1

backend/app/crypto_agent/crypto_agent.py

@@ -3304,7 +3304,7 @@ class CryptoAgent:
 
     def _validate_data(self, data: Dict[str, pd.DataFrame]) -> bool:
         """验证数据完整性"""
-        required_intervals = ['1m', '5m', '15m', '30m', '1h', '4h']
+        required_intervals = ['5m', '15m', '1h', '4h', '1d']
         for interval in required_intervals:
             if interval not in data or data[interval].empty:
                 return False

backend/app/crypto_agent/market_signal_analyzer.py

@@ -31,8 +31,8 @@ class MarketSignalAnalyzer:
     TREND_ANALYSIS_TEMPERATURE = 0.10
     ANALYSIS_MAX_TOKENS = 1200
     LANE_MIN_CONFIDENCE = {
-        'short_term': 60,
-        'medium_term': 65,
+        'short_term': 70,
+        'medium_term': 70,
     }
     LANE_MIN_RISK_REWARD = {
         'short_term': 1.5,
@@ -51,7 +51,7 @@ class MarketSignalAnalyzer:
 
     INTRADAY_ANALYSIS_PROMPT = """你是一位专业的加密货币日内交易员，只负责生成 short_term 信号。
 
-你的任务是基于 5m / 15m / 30m、当日开盘、VWAP、开盘区间、关键位、Fib 回撤位和衍生品拥挤度，判断未来 30 分钟到 4 小时内是否存在可执行 setup。
+你的任务是基于 5m / 15m、当日开盘、VWAP、开盘区间、关键位、Fib 回撤位和衍生品拥挤度，判断未来 30 分钟到 4 小时内是否存在可执行 setup。
 
 执行原则：
 1. 先判断日内 regime：trending / ranging / neutral。
@@ -75,8 +75,8 @@ class MarketSignalAnalyzer:
 8. grade / confidence 约束：
    - A: 80-100，结构、位置、量价、时机都对齐
    - B: 70-79，条件较完整但仍有一项次优
-   - C: 60-69，只有轻仓试错级别
-   - 60 以下不要输出交易信号
+   - C: 70-71，只有轻仓试错级别
+   - 70 以下不要输出交易信号
 9. 止损止盈距离下限：
    - short_term 止损距离至少 0.6%
    - short_term 止盈距离至少 1.0%
@@ -118,13 +118,13 @@ class MarketSignalAnalyzer:
 
     TREND_ANALYSIS_PROMPT = """你是一位专业的加密货币趋势交易员，只负责生成 medium_term 信号。
 
-你的任务是基于 1h / 4h、关键位、Fib 回撤/扩展位、趋势阶段、反转检测、衍生品拥挤度和新闻催化，判断未来 4 小时到 3 天内是否存在趋势 setup。
+你的任务是基于 1h / 4h / 1d、关键位、Fib 回撤/扩展位、趋势阶段、反转检测、衍生品拥挤度和新闻催化，判断未来 4 小时到 1 周内是否存在趋势 setup。
 
 执行原则：
-1. 4h 决定大方向，1h 决定节奏与入场位置。
+1. 4h/1d 决定大方向，1h 决定节奏与入场位置。
 2. 只做两类交易：
-   - 趋势延续：4h 趋势明确，1h 回踩关键位后确认继续
-   - 趋势反转：4h 结构和 1h 动能同时改善，且反转证据充分
+   - 趋势延续：4h/1d 趋势明确，1h 回踩关键位后确认继续
+   - 趋势反转：4h/1d 结构和 1h 动能同时改善，且反转证据充分
 3. 禁止仅凭 15m 噪音逆 4h 开仓。
 4. 趋势晚期、资金费率过热或价格过度偏离关键均线时，要显著降低开仓积极性。
 5. 没有清晰位置优势就不交易。
@@ -134,16 +134,16 @@ class MarketSignalAnalyzer:
 信号要求：
 1. 只允许输出 0 或 1 个 medium_term 信号。
 2. 盈亏比至少 1:1.8。
-3. 如果 4h 与 1h 明显冲突，优先返回空信号。
+3. 如果 4h/1d 与 1h 明显冲突，优先返回空信号。
 4. 反转信号必须比延续信号更严格。
 5. 如果趋势处于晚期且没有回踩确认，或反转证据不足，必须返回空信号。
 6. 只有在位置优势和方向一致性都充分时才允许开仓。
 7. 趋势延续单的 entry 应优先靠近优先支撑/阻力或对应共振区，不在远离关键位的位置追价。
 8. grade / confidence 约束：
-   - A: 82-100，4h/1h 同向且位置优
+   - A: 82-100，4h/1d/1h 同向且位置优
    - B: 72-81，趋势或反转证据较完整
-   - C: 65-71，仅限早期确认不足的轻仓趋势尝试
-   - 65 以下不要输出交易信号
+   - C: 70-71，仅限早期确认不足的轻仓趋势尝试
+   - 70 以下不要输出交易信号
 9. 止损止盈距离下限：
    - medium_term 止损距离至少 1.0%
    - medium_term 止盈距离至少 2.0%
@@ -270,12 +270,12 @@ class MarketSignalAnalyzer:
 
         feature_5m = self._summarize_timeframe_features(data.get('5m'), '5m')
         feature_15m = self._summarize_timeframe_features(data.get('15m'), '15m')
-        feature_30m = self._summarize_timeframe_features(data.get('30m'), '30m')
         feature_1h = self._summarize_timeframe_features(data.get('1h'), '1h')
         feature_4h = self._summarize_timeframe_features(data.get('4h'), '4h')
+        feature_1d = self._summarize_timeframe_features(data.get('1d'), '1d')
 
-        intraday_alignment = self._describe_alignment([feature_5m, feature_15m, feature_30m])
-        trend_alignment = self._describe_alignment([feature_1h, feature_4h])
+        intraday_alignment = self._describe_alignment([feature_5m, feature_15m])
+        trend_alignment = self._describe_alignment([feature_1h, feature_4h, feature_1d])
         range_zone = self._detect_range_zone(data)
         reversal_detection = self._detect_trend_reversal(data)
         trend_stage = self._detect_trend_stage(data)
@@ -304,7 +304,6 @@ class MarketSignalAnalyzer:
             "## 日内特征",
             self._format_feature_line(feature_5m),
             self._format_feature_line(feature_15m),
-            self._format_feature_line(feature_30m),
             f"- 日内级别一致性: {intraday_alignment}",
         ]
 
@@ -312,6 +311,7 @@ class MarketSignalAnalyzer:
             "## 趋势特征",
             self._format_feature_line(feature_1h),
             self._format_feature_line(feature_4h),
+            self._format_feature_line(feature_1d),
             f"- 趋势级别一致性: {trend_alignment}",
         ]
 
@@ -356,11 +356,35 @@ class MarketSignalAnalyzer:
         else:
             trend_parts.append("- 反转检测: 无显著反转信号")
 
+        # 震荡市场量化
+        range_metrics = self._quantify_ranging_state(data)
+        range_warning = ""
+        if range_metrics['regime'] in ('ranging', 'transitional'):
+            range_warning = (
+                f"\n## 震荡市场警告\n"
+                f"- 当前市场状态: {range_metrics['regime']} (score {range_metrics['regime_score']}/100)\n"
+                f"- ATR(1h): {range_metrics['atr_pct']:.3f}%\n"
+                f"- EMA收敛度: {range_metrics['ema_convergence_pct']:.3f}%\n"
+                f"- 方向效率: {range_metrics['range_efficiency']:.2f} (0=纯震荡, 1=纯趋势)\n"
+                f"- ADX: {range_metrics['adx']:.1f}\n"
+                f"- 建议: {'避免趋势策略，只做区间边界反转' if range_metrics['regime'] == 'ranging' else '谨慎开仓，降低仓位'}\n"
+            )
+            intraday_parts.append(
+                f"- ⚠️ 震荡市场: regime={range_metrics['regime']} (score {range_metrics['regime_score']}) | "
+                f"ATR={range_metrics['atr_pct']:.2f}% | EMA收敛={range_metrics['ema_convergence_pct']:.2f}% | "
+                f"效率={range_metrics['range_efficiency']:.2f} | ADX={range_metrics['adx']:.1f}"
+            )
+            trend_parts.append(
+                f"- 市场结构: {range_metrics['regime']} | ADX={range_metrics['adx']:.1f} | "
+                f"{'BB收口' if range_metrics['bb_squeeze'] else 'BB正常'}"
+            )
+
         return {
             'snapshot': "\n".join(snapshot_parts),
             'intraday': "\n".join(intraday_parts),
             'trend': "\n".join(trend_parts),
             'levels': "\n".join(levels_parts),
+            'range_warning': range_warning,
         }
 
     def _get_session_open(self, df: Optional[pd.DataFrame]) -> Optional[float]:
@@ -449,6 +473,8 @@ class MarketSignalAnalyzer:
             'distance_to_recent_high': None,
             'distance_to_recent_low': None,
             'is_accelerating': False,
+            'adx': None,
+            'trend_strength_adx': 'unknown',
         }
 
         if df is None or df.empty or len(df) < 20:
@@ -475,6 +501,19 @@ class MarketSignalAnalyzer:
             'is_accelerating': self._is_accelerating(df),
         })
 
+        # ADX 趋势强度
+        adx = latest.get('adx')
+        if pd.notna(adx):
+            feature['adx'] = float(adx)
+            if adx >= 40:
+                feature['trend_strength_adx'] = 'strong'
+            elif adx >= 25:
+                feature['trend_strength_adx'] = 'moderate'
+            elif adx >= 20:
+                feature['trend_strength_adx'] = 'weak'
+            else:
+                feature['trend_strength_adx'] = 'ranging'
+
         if pd.notna(ema5) and pd.notna(ema10) and pd.notna(ema20):
             if ema5 > ema10 > ema20:
                 feature['ema_alignment'] = 'bull'
@@ -499,15 +538,21 @@ class MarketSignalAnalyzer:
         def fmt(value: Optional[float], digits: int = 2) -> str:
             return "N/A" if value is None else f"{value:+.{digits}f}%"
 
+        def safe(value: Optional[float], digits: int = 1) -> str:
+            return "N/A" if value is None else f"{value:.{digits}f}"
+
+        adx_part = f" | ADX={safe(feature.get('adx'), 1)}({feature['trend_strength_adx']})" if feature.get('adx') else ""
+
         return (
             f"- {feature['timeframe']}: 结构={feature['structure']} | EMA={feature['ema_alignment']} | "
             f"3bar={fmt(feature['momentum_3'])} | 12bar={fmt(feature['momentum_12'])} | "
-            f"RSI={feature['rsi']:.1f} | ATR={feature['atr_pct']:.2f}% | "
-            f"量比={feature['volume_ratio']:.2f} | "
+            f"RSI={safe(feature['rsi'], 1)} | ATR={safe(feature['atr_pct'], 2)}% | "
+            f"量比={safe(feature['volume_ratio'], 2)} | "
             f"距EMA20={fmt(feature['distance_to_ema20'])} | "
             f"距20bar高点={fmt(feature['distance_to_recent_high'])} | "
             f"距20bar低点={fmt(feature['distance_to_recent_low'])} | "
             f"加速={'是' if feature['is_accelerating'] else '否'}"
+            f"{adx_part}"
         )
 
     def _describe_alignment(self, features: List[Dict[str, Any]]) -> str:
@@ -543,7 +588,7 @@ class MarketSignalAnalyzer:
         if range_zone.get('resistance_level'):
             resistance_candidates.append(self._make_level_candidate(float(range_zone['resistance_level']), 1.2, "区间上沿"))
 
-        for timeframe, count, tf_weight in [('30m', 20, 1.0), ('1h', 20, 1.15), ('4h', 12, 1.3)]:
+        for timeframe, count, tf_weight in [('15m', 20, 0.95), ('1h', 20, 1.15), ('4h', 12, 1.3), ('1d', 10, 1.5)]:
             df = data.get(timeframe)
             if df is None or len(df) < count:
                 continue
@@ -603,8 +648,9 @@ class MarketSignalAnalyzer:
         }
 
         fib_specs = [
-            ('30m', 48, 'intraday', '日内'),
+            ('15m', 48, 'intraday', '日内'),
             ('4h', 60, 'trend', '趋势'),
+            ('1d', 30, 'trend', '日线'),
         ]
 
         for timeframe, lookback, key, label in fib_specs:
@@ -623,7 +669,10 @@ class MarketSignalAnalyzer:
             if fib_result.get('trade_zone'):
                 summary += f" | 可交易区={fib_result['trade_zone']}"
 
-            contexts[key] = summary
+            if key in contexts and contexts[key]:
+                contexts[key] += f"\n{summary}"
+            else:
+                contexts[key] = summary
             contexts['support_levels'].extend(fib_result.get('support_levels', []))
             contexts['resistance_levels'].extend(fib_result.get('resistance_levels', []))
             contexts['support_details'].extend(fib_result.get('support_details', []))
@@ -1187,13 +1236,13 @@ class MarketSignalAnalyzer:
         lane_scope = (
             [
                 "只根据下面提供的日内结构化特征做判断，不要脑补未提供的数据。",
-                "重点阅读 5m/15m/30m、当日开盘、VWAP、开盘区间、区间状态、关键位、Fib 回撤位和衍生品过热程度。",
+                "重点阅读 5m/15m、当日开盘、VWAP、开盘区间、区间状态、关键位、Fib 回撤位和衍生品过热程度。",
                 "优先参考“优先支撑/优先阻力”和“可交易多头区/可交易空头区”，不要在远离关键位的位置给 entry。",
             ]
             if lane == "intraday"
             else [
                 "只根据下面提供的趋势结构化特征做判断，不要脑补未提供的数据。",
-                "重点阅读 1h/4h、一致性、趋势阶段、反转检测、关键位、Fib 回撤/扩展位、新闻催化和衍生品拥挤度。",
+                "重点阅读 1h/4h/1d、一致性、趋势阶段、反转检测、关键位、Fib 回撤/扩展位、新闻催化和衍生品拥挤度。",
                 "优先参考“优先支撑/优先阻力”和“可交易多头区/可交易空头区”，趋势单必须体现位置优势，不接受远离关键位追价。",
             ]
         )
@@ -1222,6 +1271,10 @@ class MarketSignalAnalyzer:
             prompt_parts.append("")
             prompt_parts.append(futures_context)
 
+        if market_context.get('range_warning'):
+            prompt_parts.append("")
+            prompt_parts.append(market_context['range_warning'])
+
         prompt_parts.append("")
         prompt_parts.append("输出要求：只返回 system prompt 定义的 JSON 对象。没有高质量 setup 就返回 signals: []。")
 
@@ -1687,16 +1740,16 @@ class MarketSignalAnalyzer:
         }
 
     def _analyze_volatility(self, data: Dict[str, pd.DataFrame]) -> str:
-        """分析波动率变化（使用 30m 作为日内主周期）"""
-        df = data.get('30m')
-        if df is None or len(df) < 24 or 'atr' not in df.columns:
+        """分析波动率变化（使用 1h 作为日内主周期）"""
+        df = data.get('1h')
+        if df is None or len(df) < 12 or 'atr' not in df.columns:
             return ""
 
         lines = []
 
         # ATR 变化趋势
-        recent_atr = df['atr'].iloc[-6:].mean()  # 最近 6 根（3小时）
-        older_atr = df['atr'].iloc[-12:-6].mean()  # 之前 6 根
+        recent_atr = df['atr'].iloc[-6:].mean()  # 最近 6 根（6小时）
+        older_atr = df['atr'].iloc[-12:-6].mean()  # 之前 6 根（6小时）
 
         if pd.isna(recent_atr) or pd.isna(older_atr) or older_atr == 0:
             return ""
@@ -1707,7 +1760,7 @@ class MarketSignalAnalyzer:
         current_price = float(df['close'].iloc[-1])
         atr_percent = current_atr / current_price * 100
 
-        lines.append(f"当前 ATR (30m): ${current_atr:.2f} ({atr_percent:.2f}%)")
+        lines.append(f"当前 ATR (1h): ${current_atr:.2f} ({atr_percent:.2f}%)")
 
         if atr_change > 20:
             lines.append(f"**波动率扩张**: ATR 上升 {atr_change:.0f}%，日内趋势可能启动")
@@ -1728,6 +1781,109 @@ class MarketSignalAnalyzer:
 
         return "\n".join(lines)
 
+    def _quantify_ranging_state(self, data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
+        """
+        量化震荡市场指标，帮助 LLM 在震荡行情中避免追涨杀跌
+
+        Returns:
+            包含 ATR%、EMA 收敛度、方向效率、BB 挤压、ADX、regime 的字典
+        """
+        result = {
+            'atr_pct': 0.0,
+            'atr_ratio_trend': 0.0,
+            'ema_convergence_pct': 0.0,
+            'range_efficiency': 0.0,
+            'bb_squeeze': False,
+            'adx': 0.0,
+            'regime': 'unknown',
+            'regime_score': 0,
+        }
+
+        try:
+            df_1h = data.get('1h')
+            if df_1h is None or len(df_1h) < 20:
+                return result
+
+            price = float(df_1h['close'].iloc[-1])
+
+            # ATR 占价格百分比
+            if 'atr' in df_1h.columns:
+                atr = float(df_1h['atr'].iloc[-1])
+                result['atr_pct'] = atr / price * 100 if price > 0 else 0
+
+                # ATR 趋势（扩张/收缩）
+                if len(df_1h) >= 18:
+                    atr_recent = df_1h['atr'].iloc[-6:].mean()
+                    atr_older = df_1h['atr'].iloc[-18:-6].mean()
+                    if atr_older > 0:
+                        result['atr_ratio_trend'] = float((atr_recent - atr_older) / atr_older)
+
+            # EMA 收敛度
+            if all(col in df_1h.columns for col in ['ema5', 'ema10', 'ema20']):
+                ema_short = float(df_1h['ema5'].iloc[-1])
+                ema_mid = float(df_1h['ema10'].iloc[-1])
+                ema_long = float(df_1h['ema20'].iloc[-1])
+                ema_spread = (max(ema_short, ema_mid, ema_long) - min(ema_short, ema_mid, ema_long))
+                result['ema_convergence_pct'] = ema_spread / price * 100 if price > 0 else 0
+
+            # 方向效率（Choppiness Index 近似）
+            lookback = 14
+            if len(df_1h) >= lookback:
+                high_low_range = df_1h['high'].iloc[-lookback:].max() - df_1h['low'].iloc[-lookback:].min()
+                directional_move = abs(df_1h['close'].iloc[-1] - df_1h['close'].iloc[-lookback])
+                result['range_efficiency'] = float(directional_move / high_low_range) if high_low_range > 0 else 0
+
+            # Bollinger Band 挤压
+            if all(col in df_1h.columns for col in ['bb_upper', 'bb_lower']) and len(df_1h) >= 20:
+                bb_width_current = (df_1h['bb_upper'].iloc[-1] - df_1h['bb_lower'].iloc[-1]) / price * 100
+                bb_width_ma = (df_1h['bb_upper'].iloc[-20:] - df_1h['bb_lower'].iloc[-20:]).mean() / \
+                              df_1h['close'].iloc[-20:].mean() * 100
+                result['bb_squeeze'] = bb_width_ma > 0 and bb_width_current < bb_width_ma * 0.7
+
+            # ADX
+            if 'adx' in df_1h.columns:
+                adx_val = df_1h['adx'].iloc[-1]
+                if pd.notna(adx_val):
+                    result['adx'] = float(adx_val)
+
+            # Regime 分类
+            score = 0
+            if result['ema_convergence_pct'] < 0.5:
+                score += 30
+            elif result['ema_convergence_pct'] < 1.0:
+                score += 20
+
+            if result['range_efficiency'] < 0.2:
+                score += 30
+            elif result['range_efficiency'] < 0.4:
+                score += 15
+
+            if result['atr_ratio_trend'] < -0.2:
+                score += 20
+
+            if result['bb_squeeze']:
+                score += 10
+
+            if result['adx'] < 20:
+                score += 20
+            elif result['adx'] < 25:
+                score += 10
+
+            result['regime_score'] = score
+            if score >= 75:
+                result['regime'] = 'ranging'
+            elif score >= 50:
+                result['regime'] = 'transitional'
+            elif score >= 25:
+                result['regime'] = 'weak_trend'
+            else:
+                result['regime'] = 'strong_trend'
+
+        except Exception as e:
+            logger.warning(f"震荡市场量化失败: {e}")
+
+        return result
+
     def _detect_range_zone(self, data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
         """
         检测震荡区间 - 计算明确的支撑位和压力位
@@ -1750,23 +1906,22 @@ class MarketSignalAnalyzer:
         }
 
         try:
-            df_30m = data.get('30m')
             df_1h = data.get('1h')
             df_15m = data.get('15m')
 
-            if df_30m is None or len(df_30m) < 48:  # 需要至少48根K线（24小时）
+            if df_1h is None or len(df_1h) < 24:  # 需要至少24根K线（24小时）
                 return result
 
-            current_price = float(df_30m['close'].iloc[-1])
+            current_price = float(df_1h['close'].iloc[-1])
 
             # ========== 1. 价格通道分析 ==========
-            # 使用最近24-48根K线（12-24小时）计算价格通道
-            lookback_periods = [24, 36, 48]
+            # 使用最近12-24根K线（12-24小时）计算价格通道
+            lookback_periods = [12, 18, 24]
             price_channels = []
 
             for period in lookback_periods:
-                if len(df_30m) >= period:
-                    period_data = df_30m.iloc[-period:]
+                if len(df_1h) >= period:
+                    period_data = df_1h.iloc[-period:]
                     high = period_data['high'].max()
                     low = period_data['low'].min()
                     price_channels.append({'high': high, 'low': low, 'width': high - low})
@@ -1782,38 +1937,32 @@ class MarketSignalAnalyzer:
                 range_width_pct = (range_width / current_price) * 100
 
                 # 震荡区间判断标准
-                # 1. 区间宽度 < 5%（震荡市）
-                # 2. 价格在区间中位数附近
-                # 3. EMA 纠缠
                 is_narrow_range = range_width_pct < 5.0
                 price_in_middle = (current_price - support) / range_width > 0.3 and \
                                  (current_price - support) / range_width < 0.7
 
                 # EMA 纠缠检查
-                ema5 = df_30m['ma5'].iloc[-1] if 'ma5' in df_30m.columns else None
-                ema10 = df_30m['ma10'].iloc[-1] if 'ma10' in df_30m.columns else None
-                ema20 = df_30m['ma20'].iloc[-1] if 'ma20' in df_30m.columns else None
+                ema5 = df_1h['ma5'].iloc[-1] if 'ma5' in df_1h.columns else None
+                ema10 = df_1h['ma10'].iloc[-1] if 'ma10' in df_1h.columns else None
+                ema20 = df_1h['ma20'].iloc[-1] if 'ma20' in df_1h.columns else None
                 ema_entangled = False
                 if all([ema5, ema10, ema20]):
                     ema_spread = (max(ema5, ema10, ema20) - min(ema5, ema10, ema20)) / current_price * 100
-                    ema_entangled = ema_spread < 1.0  # EMA 排列差距 < 1%
+                    ema_entangled = ema_spread < 1.0
 
                 # ========== 2. 成交量密集区分析 ==========
                 volume_profile_support = None
                 volume_profile_resistance = None
 
-                if len(df_30m) >= 48:
-                    # 找出成交量最大的价格区间
-                    df_30m_copy = df_30m.iloc[-48:].copy()
-                    df_30m_copy['avg_price'] = (df_30m_copy['high'] + df_30m_copy['low'] + df_30m_copy['close']) / 3
-                    df_30m_copy['volume_weight'] = df_30m_copy['volume'] * df_30m_copy['avg_price']
+                if len(df_1h) >= 24:
+                    df_1h_copy = df_1h.iloc[-24:].copy()
+                    df_1h_copy['avg_price'] = (df_1h_copy['high'] + df_1h_copy['low'] + df_1h_copy['close']) / 3
+                    df_1h_copy['volume_weight'] = df_1h_copy['volume'] * df_1h_copy['avg_price']
 
-                    # 按价格分层，找出高成交量区域
-                    price_bins = pd.cut(df_30m_copy['avg_price'], bins=10)
-                    volume_by_price = df_30m_copy.groupby(price_bins, observed=True)['volume'].sum()
+                    price_bins = pd.cut(df_1h_copy['avg_price'], bins=10)
+                    volume_by_price = df_1h_copy.groupby(price_bins, observed=True)['volume'].sum()
 
                     if len(volume_by_price) > 0:
-                        # 高成交量区作为支撑/压力
                         max_vol_bin = volume_by_price.idxmax()
                         if max_vol_bin is not None:
                             vp_level = (max_vol_bin.left + max_vol_bin.right) / 2
@@ -1825,12 +1974,11 @@ class MarketSignalAnalyzer:
                 # ========== 3. 布林带支撑/压力 ==========
                 bb_support = None
                 bb_resistance = None
-                if 'bb_lower' in df_30m.columns and 'bb_upper' in df_30m.columns:
-                    bb_support = float(df_30m['bb_lower'].iloc[-1])
-                    bb_resistance = float(df_30m['bb_upper'].iloc[-1])
+                if 'bb_lower' in df_1h.columns and 'bb_upper' in df_1h.columns:
+                    bb_support = float(df_1h['bb_lower'].iloc[-1])
+                    bb_resistance = float(df_1h['bb_upper'].iloc[-1])
 
                 # ========== 4. 关键价格点综合 ==========
-                # 综合多个指标得出最可靠的支撑/压力位
                 support_candidates = []
                 resistance_candidates = []
 
@@ -1848,7 +1996,6 @@ class MarketSignalAnalyzer:
                 if bb_resistance:
                     resistance_candidates.append(bb_resistance)
 
-                # 取中位数作为最终的支撑/压力位
                 final_support = np.median(support_candidates) if support_candidates else None
                 final_resistance = np.median(resistance_candidates) if resistance_candidates else None
 
@@ -1868,24 +2015,22 @@ class MarketSignalAnalyzer:
                     confidence += 25
                     reasons.append("EMA纠缠")
 
-                # 成交量分布检查 - 如果成交量在区间两端较小，说明是有效震荡
-                if len(df_30m) >= 24:
-                    recent_vol = df_30m['volume'].iloc[-12:].mean()
-                    older_vol = df_30m['volume'].iloc[-24:-12].mean()
+                # 成交量分布检查
+                if len(df_1h) >= 12:
+                    recent_vol = df_1h['volume'].iloc[-6:].mean()
+                    older_vol = df_1h['volume'].iloc[-12:-6].mean()
                     if abs(recent_vol - older_vol) / older_vol < 0.3:
                         confidence += 15
                         reasons.append("成交量平稳")
 
-                # 价格反弹次数 - 检查在支撑/压力位附近是否有多次反弹
+                # 价格反弹次数
                 if final_support and final_resistance:
                     bounce_count = 0
-                    for i in range(-24, 0):
-                        if i >= -len(df_30m):
-                            row = df_30m.iloc[i]
-                            # 检查是否在支撑位附近反弹
+                    for i in range(-12, 0):
+                        if i >= -len(df_1h):
+                            row = df_1h.iloc[i]
                             if abs(row['low'] - final_support) / final_support < 0.005 and row['close'] > row['open']:
                                 bounce_count += 1
-                            # 检查是否在压力位附近回落
                             if abs(row['high'] - final_resistance) / final_resistance < 0.005 and row['close'] < row['open']:
                                 bounce_count += 1
 
@@ -1932,7 +2077,6 @@ class MarketSignalAnalyzer:
 
         try:
             df_15m = data.get('15m')
-            df_30m = data.get('30m')
             df_1h = data.get('1h')
 
             if df_15m is None or len(df_15m) < 30:
@@ -2118,7 +2262,6 @@ class MarketSignalAnalyzer:
 
             # ========== 5. 多周期趋势不一致 ==========
             trend_15m = self._get_trend_direction(df_15m)
-            trend_30m = self._get_trend_direction(df_30m)
             trend_1h = self._get_trend_direction(df_1h)
 
             # 小周期反转但大周期未反应
@@ -2216,13 +2359,12 @@ class MarketSignalAnalyzer:
         }
 
         try:
-            df_30m = data.get('30m')
             df_1h = data.get('1h')
 
-            if df_30m is None or len(df_30m) < 30:
+            if df_1h is None or len(df_1h) < 30:
                 return result
 
-            current_price = float(df_30m['close'].iloc[-1])
+            current_price = float(df_1h['close'].iloc[-1])
 
             stage_signals = []
             early_score = 0
@@ -2230,10 +2372,10 @@ class MarketSignalAnalyzer:
             late_score = 0
 
             # ========== 1. EMA 排列状态 ==========
-            ema5 = df_30m['ma5'].iloc[-1] if 'ma5' in df_30m.columns else None
-            ema10 = df_30m['ma10'].iloc[-1] if 'ma10' in df_30m.columns else None
-            ema20 = df_30m['ma20'].iloc[-1] if 'ma20' in df_30m.columns else None
-            ema50 = df_30m['ma50'].iloc[-1] if 'ma50' in df_30m.columns else None
+            ema5 = df_1h['ma5'].iloc[-1] if 'ma5' in df_1h.columns else None
+            ema10 = df_1h['ma10'].iloc[-1] if 'ma10' in df_1h.columns else None
+            ema20 = df_1h['ma20'].iloc[-1] if 'ma20' in df_1h.columns else None
+            ema50 = df_1h['ma50'].iloc[-1] if 'ma50' in df_1h.columns else None
 
             if all([ema5, ema10, ema20, ema50]):
                 # 检查EMA排列是否形成
@@ -2241,11 +2383,11 @@ class MarketSignalAnalyzer:
                     # 多头排列
                     # 检查排列刚刚形成（早期）还是已经稳定（中期/晚期）
                     ema5_cross_ma20 = False
-                    if len(df_30m) >= 10:
+                    if len(df_1h) >= 10:
                         # 检查最近10根内是否发生过金叉
                         for i in range(-10, 0):
-                            if df_30m['ma5'].iloc[i] > df_30m['ma20'].iloc[i]:
-                                if i > -10 and df_30m['ma5'].iloc[i-1] <= df_30m['ma20'].iloc[i-1]:
+                            if df_1h['ma5'].iloc[i] > df_1h['ma20'].iloc[i]:
+                                if i > -10 and df_1h['ma5'].iloc[i-1] <= df_1h['ma20'].iloc[i-1]:
                                     ema5_cross_ma20 = True
                                     break
 
@@ -2265,10 +2407,10 @@ class MarketSignalAnalyzer:
                 elif ema5 < ema10 < ema20 < ema50:
                     # 空头排列
                     ema5_cross_ma20 = False
-                    if len(df_30m) >= 10:
+                    if len(df_1h) >= 10:
                         for i in range(-10, 0):
-                            if df_30m['ma5'].iloc[i] < df_30m['ma20'].iloc[i]:
-                                if i > -10 and df_30m['ma5'].iloc[i-1] >= df_30m['ma20'].iloc[i-1]:
+                            if df_1h['ma5'].iloc[i] < df_1h['ma20'].iloc[i]:
+                                if i > -10 and df_1h['ma5'].iloc[i-1] >= df_1h['ma20'].iloc[i-1]:
                                     ema5_cross_ma20 = True
                                     break
 
@@ -2285,9 +2427,9 @@ class MarketSignalAnalyzer:
                             stage_signals.append("EMA排列稳定（中期）")
 
             # ========== 2. RSI 状态 ==========
-            if 'rsi' in df_30m.columns:
-                rsi_current = df_30m['rsi'].iloc[-1]
-                rsi_prev = df_30m['rsi'].iloc[-5:-1].values
+            if 'rsi' in df_1h.columns:
+                rsi_current = df_1h['rsi'].iloc[-1]
+                rsi_prev = df_1h['rsi'].iloc[-5:-1].values
 
                 # RSI极端区 - 晚期信号
                 if rsi_current > 70:
@@ -2326,12 +2468,12 @@ class MarketSignalAnalyzer:
                         stage_signals.append("价格贴近EMA20 - 趋势稳固")
 
             # ========== 4. 量价关系 ==========
-            if 'volume' in df_30m.columns and len(df_30m) >= 10:
-                recent_vol = df_30m['volume'].iloc[-5:].mean()
-                older_vol = df_30m['volume'].iloc[-10:-5].mean()
+            if 'volume' in df_1h.columns and len(df_1h) >= 10:
+                recent_vol = df_1h['volume'].iloc[-5:].mean()
+                older_vol = df_1h['volume'].iloc[-10:-5].mean()
                 vol_change = (recent_vol - older_vol) / older_vol * 100
 
-                price_change_5 = (df_30m['close'].iloc[-1] - df_30m['close'].iloc[-5]) / df_30m['close'].iloc[-5] * 100
+                price_change_5 = (df_1h['close'].iloc[-1] - df_1h['close'].iloc[-5]) / df_1h['close'].iloc[-5] * 100
 
                 # 价格上涨但成交量下降（量价背离）- 晚期信号
                 if price_change_5 > 1 and vol_change < -20:
@@ -2348,9 +2490,9 @@ class MarketSignalAnalyzer:
                     stage_signals.append(f"放量下跌（跌{price_change_5:.1f}%量增{vol_change:.0f}%）- 可能早期")
 
             # ========== 5. 波动率状态 ==========
-            if 'atr' in df_30m.columns and len(df_30m) >= 20:
-                recent_atr = df_30m['atr'].iloc[-5:].mean()
-                older_atr = df_30m['atr'].iloc[-15:-5].mean()
+            if 'atr' in df_1h.columns and len(df_1h) >= 20:
+                recent_atr = df_1h['atr'].iloc[-5:].mean()
+                older_atr = df_1h['atr'].iloc[-15:-5].mean()
                 atr_change = (recent_atr - older_atr) / older_atr * 100 if older_atr > 0 else 0
 
                 if atr_change > 30:
@@ -2361,8 +2503,8 @@ class MarketSignalAnalyzer:
                     stage_signals.append(f"ATR收缩({atr_change:.0f}%) - 动能衰竭")
 
             # ========== 6. 连续同向K线数量 ==========
-            if len(df_30m) >= 5:
-                recent_closes = df_30m['close'].iloc[-5:].values
+            if len(df_1h) >= 5:
+                recent_closes = df_1h['close'].iloc[-5:].values
                 consecutive_up = sum(1 for i in range(1, len(recent_closes)) if recent_closes[i] > recent_closes[i-1])
                 consecutive_down = sum(1 for i in range(1, len(recent_closes)) if recent_closes[i] < recent_closes[i-1])
 

backend/app/services/bitget_service.py

@@ -14,12 +14,11 @@ class BitgetService:
 
     # K线周期映射 - 注意 Bitget 使用大写 H
     INTERVALS = {
-        '1m': '1m',
         '5m': '5m',
         '15m': '15m',
-        '30m': '30m',
         '1h': '1H',   # Bitget 大写
         '4h': '4H',   # Bitget 大写
+        '1d': '1D',   # Bitget 大写
     }
 
     # Bitget API 基础 URL
@@ -105,26 +104,24 @@ class BitgetService:
             category: 产品类型，默认 USDT-FUTURES
 
         Returns:
-            包含 1m, 5m, 15m, 30m, 1h, 4h 数据的字典
+            包含 5m, 15m, 1h, 4h, 1d 数据的字典
         """
         # 不同周期使用不同的数据量，平衡分析深度和性能
-        # 1m: 200根 = 3.3小时（超短线精确入场）
-        # 5m: 200根 = 16.7小时（日内分析）
-        # 15m: 200根 = 2.1天（短线分析）
-        # 30m: 200根 = 4.2天（日内趋势）
-        # 1h: 300根 = 12.5天（日内主趋势）
-        # 4h: 180根 = 30天（趋势判断）
+        # 5m: 200根 = 16.7小时（日内微结构）
+        # 15m: 200根 = 2.1天（日内主时间级别）
+        # 1h: 300根 = 12.5天（短期趋势 + 24h 涨跌）
+        # 4h: 180根 = 30天（趋势确认）
+        # 1d: 120根 = 120天（宏观趋势，日线 S/R）
         limits = {
-            '1m': 200,
             '5m': 200,
             '15m': 200,
-            '30m': 200,
             '1h': 300,
-            '4h': 180
+            '4h': 180,
+            '1d': 120,
         }
 
         data = {}
-        for interval in ['1m', '5m', '15m', '30m', '1h', '4h']:
+        for interval in ['5m', '15m', '1h', '4h', '1d']:
             df = self.get_klines(symbol, interval, limit=limits.get(interval, 100),
                                  category=category)
             if not df.empty:
@@ -179,14 +176,13 @@ class BitgetService:
         if df.empty:
             return df
 
-        # 根据周期调整 MA 参数
+        # 根据周期调整 MA 参数（每个时间级别有不同的覆盖范围）
         ma_config = {
-            '1m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
-            '5m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
-            '15m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
-            '30m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
-            '1h': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
-            '4h': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
+            '5m':  {'ma_short': 8,  'ma_mid': 21, 'ma_long': 55,  'ma_extra': 89},
+            '15m': {'ma_short': 7,  'ma_mid': 20, 'ma_long': 50,  'ma_extra': 100},
+            '1h':  {'ma_short': 7,  'ma_mid': 25, 'ma_long': 60,  'ma_extra': 99},
+            '4h':  {'ma_short': 7,  'ma_mid': 25, 'ma_long': 50,  'ma_extra': 100},
+            '1d':  {'ma_short': 5,  'ma_mid': 20, 'ma_long': 60,  'ma_extra': 120},
         }
 
         config = ma_config.get(interval, ma_config['1h'])
@@ -222,6 +218,11 @@ class BitgetService:
         # ATR
         df['atr'] = self._calculate_atr(df['high'], df['low'], df['close'])
 
+        # ADX (趋势强度)
+        df['adx'], df['plus_di'], df['minus_di'] = self._calculate_adx(
+            df['high'], df['low'], df['close']
+        )
+
         # 成交量均线
         df['volume_ma5'] = self._calculate_ma(df['volume'], 5)
         df['volume_ma20'] = self._calculate_ma(df['volume'], 20)
@@ -305,6 +306,40 @@ class BitgetService:
 
         return atr
 
+    @staticmethod
+    def _calculate_adx(high: pd.Series, low: pd.Series, close: pd.Series, period: int = 14):
+        """ADX 趋势强度指标"""
+        # True Range
+        tr1 = high - low
+        tr2 = abs(high - close.shift(1))
+        tr3 = abs(low - close.shift(1))
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+
+        # Directional Movement
+        up_move = high - high.shift(1)
+        down_move = low.shift(1) - low
+
+        plus_dm = pd.Series(
+            np.where((up_move > down_move) & (up_move > 0), up_move, 0.0),
+            index=high.index
+        )
+        minus_dm = pd.Series(
+            np.where((down_move > up_move) & (down_move > 0), down_move, 0.0),
+            index=high.index
+        )
+
+        # Wilder's smoothing
+        atr = tr.ewm(alpha=1/period, adjust=False).mean()
+        plus_di = 100 * (plus_dm.ewm(alpha=1/period, adjust=False).mean() / atr)
+        minus_di = 100 * (minus_dm.ewm(alpha=1/period, adjust=False).mean() / atr)
+
+        di_sum = plus_di + minus_di
+        di_sum = di_sum.replace(0, np.nan)
+        dx = 100 * abs(plus_di - minus_di) / di_sum
+        adx = dx.ewm(alpha=1/period, adjust=False).mean()
+
+        return adx, plus_di, minus_di
+
     def get_current_price(self, symbol: str, category: str = None) -> Optional[float]:
         """
         获取当前价格

backend/app/utils/indicators.py

@@ -144,6 +144,63 @@ def calculate_boll(
     return upper, middle, lower
 
 
+def calculate_adx(
+    high: pd.Series,
+    low: pd.Series,
+    close: pd.Series,
+    period: int = 14
+) -> Tuple[pd.Series, pd.Series, pd.Series]:
+    """
+    计算 ADX (Average Directional Index) 趋势强度指标
+
+    ADX 衡量趋势强度（非方向）：
+    - ADX < 20: 震荡 / 无趋势
+    - ADX 20-25: 过渡期
+    - ADX 25-50: 趋势中
+    - ADX > 50: 强趋势
+
+    Args:
+        high: 最高价
+        low: 最低价
+        close: 收盘价
+        period: 回溯周期 (默认 14)
+
+    Returns:
+        (adx, plus_di, minus_di) 元组
+    """
+    # True Range
+    tr1 = high - low
+    tr2 = abs(high - close.shift(1))
+    tr3 = abs(low - close.shift(1))
+    tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+
+    # Directional Movement
+    up_move = high - high.shift(1)
+    down_move = low.shift(1) - low
+
+    plus_dm = pd.Series(
+        np.where((up_move > down_move) & (up_move > 0), up_move, 0.0),
+        index=high.index
+    )
+    minus_dm = pd.Series(
+        np.where((down_move > up_move) & (down_move > 0), down_move, 0.0),
+        index=high.index
+    )
+
+    # Wilder's smoothing (EWM alpha=1/period)
+    atr = tr.ewm(alpha=1/period, adjust=False).mean()
+    plus_di = 100 * (plus_dm.ewm(alpha=1/period, adjust=False).mean() / atr)
+    minus_di = 100 * (minus_dm.ewm(alpha=1/period, adjust=False).mean() / atr)
+
+    # DX and ADX
+    di_sum = plus_di + minus_di
+    di_sum = di_sum.replace(0, np.nan)
+    dx = 100 * abs(plus_di - minus_di) / di_sum
+    adx = dx.ewm(alpha=1/period, adjust=False).mean()
+
+    return adx, plus_di, minus_di
+
+
 def calculate_volume_ma(volume: pd.Series, period: int = 5) -> pd.Series:
     """
     计算成交量移动平均