from data_provider.akshare_provider import AkShareProvider

provider = AkShareProvider()
# 获取A股实时行情
df = provider.realtime_quote("000001")
# 获取历史 K 线
kline = provider.historical_data("000001", start="2024-01-01")

from litellm import completion

# 配置多模型渠道
model_config = {
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "分析贵州茅台走势"}],
    "temperature": 0.7
}

# 支持的模型列表
models = [
    "gpt-4", "gpt-3.5-turbo",           # OpenAI
    "claude-3-opus", "claude-3-sonnet",   # Anthropic
    "gemini-pro",                           # Google
    "deepseek-chat",                        # DeepSeek
    "qwen-turbo",                          # 阿里通义
    "mistral-medium",                       # Mistral
]

## 📈 贵州茅台（600519）分析

### 💡 一句话结论
**多头趋势延续，短期回调是买入机会，目标价 1850 元**

### 🎯 决策点位
| 类型 | 价格 | 理由 |
|------|------|------|
| 买入价 | 1750 | MA5 支撑位 |
| 止损价 | 1700 | MA10 跌破 |
| 目标价 | 1850 | 前高压力位 |

### ✅ 操作检查清单
- [x] MA5 > MA10 > MA20 多头排列 ✓
- [x] 乖离率 3.2% < 5% 阈值 ✓
- [x] 成交量放大 20% ✓
- [ ] 北向资金净流入（需确认）

class AShareThreeStageStrategy:
    """
    A股三段式复盘策略
    
    原理：
    1. 趋势判断：MA 多头排列确认方向
    2. 位置评估：乖离率判断贵贱
    3. 仓位决策：综合给出买入/持有/卖出建议
    """
    
    def evaluate(self, stock_code: str) -> StrategyResult:
        # 阶段 1：趋势判断
        trend = self.check_trend(stock_code)  # MA5 > MA10 > MA20
        
        # 阶段 2：位置评估
        deviation = self.calc_deviation(stock_code)  # 当前价偏离 MA5 的百分比
        
        # 阶段 3：综合决策
        if trend.bullish and deviation < 5:
            return StrategyResult(action=ACTION.BUY, confidence=0.85)
        elif trend.bullish and deviation > 8:
            return StrategyResult(action=ACTION.HOLD, confidence=0.60)
        else:
            return StrategyResult(action=ACTION.WAIT, confidence=0.50)

from bot.notifier import Notifier

notifier = Notifier()

# 企业微信
notifier.send_wechat(
    title="📈 每日股票分析",
    content=report_content,
    webhook_url=WEBHOOK_URL
)

# 飞书
notifier.send_feishu(
    content=report_content,
    webhook_url=FEISHU_WEBHOOK
)

# Telegram
notifier.send_telegram(
    text=report_content,
    chat_id=CHAT_ID,
    bot_token=BOT_TOKEN
)

on:
  schedule:
    - cron: '0 9 * * 1-5'  # 周一至周五 09:00 执行
  workflow_dispatch:           # 支持手动触发

# 克隆仓库
git clone https://github.com/ZhuLinsen/daily_stock_analysis.git
cd daily_stock_analysis

# 安装依赖
pip install -r requirements.txt

# 配置环境变量
cp .env.example .env
# 编辑 .env 填入 API Key

# 运行分析
python main.py --stock 600519 --market cn

# .env 文件
AIHUBMIX_KEY=your-key-here

LITELLM_MODEL=gpt-4
LITELLM_FALLBACK_MODELS=gpt-3.5-turbo,claude-3-haiku

# 渠道 1
LLM_CHANNELS=openai,anthropic
LLM_OPENAI_PROTOCOL=https://api.openai.com/v1
LLM_OPENAI_API_KEY=sk-xxx
LLM_OPENAI_MODELS=gpt-4,gpt-3.5-turbo
LLM_OPENAI_ENABLED=true

# 渠道 2
LLM_ANTHROPIC_PROTOCOL=https://api.anthropic.com
LLM_ANTHROPIC_API_KEY=sk-ant-xxx
LLM_ANTHROPIC_MODELS=claude-3-opus,claude-3-sonnet
LLM_ANTHROPIC_ENABLED=true

from data_provider.base import DataProviderBase

class MyDataProvider(DataProviderBase):
    """自定义数据源示例"""
    
    def realtime_quote(self, code: str) -> pd.DataFrame:
        # 实现实时行情获取
        pass
    
    def historical_data(self, code: str, days: int) -> pd.DataFrame:
        # 实现历史数据获取
        pass
    
    def fundamental_data(self, code: str) -> dict:
        # 实现基本面数据获取
        pass

from bot.base import NotifierBase

class CustomNotifier(NotifierBase):
    """自定义推送渠道示例"""
    
    def send(self, title: str, content: str, **kwargs) -> bool:
        # 实现推送逻辑
        pass

a_stock:
  - code: 600519
    name: 贵州茅台
  - code: 000858
    name: 五粮液

us_stock:
  - code: AAPL
    name: Apple
  - code: TSLA
    name: Tesla