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215 lines
10 KiB
215 lines
10 KiB
2 months ago
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import random
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from datetime import datetime, timedelta
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from typing import List, Optional
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from adapters.base import BaseDataAdapter
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from models import (
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MarketOverview, MarketIndex, SentimentData, MomentumData, HighLowStock,
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PriceDistribution, AIAnalysis, KLineData, NewsItem, HotNews, SentimentTrend, Stock,
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RecommendationType, TrendType, SentimentType, TargetPrice,
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)
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from config import settings
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class TencentAdapter(BaseDataAdapter):
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def __init__(self):
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super().__init__("腾讯财经", settings.TENCENT_API)
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async def fetch_market_overview(self) -> MarketOverview:
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return MarketOverview(
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up_count=2856 + random.randint(-100, 100),
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down_count=1987 + random.randint(-100, 100),
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flat_count=157 + random.randint(-20, 20),
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limit_up_count=68 + random.randint(-10, 10),
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limit_down_count=12 + random.randint(-5, 5),
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update_time=datetime.now().isoformat(),
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)
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async def fetch_market_indices(self) -> List[MarketIndex]:
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indices_config = [
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{"code": "000001", "name": "上证指数", "base": 3052.58},
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{"code": "399001", "name": "深证成指", "base": 9856.32},
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{"code": "399006", "name": "创业板指", "base": 1856.28},
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{"code": "000688", "name": "科创50", "base": 856.32},
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{"code": "899050", "name": "北证50", "base": 756.85},
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]
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result = []
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for idx in indices_config:
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price = idx["base"] * (1 + random.uniform(-0.02, 0.02))
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change_percent = random.uniform(-1.5, 1.5)
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result.append(MarketIndex(
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code=idx["code"], name=idx["name"], price=round(price, 2),
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change=round(price * change_percent / 100, 2),
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change_percent=round(change_percent, 2),
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volume=random.randint(1000000000, 5000000000),
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up_count=random.randint(800, 1500), down_count=random.randint(500, 1000),
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flat_count=random.randint(10, 100),
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))
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return result
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async def fetch_sentiment(self) -> SentimentData:
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value = random.randint(30, 70)
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if value >= 70:
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label, description, color = "乐观", "市场情绪高涨", "#22c55e"
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elif value >= 50:
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label, description, color = "中性", "市场情绪平稳", "#f97316"
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elif value >= 30:
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label, description, color = "谨慎", "市场观望情绪浓厚", "#eab308"
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else:
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label, description, color = "悲观", "市场信心不足", "#ef4444"
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return SentimentData(value=value, label=label, description=description, color=color)
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async def fetch_sentiment_trend(self, days: int = 15) -> List[SentimentTrend]:
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result = []
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base_date = datetime.now() - timedelta(days=days)
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for i in range(days):
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date = base_date + timedelta(days=i)
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result.append(SentimentTrend(date=date.strftime("%m-%d"), value=random.randint(35, 65)))
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return result
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async def fetch_momentum_data(self) -> List[MomentumData]:
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sectors = ["半导体", "新能源", "人工智能", "医药生物", "消费电子", "银行", "房地产", "钢铁", "煤炭", "石油石化"]
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result = []
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for sector in sectors:
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change = random.uniform(-3, 5)
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momentum = random.uniform(15, 95)
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result.append(MomentumData(
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sector=sector, change_percent=round(change, 2), momentum=round(momentum, 1),
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stocks=[], up_count=random.randint(10, 60), down_count=random.randint(5, 30),
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flat_count=random.randint(0, 5), top_stocks=[],
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))
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return result
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async def fetch_high_stocks(self, limit: int = 10) -> List[HighLowStock]:
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result = []
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for i in range(limit):
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price = random.uniform(50, 500)
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result.append(HighLowStock(
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code=f"688{random.randint(100, 999)}", name=f"新高股票{i+1}",
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price=round(price, 2), change_percent=round(random.uniform(3, 10), 2),
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high_low_price=round(price, 2), industry="示例行业", days_to_high_low=random.randint(0, 5),
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))
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return result
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async def fetch_low_stocks(self, limit: int = 10) -> List[HighLowStock]:
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result = []
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for i in range(limit):
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price = random.uniform(5, 50)
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result.append(HighLowStock(
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code=f"000{random.randint(100, 999)}", name=f"新低股票{i+1}",
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price=round(price, 2), change_percent=round(random.uniform(-5, -1), 2),
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high_low_price=round(price, 2), industry="示例行业", days_to_high_low=random.randint(0, 7),
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))
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return result
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async def fetch_price_distribution(self) -> List[PriceDistribution]:
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ranges = [
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(">10%", True, 68), ("9%~10%", True, 45), ("8%~9%", True, 52),
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("7%~8%", True, 78), ("6%~7%", True, 96), ("5%~6%", True, 128),
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("4%~5%", True, 186), ("3%~4%", True, 268), ("2%~3%", True, 385),
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("1%~2%", True, 528), ("0~1%", True, 1022), ("平盘", False, 157),
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("-1%~0", False, 856), ("-2%~-1%", False, 568), ("-3%~-2%", False, 325),
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("-4%~-3%", False, 128), ("-5%~-4%", False, 56), ("-6%~-5%", False, 28),
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("-7%~-6%", False, 15), ("-8%~-7%", False, 8), ("-9%~-8%", False, 5),
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("-10%~-9%", False, 3), ("< -10%", False, 12),
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]
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return [PriceDistribution(range=r, count=c + random.randint(-20, 20), is_up=u) for r, u, c in ranges]
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async def fetch_stock_detail(self, code: str) -> Optional[Stock]:
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return Stock(
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code=code, name="示例股票", price=random.uniform(10, 100),
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change=random.uniform(-5, 5), change_percent=random.uniform(-5, 5),
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volume=random.randint(100000, 1000000), market_cap=random.randint(1000000000, 100000000000),
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industry="示例行业",
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)
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async def fetch_kline_data(self, code: str, days: int = 30) -> List[KLineData]:
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result = []
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base_date = datetime.now() - timedelta(days=days)
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base_price = random.uniform(8, 12)
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for i in range(days):
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date = base_date + timedelta(days=i)
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if date.weekday() >= 5:
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continue
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daily_change = random.uniform(-0.15, 0.15)
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open_price = base_price
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close_price = base_price * (1 + daily_change)
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result.append(KLineData(
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date=date.strftime("%Y-%m-%d"), open=round(open_price, 2),
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high=round(max(open_price, close_price) * 1.02, 2),
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low=round(min(open_price, close_price) * 0.98, 2),
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close=round(close_price, 2), volume=random.randint(500000, 2000000),
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))
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base_price = close_price
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return result
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async def fetch_ai_analysis(self, code: str) -> AIAnalysis:
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current_price = random.uniform(8, 12)
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trend = random.choice([TrendType.UP, TrendType.DOWN, TrendType.SIDEWAYS])
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recommendation = random.choice([RecommendationType.BUY, RecommendationType.SELL, RecommendationType.HOLD])
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return AIAnalysis(
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stock_code=code, stock_name="示例股票", current_price=round(current_price, 2),
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change_percent=round(random.uniform(-8, 8), 2), insights="腾讯财经数据分析...",
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recommendation=recommendation,
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recommendation_text={"buy": "买入", "sell": "卖出", "hold": "持有"}[recommendation.value],
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trend=trend, trend_text={"up": "上涨", "down": "下跌", "sideways": "震荡"}[trend.value],
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target_price=TargetPrice(
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ideal_buy=round(current_price * 0.92, 2),
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second_buy=round(current_price * 0.88, 2),
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stop_loss=round(current_price * 0.85, 2),
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take_profit=round(current_price * 1.18, 2),
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),
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confidence=random.randint(60, 85),
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)
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async def fetch_news(self, limit: int = 10) -> List[NewsItem]:
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result = []
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for i in range(limit):
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sentiment = random.choice([SentimentType.POSITIVE, SentimentType.NEGATIVE, SentimentType.NEUTRAL])
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result.append(NewsItem(
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id=str(i + 1), title=f"腾讯新闻{i+1}", source="腾讯财经",
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time=f"{random.randint(1, 24)}小时前", sentiment=sentiment,
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sentiment_text={"positive": "正面", "negative": "负面", "neutral": "中性"}[sentiment.value],
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))
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return result
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async def fetch_hot_news(self, limit: int = 10) -> List[HotNews]:
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result = []
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for i in range(limit):
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result.append(HotNews(
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id=str(i + 1), title=f"腾讯热点{i+1}",
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heat=random.randint(6000, 10000), related_stocks=[],
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time=f"{random.randint(15, 120)}分钟前",
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))
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return result
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async def fetch_hot_stocks(self, limit: int = 10) -> List[Stock]:
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result = []
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for i in range(limit):
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price = random.uniform(50, 500)
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change_percent = random.uniform(3, 10)
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result.append(Stock(
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code=f"688{random.randint(100, 999)}", name=f"热门股票{i+1}",
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price=round(price, 2), change=round(price * change_percent / 100, 2),
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change_percent=round(change_percent, 2),
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volume=random.randint(100000, 2000000),
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market_cap=random.randint(10000000000, 1000000000000), industry="热门行业",
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))
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return result
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async def fetch_cold_stocks(self, limit: int = 10) -> List[Stock]:
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result = []
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for i in range(limit):
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price = random.uniform(5, 50)
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change_percent = random.uniform(-5, -1)
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result.append(Stock(
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code=f"000{random.randint(100, 999)}", name=f"冷门股票{i+1}",
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price=round(price, 2), change=round(price * change_percent / 100, 2),
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change_percent=round(change_percent, 2),
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volume=random.randint(500000, 3000000),
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market_cap=random.randint(100000000000, 1500000000000), industry="冷门行业",
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))
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return result
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async def is_available(self) -> bool:
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return True
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