From a65cca62f8d907ade5f40bec1085ce8e6059612b Mon Sep 17 00:00:00 2001 From: Lxy Date: Sat, 23 May 2026 00:22:34 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E5=A2=9E=E5=8A=A0AI=E5=88=86=E6=9E=90?= =?UTF-8?q?=E5=B9=B6=E8=B0=83=E9=80=9A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/analysis_db.py | 4 + app/analysis_models.py | 13 + .../futures_analysis.cpython-311.pyc | Bin 44949 -> 50015 bytes app/api/futures_analysis.py | 112 ++++- app/services/ai_analysis.py | 459 ++++++++++++++++++ app/static/futures_analysis.css | 390 +++++++++++++++ app/static/futures_analysis.html | 33 ++ app/static/futures_analysis.js | 348 +++++++++++++ check_db.py | 55 +++ data/ai_analysis_prompt.md | 201 ++++++++ data/buffer.db | Bin 2035712 -> 2035712 bytes init_analysis_db.py | 24 + verify_db.py | 27 ++ 13 files changed, 1665 insertions(+), 1 deletion(-) create mode 100644 app/services/ai_analysis.py create mode 100644 check_db.py create mode 100644 data/ai_analysis_prompt.md create mode 100644 init_analysis_db.py create mode 100644 verify_db.py diff --git a/app/analysis_db.py b/app/analysis_db.py index 930f25f..8593935 100644 --- a/app/analysis_db.py +++ b/app/analysis_db.py @@ -33,4 +33,8 @@ def get_analysis_db(): def init_analysis_db(): """初始化期货智析数据库表""" + # 确保导入所有模型类，使其注册到 AnalysisBase + from app import analysis_models + # 直接导入 analysis_models 模块中的所有类 + from app.analysis_models import FuturesAnalysis, WatchedSymbol, AIModelConfig, AnalysisSettings, AIAnalysisCache AnalysisBase.metadata.create_all(bind=analysis_engine) diff --git a/app/analysis_models.py b/app/analysis_models.py index 8a84288..eb9bfb7 100644 --- a/app/analysis_models.py +++ b/app/analysis_models.py @@ -86,3 +86,16 @@ class AnalysisSettings(AnalysisBase): def __repr__(self): return f"" + + +class AIAnalysisCache(AnalysisBase): + """AI分析缓存表""" + __tablename__ = "ai_analysis_cache" + + id = Column(Integer, primary_key=True, autoincrement=True) + symbol = Column(String(32), nullable=False, index=True, comment="品种合约代码") + analysis_data = Column(JSON, nullable=False, comment="AI分析结果数据") + created_at = Column(DateTime, nullable=False, default=datetime.now, index=True, comment="分析时间") + + def __repr__(self): + return f"" diff --git a/app/api/__pycache__/futures_analysis.cpython-311.pyc b/app/api/__pycache__/futures_analysis.cpython-311.pyc index 3044ec9fd4c0a792d30550d57f065d9cc5173d73..377031de042f360d3afde21348b0c0d7e3dea1d8 100644 GIT binary patch delta 10430 zcmb7K4RjONm7b9_vTXSu*O2X7rmBgfWBgZvbB#W5L zN;AY1skBKWrXJNbTBX9?nY1d)L|@{U3T2VEC3}+S6+T^?E|t?fO{P#0Y1yoqDU^%Z zP~WM^^v@E30{E3T>BL;%S1IO6v&DS)Gs7R&8^i*ruVQqCW3+m)21==JrmZn?&LfOaWU*1u~@`BM6S+Du_tr2lc9 zg>Khn2+Jc>$91LorHq?StiFp|EzqrC+(vYzd^t@NGKG~9Zf6AxZ5A?wRS|Ayg>i06 z?&7u@=;|1^uZ7a0`JgIwWWBiTF4{Gbk%5+xci`Ie(*MbSJYh35PxA9(PzAIeo z-5VM=F!PPsvypCgq?I?;iz|VY7gvd^dvx{&v958GI9*B{vx8Dl+!|2p=CIiOT6&%@ 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app.database import get_db from app.analysis_db import get_analysis_db -from app.analysis_models import FuturesAnalysis, WatchedSymbol, AIModelConfig, AnalysisSettings +from app.analysis_models import FuturesAnalysis, WatchedSymbol, AIModelConfig, AnalysisSettings, AIAnalysisCache from app.services.cache import get_cached_data, get_latest_cached, save_market_data from app.services.collector import fetch_symbol_data @@ -694,6 +694,7 @@ def delete_ai_model(model_id: int, adb: Session = Depends(get_analysis_db)): # ==================== 数据刷新接口 ==================== from app.services.cache import needs_refresh, get_symbol_timestamp +from app.services.ai_analysis import AIFuturesAnalyzer refresh_lock = threading.Lock() refresh_status = {"running": False, "progress": 0, "total": 0, "message": ""} @@ -786,3 +787,112 @@ def refresh_all_symbols_api(background_tasks: BackgroundTasks): def get_refresh_status(): """获取刷新状态""" return {"success": True, "data": refresh_status} + + +# ==================== AI智能分析接口 ==================== + +@router.post("/ai-analysis/{symbol}") +def run_ai_analysis(symbol: str, db: Session = Depends(get_db), analysis_db: Session = Depends(get_analysis_db)): + """执行AI智能分析""" + try: + analyzer = AIFuturesAnalyzer(db, analysis_db) + result = analyzer.analyze(symbol) + + if result.get("success"): + return { + "success": True, + "data": result["data"] + } + else: + return { + "success": False, + "error": result.get("error", "AI分析失败") + } + except Exception as e: + logger.error(f"AI分析失败: {e}") + return { + "success": False, + "error": f"AI分析失败: {str(e)}" + } + + +@router.get("/ai-analysis/{symbol}") +def get_ai_analysis(symbol: str, force_refresh: bool = False, db: Session = Depends(get_db), analysis_db: Session = Depends(get_analysis_db)): + """获取AI分析结果（可选择是否强制刷新）""" + try: + analyzer = AIFuturesAnalyzer(db, analysis_db) + + if force_refresh: + result = analyzer.analyze(symbol) + if result.get("success"): + return { + "success": True, + "data": result["data"], + "is_cached": False + } + else: + return { + "success": False, + "error": result.get("error", "AI分析失败") + } + + cache = analyzer.get_latest_cache(symbol) + if cache: + return { + "success": True, + "data": { + "id": cache.id, + "symbol": cache.symbol, + "analysis_time": cache.created_at.isoformat(), + "result": cache.analysis_data + }, + "is_cached": True + } + + result = analyzer.analyze(symbol) + if result.get("success"): + return { + "success": True, + "data": result["data"], + "is_cached": False + } + else: + return { + "success": False, + "error": result.get("error", "未找到分析结果") + } + except Exception as e: + logger.error(f"获取AI分析结果失败: {e}") + return { + "success": False, + "error": f"获取AI分析失败: {str(e)}" + } + + +@router.get("/ai-analysis/{symbol}/history") +def get_ai_analysis_history(symbol: str, limit: int = 10, analysis_db: Session = Depends(get_analysis_db)): + """获取AI分析历史记录""" + try: + records = analysis_db.query(AIAnalysisCache).filter( + AIAnalysisCache.symbol == symbol + ).order_by( + AIAnalysisCache.created_at.desc() + ).limit(limit).all() + + return { + "success": True, + "data": [{ + "id": r.id, + "symbol": r.symbol, + "analysis_time": r.created_at.isoformat(), + "summary": r.analysis_data.get("summary", ""), + "trading_suggestion": r.analysis_data.get("trading_suggestion", {}), + "confidence": r.analysis_data.get("trading_suggestion", {}).get("confidence", 0) + } for r in records] + } + except Exception as e: + logger.error(f"获取AI分析历史失败: {e}") + return { + "success": False, + "error": f"获取历史记录失败: {str(e)}" + } diff --git a/app/services/ai_analysis.py b/app/services/ai_analysis.py new file mode 100644 index 0000000..dddc5a3 --- /dev/null +++ b/app/services/ai_analysis.py @@ -0,0 +1,459 @@ +""" +AI分析服务 - 期货四维联合分析 +""" +import json +import re +import logging +from datetime import datetime +from typing import Dict, List, Optional +from sqlalchemy.orm import Session + +from app.analysis_models import AIAnalysisCache +from app.services.cache import get_cached_data, get_latest_cached +from pathlib import Path + +CONFIG_DIR = Path(__file__).resolve().parent.parent.parent / "config" +AI_CONFIG_FILE = CONFIG_DIR / "ai_config.json" + +logger = logging.getLogger(__name__) + + +class AIAnalysisPrompt: + """AI分析提示词管理器""" + + SYSTEM_PROMPT = """你是一位拥有20年实战经验的资深金融交易分析师，精通A股市场与商品期货的技术分析。 +你的核心使命是基于提供的K线数据，执行【四维联合判断分析法(4D-XV)】，并提供包含风控红线审查的客观交易策略。 + +你的分析必须遵循以下原则： +1. 数据驱动：所有结论必须基于输入的JSON数据，严禁凭空捏造。 +2. 四维共振：任何交易建议必须经过MACD（趋势）、成交量（资金）、KDJ（时机）、多周期（方向）的交叉验证。 +3. 红线否决：如果数据触发【17条交易红线】，必须直接给出【禁止交易】或【止损】的建议。 +4. 客观中立：不使用绝对化表述，提供情景预案（概率估算）。 +5. 动态切换：遇到关键位放量突破时，立即切换右侧思维，不逆势死扛。""" + + ANALYSIS_TEMPLATE = """请严格按照以下JSON格式输出分析结果（不要输出任何其他内容）： + +{ + "summary": "一句话总结当前市场状态", + "four_dimensional": { + "60min": { + "macd": {"trend": "up/down/neutral", "histogram": "放大/缩小/背离", "position": "零轴上/下"}, + "volume": {"status": "放量上涨/缩量回调/趋势量能/拐点量能", "ratio": 1.5}, + "kdj": {"k": 85, "d": 80, "j": 95, "status": "超买/超卖/中性", "signal": "金叉/死叉/钝化"}, + "conclusion": "定大势结论" + }, + "30min": { + "macd": {"trend": "up/down/neutral", "histogram": "放大/缩小/背离", "position": "零轴上/下"}, + "volume": {"status": "放量/缩量/正常", "ratio": 1.5}, + "kdj": {"k": 50, "d": 45, "j": 60, "status": "中性", "signal": "金叉/死叉"}, + "conclusion": "找拐点结论" + }, + "15min": { + "macd": {"trend": "up/down/neutral", "histogram": "放大/缩小/背离", "position": "零轴上/下"}, + "volume": {"status": "放量/缩量/正常", "ratio": 2.0}, + "kdj": {"k": 30, "d": 25, "j": 40, "status": "超卖", "signal": "金叉/死叉"}, + "conclusion": "择入场结论" + } + }, + "kdj_diagnosis": { + "current_status": "超买/超卖/中性区域", + "divergence": "是否存在顶/底背离", + "paralysis": "是否钝化（持续>6根K线）", + "recommendation": "KDJ使用建议" + }, + "pivot_points": { + "r2": 7500, + "r1": 7350, + "pp": 7200, + "s1": 7050, + "s2": 6900, + "validation": { + "test_count": 3, + "volume_confirmed": true, + "multi_period_resonance": true, + "breakback_confirmed": false + } + }, + "red_lines_check": { + "passed": true, + "violated": [], + "warnings": ["暂无红线警告"] + }, + "discipline_score": { + "total": 9, + "max": 11, + "details": { + "trend": true, + "position": true, + "signal": true, + "risk": true, + "mindset": true + } + }, + "trading_suggestion": { + "direction": "做多/做空/观望", + "confidence": 75, + "entry_range": {"min": 7050, "max": 7100}, + "stop_loss": 6950, + "take_profit": [{"price": 7200, "ratio": 50}, {"price": 7350, "ratio": 30}, {"price": 7500, "ratio": 20}], + "position_size": "轻仓/半仓/重仓", + "reason": "做多理由" + }, + "scenario_plans": { + "breakthrough": {"probability": 35, "action": "放量突破关键位，跟随右侧思维"}, + "consolidation": {"probability": 40, "action": "R1-S1区间内高抛低吸"}, + "reversal": {"probability": 15, "action": "MACD顶/底背离+量能不足，立即止损反手"}, + "news_impact": {"probability": 10, "action": "减仓50%规避不确定性"} + }, + "risk_warnings": [ + "技术指标具有滞后性，历史表现不代表未来", + "需结合基本面和市场情绪综合判断" + ], + "experience_lessons": [ + "警惕缩量创新高，可能是诱多信号", + "KDJ超买钝化中不宜逆势做空" + ] +}""" + + @classmethod + def build_prompt(cls, symbol: str, data: Dict) -> str: + """构建完整的AI分析提示词""" + prompt = f"""{cls.SYSTEM_PROMPT} + +现在请分析以下期货品种的K线数据： + +## 品种信息 +- 合约代码：{symbol} +- 当前价格：{data.get('current_price', 'N/A')} + +## 多周期K线数据 +```json +{json.dumps(data, ensure_ascii=False, indent=2)} +``` + +{cls.ANALYSIS_TEMPLATE}""" + + return prompt + + +class AIFuturesAnalyzer: + """AI期货分析器""" + + def __init__(self, db: Session, analysis_db: Session = None): + self.db = db + self.analysis_db = analysis_db or db + + def get_active_model(self) -> Optional[Dict]: + """获取当前激活的AI模型配置""" + try: + if not AI_CONFIG_FILE.exists(): + return None + + with open(AI_CONFIG_FILE, "r", encoding="utf-8") as f: + config = json.load(f) + + models = config.get("models", []) + active_model_name = config.get("active_model") + + if active_model_name: + for model in models: + if model.get("model_name") == active_model_name and model.get("enabled", True): + return model + + for model in models: + if model.get("enabled", True): + logger.warning(f"未找到匹配的激活模型，使用第一个启用的模型: {model.get('model_name')}") + return model + + return None + except Exception as e: + logger.error(f"加载AI配置失败: {e}") + return None + + def prepare_multi_period_data(self, symbol: str) -> Optional[Dict]: + """准备多周期数据用于AI分析""" + cached_data = get_cached_data( + self.db, + symbol, + "futures", + ["5min", "15min", "30min", "60min", "daily"] + ) + + if not cached_data or not cached_data.get("timeframes"): + return None + + timeframes = cached_data.get("timeframes", {}) + current_price = cached_data.get("current_price") + + result = { + "symbol": symbol, + "current_price": current_price, + "timeframes": {} + } + + for period_name, db_period in [("5min", "5min"), ("15min", "15min"), + ("30min", "30min"), ("60min", "60min"), + ("daily", "daily")]: + if period_name in timeframes and timeframes[period_name]: + candles = timeframes[period_name] + if len(candles) >= 20: + result["timeframes"][period_name] = self._analyze_timeframe(candles, period_name) + + return result + + def _analyze_timeframe(self, candles: List[Dict], period: str) -> Dict: + """分析单个周期的技术指标""" + if not candles or len(candles) < 20: + return {} + + closes = [float(c.get("close", 0)) for c in candles] + highs = [float(c.get("high", 0)) for c in candles] + lows = [float(c.get("low", 0)) for c in candles] + volumes = [float(c.get("volume", 0)) for c in candles] + + ma10 = sum(closes[-10:]) / 10 if len(closes) >= 10 else None + ma20 = sum(closes[-20:]) / 20 if len(closes) >= 20 else None + + macd_data = self._calc_macd(closes) + kdj_data = self._calc_kdj(highs, lows, closes) + + avg_volume = sum(volumes[-20:]) / 20 if len(volumes) >= 20 else 0 + current_volume = volumes[-1] if volumes else 0 + + return { + "trend": "up" if closes[-1] > closes[0] else "down", + "ma10": round(ma10, 2) if ma10 else None, + "ma20": round(ma20, 2) if ma20 else None, + "macd_dif": round(macd_data["dif"], 4), + "macd_dea": round(macd_data["dea"], 4), + "macd_histogram": round(macd_data["histogram"], 4), + "kdj_k": kdj_data["k"], + "kdj_d": kdj_data["d"], + "kdj_j": kdj_data["j"], + "volume_avg": round(avg_volume, 2), + "volume_current": round(current_volume, 2), + "volume_ratio": round(current_volume / avg_volume, 2) if avg_volume > 0 else 1, + "candles": candles[-10:] if len(candles) > 10 else candles + } + + def _calc_macd(self, closes: List[float]) -> Dict: + """计算MACD指标""" + if len(closes) < 26: + return {"dif": 0, "dea": 0, "histogram": 0} + + ema12 = self._calc_ema(closes, 12) + ema26 = self._calc_ema(closes, 26) + + dif = ema12 - ema26 + dea = self._calc_ema([dif] * len(closes), 9) + histogram = 2 * (dif - dea) + + return {"dif": dif, "dea": dea, "histogram": histogram} + + def _calc_ema(self, data: List[float], period: int) -> float: + """计算EMA""" + if len(data) < period: + return sum(data) / len(data) if data else 0 + + multiplier = 2 / (period + 1) + ema = sum(data[:period]) / period + + for i in range(period, len(data)): + ema = (data[i] - ema) * multiplier + ema + + return ema + + def _calc_kdj(self, highs: List[float], lows: List[float], closes: List[float]) -> Dict: + """计算KDJ指标""" + if len(closes) < 9: + return {"k": 50, "d": 50, "j": 50} + + period = 9 + recent_highs = highs[-period:] + recent_lows = lows[-period:] + recent_closes = closes[-period:] + + highest = max(recent_highs) + lowest = min(recent_lows) + current = recent_closes[-1] + + if highest == lowest: + rsv = 50 + else: + rsv = (current - lowest) / (highest - lowest) * 100 + + k = rsv * 2 / 3 + 50 / 3 + d = k * 2 / 3 + 50 / 3 + j = 3 * k - 2 * d + + return {"k": round(k, 2), "d": round(d, 2), "j": round(j, 2)} + + def call_ai_model(self, prompt: str, model: Dict) -> Optional[str]: + """调用AI模型""" + try: + import requests + + api_base = model.get("api_base", "https://api.openai.com/v1") + api_key = model.get("api_key", "") + model_id = model.get("model_id") or model.get("model_name", "") + + logger.info(f"========== AI模型调用开始 ==========") + logger.info(f"API Base: {api_base}") + logger.info(f"API Key: {'已配置' if api_key else '未配置'} ({api_key[:10]}...)" if api_key else "API Key: 未配置") + logger.info(f"Model ID: {model_id}") + logger.info(f"Temperature: {model.get('temperature', 0.7)}") + logger.info(f"Max Tokens: {model.get('max_tokens', 2000)}") + + if not api_key: + logger.error("API Key 未配置，无法调用AI模型") + return None + + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {api_key}" + } + + payload = { + "model": model_id, + "messages": [ + {"role": "user", "content": prompt} + ], + "temperature": model.get("temperature", 0.7), + "max_tokens": model.get("max_tokens", 2000) + } + + url = f"{api_base}/chat/completions" + logger.info(f"请求 URL: {url}") + logger.info(f"请求 Payload 大小: {len(str(payload))} 字符") + + response = requests.post( + url, + headers=headers, + json=payload, + timeout=180 # 增加到180秒（3分钟）超时 + ) + + logger.info(f"响应状态码: {response.status_code}") + + if response.status_code == 200: + result = response.json() + content = result["choices"][0]["message"]["content"] + logger.info(f"AI响应成功，内容长度: {len(content)} 字符") + logger.info(f"========== AI模型调用成功 ==========") + return content + else: + logger.error(f"AI模型调用失败:") + logger.error(f" 状态码: {response.status_code}") + logger.error(f" 响应内容: {response.text}") + logger.error(f"========== AI模型调用失败 ==========") + return None + + except requests.exceptions.Timeout: + logger.error("AI模型调用超时（超过60秒）") + logger.error(f"========== AI模型调用失败 ==========") + return None + except requests.exceptions.ConnectionError as e: + logger.error(f"AI模型连接错误: {e}") + logger.error(f"========== AI模型调用失败 ==========") + return None + except requests.exceptions.RequestException as e: + logger.error(f"AI模型请求异常: {e}") + logger.error(f"========== AI模型调用失败 ==========") + return None + except Exception as e: + logger.error(f"调用AI模型未知异常: {e}", exc_info=True) + logger.error(f"========== AI模型调用失败 ==========") + return None + + def parse_ai_response(self, response: str) -> Optional[Dict]: + """解析AI返回的JSON响应""" + try: + json_match = re.search(r'\{[\s\S]*\}', response) + if json_match: + return json.loads(json_match.group(0)) + return None + except Exception as e: + logger.error(f"解析AI响应失败: {e}") + return None + + def save_analysis_cache(self, symbol: str, analysis_data: Dict) -> AIAnalysisCache: + """保存AI分析结果到缓存""" + cache = AIAnalysisCache( + symbol=symbol, + analysis_data=analysis_data, + created_at=datetime.now() + ) + + self.analysis_db.add(cache) + self.analysis_db.commit() + self.analysis_db.refresh(cache) + + return cache + + def get_latest_cache(self, symbol: str) -> Optional[AIAnalysisCache]: + """获取最新的AI分析缓存""" + return self.analysis_db.query(AIAnalysisCache).filter( + AIAnalysisCache.symbol == symbol + ).order_by(AIAnalysisCache.created_at.desc()).first() + + def analyze(self, symbol: str) -> Dict: + """执行完整的AI分析流程""" + logger.info(f"===== 开始AI分析: {symbol} =====") + + model = self.get_active_model() + if not model: + logger.error("未找到激活的AI模型配置") + return { + "success": False, + "error": "未配置AI模型或模型未激活" + } + + logger.info(f"使用AI模型: {model.get('model_name')}") + + data = self.prepare_multi_period_data(symbol) + if not data: + logger.error(f"未找到 {symbol} 的市场数据") + return { + "success": False, + "error": f"未找到 {symbol} 的市场数据" + } + + logger.info(f"市场数据准备成功，包含周期: {list(data.get('timeframes', {}).keys())}") + + prompt = AIAnalysisPrompt.build_prompt(symbol, data) + logger.info(f"AI提示词生成完成，长度: {len(prompt)} 字符") + + response = self.call_ai_model(prompt, model) + + if not response: + logger.error("AI模型返回空响应") + return { + "success": False, + "error": "AI模型调用失败" + } + + logger.info(f"AI模型响应接收成功，长度: {len(response)} 字符") + + analysis_result = self.parse_ai_response(response) + if not analysis_result: + logger.error(f"AI响应解析失败，原始响应前100字符: {response[:100]}") + return { + "success": False, + "error": "AI响应解析失败" + } + + logger.info(f"AI响应解析成功") + + cache = self.save_analysis_cache(symbol, analysis_result) + logger.info(f"分析结果已保存到缓存，ID: {cache.id}") + logger.info(f"===== AI分析完成: {symbol} =====") + + return { + "success": True, + "data": { + "id": cache.id, + "symbol": symbol, + "analysis_time": cache.created_at.isoformat(), + "result": analysis_result + } + } diff --git a/app/static/futures_analysis.css b/app/static/futures_analysis.css index 3e5106e..d60e210 100644 --- a/app/static/futures_analysis.css +++ b/app/static/futures_analysis.css @@ -1597,6 +1597,396 @@ body.theme-minimal .sort-select select:hover { border-color: var(--text-muted); } +/* ============================================ + AI智能分析样式 + ============================================ */ + +.panel-header-actions { + display: flex; + gap: 8px; + margin-left: auto; +} + +.ai-analyze-btn { + display: flex; + align-items: center; + gap: 6px; + padding: 6px 12px; + background: linear-gradient(135deg, var(--purple), var(--cyan)); + border: none; + border-radius: 6px; + color: white; + font-size: 12px; + font-weight: 500; + cursor: pointer; + transition: all 0.3s ease; +} + +.ai-analyze-btn:hover { + transform: translateY(-2px); + box-shadow: 0 4px 12px var(--purple-glow); +} + +.ai-analyze-btn:active { + transform: translateY(0); +} + +.ai-analyze-btn:disabled { + opacity: 0.6; + cursor: not-allowed; + transform: none; +} + +.ai-analyze-btn i { + font-size: 11px; +} + +.ai-analysis-content { + min-height: 120px; +} + +.ai-analysis-placeholder { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + padding: 24px; + color: var(--text-muted); +} + +.ai-analysis-placeholder i { + font-size: 32px; + margin-bottom: 12px; + opacity: 0.5; +} + +.ai-analysis-placeholder p { + font-size: 13px; + text-align: center; +} + +.ai-analysis-loading { + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + padding: 24px; + gap: 12px; +} + +.ai-analysis-loading i { + font-size: 28px; + color: var(--purple); + animation: pulse 1.5s ease-in-out infinite; +} + +@keyframes pulse { + 0%, 100% { opacity: 0.5; transform: scale(1); } + 50% { opacity: 1; transform: scale(1.1); } +} + +.ai-analysis-result { + display: flex; + flex-direction: column; + gap: 12px; +} + +.ai-summary { + padding: 12px; + background: rgba(139, 92, 246, 0.08); + border: 1px solid rgba(139, 92, 246, 0.15); + border-radius: 8px; + font-size: 13px; + line-height: 1.6; + color: var(--text-primary); +} + +.ai-suggestion-row { + display: flex; + justify-content: space-between; + align-items: center; + padding: 10px 12px; + background: rgba(255, 255, 255, 0.03); + border-radius: 6px; +} + +.ai-suggestion-direction { + display: flex; + align-items: center; + gap: 8px; +} + +.ai-suggestion-direction i { + font-size: 16px; +} + +.ai-suggestion-direction.long i { + color: var(--green); +} + +.ai-suggestion-direction.short i { + color: var(--red); +} + +.ai-suggestion-direction.neutral i { + color: var(--amber); +} + +.ai-confidence { + display: flex; + align-items: center; + gap: 6px; + font-size: 12px; + color: var(--text-secondary); +} + +.ai-confidence-bar { + width: 60px; + height: 4px; + background: rgba(255, 255, 255, 0.1); + border-radius: 2px; + overflow: hidden; +} + +.ai-confidence-fill { + height: 100%; + background: linear-gradient(90deg, var(--amber), var(--green)); + border-radius: 2px; + transition: width 0.5s ease; +} + +.ai-key-metrics { + display: grid; + grid-template-columns: repeat(2, 1fr); + gap: 8px; +} + +.ai-metric-item { + display: flex; + justify-content: space-between; + padding: 8px 10px; + background: rgba(255, 255, 255, 0.02); + border-radius: 6px; + font-size: 12px; +} + +.ai-metric-item .label { + color: var(--text-muted); +} + +.ai-metric-item .value { + font-weight: 600; + color: var(--text-primary); +} + +.ai-metric-item .value.up { + color: var(--green); +} + +.ai-metric-item .value.down { + color: var(--red); +} + +.ai-timestamp { + text-align: center; + font-size: 11px; + color: var(--text-muted); + padding-top: 8px; + border-top: 1px solid var(--border-color); +} + +/* AI分析详情模态框 */ +.modal-large { + max-width: 900px; + width: 90%; +} + +.modal-large .modal-body { + max-height: 80vh; + overflow-y: auto; +} + +.ai-modal-section { + margin-bottom: 24px; +} + +.ai-modal-section:last-child { + margin-bottom: 0; +} + +.ai-modal-section-title { + display: flex; + align-items: center; + gap: 8px; + font-size: 15px; + font-weight: 600; + color: var(--cyan); + margin-bottom: 12px; + padding-bottom: 8px; + border-bottom: 1px solid var(--border-color); +} + +.ai-modal-section-title i { + font-size: 16px; +} + +.four-dimensional-table { + width: 100%; + border-collapse: collapse; + font-size: 13px; +} + +.four-dimensional-table th, +.four-dimensional-table td { + padding: 10px 12px; + text-align: left; + border-bottom: 1px solid var(--border-color); +} + +.four-dimensional-table th { + background: rgba(139, 92, 246, 0.08); + color: var(--purple); + font-weight: 600; + font-size: 12px; +} + +.four-dimensional-table td { + color: var(--text-secondary); +} + +.four-dimensional-table tr:last-child td { + border-bottom: none; +} + +.four-dimensional-table .period-cell { + font-weight: 600; + color: var(--text-primary); +} + +.scenario-cards { + display: grid; + grid-template-columns: repeat(2, 1fr); + gap: 12px; +} + +.scenario-card { + padding: 12px; + background: rgba(255, 255, 255, 0.03); + border-radius: 8px; + border-left: 3px solid var(--cyan); +} + +.scenario-card.breakthrough { + border-left-color: var(--green); +} + +.scenario-card.consolidation { + border-left-color: var(--amber); +} + +.scenario-card.reversal { + border-left-color: var(--red); +} + +.scenario-card.news { + border-left-color: var(--purple); +} + +.scenario-probability { + display: inline-block; + padding: 2px 8px; + background: rgba(139, 92, 246, 0.15); + border-radius: 4px; + font-size: 11px; + font-weight: 600; + color: var(--purple); + margin-bottom: 6px; +} + +.scenario-action { + font-size: 12px; + color: var(--text-secondary); + line-height: 1.5; +} + +.red-lines-list { + display: flex; + flex-direction: column; + gap: 8px; +} + +.red-line-item { + display: flex; + align-items: center; + gap: 8px; + padding: 8px 12px; + background: rgba(239, 68, 68, 0.08); + border: 1px solid rgba(239, 68, 68, 0.15); + border-radius: 6px; + font-size: 12px; + color: var(--red); +} + +.red-line-item.pass { + background: rgba(16, 185, 129, 0.08); + border-color: rgba(16, 185, 129, 0.15); + color: var(--green); +} + +.discipline-grid { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 10px; +} + +.discipline-item { + display: flex; + align-items: center; + gap: 8px; + padding: 10px; + background: rgba(255, 255, 255, 0.03); + border-radius: 6px; +} + +.discipline-item i { + font-size: 16px; +} + +.discipline-item.pass i { + color: var(--green); +} + +.discipline-item.fail i { + color: var(--red); +} + +.discipline-label { + font-size: 12px; + color: var(--text-secondary); +} + +.ai-experience-list { + display: flex; + flex-direction: column; + gap: 8px; +} + +.experience-item { + display: flex; + align-items: flex-start; + gap: 8px; + padding: 10px 12px; + background: rgba(245, 158, 11, 0.08); + border: 1px solid rgba(245, 158, 11, 0.15); + border-radius: 6px; + font-size: 12px; + color: var(--amber); +} + +.experience-item i { + margin-top: 2px; +} + body.theme-minimal .sort-select select:focus { border-color: var(--cyan); box-shadow: 0 0 0 0.125rem rgba(73, 79, 223, 0.2); diff --git a/app/static/futures_analysis.html b/app/static/futures_analysis.html index c1e1662..853594e 100644 --- a/app/static/futures_analysis.html +++ b/app/static/futures_analysis.html @@ -362,6 +362,26 @@ + +
+
+ + AI 四维分析 +
+ +
+
+
+
+ +

点击"智能分析"按钮获取AI分析结果

+
+
+
+
@@ -385,6 +405,19 @@
+ + +