text.append('RSI6/RSI14背离，注意假突破')
elif iv['rsi6'] < 30 and iv['rsi14'] > 50:
rsi_text.append('RSI6/RSI14背离，注意假跌破')
signals.append({
'indicator': 'RSI(6,14)',
'score': rsi_score,
'max_score': 2,
'signal': 'bullish' if rsi_score > 0 else 'bearish' if rsi_score < 0 else 'neutral',
'details': rsi_text
})
# --- CCI(20) ---
cci_score = 0
cci_text = []
if not np.isnan(iv['cci']):
c = iv['cci']
if c > 200:
cci_score -= 1
cci_text.append('CCI={:.0f}，极端强势多头'.format(c))
elif c > 100:
cci_score -= 0.5
cci_text.append('CCI={:.0f}，强势多头区间'.format(c))
elif c < -200:
cci_score += 1
cci_text.append('CCI={:.0f}，极端弱势空头'.format(c))
elif c < -100:
cci_score += 0.5
cci_text.append('CCI={:.0f}，弱势空头区间'.format(c))
else:
cci_text.append('CCI={:.0f}，正常波动区间'.format(c))
signals.append({
'indicator': 'CCI(20)',
'score': cci_score,
'max_score': 1.5,
'signal': 'bullish' if cci_score > 0 else 'bearish' if cci_score < 0 else 'neutral',
'details': cci_text
})
total = sum(s['score'] for s in signals)
max_total = sum(s['max_score'] for s in signals)
return {
'name': '拐点判定',
'description': 'KDJ+RSI+CCI组合捕捉短期高低点反转',
'signals': signals,
'score': total,
'max_score': max_total,
'verdict': 'bullish' if total > max_total * 0.3 else 'bearish' if total < -max_total * 0.3 else 'neutral',
'verdict_text': '拐点偏多/超卖反弹' if total > max_total * 0.3 else '拐点偏空/超买回调' if total < -max_total * 0.3 else '无明显拐点信号',
}
# ------------------------------------------------------------------
# 维度3：行情验证 (成交量 + OBV)
# ------------------------------------------------------------------
def _judge_verification(self, iv, df):
"""行情验证：成交量+OBV验证趋势真实性"""
signals = []
# --- 成交量体系 ---
vol_score = 0
vol_text = []
close = iv['close']
close_prev = iv['close_prev']
# 量比判断
if not np.isnan(iv['vol_ratio5']):
vr = iv['vol_ratio5']
if vr > 2.0:
vol_text.append('5日量比={:.1f}，显著放量'.format(vr))
if close > close_prev:
vol_score += 1.5 # 放量上涨
else:
vol_score -= 1.0 # 放量下跌
elif vr > 1.3:
vol_text.append('5日量比={:.1f}，温和放量'.format(vr))
if close > close_prev:
vol_score += 0.5
elif vr < 0.5:
vol_text.append('5日量比={:.1f}，显著缩量'.format(vr))
# 缩量下跌视为惜售，缩量上涨视为动力不足
if close < close_prev:
vol_score += 0.5 # 缩量下跌=惜售
else:
vol_score -= 0.5
else:
vol_text.append('5日量比={:.1f}，成交量正常'.format(vr))
# 10日量比
if not np.isnan(iv['vol_ratio10']):
vr10 = iv['vol_ratio10']
if vr10 > 1.5:
vol_text.append('10日量比={:.1f}，中期放量'.format(vr10))
signals.append({
'indicator': '成交量体系',
'score': vol_score,
'max_score': 2,
'signal': 'bullish' if vol_score > 0 else 'bearish' if vol_score < 0 else 'neutral',
'details': vol_text
})
# --- OBV ---
obv_score = 0
obv_text = []
if not np.isnan(iv['obv']) and not np.isnan(iv['obv_ma10']):
obv = iv['obv']
obv_ma = iv['obv_ma10']
if obv > obv_ma:
obv_score += 1
obv_text.append('OBV位于均线上方，资金流入')
else:
obv_score -= 1
obv_text.append('OBV位于均线下方，资金流出')
# OBV趋势与价格趋势一致性
if not np.isnan(close_prev) and close > close_prev:
if obv > obv_ma:
obv_text.append('量价齐升，趋势健康')
obv_score += 0.5
else:
obv_text.append('价涨量缩，需警惕量价背离')
obv_score -= 0.5
elif not np.isnan(close_prev) and close < close_prev:
if obv < obv_ma:
obv_text.append('量价齐跌，空头确认')
obv_score -= 0.5
else:
obv_text.append('价跌量升，主力吸筹可能')
obv_score += 0.5
signals.append({
'indicator': 'OBV能量潮',
'score': obv_score,
'max_score': 1.5,
'signal': 'bullish' if obv_score > 0 else 'bearish' if obv_score < 0 else 'neutral',
'details': obv_text
})
total = sum(s['score'] for s in signals)
max_total = sum(s['max_score'] for s in signals)
return {
'name': '行情验证',
'description': '成交量+OBV验证趋势真实性，排除无量假突破/假回落',
'signals': signals,
'score': total,
'max_score': max_total,
'verdict': 'bullish' if total > max_total * 0.2 else 'bearish' if total < -max_total * 0.2 else 'neutral',
'verdict_text': '量能验证通过，趋势可信' if total > max_total * 0.2 else '量能不支持，警惕假信号' if total < -max_total * 0.2 else '量能中性',
}
# ------------------------------------------------------------------
# 维度4：强度确认 (DMI + MFI)
# ------------------------------------------------------------------
def _judge_strength(self, iv):
"""强度确认：DMI+MFI确认行情动能强弱与资金认可度"""
signals = []
# --- DMI(14,6) ---
dmi_score = 0
dmi_text = []
if not np.isnan(iv['adx']):
adx = iv['adx']
# ADX > 25 趋势明确
if adx > 50:
dmi_text.append('ADX={:.1f}，极强趋势'.format(adx))
dmi_score += 1.5
elif adx > 25:
dmi_text.append('ADX={:.1f}，趋势明确'.format(adx))
dmi_score += 1
elif adx > 20:
dmi_text.append('ADX={:.1f}，趋势形成中'.format(adx))
dmi_score += 0.5
else:
dmi_text.append('ADX={:.1f}，无明确趋势/震荡市'.format(adx))
# +DI/-DI 判断多空力量
if not np.isnan(iv['plus_di']) and not np.isnan(iv['minus_di']):
pdi, mdi = iv['plus_di'], iv['minus_di']
if pdi > mdi:
dmi_score += 1
dmi_text.append('+DI({:.1f})>-DI({:.1f})，多头主导'.format(pdi, mdi))
else:
dmi_score -= 1
dmi_text.append('-DI({:.1f})>+DI({:.1f})，空头主导'.format(mdi, pdi))
# DI差值
if not np.isnan(iv['di_diff']):
diff = iv['di_diff']
if diff > 20:
dmi_text.append('多空力量差={:.1f}，多方优势明显'.format(diff))
dmi_score += 0.5
elif diff < -20:
dmi_text.append('多空力量差={:.1f}，空方优势明显'.format(diff))
dmi_score -= 0.5
signals.append({
'indicator': 'DMI(14,6)',
'score': dmi_score,
'max_score': 3,
'signal': 'bullish' if dmi_score > 0 else 'bearish' if dmi_score < 0 else 'neutral',
'details': dmi_text
})
# --- MFI(14) ---
mfi_score = 0
mfi_text = []
if not np.isnan(iv['mfi']):
mfi = iv['mfi']
if mfi > 80:
mfi_score -= 1
mfi_text.append('MFI={:.1f}，资金过度流入/超买'.format(mfi))
elif mfi > 60:
mfi_score += 0.5
mfi_text.append('MFI={:.1f}，资金持续流入'.format(mfi))
elif mfi < 20:
mfi_score += 1
mfi_text.append('MFI={:.1f}，资金过度流出/超卖'.format(mfi))
elif mfi < 40:
mfi_score -= 0.5
mfi_text.append('MFI={:.1f}，资金持续流出'.format(mfi))
else:
mfi_text.append('MFI={:.1f}，资金流向中性'.format(mfi))
signals.append({
'indicator': 'MFI(14)',
'score': mfi_score,
'max_score': 1.5,
'signal': 'bullish' if mfi_score > 0 else 'bearish' if mfi_score < 0 else 'neutral',
'details': mfi_text
})
total = sum(s['score'] for s in signals)
max_total = sum(s['max_score'] for s in signals)
return {
'name': '强度确认',
'description': 'DMI+MFI确认行情动能强弱与资金认可度',
'signals': signals,
'score': total,
'max_score': max_total,
'verdict': 'bullish' if total > max_total * 0.3 else 'bearish' if total < -max_total * 0.3 else 'neutral',
'verdict_text': '动能强劲，资金认可' if total > max_total * 0.3 else '动能衰竭，资金撤离' if total < -max_total * 0.3 else '动能中性',
}
# ------------------------------------------------------------------
# 维度5：风控配套 (SAR)
# ------------------------------------------------------------------
def _judge_risk(self, iv):
"""风控配套：SAR自动锚定趋势止盈止损点位"""
signals = []
sar_score = 0
sar_text = []
if not np.isnan(iv['sar']) and not np.isnan(iv['sar_signal']):
close = iv['close']
sar = iv['sar']
if iv['sar_signal'] == 1:
# SAR在价格下方 = 多头趋势
sar_score += 2
sar_text.append('SAR={:.2f}在价格下方，多头趋势运行'.format(sar))
sar_text.append('SAR可作为止损参考位: {:.2f}'.format(sar))
else:
# SAR在价格上方 = 空头趋势
sar_score -= 2
sar_text.append('SAR={:.2f}在价格上方，空头趋势运行'.format(sar))
sar_text.append('SAR可作为止盈/止损参考位: {:.2f}'.format(sar))
# SAR与价格距离（衡量风险敞口）
distance = abs(close - sar) / close * 100
if distance > 5:
sar_text.append('价格偏离SAR {:.1f}%，趋势强劲但注意回撤'.format(distance))
elif distance < 1:
sar_text.append('价格接近SAR {:.1f}%，趋势可能反转'.format(distance))
signals.append({
'indicator': 'SAR(0.02,0.2)',
'score': sar_score,
'max_score': 2,
'signal': 'bullish' if sar_score > 0 else 'bearish' if sar_score < 0 else 'neutral',
'details': sar_text
})
return {
'name': '风控配套',
'description': 'SAR自动锚定趋势止盈止损点位，辅助风控量化',
'signals': signals,
'score': sar_score,
'max_score': 2,
'verdict': 'bullish' if sar_score > 0 else 'bearish' if sar_score < 0 else 'neutral',
'verdict_text': 'SAR多头运行，可持仓' if sar_score > 0 else 'SAR空头运行，宜止损' if sar_score < 0 else 'SAR中性',
}
# ------------------------------------------------------------------
# 信号共振计算
# ------------------------------------------------------------------
def _calc_resonance(self, dimensions):
"""
多指标同向共振优先采信，指标背离则暂缓判定
共振等级：
- 强共振：5个维度全部同向
- 弱共振：4个维度同向
- 共振：3个维度同向
- 分歧：2/3 维度反向
- 强分歧：各维度方向混乱
- 背离：方向完全不一致
"""
verdicts = {k: v['verdict'] for k, v in dimensions.items()}
bullish_count = sum(1 for v in verdicts.values() if v == 'bullish')
bearish_count = sum(1 for v in verdicts.values() if v == 'bearish')
neutral_count = sum(1 for v in verdicts.values() if v == 'neutral')
total = len(verdicts) # 5
if bullish_count == total:
level = 'strong_bullish'
text = '5维度强共振看多'
elif bearish_count == total:
level = 'strong_bearish'
text = '5维度强共振看空'
elif bullish_count >= 4:
level = 'bullish'
text = '4维度共振看多'
elif bearish_count >= 4:
level = 'bearish'
text = '4维度共振看空'
elif bullish_count >= 3:
level = 'weak_bullish'
text = '3维度偏多共振'
elif bearish_count >= 3:
level = 'weak_bearish'
text = '3维度偏空共振'
elif neutral_count >= 3:
level = 'neutral'
text = '多维度中性，方向不明'
elif bullish_count == 2 and bearish_count == 2:
level = 'divergence'
text = '指标分歧，多空对峙'
elif bullish_count == 2 and bearish_count == 1:
level = 'slight_bullish'
text = '略偏多，但缺乏共振确认'
elif bearish_count == 2 and bullish_count == 1:
level = 'slight_bearish'
text = '略偏空，但缺乏共振确认'
else:
level = 'divergence'
text = '指标严重分歧，暂缓操作'
# 共振分数（-100 到 +100）
score_map = {
'strong_bullish': 90, 'bullish': 70, 'weak_bullish': 40,
'slight_bullish': 20, 'neutral': 0, 'slight_bearish': -20,
'weak_bearish': -40, 'bearish': -70, 'strong_bearish': -90,
'divergence': 0,
}
return {
'level': level,
'text': text,
'score': score_map.get(level, 0),
'bullish_count': bullish_count,
'bearish_count': bearish_count,
'neutral_count': neutral_count,
'distribution': {k: v for k, v in verdicts.items()},
}
# ------------------------------------------------------------------
# 综合研判
# ------------------------------------------------------------------
def _calc_overall(self, dimensions, resonance):
"""综合5维度和共振得出最终信号"""
# 加权总分
weights = {
'trend': 0.30, # 趋势最重要
'inflection': 0.20, # 拐点次之
'verification': 0.20, # 行情验证
'strength': 0.15, # 强度确认
'risk_control': 0.15, # 风控
}
weighted_score = 0
for dim_name, dim_data in dimensions.items():
w = weights.get(dim_name, 0.2)
max_s = dim_data['max_score'] if dim_data['max_score'] > 0 else 1
normalized = dim_data['score'] / max_s # -1 到 +1
weighted_score += normalized * w
# -1 到 +1 映射到分数
total_score = round(weighted_score * 100, 1)
# 结合共振等级修正
res_level = resonance['level']
if 'strong' in res_level:
# 强共振放大信号
total_score = total_score * 1.2
elif 'divergence' in res_level:
# 分歧缩小信号
total_score = total_score * 0.5
total_score = max(-100, min(100, round(total_score, 1)))
# 最终信号
if total_score > 40:
signal = 'bullish'
signal_text = '多头信号'
elif total_score > 15:
signal = 'slightly_bullish'
signal_text = '偏多信号'
elif total_score > -15:
signal = 'neutral'
signal_text = '观望信号'
elif total_score > -40:
signal = 'slightly_bearish'
signal_text = '偏空信号'
else:
signal = 'bearish'
signal_text = '空头信号'
# 操作建议
if signal == 'bullish':
action = '积极做多，注意SAR止损位'
elif signal == 'slightly_bullish':
action = '可轻仓参与，严格风控'
elif signal == 'neutral':
action = '暂缓操作，等待明确信号'
elif signal == 'slightly_bearish':
action = '减仓观望，不宜追高'
else:
action = '回避风险，考虑止损离场'
return {
'signal': signal,
'signal_text': signal_text,
'score': total_score,
'action': action,
'resonance_level': res_level,
'resonance_text': resonance['text'],
}
# ------------------------------------------------------------------
# 指标详情构建（供前端展示）
# ------------------------------------------------------------------
def _build_detail(self, iv, dimensions):
"""构建前端展示用的指标数值详情"""
return {
'MACD': {
'DIF': self._safe_round(iv['macd'], 4),
'DEA': self._safe_round(iv['macd_signal'], 4),
'MACD柱': self._safe_round(iv['macd_hist'], 4),
},
'KDJ': {
'K': self._safe_round(iv['k'], 2),
'D': self._safe_round(iv['d'], 2),
'J': self._safe_round(iv['j'], 2),
},
'EMA': {
'EMA5': self._safe_round(iv['ema5'], 2),
'EMA10': self._safe_round(iv['ema10'], 2),
'EMA20': self._safe_round(iv['ema20'], 2),
'EMA60': self._safe_round(iv['ema60'], 2),
},
'BOLL': {
'上轨': self._safe_round(iv['bb_upper'], 2),
'中轨': self._safe_round(iv['bb_middle'], 2),
'下轨': self._safe_round(iv['bb_lower'], 2),
'位置': self._safe_round(iv['bb_position'], 3),
},
'RSI': {
'RSI6': self._safe_round(iv['rsi6'], 2),
'RSI14': self._safe_round(iv['rsi14'], 2),
},
'CCI': {
'CCI': self._safe_round(iv['cci'], 2),
},
'成交量': {
'5日均量': self._safe_round(iv['vol_ma5'], 0),
'10日均量': self._safe_round(iv['vol_ma10'], 0),
'5日量比': self._safe_round(iv['vol_ratio5'], 2),
},
'OBV': {
'OBV': self._safe_round(iv['obv'], 0),
'OBV_MA10': self._safe_round(iv['obv_ma10'], 0),
},
'DMI': {
'ADX': self._safe_round(iv['adx'], 2),
'+DI': self._safe_round(iv['plus_di'], 2),
'-DI': self._safe_round(iv['minus_di'], 2),
},
'SAR': {
'SAR': self._safe_round(iv['sar'], 2),
'信号': '多头' if iv.get('sar_signal') == 1 else '空头',
},
'MFI': {
'MFI': self._safe_round(iv['mfi'], 2),
},
}
@staticmethod
def _safe_round(val, digits):
if val is None or (isinstance(val, float) and np.isnan(val)):
return None
return round(float(val), digits)
# ============================================================================
# 向后兼容的增强入口
# ============================================================================
def enhance_kronos_features(df: pd.DataFrame) -> pd.DataFrame:
"""为Kronos模型增强特征（保持向后兼容）"""
indicators = TechnicalIndicators()
enhanced_df = indicators.calculate_all_indicators(df)
vp_analyzer = VolumePriceAnalyzer()
vp = vp_analyzer.analyze_volume_price_relationship(enhanced_df)
for k, v in vp.items():
enhanced_df[f'vp_{k}'] = v
w_analyzer = WaveTheoryAnalyzer()
wa = w_analyzer.analyze_wave_patterns(enhanced_df)
for k, v in wa.items():
if isinstance(v, dict):
for sk, sv in v.items():
enhanced_df[f'wave_{k}_{sk}'] = sv
else:
enhanced_df[f'wave_{k}'] = v
return enhanced_df
def run_multidimension_analysis(df: pd.DataFrame) -> Dict:
"""
执行多维度联动研判（供路由调用）
Parameters
----------
df : DataFrame OHLCV 数据
Returns
-------
dict 完整的多维度研判结果
"""
analyzer = MultiDimensionSignalAnalyzer()
return analyzer.analyze(df)
# ============================================================================
# 测试
# ============================================================================
if __name__ == "__main__":
dates = pd.date_range('2024-01-01', periods=120, freq='D')
np.random.seed(42)
base = 100
prices = [base]
for i in range(119):
change = np.random.normal(0, 0.02)
prices.append(max(0.01, prices[-1] * (1 + change)))
df = pd.DataFrame({
'timestamps': dates,
'open': prices,
'high': [p * (1 + abs(np.random.normal(0, 0.015))) for p in prices],
'low': [p * (1 - abs(np.random.normal(0, 0.015))) for p in prices],
'close': prices,
'volume': np.random.normal(1000000, 300000, 120),
})
# 测试多维度研判
result = run_multidimension_analysis(df)
print("=" * 60)
print("多维度技术指标联动研判结果")
print("=" * 60)
for dim_name, dim_data in result['dimensions'].items():
print(f"\n【{dim_data['name']}】 {dim_data['description']}")
print(f" 判定: {dim_data['verdict_text']} (得分: {dim_data['score']:.1f}/{dim_data['max_score']:.1f})")
for sig in dim_data['signals']:
print(f" - {sig['indicator']}: {sig['signal']} ({sig['score']:.1f}/{sig['max_score']:.1f})")
for d in sig['details']:
print(f" {d}")
print(f"\n{'=' * 60}")
print(f"【信号共振】 {result['resonance']['text']}")
print(f" 共振等级: {result['resonance']['level']}")
print(f" 共振分数: {result['resonance']['score']}")
print(f" 分布: 看多={result['resonance']['bullish_count']} "
f"看空={result['resonance']['bearish_count']} "
f"中性={result['resonance']['neutral_count']}")
print(f"\n【综合研判】 {result['overall_signal']['signal_text']}")
print(f" 综合分数: {result['overall_signal']['score']}")
print(f" 操作建议: {result['overall_signal']['action']}")
print(f"\n【指标详情】")
for name, vals in result['indicators_detail'].items():
val_str = ' | '.join(f"{k}={v}" for k, v in vals.items() if v is not None)
print(f" {name}: {val_str}")
|