selfrelease
2b5a32ca1e
新增模块: - fund_flow_analyzer.py: 主力资金流向分析(P0, ±20) - market_sentiment.py: 市场情绪指标(P1, ±10) - external_factors.py: 北向资金/美股/大宗商品/汇率(P2-P4,P7) - news_analyzer.py: 公告/并购/政策面LLM分析(P5-P6) - score_engine.py: 综合评分引擎，整合技术面+外部因素 路由更新: - analysis.py: deep_analyze接入综合评分，根据最终评级修正买卖建议 - market.py: 新增4个外部因素API端点 - trades.py: 交易路由更新 算法文档重构: - 章节重排: 技术面(二三)→外部因素(四)→买卖决策(五)→数据源(六)→性能(七) - 架构图更新为五层，标注章节对应 - 5.1/5.2标注纯技术面，5.3整合外部因素修正推荐
255 lines
9.9 KiB
Python
255 lines
9.9 KiB
Python
"""
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豆包AI服务模块
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用于股票分析的AI对话（流式输出版本）
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"""
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import requests
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import json
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from config import Config
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# API配置
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API_KEY = Config.DOUBAO_API_KEY or "9fd8383f-5776-4366-855d-c6f40e867940"
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API_URL = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
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MODEL = "doubao-seed-1-6-251015"
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def analyze_stock(stock_code, stock_name, stock_data):
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"""
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使用豆包AI分析股票（流式输出，获取思考和结论）
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"""
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# 构建分析提示词（基于推荐模型v3.0）
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fund_flow_str = format_fund_flow(stock_data.get('fund_flow_3days', []))
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prompt = f"""分析{stock_name}({stock_code})投资价值。
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数据：价格{stock_data.get('price', 'N/A')}元，PE={stock_data.get('pe', 'N/A')}，PB={stock_data.get('pb', 'N/A')}，ROE={stock_data.get('roe', 'N/A')}%，市值{format_market_cap(stock_data.get('total_market_cap'))}
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资金流向：{fund_flow_str}
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评分模型：价格位置(30分,低于50%为低位)、资金流向(25分,主力净流入占比)、趋势(15分)、连续性(15分,≥3天强连续)、涨跌配合(10分)、量能(5分)
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推荐率：80-100强买/卖，60-79可操作，40-59观望，0-39不建议
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按以下格式输出：
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## 技术面分析
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分析资金流向趋势和连续性
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## 基本面分析
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分析PE/ROE估值和盈利能力
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## 操作建议
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明确建议买入/持有/卖出，给出预估推荐率(0-100)
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## 风险提示
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列出2-3个风险点"""
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try:
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {API_KEY}"
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}
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payload = {
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"model": MODEL,
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"max_completion_tokens": 2048,
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"stream": True, # 启用流式输出
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"messages": [
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{
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"role": "user",
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"content": prompt
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}
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]
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}
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# 流式请求
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response = requests.post(API_URL, headers=headers, json=payload, timeout=120, stream=True)
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if response.status_code == 200:
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reasoning_content = "" # 思考过程
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content = "" # 最终结论
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for line in response.iter_lines():
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if line:
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line_str = line.decode('utf-8')
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if line_str.startswith('data: '):
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data_str = line_str[6:]
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if data_str == '[DONE]':
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break
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try:
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data = json.loads(data_str)
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if 'choices' in data and len(data['choices']) > 0:
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delta = data['choices'][0].get('delta', {})
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# 获取思考过程
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if 'reasoning_content' in delta and delta['reasoning_content']:
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reasoning_content += delta['reasoning_content']
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# 获取最终内容
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if 'content' in delta and delta['content']:
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content += delta['content']
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except json.JSONDecodeError:
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continue
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# 组合思考过程和结论
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full_analysis = ""
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if reasoning_content:
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full_analysis += "## 💭 AI思考过程\n" + reasoning_content + "\n\n---\n\n"
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if content:
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full_analysis += content
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if full_analysis:
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return {'success': True, 'analysis': full_analysis}
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else:
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return {'success': False, 'error': 'AI返回内容为空'}
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else:
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return {'success': False, 'error': f'API请求失败: {response.status_code}'}
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except requests.exceptions.Timeout:
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return {'success': False, 'error': 'AI分析超时，请稍后重试'}
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except Exception as e:
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return {'success': False, 'error': f'AI分析失败: {str(e)}'}
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def analyze_stock_stream(stock_code, stock_name, stock_data):
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"""
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使用豆包AI分析股票（流式生成器，用于SSE）
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"""
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# 构建分析提示词
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fund_flow_str = format_fund_flow(stock_data.get('fund_flow_3days', []))
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signal_status = stock_data.get('signal_status', [])
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indicators = stock_data.get('indicators', {})
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signal_lines = []
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triggered_names = []
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for s in signal_status:
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status = '✅已触发' if s.get('triggered') else '○未触发'
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signal_lines.append(f" {s.get('name','')}: {status} (胜率{s.get('strength',0)}%) — {s.get('description','')}")
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if s.get('triggered'):
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triggered_names.append(s.get('name', ''))
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signal_text = '\n'.join(signal_lines) if signal_lines else ' 暂无扫描数据'
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triggered_text = '、'.join(triggered_names) if triggered_names else '无'
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macd = indicators.get('macd', {})
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skdj = indicators.get('skdj', {})
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ema = indicators.get('ema', {})
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indicator_text = f"MACD: DIF={macd.get('dif','N/A')}, DEA={macd.get('dea','N/A')} | SKDJ: K={skdj.get('k','N/A')}, D={skdj.get('d','N/A')} | EMA: EMA3={ema.get('ema3','N/A')}, EMA21={ema.get('ema21','N/A')}"
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prompt = f"""基于"交易信号实战体系"分析{stock_name}({stock_code})。
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【基本面数据】
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价格{stock_data.get('price', 'N/A')}元，PE={stock_data.get('pe', 'N/A')}，PB={stock_data.get('pb', 'N/A')}，ROE={stock_data.get('roe', 'N/A')}%，市值{format_market_cap(stock_data.get('total_market_cap'))}
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资金流向：{fund_flow_str}
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【技术指标】
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{indicator_text}
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【7大交易信号状态】（当前已触发: {triggered_text}）
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{signal_text}
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【交易信号体系规则】
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信号胜率排行: ★主升浪85% > 日线底背离80% > 龙抬头75% > 真龙70% > 短底背离65% > 老鼠仓60% > 反弹55%
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标准牛股启动顺序: 日线底背离→龙抬头→真龙→★主升浪→反弹
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体系最强战法: 1)日线底背离出现→关注 2)龙抬头出现→买入 3)真龙/★主升浪→持有加仓 4)不见主升浪→不出场
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请按以下格式分析：
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## 交易信号分析
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根据7大信号的触发状态，判断该股在"底部→拉升"流程中处于哪个阶段。已触发的信号说明什么？距离下一个关键信号还有多远？
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## 基本面分析
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简要分析PE/ROE估值水平和盈利能力（2-3句话）
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## 操作建议
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基于体系最强战法规则，给出明确建议（关注/买入/持有加仓/减仓/观望），并解释理由。给出推荐率(0-100)
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## 风险提示
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列出2-3个关键风险点"""
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try:
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {API_KEY}"
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}
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payload = {
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"model": MODEL,
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"max_completion_tokens": 2048,
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"stream": True,
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"messages": [
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{"role": "user", "content": prompt}
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]
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}
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response = requests.post(API_URL, headers=headers, json=payload, timeout=120, stream=True)
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if response.status_code == 200:
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in_reasoning = False
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for line in response.iter_lines():
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if line:
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line_str = line.decode('utf-8')
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if line_str.startswith('data: '):
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data_str = line_str[6:]
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if data_str == '[DONE]':
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break
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try:
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data = json.loads(data_str)
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if 'choices' in data and len(data['choices']) > 0:
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delta = data['choices'][0].get('delta', {})
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# 思考过程
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if 'reasoning_content' in delta and delta['reasoning_content']:
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if not in_reasoning:
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yield {'type': 'reasoning_start'}
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in_reasoning = True
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yield {'type': 'reasoning', 'content': delta['reasoning_content']}
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# 最终内容
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if 'content' in delta and delta['content']:
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if in_reasoning:
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yield {'type': 'reasoning_end'}
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in_reasoning = False
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yield {'type': 'content', 'content': delta['content']}
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except json.JSONDecodeError:
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continue
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if in_reasoning:
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yield {'type': 'reasoning_end'}
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else:
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yield {'type': 'error', 'content': f'API请求失败: {response.status_code}'}
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except requests.exceptions.Timeout:
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yield {'type': 'error', 'content': 'AI分析超时，请稍后重试'}
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except Exception as e:
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yield {'type': 'error', 'content': f'AI分析失败: {str(e)}'}
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def format_market_cap(value):
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"""格式化市值"""
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if not value:
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return 'N/A'
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try:
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value = float(value)
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if value >= 100000000000: # 千亿
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return f"{value/100000000000:.2f}千亿"
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elif value >= 100000000: # 亿
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return f"{value/100000000:.2f}亿"
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else:
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return f"{value/10000:.2f}万"
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except:
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return str(value)
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def format_fund_flow(fund_flow_list):
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"""格式化资金流向数据"""
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if not fund_flow_list:
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return "暂无数据"
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lines = []
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for item in fund_flow_list:
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date = item.get('date', '')
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change = item.get('change_pct', 0)
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main = item.get('main_pct', 0)
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super_pct = item.get('super_pct', 0)
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lines.append(f"- {date}: 涨跌{change:+.2f}%, 主力{main:+.2f}%, 超大单{super_pct:+.2f}%")
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return '\n'.join(lines)
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