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tushare

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songzhuoyuan
2026-08-11 20:41:31 +08:00
parent 2ce13e5bae
commit befdc32aea
24 changed files with 1311 additions and 530 deletions
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ai_agents.py
+25 -31
View File
@@ -1,5 +1,6 @@
from deepseek_client import DeepSeekClient
from typing import Dict, Any
import concurrent.futures
import time
import config
@@ -13,7 +14,6 @@ class StockAnalysisAgents:
def technical_analyst_agent(self, stock_info: Dict, stock_data: Any, indicators: Dict) -> Dict[str, Any]:
"""技术面分析智能体"""
print("🔍 技术分析师正在分析中...")
time.sleep(1) # 模拟分析时间
analysis = self.deepseek_client.technical_analysis(stock_info, stock_data, indicators)
@@ -38,8 +38,6 @@ class StockAnalysisAgents:
else:
print(" ⚠ 未获取到季报数据，将基于基本财务数据分析")
time.sleep(1)
analysis = self.deepseek_client.fundamental_analysis(stock_info, financial_data, quarterly_data)
return {
@@ -61,8 +59,6 @@ class StockAnalysisAgents:
else:
print(" ⚠ 未获取到资金流向数据，将基于技术指标分析")
time.sleep(1)
analysis = self.deepseek_client.fund_flow_analysis(stock_info, indicators, fund_flow_data)
return {
@@ -84,8 +80,6 @@ class StockAnalysisAgents:
else:
print(" ⚠ 未获取到风险数据，将基于基本信息分析")
time.sleep(1)
# 构建风险数据文本
risk_data_text = ""
if risk_data and risk_data.get('data_success'):
@@ -227,8 +221,6 @@ class StockAnalysisAgents:
else:
print(" ⚠ 未获取到详细情绪数据，将基于基本信息分析")
time.sleep(1)
# 构建带有市场情绪数据的prompt
sentiment_data_text = ""
if sentiment_data and sentiment_data.get('data_success'):
@@ -317,8 +309,6 @@ class StockAnalysisAgents:
else:
print(" ⚠ 未获取到新闻数据，将基于基本信息分析")
time.sleep(1)
# 构建带有新闻数据的prompt
news_text = ""
if news_data and news_data.get('data_success'):
@@ -436,32 +426,38 @@ class StockAnalysisAgents:
print(f"📋 参与分析的分析师: {', '.join(active_analysts)}")
print("=" * 50)
# 并行运行各个分析师
# 并行运行各个分析师（全部同时启动，等待最慢的一个完成）
agents_results = {}
# 技术面分析
agent_jobs = []
if enabled_analysts.get('technical', True):
agents_results["technical"] = self.technical_analyst_agent(stock_info, stock_data, indicators)
# 基本面分析
agent_jobs.append(("technical", lambda: self.technical_analyst_agent(stock_info, stock_data, indicators)))
if enabled_analysts.get('fundamental', True):
agents_results["fundamental"] = self.fundamental_analyst_agent(stock_info, financial_data, quarterly_data)
# 资金面分析（传入资金流向数据）
agent_jobs.append(("fundamental", lambda: self.fundamental_analyst_agent(stock_info, financial_data, quarterly_data)))
if enabled_analysts.get('fund_flow', True):
agents_results["fund_flow"] = self.fund_flow_analyst_agent(stock_info, indicators, fund_flow_data)
# 风险管理分析（传入风险数据）
agent_jobs.append(("fund_flow", lambda: self.fund_flow_analyst_agent(stock_info, indicators, fund_flow_data)))
if enabled_analysts.get('risk', True):
agents_results["risk_management"] = self.risk_management_agent(stock_info, indicators, risk_data)
# 市场情绪分析（传入市场情绪数据）
agent_jobs.append(("risk_management", lambda: self.risk_management_agent(stock_info, indicators, risk_data)))
if enabled_analysts.get('sentiment', False):
agents_results["market_sentiment"] = self.market_sentiment_agent(stock_info, sentiment_data)
# 新闻分析（传入新闻数据）
agent_jobs.append(("market_sentiment", lambda: self.market_sentiment_agent(stock_info, sentiment_data)))
if enabled_analysts.get('news', False):
agents_results["news"] = self.news_analyst_agent(stock_info, news_data)
agent_jobs.append(("news", lambda: self.news_analyst_agent(stock_info, news_data)))
with concurrent.futures.ThreadPoolExecutor(max_workers=max(min(len(agent_jobs), 6), 1)) as executor:
future_to_key = {executor.submit(job): key for key, job in agent_jobs}
for future in concurrent.futures.as_completed(future_to_key):
key = future_to_key[future]
try:
agents_results[key] = future.result()
except Exception as e:
print(f"❌ {key} 分析师并行分析失败: {e}")
agents_results[key] = {
"agent_name": key,
"agent_role": "",
"analysis": f"分析失败: {e}",
"focus_areas": [],
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
}
print("✅ 所有已选择的分析师完成分析")
print("=" * 50)
@@ -471,7 +467,6 @@ class StockAnalysisAgents:
def conduct_team_discussion(self, agents_results: Dict[str, Any], stock_info: Dict) -> str:
"""进行团队讨论"""
print("🤝 分析团队正在进行综合讨论...")
time.sleep(2)
# 收集参与分析的分析师名单和报告
participants = []
@@ -538,7 +533,6 @@ class StockAnalysisAgents:
def make_final_decision(self, discussion_result: str, stock_info: Dict, indicators: Dict) -> Dict[str, Any]:
"""制定最终投资决策"""
print("📋 正在制定最终投资决策...")
time.sleep(1)
decision = self.deepseek_client.final_decision(discussion_result, stock_info, indicators)
app.py
+4
View File
@@ -9,6 +9,10 @@ import base64
import os
import config
# 注入所有外部请求（akshare/tushare等）的默认超时
from http_timeout import install_default_requests_timeout
install_default_requests_timeout()
from stock_data import StockDataFetcher
from ai_agents import StockAnalysisAgents
from pdf_generator import display_pdf_export_section
data_source_manager.py
+159 -130
View File
@@ -11,6 +11,10 @@ from dotenv import load_dotenv
# 加载环境变量
load_dotenv()
# 注入外部请求默认超时（覆盖akshare、tushare等基于requests的调用）
from http_timeout import install_default_requests_timeout
install_default_requests_timeout()
class DataSourceManager:
"""数据源管理器 - 实现akshare与tushare自动切换"""
@@ -25,7 +29,7 @@ class DataSourceManager:
try:
import tushare as ts
ts.set_token(self.tushare_token)
self.tushare_api = ts.pro_api()
self.tushare_api = ts.pro_api(timeout=float(os.getenv('TUSHARE_TIMEOUT', '15')))
self.tushare_available = True
print("✅ Tushare数据源初始化成功")
except Exception as e:
@@ -36,7 +40,7 @@ class DataSourceManager:
def get_stock_hist_data(self, symbol, start_date=None, end_date=None, adjust='qfq'):
"""
获取股票历史数据（优先akshare，失败时使用tushare）
获取股票历史数据（优先tushare，失败时使用akshare）
Args:
symbol: 股票代码（6位数字）
@@ -55,10 +59,63 @@ class DataSourceManager:
else:
end_date = datetime.now().strftime('%Y%m%d')
# 优先使用akshare
# 优先使用tushare
if self.tushare_available:
try:
import tushare as ts
print(f"[Tushare] 正在获取 {symbol} 的历史数据（主要数据源）...")
# 转换股票代码格式（添加市场后缀）
ts_code = self._convert_to_ts_code(symbol)
# 转换复权类型
adj_dict = {'qfq': 'qfq', 'hfq': 'hfq', '': None}
adj = adj_dict.get(adjust, 'qfq')
if adj is None:
# 不复权数据直接使用daily接口
df = self.tushare_api.daily(
ts_code=ts_code,
start_date=start_date,
end_date=end_date
)
else:
# 复权数据使用pro_bar（daily接口不支持adj参数）
df = ts.pro_bar(
api=self.tushare_api,
ts_code=ts_code,
start_date=start_date,
end_date=end_date,
adj=adj,
retry_count=1
)
if df is not None and not df.empty:
# 标准化列名和数据格式
df = df.rename(columns={
'trade_date': 'date',
'vol': 'volume',
'amount': 'amount'
})
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values('date')
# 转换成交量单位（tushare单位是手，转换为股）
df['volume'] = df['volume'] * 100
# 转换成交额单位（tushare单位是千元，转换为元）
df['amount'] = df['amount'] * 1000
print(f"[Tushare] ✅ 成功获取 {len(df)} 条数据")
return df
else:
print(f"[Tushare] ❌ 未获取到数据，尝试备用数据源")
except Exception as e:
print(f"[Tushare] ❌ 获取失败: {e}")
# tushare失败，回退到akshare
try:
import akshare as ak
print(f"[Akshare] 正在获取 {symbol} 的历史数据...")
print(f"[Akshare] 正在获取 {symbol} 的历史数据（备用数据源）...")
df = ak.stock_zh_a_hist(
symbol=symbol,
@@ -86,60 +143,18 @@ class DataSourceManager:
df['date'] = pd.to_datetime(df['date'])
print(f"[Akshare] ✅ 成功获取 {len(df)} 条数据")
return df
else:
print(f"[Akshare] ❌ 未获取到数据")
except Exception as e:
print(f"[Akshare] ❌ 获取失败: {e}")
# akshare失败，尝试tushare
if self.tushare_available:
try:
print(f"[Tushare] 正在获取 {symbol} 的历史数据（备用数据源）...")
# 转换股票代码格式（添加市场后缀）
ts_code = self._convert_to_ts_code(symbol)
# 转换复权类型
adj_dict = {'qfq': 'qfq', 'hfq': 'hfq', '': None}
adj = adj_dict.get(adjust, 'qfq')
# 格式化日期
start = f"{start_date[:4]}-{start_date[4:6]}-{start_date[6:]}" if start_date else None
end = f"{end_date[:4]}-{end_date[4:6]}-{end_date[6:]}" if end_date else None
# 获取数据
df = self.tushare_api.daily(
ts_code=ts_code,
start_date=start_date,
end_date=end_date,
adj=adj
)
if df is not None and not df.empty:
# 标准化列名和数据格式
df = df.rename(columns={
'trade_date': 'date',
'vol': 'volume',
'amount': 'amount'
})
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values('date')
# 转换成交量单位（tushare单位是手，转换为股）
df['volume'] = df['volume'] * 100
# 转换成交额单位（tushare单位是千元，转换为元）
df['amount'] = df['amount'] * 1000
print(f"[Tushare] ✅ 成功获取 {len(df)} 条数据")
return df
except Exception as e:
print(f"[Tushare] ❌ 获取失败: {e}")
# 两个数据源都失败
print("❌ 所有数据源均获取失败")
return None
def get_stock_basic_info(self, symbol):
"""
获取股票基本信息（优先akshare，失败时使用tushare）
获取股票基本信息（优先tushare，失败时使用akshare）
Args:
symbol: 股票代码
@@ -154,10 +169,34 @@ class DataSourceManager:
"market": "未知"
}
# 优先使用akshare
# 优先使用tushare
if self.tushare_available:
try:
print(f"[Tushare] 正在获取 {symbol} 的基本信息（主要数据源）...")
ts_code = self._convert_to_ts_code(symbol)
df = self.tushare_api.stock_basic(
ts_code=ts_code,
fields='ts_code,name,area,industry,market,list_date'
)
if df is not None and not df.empty:
info['name'] = df.iloc[0]['name']
info['industry'] = df.iloc[0]['industry']
info['market'] = df.iloc[0]['market']
info['list_date'] = df.iloc[0]['list_date']
print(f"[Tushare] ✅ 成功获取基本信息")
return info
else:
print(f"[Tushare] ❌ 未获取到基本信息，尝试备用数据源")
except Exception as e:
print(f"[Tushare] ❌ 获取失败: {e}")
# tushare失败，回退akshare
try:
import akshare as ak
print(f"[Akshare] 正在获取 {symbol} 的基本信息...")
print(f"[Akshare] 正在获取 {symbol} 的基本信息（备用数据源）...")
stock_info = ak.stock_individual_info_em(symbol=symbol)
if stock_info is not None and not stock_info.empty:
@@ -181,33 +220,11 @@ class DataSourceManager:
except Exception as e:
print(f"[Akshare] ❌ 获取失败: {e}")
# akshare失败，尝试tushare
if self.tushare_available:
try:
print(f"[Tushare] 正在获取 {symbol} 的基本信息（备用数据源）...")
ts_code = self._convert_to_ts_code(symbol)
df = self.tushare_api.stock_basic(
ts_code=ts_code,
fields='ts_code,name,area,industry,market,list_date'
)
if df is not None and not df.empty:
info['name'] = df.iloc[0]['name']
info['industry'] = df.iloc[0]['industry']
info['market'] = df.iloc[0]['market']
info['list_date'] = df.iloc[0]['list_date']
print(f"[Tushare] ✅ 成功获取基本信息")
return info
except Exception as e:
print(f"[Tushare] ❌ 获取失败: {e}")
return info
def get_realtime_quotes(self, symbol):
"""
获取实时行情数据（优先akshare，失败时使用tushare）
获取实时行情数据（优先tushare，失败时使用akshare）
Args:
symbol: 股票代码
@@ -217,10 +234,51 @@ class DataSourceManager:
"""
quotes = {}
# 优先使用akshare
# 优先使用tushare
if self.tushare_available:
try:
print(f"[Tushare] 正在获取 {symbol} 的实时行情（主要数据源）...")
ts_code = self._convert_to_ts_code(symbol)
today = datetime.now().strftime('%Y%m%d')
df = self.tushare_api.daily(
ts_code=ts_code,
start_date=today,
end_date=today
)
if df is None or df.empty:
# 非交易日时，获取最近10个交易日的最新数据
start = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
df = self.tushare_api.daily(
ts_code=ts_code,
start_date=start,
end_date=today
)
if df is not None and not df.empty:
row = df.iloc[0]
quotes = {
'symbol': symbol,
'price': row['close'],
'change_percent': row['pct_chg'],
'volume': row['vol'] * 100,
'amount': row['amount'] * 1000,
'high': row['high'],
'low': row['low'],
'open': row['open'],
'pre_close': row['pre_close']
}
print(f"[Tushare] ✅ 成功获取实时行情")
return quotes
else:
print(f"[Tushare] ❌ 未获取到实时行情，尝试备用数据源")
except Exception as e:
print(f"[Tushare] ❌ 获取失败: {e}")
# tushare失败，回退akshare
try:
import akshare as ak
print(f"[Akshare] 正在获取 {symbol} 的实时行情...")
print(f"[Akshare] 正在获取 {symbol} 的实时行情（备用数据源）...")
df = ak.stock_zh_a_spot_em()
stock_df = df[df['代码'] == symbol]
@@ -245,41 +303,11 @@ class DataSourceManager:
except Exception as e:
print(f"[Akshare] ❌ 获取失败: {e}")
# akshare失败，尝试tushare
if self.tushare_available:
try:
print(f"[Tushare] 正在获取 {symbol} 的实时行情（备用数据源）...")
ts_code = self._convert_to_ts_code(symbol)
df = self.tushare_api.daily(
ts_code=ts_code,
start_date=datetime.now().strftime('%Y%m%d'),
end_date=datetime.now().strftime('%Y%m%d')
)
if df is not None and not df.empty:
row = df.iloc[0]
quotes = {
'symbol': symbol,
'price': row['close'],
'change_percent': row['pct_chg'],
'volume': row['vol'] * 100,
'amount': row['amount'] * 1000,
'high': row['high'],
'low': row['low'],
'open': row['open'],
'pre_close': row['pre_close']
}
print(f"[Tushare] ✅ 成功获取实时行情")
return quotes
except Exception as e:
print(f"[Tushare] ❌ 获取失败: {e}")
return quotes
def get_financial_data(self, symbol, report_type='income'):
"""
获取财务数据（优先akshare，失败时使用tushare）
获取财务数据（优先tushare，失败时使用akshare）
Args:
symbol: 股票代码
@@ -288,30 +316,10 @@ class DataSourceManager:
Returns:
DataFrame: 财务数据
"""
# 优先使用akshare
try:
import akshare as ak
print(f"[Akshare] 正在获取 {symbol} 的财务数据...")
if report_type == 'income':
df = ak.stock_financial_report_sina(stock=symbol, symbol="利润表")
elif report_type == 'balance':
df = ak.stock_financial_report_sina(stock=symbol, symbol="资产负债表")
elif report_type == 'cashflow':
df = ak.stock_financial_report_sina(stock=symbol, symbol="现金流量表")
else:
df = None
if df is not None and not df.empty:
print(f"[Akshare] ✅ 成功获取财务数据")
return df
except Exception as e:
print(f"[Akshare] ❌ 获取失败: {e}")
# akshare失败，尝试tushare
# 优先使用tushare
if self.tushare_available:
try:
print(f"[Tushare] 正在获取 {symbol} 的财务数据（备用数据源）...")
print(f"[Tushare] 正在获取 {symbol} 的财务数据（主要数据源）...")
ts_code = self._convert_to_ts_code(symbol)
@@ -327,9 +335,31 @@ class DataSourceManager:
if df is not None and not df.empty:
print(f"[Tushare] ✅ 成功获取财务数据")
return df
else:
print(f"[Tushare] ❌ 未获取到财务数据，尝试备用数据源")
except Exception as e:
print(f"[Tushare] ❌ 获取失败: {e}")
# tushare失败，回退akshare
try:
import akshare as ak
print(f"[Akshare] 正在获取 {symbol} 的财务数据（备用数据源）...")
if report_type == 'income':
df = ak.stock_financial_report_sina(stock=symbol, symbol="利润表")
elif report_type == 'balance':
df = ak.stock_financial_report_sina(stock=symbol, symbol="资产负债表")
elif report_type == 'cashflow':
df = ak.stock_financial_report_sina(stock=symbol, symbol="现金流量表")
else:
df = None
if df is not None and not df.empty:
print(f"[Akshare] ✅ 成功获取财务数据")
return df
except Exception as e:
print(f"[Akshare] ❌ 获取失败: {e}")
return None
def _convert_to_ts_code(self, symbol):
@@ -376,4 +406,3 @@ class DataSourceManager:
# 全局数据源管理器实例
data_source_manager = DataSourceManager()
deepseek_client.py
+4 -1
View File
@@ -1,5 +1,6 @@
import openai
import json
import os
from typing import Dict, List, Any, Optional
import config
@@ -10,7 +11,9 @@ class DeepSeekClient:
self.model = model or config.DEFAULT_MODEL_NAME
self.client = openai.OpenAI(
api_key=config.DEEPSEEK_API_KEY,
base_url=config.DEEPSEEK_BASE_URL
base_url=config.DEEPSEEK_BASE_URL,
timeout=float(os.getenv('DEEPSEEK_TIMEOUT', '180')),
max_retries=int(os.getenv('DEEPSEEK_MAX_RETRIES', '1'))
)
def call_api(self, messages: List[Dict[str, str]], model: Optional[str] = None,
fund_flow_akshare.py
+45 -48
View File
@@ -109,56 +109,54 @@ class FundFlowAkshareDataFetcher:
def _get_individual_fund_flow(self, symbol, market):
"""获取个股资金流向数据（支持akshare和tushare自动切换）"""
try:
# 优先使用akshare的stock_individual_fund_flow接口
print(f" [Akshare] 正在获取资金流向 (市场: {market})...")
# 优先使用tushare
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取资金流向数据（主要数据源）...")
ts_code = data_source_manager._convert_to_ts_code(symbol)
df = ak.stock_individual_fund_flow(stock=symbol, market=market)
# 计算日期范围（最近N个交易日）
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=self.days * 2)).strftime('%Y%m%d')
df = data_source_manager.tushare_api.moneyflow(
ts_code=ts_code,
start_date=start_date,
end_date=end_date
)
if df is not None and not df.empty:
# 标准化列名以匹配akshare格式（金额单位：千元→元）
df = df.rename(columns={
'trade_date': '日期',
'close': '收盘价',
'pct_chg': '涨跌幅',
'net_mf_amount': '主力净流入-净额'
})
df['主力净流入-净额'] = df['主力净流入-净额'] * 1000
df['超大单净流入-净额'] = (df['buy_elg_amount'] - df['sell_elg_amount']) * 1000
df['大单净流入-净额'] = (df['buy_lg_amount'] - df['sell_lg_amount']) * 1000
df['中单净流入-净额'] = (df['buy_md_amount'] - df['sell_md_amount']) * 1000
df['小单净流入-净额'] = (df['buy_sm_amount'] - df['sell_sm_amount']) * 1000
# tushare按日期倒序返回，取最近N天后转为正序（与akshare一致）
df = df.head(self.days)
df = df.iloc[::-1].reset_index(drop=True)
print(f" [Tushare] ✅ 成功获取 {len(df)} 条资金流向数据")
else:
print(f" [Tushare] ❌ 未找到资金流向数据，尝试备用数据源")
df = None
except Exception as te:
print(f" [Tushare] ❌ 获取失败: {te}")
df = None
else:
df = None
# tushare不可用或失败时，回退akshare
if df is None or df.empty:
print(f" [Akshare] 未找到资金流向数据，尝试备用数据源...")
# akshare失败，尝试tushare
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取资金流向数据（备用数据源）...")
ts_code = data_source_manager._convert_to_ts_code(symbol)
# 计算日期范围（最近N个交易日）
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=self.days * 2)).strftime('%Y%m%d')
# 获取资金流向数据
df = data_source_manager.tushare_api.moneyflow(
ts_code=ts_code,
start_date=start_date,
end_date=end_date
)
if df is not None and not df.empty:
# 标准化列名以匹配akshare格式
df = df.rename(columns={
'trade_date': '日期',
'buy_sm_amount': '小单买入',
'sell_sm_amount': '小单卖出',
'buy_md_amount': '中单买入',
'sell_md_amount': '中单卖出',
'buy_lg_amount': '大单买入',
'sell_lg_amount': '大单卖出',
'buy_elg_amount': '超大单买入',
'sell_elg_amount': '超大单卖出',
'net_mf_amount': '净额'
})
# 限制为最近N天
df = df.head(self.days)
print(f" [Tushare] ✅ 成功获取 {len(df)} 条资金流向数据")
else:
print(f" [Tushare] ❌ 未找到资金流向数据")
return None
except Exception as te:
print(f" [Tushare] ❌ 获取失败: {te}")
return None
else:
print(f" [Akshare] 正在获取资金流向 (市场: {market})（备用数据源）...")
df = ak.stock_individual_fund_flow(stock=symbol, market=market)
if df is None or df.empty:
print(f" [Akshare] 未找到资金流向数据")
return None
# akshare 返回的数据是按时间正序排列（从旧到新），所以使用 tail() 获取最近N天的数据
@@ -341,4 +339,3 @@ if __name__ == "__main__":
print(f"\n获取失败: {data.get('error', '未知错误')}")
print("\n")
http_timeout.py
+77
View File
@@ -0,0 +1,77 @@
"""
统一的外部请求超时控制工具
- install_default_requests_timeout: 给所有基于 requests 的外部请求
（akshare、tushare、pywencai 等）注入默认超时，调用方未指定 timeout 时生效。
- call_with_timeout: 在独立线程中执行阻塞调用，超过时限立即放弃等待，
防止个别环节长时间卡死整个程序。
"""
import os
import threading
# 默认连接超时 / 读取超时（单位：秒），可通过环境变量覆盖
DEFAULT_CONNECT_TIMEOUT = float(os.getenv("HTTP_CONNECT_TIMEOUT", "6"))
DEFAULT_READ_TIMEOUT = float(os.getenv("HTTP_READ_TIMEOUT", "20"))
DEFAULT_CALL_TIMEOUT = float(os.getenv("HTTP_CALL_TIMEOUT", "30"))
_installed = False
_install_lock = threading.Lock()
def install_default_requests_timeout(connect_timeout=None, read_timeout=None):
"""给 requests 库注入默认超时（仅当调用方未显式指定 timeout 时生效）。"""
global _installed
with _install_lock:
if _installed:
return
connect = DEFAULT_CONNECT_TIMEOUT if connect_timeout is None else connect_timeout
read = DEFAULT_READ_TIMEOUT if read_timeout is None else read_timeout
try:
import requests.sessions
original_request = requests.sessions.Session.request
def request_with_timeout(self, method, url, **kwargs):
if kwargs.get("timeout") is None:
kwargs["timeout"] = (connect, read)
return original_request(self, method, url, **kwargs)
requests.sessions.Session.request = request_with_timeout
_installed = True
print(f"✅ 已为外部请求注入默认超时（连接 {connect}s / 读取 {read}s）")
except Exception as e:
print(f"⚠️ 注入 requests 默认超时失败: {e}")
def call_with_timeout(func, timeout=None, *args, **kwargs):
"""在线程中执行函数，超过 timeout 秒则放弃等待并抛出 TimeoutError。
注意：超时后线程会继续在后台运行直至结束（守护线程），不会影响主程序。
"""
if timeout is None:
timeout = DEFAULT_CALL_TIMEOUT
if timeout <= 0:
return func(*args, **kwargs)
result_box = {}
def _run():
try:
result_box["ok"] = True
result_box["value"] = func(*args, **kwargs)
except BaseException as e: # noqa: BLE001
result_box["ok"] = False
result_box["error"] = e
worker = threading.Thread(target=_run, daemon=True)
worker.start()
worker.join(timeout)
if worker.is_alive():
func_name = getattr(func, "__name__", "function")
raise TimeoutError(f"调用 {func_name} 超过 {timeout}s 未返回，已放弃等待")
if result_box.get("ok"):
return result_box["value"]
raise result_box.get("error", RuntimeError("未知错误"))
low_price_bull_selector.py
+2 -1
View File
@@ -10,6 +10,7 @@ import pywencai
from datetime import datetime
from typing import Tuple, Optional
import time
from http_timeout import call_with_timeout
class LowPriceBullSelector:
@@ -60,7 +61,7 @@ class LowPriceBullSelector:
print(f"正在调用问财接口...")
# 调用pywencai
result = pywencai.get(query=query, loop=True)
result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if result is None:
return False, None, "问财接口返回None，请检查网络或稍后重试"
main_force_selector.py
+2 -2
View File
@@ -11,6 +11,7 @@ import pywencai
from datetime import datetime, timedelta
from typing import Dict, List, Tuple
import time
from http_timeout import call_with_timeout
class MainForceStockSelector:
"""主力选股类"""
@@ -71,7 +72,7 @@ class MainForceStockSelector:
print(f"查询语句: {query[:100]}...")
try:
result = pywencai.get(query=query, loop=True)
result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if result is None:
print(f" ⚠️ 方案{i}返回None，尝试下一个方案")
@@ -387,4 +388,3 @@ class MainForceStockSelector:
# 全局实例
main_force_selector = MainForceStockSelector()
market_sentiment_data.py
+160 -86
View File
@@ -329,11 +329,56 @@ class MarketSentimentDataFetcher:
}
def _get_turnover_rate(self, symbol):
"""获取换手率数据（支持akshare和tushare自动切换）"""
"""获取换手率数据（优先tushare，失败时使用akshare）"""
try:
# 优先使用akshare获取最近的换手率数据
print(f" [Akshare] 正在获取换手率数据...")
# 获取A股实时行情数据（不需要参数）
# 优先使用tushare（daily_basic，取最近10个交易日保证非交易日也有数据）
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取换手率数据（主要数据源）...")
ts_code = data_source_manager._convert_to_ts_code(symbol)
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
df = data_source_manager.tushare_api.daily_basic(
ts_code=ts_code,
start_date=start_date,
end_date=end_date
)
if df is not None and not df.empty:
row = df.iloc[0]
turnover_rate = row.get('turnover_rate', 'N/A')
# 解读换手率
interpretation = ""
if turnover_rate != 'N/A':
try:
turnover = float(turnover_rate)
if turnover > 20:
interpretation = "换手率极高（>20%），资金活跃度极高，可能存在炒作"
elif turnover > 10:
interpretation = "换手率较高（>10%），交易活跃"
elif turnover > 5:
interpretation = "换手率正常（5%-10%），交易适中"
elif turnover > 2:
interpretation = "换手率偏低（2%-5%），交易相对清淡"
else:
interpretation = "换手率很低（<2%），交易清淡"
except:
pass
print(f" [Tushare] ✅ 成功获取换手率: {turnover_rate}%")
return {
"current_turnover_rate": turnover_rate,
"interpretation": interpretation
}
else:
print(f" [Tushare] ❌ 未获取到换手率，尝试备用数据源")
except Exception as te:
print(f" [Tushare] ❌ 获取失败: {te}")
# tushare失败，回退akshare
print(f" [Akshare] 正在获取换手率数据（备用数据源）...")
df = ak.stock_zh_a_spot_em()
if df is not None and not df.empty:
stock_data = df[df['代码'] == symbol]
@@ -367,56 +412,41 @@ class MarketSentimentDataFetcher:
except Exception as e:
print(f" [Akshare] ❌ 获取换手率失败: {e}")
# akshare失败，尝试tushare
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取换手率数据（备用数据源）...")
ts_code = data_source_manager._convert_to_ts_code(symbol)
# 获取最近一个交易日的数据
df = data_source_manager.tushare_api.daily_basic(
ts_code=ts_code,
trade_date=datetime.now().strftime('%Y%m%d')
)
if df is not None and not df.empty:
row = df.iloc[0]
turnover_rate = row.get('turnover_rate', 'N/A')
# 解读换手率
interpretation = ""
if turnover_rate != 'N/A':
try:
turnover = float(turnover_rate)
if turnover > 20:
interpretation = "换手率极高（>20%），资金活跃度极高，可能存在炒作"
elif turnover > 10:
interpretation = "换手率较高（>10%），交易活跃"
elif turnover > 5:
interpretation = "换手率正常（5%-10%），交易适中"
elif turnover > 2:
interpretation = "换手率偏低（2%-5%），交易相对清淡"
else:
interpretation = "换手率很低（<2%），交易清淡"
except:
pass
print(f" [Tushare] ✅ 成功获取换手率: {turnover_rate}%")
return {
"current_turnover_rate": turnover_rate,
"interpretation": interpretation
}
except Exception as te:
print(f" [Tushare] ❌ 获取失败: {te}")
return None
def _get_market_index_sentiment(self):
"""获取大盘指数情绪（支持akshare和tushare自动切换）"""
try:
# 优先使用akshare获取上证指数实时数据
print(f" [Akshare] 正在获取大盘指数数据...")
# 使用正确的symbol参数
# 优先使用tushare（index_daily，取最近10个交易日保证非交易日也有数据）
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取大盘指数数据（主要数据源）...")
# 获取上证指数数据
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
df = data_source_manager.tushare_api.index_daily(
ts_code='000001.SH',
start_date=start_date,
end_date=end_date
)
if df is not None and not df.empty:
row = df.iloc[0]
change_pct = row.get('pct_chg', 0)
print(f" [Tushare] ✅ 成功获取大盘指数涨跌幅: {change_pct}%")
return {
"index_name": "上证指数",
"change_percent": change_pct
}
else:
print(f" [Tushare] ❌ 未获取到大盘指数，尝试备用数据源")
except Exception as te:
print(f" [Tushare] ❌ 获取失败: {te}")
# tushare失败，回退akshare
print(f" [Akshare] 正在获取大盘指数数据（备用数据源）...")
df = ak.stock_zh_index_spot_em(symbol="上证系列指数")
if df is not None and not df.empty:
# 查找上证指数（代码为000001）
@@ -471,30 +501,6 @@ class MarketSentimentDataFetcher:
except Exception as e:
print(f" [Akshare] ❌ 获取大盘指数失败: {e}")
# akshare失败，尝试tushare
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取大盘指数数据（备用数据源）...")
# 获取上证指数数据
df = data_source_manager.tushare_api.index_daily(
ts_code='000001.SH',
start_date=datetime.now().strftime('%Y%m%d'),
end_date=datetime.now().strftime('%Y%m%d')
)
if df is not None and not df.empty:
row = df.iloc[0]
change_pct = row.get('pct_chg', 0)
print(f" [Tushare] ✅ 成功获取大盘指数涨跌幅: {change_pct}%")
return {
"index_name": "上证指数",
"change_percent": change_pct
}
except Exception as te:
print(f" [Tushare] ❌ 获取失败: {te}")
return None
def _get_limit_up_down_stats(self):
@@ -503,19 +509,50 @@ class MarketSentimentDataFetcher:
# 获取今日涨停和跌停统计
today = datetime.now().strftime('%Y%m%d')
# 获取涨停股票
try:
limit_up_df = ak.stock_zt_pool_em(date=today)
limit_up_count = len(limit_up_df) if limit_up_df is not None and not limit_up_df.empty else 0
except:
limit_up_count = 0
limit_up_count = 0
limit_down_count = 0
# 获取跌停股票
try:
limit_down_df = ak.stock_zt_pool_dtgc_em(date=today)
limit_down_count = len(limit_down_df) if limit_down_df is not None and not limit_down_df.empty else 0
except:
limit_down_count = 0
# 优先使用tushare的涨跌停列表
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取涨跌停数据（主要数据源）...")
df_ll = data_source_manager.tushare_api.limit_list_d(trade_date=today)
if df_ll is not None and not df_ll.empty:
if 'limit_type' in df_ll.columns:
limit_up_count = int((df_ll['limit_type'].fillna('') == 'U').sum())
limit_down_count = int((df_ll['limit_type'].fillna('') == 'D').sum())
print(f" [Tushare] ✅ 成功获取涨跌停: 涨停{limit_up_count} / 跌停{limit_down_count}")
elif 'pct_chg' in df_ll.columns:
limit_up_count = int((df_ll['pct_chg'] >= 9.5).sum())
limit_down_count = int((df_ll['pct_chg'] <= -9.5).sum())
print(f" [Tushare] ✅ 成功获取涨跌停: 涨停{limit_up_count} / 跌停{limit_down_count}")
elif '涨跌幅' in df_ll.columns:
limit_up_count = int((df_ll['涨跌幅'] >= 9.5).sum())
limit_down_count = int((df_ll['涨跌幅'] <= -9.5).sum())
print(f" [Tushare] ✅ 成功获取涨跌停: 涨停{limit_up_count} / 跌停{limit_down_count}")
else:
# 无法识别的列结构，按0处理并回退akshare
print(f" [Tushare] ⚠ 涨跌停返回列无法识别: {list(df_ll.columns)[:10]}，尝试备用数据源")
else:
print(f" [Tushare] ❌ 未获取到涨跌停数据，尝试备用数据源")
except Exception as e:
print(f" [Tushare] ❌ 获取涨跌停数据失败: {e}")
# tushare不可用或失败时，回退akshare
if limit_up_count == 0 and limit_down_count == 0:
# 获取涨停股票
try:
limit_up_df = ak.stock_zt_pool_em(date=today)
limit_up_count = len(limit_up_df) if limit_up_df is not None and not limit_up_df.empty else 0
except:
limit_up_count = 0
# 获取跌停股票
try:
limit_down_df = ak.stock_zt_pool_dtgc_em(date=today)
limit_down_count = len(limit_down_df) if limit_down_df is not None and not limit_down_df.empty else 0
except:
limit_down_count = 0
# 计算涨跌停比例
if limit_up_count + limit_down_count > 0:
@@ -549,6 +586,44 @@ class MarketSentimentDataFetcher:
def _get_margin_trading_data(self, symbol):
"""获取融资融券数据"""
try:
# 优先使用tushare的个股融资融券明细
if data_source_manager.tushare_available:
try:
print(f" [Tushare] 正在获取融资融券数据（主要数据源）...")
ts_code = data_source_manager._convert_to_ts_code(symbol)
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=15)).strftime('%Y%m%d')
df = data_source_manager.tushare_api.margin_detail(
ts_code=ts_code,
start_date=start_date,
end_date=end_date
)
if df is not None and not df.empty:
latest = df.iloc[0]
margin_balance = latest.get('rzye', 0) or 0
short_balance = latest.get('rqye', 0) or 0
# 解读融资融券
interpretation = []
if margin_balance > short_balance * 10:
interpretation.append("融资余额远大于融券余额，投资者看多情绪强")
elif margin_balance > short_balance * 3:
interpretation.append("融资余额大于融券余额，投资者偏看多")
else:
interpretation.append("融资融券相对平衡")
print(f" [Tushare] ✅ 成功获取融资融券数据")
return {
"margin_balance": margin_balance,
"short_balance": short_balance,
"interpretation": interpretation,
"date": str(latest.get('trade_date', datetime.now().strftime('%Y-%m-%d')))
}
else:
print(f" [Tushare] ❌ 未获取到融资融券数据，尝试备用数据源")
except Exception as e:
print(f" [Tushare] ❌ 获取融资融券数据失败: {e}")
# 获取个股融资融券数据（尝试多个API）
try:
# 方法1：获取沪深融资融券明细
@@ -762,4 +837,3 @@ if __name__ == "__main__":
print(formatted_text)
else:
print(f"\n获取失败: {sentiment_data.get('error', '未知错误')}")
news_announcement_data.py
+3 -3
View File
@@ -9,6 +9,7 @@ import sys
import io
import warnings
from datetime import datetime
from http_timeout import call_with_timeout
warnings.filterwarnings('ignore')
@@ -100,7 +101,7 @@ class NewsAnnouncementDataFetcher:
print(f" 使用问财查询: {query}")
# 使用pywencai查询
result = pywencai.get(query=query, loop=True)
result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if result is None:
print(f" 问财查询返回None")
@@ -191,7 +192,7 @@ class NewsAnnouncementDataFetcher:
print(f" 使用问财查询: {query}")
# 使用pywencai查询
result = pywencai.get(query=query, loop=True)
result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if result is None:
print(f" 问财查询返回None")
@@ -342,4 +343,3 @@ if __name__ == "__main__":
print(formatted_text)
else:
print(f"\n获取失败: {data.get('error', '未知错误')}")
profit_growth_selector.py
+2 -1
View File
@@ -8,6 +8,7 @@
import logging
from typing import Tuple, Optional
import pandas as pd
from http_timeout import call_with_timeout
class ProfitGrowthSelector:
@@ -50,7 +51,7 @@ class ProfitGrowthSelector:
self.logger.info(f"开始执行净利增长选股，查询条件: {query}")
# 调用pywencai
result = pywencai.get(query=query, loop=True)
result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if result is None or result.empty:
self.logger.warning("未获取到符合条件的股票")
qstock_news_data.py
+84 -7
View File
@@ -1,6 +1,6 @@
"""
新闻数据获取模块
使用akshare获取股票的最新新闻信息（替代qstock）
优先使用tushare，失败时使用akshare获取股票的最新新闻信息
"""
import pandas as pd
@@ -9,6 +9,7 @@ import io
import warnings
from datetime import datetime, timedelta
import akshare as ak
from data_source_manager import data_source_manager
warnings.filterwarnings('ignore')
@@ -30,6 +31,9 @@ def _setup_stdout_encoding():
_setup_stdout_encoding()
# 记录tushare news接口是否无权限（避免每次分析都重复请求失败）
_tushare_news_unavailable = False
class QStockNewsDataFetcher:
"""新闻数据获取类（使用akshare作为数据源）"""
@@ -37,7 +41,7 @@ class QStockNewsDataFetcher:
def __init__(self):
self.max_items = 30 # 最多获取的新闻数量
self.available = True
print("✓ 新闻数据获取器初始化成功（akshare数据源）")
print("✓ 新闻数据获取器初始化成功（tushare优先/akshare备用）")
def get_stock_news(self, symbol):
"""
@@ -67,7 +71,7 @@ class QStockNewsDataFetcher:
try:
# 获取新闻数据
print(f"📰 正在使用qstock获取 {symbol} 的最新新闻...")
print(f"📰 正在获取 {symbol} 的最新新闻...")
news_data = self._get_news_data(symbol)
if news_data:
@@ -89,9 +93,20 @@ class QStockNewsDataFetcher:
return symbol.isdigit() and len(symbol) == 6
def _get_news_data(self, symbol):
"""获取新闻数据（使用akshare）"""
"""获取新闻数据（优先tushare，失败时使用akshare）"""
try:
print(f" 使用 akshare 获取新闻...")
# 优先使用tushare新闻接口
tushare_items = self._get_news_from_tushare(symbol)
if tushare_items:
print(f" ✓ 从tushare获取到 {len(tushare_items)} 条相关新闻")
return {
"items": tushare_items,
"count": len(tushare_items),
"query_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
"date_range": "最近新闻"
}
print(f" 使用 akshare 获取新闻（备用数据源）...")
news_items = []
@@ -223,6 +238,69 @@ class QStockNewsDataFetcher:
traceback.print_exc()
return None
def _get_news_from_tushare(self, symbol):
"""从tushare获取个股新闻（按股票名称/代码过滤）"""
global _tushare_news_unavailable
try:
if _tushare_news_unavailable or not data_source_manager.tushare_available:
return None
# 获取股票名称
stock_name = None
try:
basic = data_source_manager.get_stock_basic_info(symbol)
if basic and basic.get('name') and basic['name'] != '未知':
stock_name = basic['name']
except Exception as e:
print(f" 获取股票名称失败: {e}")
# 查询最近7天的全市场新闻（东方财富源）
end_date = datetime.now().strftime('%Y-%m-%d')
start_date = (datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d')
df = data_source_manager.tushare_api.news(
src='eastmoney',
start_date=start_date,
end_date=end_date
)
if df is None or df.empty:
return None
# 按股票代码或名称过滤
mask = df['title'].str.contains(symbol, na=False) | df['title'].str.contains(stock_name, na=False) if stock_name else df['title'].str.contains(symbol, na=False)
if 'content' in df.columns:
mask = mask | df['content'].str.contains(symbol, na=False)
if stock_name:
mask = mask | df['content'].str.contains(stock_name, na=False)
df_filtered = df[mask]
if df_filtered.empty:
return None
news_items = []
for _, row in df_filtered.head(self.max_items).iterrows():
item = {'source': 'tushare-东方财富'}
for col in ['title', 'content', 'pub_time']:
if col in df_filtered.columns:
value = row.get(col)
if value is None or (isinstance(value, float) and pd.isna(value)):
continue
try:
item[col] = str(value)
except:
item[col] = "无法解析"
if len(item) > 1:
news_items.append(item)
return news_items or None
except Exception as e:
error_msg = str(e)
if "权限" in error_msg or "积分" in error_msg:
_tushare_news_unavailable = True
print(" ⚠ tushare news 接口需要较高积分，当前账号无权限，已自动使用 akshare 获取新闻")
else:
print(f" ⚠ 从tushare获取新闻失败: {error_msg}")
return None
def format_news_for_ai(self, data):
"""
将新闻数据格式化为适合AI阅读的文本
@@ -236,7 +314,7 @@ class QStockNewsDataFetcher:
if data.get("news_data"):
news_data = data["news_data"]
text_parts.append(f"""
【最新新闻 - akshare数据源】
【最新新闻 - tushare/akshare自动切换】
查询时间：{news_data.get('query_time', 'N/A')}
时间范围：{news_data.get('date_range', 'N/A')}
新闻数量：{news_data.get('count', 0)}条
@@ -303,4 +381,3 @@ if __name__ == "__main__":
print(f"\n获取失败: {data.get('error', '未知错误')}")
print("\n")
quarterly_report_data.py
+174 -14
View File
@@ -9,6 +9,8 @@ import io
import warnings
from datetime import datetime
import akshare as ak
from http_timeout import call_with_timeout
from data_source_manager import data_source_manager
warnings.filterwarnings('ignore')
@@ -67,26 +69,34 @@ class QuarterlyReportDataFetcher:
try:
print(f"📊 正在获取 {symbol} 的季报数据...")
# 获取利润表
income_data = self._get_income_statement(symbol)
# 获取利润表（优先tushare，失败时回退akshare）
income_data = self._get_income_statement_from_tushare(symbol)
if income_data is None:
income_data = self._get_income_statement(symbol)
if income_data:
data["income_statement"] = income_data
print(f" ✓ 成功获取 {len(income_data.get('data', []))} 期利润表数据")
# 获取资产负债表
balance_data = self._get_balance_sheet(symbol)
# 获取资产负债表（优先tushare，失败时回退akshare）
balance_data = self._get_balance_sheet_from_tushare(symbol)
if balance_data is None:
balance_data = self._get_balance_sheet(symbol)
if balance_data:
data["balance_sheet"] = balance_data
print(f" ✓ 成功获取 {len(balance_data.get('data', []))} 期资产负债表数据")
# 获取现金流量表
cash_flow_data = self._get_cash_flow(symbol)
# 获取现金流量表（优先tushare，失败时回退akshare）
cash_flow_data = self._get_cash_flow_from_tushare(symbol)
if cash_flow_data is None:
cash_flow_data = self._get_cash_flow(symbol)
if cash_flow_data:
data["cash_flow"] = cash_flow_data
print(f" ✓ 成功获取 {len(cash_flow_data.get('data', []))} 期现金流量表数据")
# 获取财务指标
indicators_data = self._get_financial_indicators(symbol)
# 获取财务指标（优先tushare，失败时回退akshare）
indicators_data = self._get_financial_indicators_from_tushare(symbol)
if indicators_data is None:
indicators_data = self._get_financial_indicators(symbol)
if indicators_data:
data["financial_indicators"] = indicators_data
print(f" ✓ 成功获取 {len(indicators_data.get('data', []))} 期财务指标数据")
@@ -108,11 +118,160 @@ class QuarterlyReportDataFetcher:
"""判断是否为中国股票"""
return symbol.isdigit() and len(symbol) == 6
def _convert_ts_records(self, df, field_map, periods):
"""将tushare返回的财务表转换为统一的记录结构"""
try:
data_list = []
for _, row in df.head(periods).iterrows():
item = {}
for ch_name, ts_name in field_map.items():
if ts_name in df.columns:
value = row.get(ts_name)
if value is None or (isinstance(value, float) and pd.isna(value)):
continue
try:
item[ch_name] = str(value)
except:
item[ch_name] = "N/A"
if item:
data_list.append(item)
return {
"data": data_list,
"periods": len(data_list),
"columns": list(field_map.keys()),
"query_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
except Exception as e:
print(f" 转换tushare财务数据异常: {e}")
return None
def _get_income_statement_from_tushare(self, symbol):
"""从tushare获取利润表（优先数据源）"""
try:
if not data_source_manager.tushare_available:
return None
ts_code = data_source_manager._convert_to_ts_code(symbol)
df = data_source_manager.tushare_api.income(ts_code=ts_code)
if df is None or df.empty:
return None
df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
field_map = {
'报告期': 'end_date',
'营业总收入': 'total_revenue',
'营业收入': 'revenue',
'营业总成本': 'total_operate_cost',
'营业利润': 'operate_profit',
'利润总额': 'total_profit',
'净利润': 'n_income',
'归属于母公司所有者的净利润': 'n_income_attr_p',
'基本每股收益': 'basic_eps',
'稀释每股收益': 'diluted_eps',
'销售费用': 'sell_exp',
'管理费用': 'admin_exp',
'财务费用': 'fin_exp',
'研发费用': 'rd_exp',
}
result = self._convert_ts_records(df, field_map, self.periods)
if result and result.get('periods'):
print(f" ✓ tushare成功获取 {result['periods']} 期利润表数据")
return result
except Exception as e:
print(f" tushare获取利润表异常: {e}")
return None
def _get_balance_sheet_from_tushare(self, symbol):
"""从tushare获取资产负债表（优先数据源）"""
try:
if not data_source_manager.tushare_available:
return None
ts_code = data_source_manager._convert_to_ts_code(symbol)
df = data_source_manager.tushare_api.balancesheet(ts_code=ts_code)
if df is None or df.empty:
return None
df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
field_map = {
'报告期': 'end_date',
'资产总计': 'total_assets',
'流动资产合计': 'total_cur_assets',
'非流动资产合计': 'total_ncur_assets',
'负债合计': 'total_liab',
'流动负债合计': 'total_cur_liab',
'非流动负债合计': 'total_ncur_liab',
'所有者权益合计': 'total_hldr_eqy_inc_min_int',
'归属于母公司股东权益合计': 'total_hldr_eqy_exc_min_int',
}
result = self._convert_ts_records(df, field_map, self.periods)
if result and result.get('periods'):
print(f" ✓ tushare成功获取 {result['periods']} 期资产负债表数据")
return result
except Exception as e:
print(f" tushare获取资产负债表异常: {e}")
return None
def _get_cash_flow_from_tushare(self, symbol):
"""从tushare获取现金流量表（优先数据源）"""
try:
if not data_source_manager.tushare_available:
return None
ts_code = data_source_manager._convert_to_ts_code(symbol)
df = data_source_manager.tushare_api.cashflow(ts_code=ts_code)
if df is None or df.empty:
return None
df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
field_map = {
'报告期': 'end_date',
'经营活动产生的现金流量净额': 'n_cashflow_act',
'投资活动产生的现金流量净额': 'n_cashflow_inv_act',
'筹资活动产生的现金流量净额': 'n_cash_flows_fnc_act',
}
result = self._convert_ts_records(df, field_map, self.periods)
if result and result.get('periods'):
print(f" ✓ tushare成功获取 {result['periods']} 期现金流量表数据")
return result
except Exception as e:
print(f" tushare获取现金流量表异常: {e}")
return None
def _get_financial_indicators_from_tushare(self, symbol):
"""从tushare获取财务指标（优先数据源）"""
try:
if not data_source_manager.tushare_available:
return None
ts_code = data_source_manager._convert_to_ts_code(symbol)
df = data_source_manager.tushare_api.fina_indicator(ts_code=ts_code)
if df is None or df.empty:
return None
df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
field_map = {
'报告期': 'end_date',
'净资产收益率': 'roe',
'总资产净利率': 'roa',
'销售净利率': 'netprofit_margin',
'销售毛利率': 'grossprofit_margin',
'资产负债率': 'debt_to_assets',
'流动比率': 'current_ratio',
'速动比率': 'quick_ratio',
'应收账款周转率': 'ar_turnover',
'存货周转率': 'inventory_turnover',
'总资产周转率': 'assets_turnover',
'每股收益': 'eps',
'每股净资产': 'bps',
'每股经营现金流': 'cfps',
}
result = self._convert_ts_records(df, field_map, self.periods)
if result and result.get('periods'):
print(f" ✓ tushare成功获取 {result['periods']} 期财务指标数据")
return result
except Exception as e:
print(f" tushare获取财务指标异常: {e}")
return None
def _get_income_statement(self, symbol):
"""获取利润表数据"""
try:
# stock_financial_report_sina - 新浪财经季度利润表
df = ak.stock_financial_report_sina(stock=symbol, symbol="利润表")
df = call_with_timeout(ak.stock_financial_report_sina, timeout=25,
stock=symbol, symbol="利润表")
if df is None or df.empty:
print(f" 未找到利润表数据")
@@ -151,7 +310,8 @@ class QuarterlyReportDataFetcher:
"""获取资产负债表数据"""
try:
# stock_financial_report_sina - 新浪财经季度资产负债表
df = ak.stock_financial_report_sina(stock=symbol, symbol="资产负债表")
df = call_with_timeout(ak.stock_financial_report_sina, timeout=25,
stock=symbol, symbol="资产负债表")
if df is None or df.empty:
print(f" 未找到资产负债表数据")
@@ -190,7 +350,8 @@ class QuarterlyReportDataFetcher:
"""获取现金流量表数据"""
try:
# stock_financial_report_sina - 新浪财经季度现金流量表
df = ak.stock_financial_report_sina(stock=symbol, symbol="现金流量表")
df = call_with_timeout(ak.stock_financial_report_sina, timeout=25,
stock=symbol, symbol="现金流量表")
if df is None or df.empty:
print(f" 未找到现金流量表数据")
@@ -229,7 +390,7 @@ class QuarterlyReportDataFetcher:
"""获取财务指标数据"""
try:
# 使用stock_financial_abstract替代已失效的stock_financial_analysis_indicator
df = ak.stock_financial_abstract(symbol=symbol)
df = call_with_timeout(ak.stock_financial_abstract, timeout=25, symbol=symbol)
if df is None or df.empty:
print(f" 未找到财务指标数据")
@@ -292,7 +453,7 @@ class QuarterlyReportDataFetcher:
text_parts = []
text_parts.append(f"""
【季度财务报告数据 - akshare数据源】
【季度财务报告数据 - tushare/akshare自动切换】
股票代码：{data.get('symbol', 'N/A')}
数据期数：最近{self.periods}期季报
@@ -423,4 +584,3 @@ if __name__ == "__main__":
print(f"\n获取失败: {data.get('error', '未知错误')}")
print("\n")
risk_data_fetcher.py
+4 -4
View File
@@ -12,6 +12,7 @@ from typing import Dict, Any
import time
import warnings
import os
from http_timeout import call_with_timeout
# 屏蔽pywencai的Node.js警告信息（不影响功能）
warnings.filterwarnings('ignore', category=DeprecationWarning)
@@ -108,7 +109,7 @@ class RiskDataFetcher:
query = f"{symbol}限售解禁"
# 使用pywencai查询
response = pywencai.get(query=query, loop=True)
response = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if response is None:
return result
@@ -171,7 +172,7 @@ class RiskDataFetcher:
query = f"{symbol}大股东减持公告"
# 使用pywencai查询
response = pywencai.get(query=query, loop=True)
response = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if response is None:
return result
@@ -234,7 +235,7 @@ class RiskDataFetcher:
query = f"{symbol}近期重要事件"
# 使用pywencai查询
response = pywencai.get(query=query, loop=True)
response = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if response is None:
return result
@@ -466,4 +467,3 @@ if __name__ == "__main__":
if risk_data['data_success']:
print("\n格式化的风险数据:")
print(fetcher.format_risk_data_for_ai(risk_data))
sector_strategy_data.py
+48 -36
View File
@@ -1,6 +1,6 @@
"""
智策板块数据采集模块
使用AKShare获取板块相关数据
优先使用Tushare，失败时使用AKShare获取板块相关数据
"""
import akshare as ak
@@ -12,6 +12,7 @@ import logging
import os
from dotenv import load_dotenv
from sector_strategy_db import SectorStrategyDatabase
from data_source_manager import data_source_manager
# 加载环境变量
load_dotenv()
@@ -254,40 +255,52 @@ class SectorStrategyDataFetcher:
except:
pass
# 大盘指数
# 大盘指数（优先tushare，失败回退akshare）
def _get_index_data(index_code, ts_code, name):
if data_source_manager.tushare_available:
try:
end = datetime.now().strftime('%Y%m%d')
start = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
df = data_source_manager.tushare_api.index_daily(
ts_code=ts_code, start_date=start, end_date=end
)
if df is not None and not df.empty:
row = df.iloc[0]
return {
"code": index_code,
"name": name,
"close": row.get('close', 0),
"change_pct": row.get('pct_chg', 0),
"change": row.get('change', 0)
}
print(f" tushare未获取到{name}，尝试备用数据源")
except Exception as e:
print(f" tushare获取{name}失败: {e}")
try:
df = self._safe_request(ak.stock_zh_index_spot_em, symbol=name)
if df is not None and not df.empty:
row = df.iloc[0]
return {
"code": index_code,
"name": name,
"close": row.get('最新价', 0),
"change_pct": row.get('涨跌幅', 0),
"change": row.get('涨跌额', 0)
}
except Exception as e:
print(f" akshare获取{name}失败: {e}")
return None
try:
# 上证指数
df_sh = ak.stock_zh_index_spot_em(symbol="上证指数")
if df_sh is not None and not df_sh.empty:
overview["sh_index"] = {
"code": "000001",
"name": "上证指数",
"close": df_sh.iloc[0].get('最新价', 0),
"change_pct": df_sh.iloc[0].get('涨跌幅', 0),
"change": df_sh.iloc[0].get('涨跌额', 0)
}
# 深证成指
df_sz = self._safe_request(ak.stock_zh_index_spot_em, symbol="深证成指")
if df_sz is not None and not df_sz.empty:
overview["sz_index"] = {
"code": "399001",
"name": "深证成指",
"close": df_sz.iloc[0].get('最新价', 0),
"change_pct": df_sz.iloc[0].get('涨跌幅', 0),
"change": df_sz.iloc[0].get('涨跌额', 0)
}
# 创业板指
df_cyb = self._safe_request(ak.stock_zh_index_spot_em, symbol="创业板指")
if df_cyb is not None and not df_cyb.empty:
overview["cyb_index"] = {
"code": "399006",
"name": "创业板指",
"close": df_cyb.iloc[0].get('最新价', 0),
"change_pct": df_cyb.iloc[0].get('涨跌幅', 0),
"change": df_cyb.iloc[0].get('涨跌额', 0)
}
sh_index = _get_index_data("000001", "000001.SH", "上证指数")
if sh_index:
overview["sh_index"] = sh_index
sz_index = _get_index_data("399001", "399001.SZ", "深证成指")
if sz_index:
overview["sz_index"] = sz_index
cyb_index = _get_index_data("399006", "399006.SZ", "创业板指")
if cyb_index:
overview["cyb_index"] = cyb_index
except:
pass
@@ -310,7 +323,7 @@ class SectorStrategyDataFetcher:
try:
import tushare as ts
ts.set_token(tushare_token)
self.ts_pro = ts.pro_api()
self.ts_pro = ts.pro_api(timeout=float(os.getenv('TUSHARE_TIMEOUT', '15')))
print(" [Tushare] ✅ 初始化成功")
except Exception as e:
print(f" [Tushare] 初始化失败: {e}")
@@ -757,4 +770,3 @@ if __name__ == "__main__":
print(f"\n... (总长度: {len(formatted_text)} 字符)")
else:
print(f"\n数据采集失败: {data.get('error', '未知错误')}")
small_cap_selector.py
+2 -1
View File
@@ -8,6 +8,7 @@
import logging
from typing import Tuple, Optional
import pandas as pd
from http_timeout import call_with_timeout
class SmallCapSelector:
@@ -54,7 +55,7 @@ class SmallCapSelector:
self.logger.info(f"开始执行小市值策略选股，查询条件: {query}")
# 调用pywencai
result = pywencai.get(query=query, loop=True)
result = call_with_timeout(pywencai.get, timeout=30, query=query, loop=True)
if result is None or result.empty:
self.logger.warning("未获取到符合条件的股票")
smart_monitor.db
BIN
View File
Binary file not shown.
smart_monitor_data.py
+48 -30
View File
@@ -54,7 +54,7 @@ class SmartMonitorDataFetcher:
try:
import tushare as ts
ts.set_token(tushare_token)
self.ts_pro = ts.pro_api()
self.ts_pro = ts.pro_api(timeout=float(os.getenv('TUSHARE_TIMEOUT', '15')))
self.logger.info("Tushare备用数据源初始化成功")
except Exception as e:
self.logger.warning(f"Tushare初始化失败: {e}")
@@ -64,7 +64,7 @@ class SmartMonitorDataFetcher:
def get_realtime_quote(self, stock_code: str, retry: int = 1) -> Optional[Dict]:
"""
获取实时行情（带重试和降级机制）
优先使用TDX，失败时降级到AKShare，最后降级到Tushare
优先使用TDX，失败时降级到Tushare，最后降级到AKShare
Args:
stock_code: 股票代码（如：600519）
@@ -82,11 +82,20 @@ class SmartMonitorDataFetcher:
if quote:
return quote
else:
self.logger.warning(f"TDX获取失败 {stock_code}，尝试降级到AKShare")
self.logger.warning(f"TDX获取失败 {stock_code}，尝试降级到Tushare")
except Exception as e:
self.logger.warning(f"TDX获取异常 {stock_code}: {e}，尝试降级到AKShare")
self.logger.warning(f"TDX获取异常 {stock_code}: {e}，尝试降级到Tushare")
# 方法2: 组合使用AKShare分钟行情 + 基本信息
# 方法2: 降级到Tushare（优先于AKShare）
if self.ts_pro:
quote = self._get_realtime_quote_from_tushare(stock_code)
if quote:
return quote
self.logger.warning(f"Tushare获取失败 {stock_code}，尝试降级到AKShare")
else:
self.logger.warning(f"未配置Tushare，尝试使用AKShare")
# 方法3: 组合使用AKShare分钟行情 + 基本信息
for attempt in range(retry):
try:
# 1.1 获取股票基本信息（名称）
@@ -167,18 +176,14 @@ class SmartMonitorDataFetcher:
else:
self.logger.warning(f"AKShare获取失败 {stock_code}（已重试{retry}次），尝试降级")
# 降级到Tushare
if self.ts_pro:
self.logger.info(f"降级到Tushare获取 {stock_code}...")
return self._get_realtime_quote_from_tushare(stock_code)
else:
self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 行情")
return None
# 所有数据源都失败
self.logger.error(f"所有数据源都无法获取 {stock_code} 行情")
return None
def get_technical_indicators(self, stock_code: str, period: str = 'daily', retry: int = 1) -> Optional[Dict]:
"""
计算技术指标（带降级机制）
优先使用TDX，失败时降级到AKShare，最后降级到Tushare
优先使用TDX，失败时降级到Tushare，最后降级到AKShare
Args:
stock_code: 股票代码
@@ -197,11 +202,21 @@ class SmartMonitorDataFetcher:
if indicators:
return indicators
else:
self.logger.warning(f"TDX计算技术指标失败 {stock_code}，尝试降级到AKShare")
self.logger.warning(f"TDX计算技术指标失败 {stock_code}，尝试降级到Tushare")
except Exception as e:
self.logger.warning(f"TDX计算技术指标异常 {stock_code}: {e}，尝试降级到AKShare")
self.logger.warning(f"TDX计算技术指标异常 {stock_code}: {e}，尝试降级到Tushare")
# 方法2: 尝试使用AKShare
# 方法2: 降级到Tushare（优先于AKShare）
if self.ts_pro:
self.logger.info(f"降级到Tushare获取 {stock_code} 历史数据...")
indicators = self._get_technical_indicators_from_tushare(stock_code, period)
if indicators:
return indicators
self.logger.warning(f"Tushare获取技术指标失败 {stock_code}，尝试降级到AKShare")
else:
self.logger.warning(f"未配置Tushare，尝试使用AKShare")
# 方法3: 尝试使用AKShare
for attempt in range(retry):
try:
# 获取历史数据（最近200个交易日，用于计算指标）
@@ -237,13 +252,9 @@ class SmartMonitorDataFetcher:
self.logger.warning(f"AKShare获取历史数据失败 {stock_code}（已重试{retry}次），尝试降级到Tushare")
break
# 方法3: 降级到Tushare
if self.ts_pro:
self.logger.info(f"降级到Tushare获取 {stock_code} 历史数据...")
return self._get_technical_indicators_from_tushare(stock_code, period)
else:
self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 技术指标")
return None
# 所有数据源都失败
self.logger.error(f"所有数据源都无法获取 {stock_code} 技术指标")
return None
def _calculate_all_indicators(self, df: pd.DataFrame, stock_code: str) -> Optional[Dict]:
"""
@@ -439,6 +450,17 @@ class SmartMonitorDataFetcher:
"""
import time
# 优先使用Tushare个股资金流向
if self.ts_pro:
try:
result = self._get_main_force_from_tushare(stock_code)
if result:
self.logger.info(f"✅ Tushare成功获取 {stock_code} 资金流向（主要数据源）")
return result
self.logger.warning(f"Tushare未获取到资金流向 {stock_code}，尝试AKShare")
except Exception as e:
self.logger.warning(f"Tushare获取资金流向失败 {stock_code}: {e}")
for attempt in range(retry):
try:
# 获取个股资金流（新版AKShare API参数调整）
@@ -495,12 +517,9 @@ class SmartMonitorDataFetcher:
self.logger.warning(f"AKShare获取资金流向失败 {stock_code}（已重试{retry}次），尝试降级到Tushare")
break
# 降级到Tushare
if self.ts_pro:
return self._get_main_force_from_tushare(stock_code)
else:
self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 资金流向")
return None
# 所有数据源都失败
self.logger.error(f"所有数据源都无法获取 {stock_code} 资金流向")
return None
def get_comprehensive_data(self, stock_code: str) -> Dict:
"""
@@ -822,4 +841,3 @@ if __name__ == '__main__':
print("\n主力资金:")
print(f" 主力净额: {data['main_force']['main_net']:.2f}万")
print(f" 主力动向: {data['main_force']['trend']}")
smart_monitor_kline.py
+22 -17
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@@ -333,15 +333,26 @@ class SmartMonitorKline:
self.logger.info(f"✅ TDX获取K线数据成功 {stock_code}，共{len(df)}条")
return df
else:
self.logger.warning(f"TDX未返回K线数据 {stock_code}，尝试降级到AKShare")
self.logger.warning(f"TDX未返回K线数据 {stock_code}，尝试降级到Tushare")
except Exception as e:
self.logger.warning(f"TDX获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到AKShare")
self.logger.warning(f"TDX获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到Tushare")
# 计算日期范围
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=days + 30)).strftime('%Y%m%d') # 多取30天以确保足够数据
# 方法2: 尝试使用AKShare获取（只尝试1次，避免IP封禁）
# 方法2: 降级到Tushare（优先于AKShare）
if data_fetcher and data_fetcher.ts_pro:
self.logger.info(f"降级使用Tushare获取K线数据 {stock_code}")
df = self._get_kline_from_tushare(stock_code, days, data_fetcher.ts_pro)
if df is not None and not df.empty:
self.logger.info(f"✅ Tushare获取K线数据成功 {stock_code}，共{len(df)}条")
return df
self.logger.warning(f"Tushare未返回K线数据 {stock_code}，尝试降级到AKShare")
else:
self.logger.warning(f"未配置Tushare，尝试使用AKShare")
# 方法3: 尝试使用AKShare获取（只尝试1次，避免IP封禁）
try:
import akshare as ak
df = ak.stock_zh_a_hist(
@@ -358,17 +369,9 @@ class SmartMonitorKline:
self.logger.info(f"✅ AKShare获取K线数据成功 {stock_code}，共{len(df)}条")
return df
else:
self.logger.warning(f"AKShare未返回K线数据 {stock_code}，尝试降级到Tushare")
self.logger.warning(f"AKShare未返回K线数据 {stock_code}")
except Exception as e:
self.logger.warning(f"AKShare获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到Tushare")
# 方法3: 降级到Tushare
if data_fetcher and data_fetcher.ts_pro:
self.logger.info(f"降级使用Tushare获取K线数据 {stock_code}")
df = self._get_kline_from_tushare(stock_code, days, data_fetcher.ts_pro)
if df is not None and not df.empty:
self.logger.info(f"✅ Tushare获取K线数据成功 {stock_code}，共{len(df)}条")
return df
self.logger.warning(f"AKShare获取K线数据失败 {stock_code}: {type(e).__name__}")
self.logger.error(f"所有数据源都无法获取K线数据 {stock_code}")
return None
@@ -404,12 +407,15 @@ class SmartMonitorKline:
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=days + 60)).strftime('%Y%m%d')
# 获取日K线数据（前复权）
df = ts_pro.daily(
# 获取日K线数据（前复权，daily接口不支持adj参数，需用pro_bar）
import tushare as ts
df = ts.pro_bar(
api=ts_pro,
ts_code=ts_code,
start_date=start_date,
end_date=end_date,
adj='qfq'
adj='qfq',
retry_count=1
)
if df is None or df.empty:
@@ -493,4 +499,3 @@ if __name__ == '__main__':
print("K线图已保存到 test_kline.html")
else:
print("获取K线数据失败")
stock_analysis.db
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stock_data.py
+325 -105
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@@ -8,10 +8,46 @@ import requests
import json
import pywencai
from data_source_manager import data_source_manager
from http_timeout import call_with_timeout
class StockDataFetcher:
"""股票数据获取类"""
# tushare财务字段 -> 中文映射（供AI分析展示）
INCOME_TS_MAP = {
'报告期': 'end_date',
'营业总收入': 'total_revenue',
'营业收入': 'revenue',
'营业总成本': 'total_operate_cost',
'营业利润': 'operate_profit',
'利润总额': 'total_profit',
'净利润': 'n_income',
'归属于母公司所有者的净利润': 'n_income_attr_p',
'基本每股收益': 'basic_eps',
'稀释每股收益': 'diluted_eps',
'销售费用': 'sell_exp',
'管理费用': 'admin_exp',
'财务费用': 'fin_exp',
'研发费用': 'rd_exp',
}
BALANCE_TS_MAP = {
'报告期': 'end_date',
'资产总计': 'total_assets',
'流动资产合计': 'total_cur_assets',
'非流动资产合计': 'total_ncur_assets',
'负债合计': 'total_liab',
'流动负债合计': 'total_cur_liab',
'非流动负债合计': 'total_ncur_liab',
'所有者权益合计': 'total_hldr_eqy_inc_min_int',
'归属于母公司股东权益合计': 'total_hldr_eqy_exc_min_int',
}
CASHFLOW_TS_MAP = {
'报告期': 'end_date',
'经营活动产生的现金流量净额': 'n_cashflow_act',
'投资活动产生的现金流量净额': 'n_cashflow_inv_act',
'筹资活动产生的现金流量净额': 'n_cash_flows_fnc_act',
}
def __init__(self):
self.data = None
self.info = None
@@ -89,57 +125,70 @@ class StockDataFetcher:
if basic_info:
info.update(basic_info)
# 方法1: 尝试获取个股详细信息（akshare）
try:
stock_info = ak.stock_individual_info_em(symbol=symbol)
if stock_info is not None and not stock_info.empty:
for _, row in stock_info.iterrows():
key = row['item']
value = row['value']
if key == '股票简称':
info['name'] = value
elif key == '总市值':
try:
if value and value != '-':
info['market_cap'] = float(value)
except:
pass
elif key == '市盈率-动态':
try:
if value and value != '-':
pe_value = float(value)
if 0 < pe_value <= 1000:
info['pe_ratio'] = pe_value
except:
pass
elif key == '市净率':
try:
if value and value != '-':
pb_value = float(value)
if 0 < pb_value <= 100:
info['pb_ratio'] = pb_value
except:
pass
except Exception as e:
print(f"[Akshare] 获取个股详细信息失败: {e}")
# 如果akshare失败，尝试从tushare获取
if self.data_source_manager.tushare_available and info['name'] == '未知':
print(f"[Tushare] 尝试获取基本信息（tushare）...")
# 方法1: 获取详细估值信息（优先tushare，失败时回退akshare）
if (info.get('name') == '未知' or info.get('pe_ratio') == 'N/A' or
info.get('pb_ratio') == 'N/A' or info.get('market_cap') == 'N/A'):
# 优先使用tushare daily_basic（一次获取PE/PB/市值）
if self.data_source_manager.tushare_available:
try:
print(f"[Tushare] 正在获取 {symbol} 的估值信息（主要数据源）...")
ts_code = self.data_source_manager._convert_to_ts_code(symbol)
df = self.data_source_manager.tushare_api.daily_basic(
ts_code=ts_code,
trade_date=datetime.now().strftime('%Y%m%d')
start_date=(datetime.now() - timedelta(days=10)).strftime('%Y%m%d'),
end_date=datetime.now().strftime('%Y%m%d')
)
if df is not None and not df.empty:
row = df.iloc[0]
info['pe_ratio'] = row.get('pe', 'N/A')
info['pb_ratio'] = row.get('pb', 'N/A')
info['market_cap'] = row.get('total_mv', 'N/A')
print(f"[Tushare] ✅ 成功获取部分信息")
except Exception as te:
print(f"[Tushare] ❌ 获取失败: {te}")
if info.get('pe_ratio') == 'N/A' and 'pe' in df.columns:
info['pe_ratio'] = row.get('pe', 'N/A')
if info.get('pb_ratio') == 'N/A' and 'pb' in df.columns:
info['pb_ratio'] = row.get('pb', 'N/A')
if info.get('market_cap') == 'N/A' and 'total_mv' in df.columns:
info['market_cap'] = row.get('total_mv', 'N/A')
print(f"[Tushare] ✅ 成功获取估值信息")
else:
print(f"[Tushare] ❌ 未获取到估值信息，尝试备用数据源")
except Exception as e:
print(f"[Tushare] ❌ 获取估值信息失败: {e}")
# tushare未获取到时，回退akshare
if (info.get('name') == '未知' or info.get('pe_ratio') == 'N/A' or
info.get('pb_ratio') == 'N/A' or info.get('market_cap') == 'N/A'):
try:
print(f"[Akshare] 正在获取 {symbol} 的详细信息（备用数据源）...")
stock_info = ak.stock_individual_info_em(symbol=symbol)
if stock_info is not None and not stock_info.empty:
for _, row in stock_info.iterrows():
key = row['item']
value = row['value']
if key == '股票简称':
info['name'] = value
elif key == '总市值':
try:
if value and value != '-':
info['market_cap'] = float(value)
except:
pass
elif key == '市盈率-动态':
try:
if value and value != '-':
pe_value = float(value)
if 0 < pe_value <= 1000:
info['pe_ratio'] = pe_value
except:
pass
elif key == '市净率':
try:
if value and value != '-':
pb_value = float(value)
if 0 < pb_value <= 100:
info['pb_ratio'] = pb_value
except:
pass
except Exception as e:
print(f"[Akshare] 获取个股详细信息失败: {e}")
# 方法2: 尝试获取历史价格和涨跌幅（如果网络允许）
# try:
@@ -204,7 +253,10 @@ class StockDataFetcher:
# 方法3: 使用百度估值数据获取市盈率和市净率
if info['pe_ratio'] == 'N/A':
try:
pe_data = ak.stock_zh_valuation_baidu(symbol=symbol, indicator="市盈率(TTM)")
pe_data = call_with_timeout(
ak.stock_zh_valuation_baidu, timeout=15,
symbol=symbol, indicator="市盈率(TTM)"
)
if pe_data is not None and not pe_data.empty:
latest_pe = pe_data.iloc[-1]['value']
if latest_pe and latest_pe != '-':
@@ -216,7 +268,10 @@ class StockDataFetcher:
if info['pb_ratio'] == 'N/A':
try:
pb_data = ak.stock_zh_valuation_baidu(symbol=symbol, indicator="市净率")
pb_data = call_with_timeout(
ak.stock_zh_valuation_baidu, timeout=15,
symbol=symbol, indicator="市净率"
)
if pb_data is not None and not pb_data.empty:
latest_pb = pb_data.iloc[-1]['value']
if latest_pb and latest_pb != '-':
@@ -262,6 +317,32 @@ class StockDataFetcher:
"exchange": "香港交易所"
}
# 优先使用tushare（hk_basic + hk_daily）
if self.data_source_manager.tushare_available:
try:
print(f"[Tushare] 正在获取港股信息（主要数据源）...")
ts_code = f"{hk_code}.HK"
bdf = self.data_source_manager.tushare_api.hk_basic(ts_code=ts_code)
if bdf is not None and not bdf.empty and 'name' in bdf.columns:
info['name'] = bdf.iloc[0].get('name', '未知')
end = datetime.now().strftime('%Y%m%d')
start = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
hdf = self.data_source_manager.tushare_api.hk_daily(
ts_code=ts_code, start_date=start, end_date=end
)
if hdf is not None and not hdf.empty:
latest = hdf.iloc[0]
info['current_price'] = latest.get('close', 'N/A')
info['change_percent'] = latest.get('pct_chg', 'N/A')
if info['current_price'] != 'N/A':
print(f"[Tushare] ✅ 成功获取港股信息")
return info
print(f"[Tushare] ❌ 未获取到港股行情，尝试备用数据源")
except Exception as e:
print(f"[Tushare] 获取港股信息失败: {e}")
# 方法1: 获取港股实时行情
try:
# 使用akshare获取港股实时数据
@@ -331,12 +412,7 @@ class StockDataFetcher:
def _get_us_stock_info(self, symbol):
"""获取美股基本信息"""
import time
try:
# 添加延迟避免频率限制
time.sleep(1)
ticker = yf.Ticker(symbol)
# 先尝试获取历史数据（通常更稳定）
@@ -487,7 +563,36 @@ class StockDataFetcher:
else:
start_date = (datetime.now() - timedelta(days=365)).strftime('%Y%m%d')
# 获取港股历史数据
# 优先使用tushare（hk_daily）
if self.data_source_manager.tushare_available:
try:
print(f"[Tushare] 正在获取港股历史数据（主要数据源）...")
ts_code = f"{hk_code}.HK"
df = self.data_source_manager.tushare_api.hk_daily(
ts_code=ts_code,
start_date=start_date,
end_date=end_date
)
if df is not None and not df.empty:
df = df.rename(columns={
'trade_date': 'Date',
'open': 'Open',
'high': 'High',
'low': 'Low',
'close': 'Close',
'vol': 'Volume'
})
df['Date'] = pd.to_datetime(df['Date'])
df = df.sort_values('Date')
df.set_index('Date', inplace=True)
print(f"[Tushare] ✅ 成功获取港股历史数据")
return df
else:
print(f"[Tushare] ❌ 未获取到港股历史数据，尝试备用数据源")
except Exception as e:
print(f"[Tushare] 获取港股历史数据失败: {e}")
# 回退akshare
df = ak.stock_hk_hist(symbol=hk_code, period="daily",
start_date=start_date, end_date=end_date, adjust="qfq")
@@ -600,6 +705,112 @@ class StockDataFetcher:
except Exception as e:
return {"error": f"获取财务数据失败: {str(e)}"}
def _convert_ts_financial_records(self, df, field_map, limit=8):
"""将tushare财务表转换为统一的记录列表"""
try:
data_list = []
for _, row in df.head(limit).iterrows():
item = {}
for ch_name, ts_name in field_map.items():
if ts_name in df.columns:
value = row.get(ts_name)
if value is None or (isinstance(value, float) and pd.isna(value)):
continue
try:
item[ch_name] = str(value)
except:
item[ch_name] = "N/A"
if item:
data_list.append(item)
return data_list
except Exception as e:
print(f"转换tushare财务数据异常: {e}")
return None
def _convert_ts_ratios(self, df):
"""将tushare财务指标转换为AI使用的比率字典"""
try:
df = df.sort_values('end_date', ascending=False)
if df.empty:
return {}
row = df.iloc[0]
mapping = {
'净资产收益率(ROE)': 'roe',
'总资产报酬率(ROA)': 'roa',
'销售毛利率': 'grossprofit_margin',
'销售净利率': 'netprofit_margin',
'资产负债率': 'debt_to_assets',
'流动比率': 'current_ratio',
'速动比率': 'quick_ratio',
'存货周转率': 'inventory_turnover',
'应收账款周转率': 'ar_turnover',
'总资产周转率': 'assets_turnover',
'营业收入同比增长': 'or_yoy',
'净利润同比增长': 'netprofit_yoy',
'EPS': 'eps',
}
ratios = {'报告期': str(row.get('end_date', 'N/A'))}
for ch_name, ts_name in mapping.items():
if ts_name in df.columns:
value = row.get(ts_name)
if value is None or (isinstance(value, float) and pd.isna(value)):
ratios[ch_name] = "N/A"
else:
try:
ratios[ch_name] = str(value)
except:
ratios[ch_name] = "N/A"
return ratios
except Exception as e:
print(f"转换tushare财务指标异常: {e}")
return {}
def _get_financial_data_from_tushare(self, symbol):
"""优先从tushare获取财务三表与财务指标"""
try:
if not self.data_source_manager.tushare_available:
return None
ts_code = self.data_source_manager._convert_to_ts_code(symbol)
result = {}
# 利润表
df = self.data_source_manager.tushare_api.income(ts_code=ts_code)
if df is not None and not df.empty:
df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
records = self._convert_ts_financial_records(df, self.INCOME_TS_MAP)
if records:
result["income_statement"] = records
# 资产负债表
df = self.data_source_manager.tushare_api.balancesheet(ts_code=ts_code)
if df is not None and not df.empty:
df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
records = self._convert_ts_financial_records(df, self.BALANCE_TS_MAP)
if records:
result["balance_sheet"] = records
# 现金流量表
df = self.data_source_manager.tushare_api.cashflow(ts_code=ts_code)
if df is not None and not df.empty:
df = df.drop_duplicates(subset=['end_date']).sort_values('end_date', ascending=False)
records = self._convert_ts_financial_records(df, self.CASHFLOW_TS_MAP)
if records:
result["cash_flow"] = records
# 财务指标
df = self.data_source_manager.tushare_api.fina_indicator(ts_code=ts_code)
if df is not None and not df.empty:
ratios = self._convert_ts_ratios(df)
if ratios:
result["financial_ratios"] = ratios
if result:
print(f"[Tushare] ✅ 成功获取财务数据（主要数据源）")
return result or None
except Exception as e:
print(f"[Tushare] ❌ 获取财务数据失败: {e}")
return None
def _get_chinese_financial_data(self, symbol):
"""获取中国股票财务数据"""
financial_data = {
@@ -612,68 +823,77 @@ class StockDataFetcher:
}
try:
# 1. 获取资产负债表
try:
balance_sheet = ak.stock_financial_abstract_ths(symbol=symbol, indicator="资产负债表")
if balance_sheet is not None and not balance_sheet.empty:
financial_data["balance_sheet"] = balance_sheet.head(8).to_dict('records')
except Exception as e:
print(f"获取资产负债表失败: {e}")
# 0. 优先使用tushare获取财务三表与财务指标
ts_financial = self._get_financial_data_from_tushare(symbol)
if ts_financial:
financial_data.update(ts_financial)
# 2. 获取利润表
try:
income_statement = ak.stock_financial_abstract_ths(symbol=symbol, indicator="利润表")
if income_statement is not None and not income_statement.empty:
financial_data["income_statement"] = income_statement.head(8).to_dict('records')
except Exception as e:
print(f"获取利润表失败: {e}")
# 1. 获取资产负债表（tushare未获取到时回退akshare）
if financial_data["balance_sheet"] is None:
try:
balance_sheet = ak.stock_financial_abstract_ths(symbol=symbol, indicator="资产负债表")
if balance_sheet is not None and not balance_sheet.empty:
financial_data["balance_sheet"] = balance_sheet.head(8).to_dict('records')
except Exception as e:
print(f"获取资产负债表失败: {e}")
# 3. 获取现金流量表
try:
cash_flow = ak.stock_financial_abstract_ths(symbol=symbol, indicator="现金流量表")
if cash_flow is not None and not cash_flow.empty:
financial_data["cash_flow"] = cash_flow.head(8).to_dict('records')
except Exception as e:
print(f"获取现金流量表失败: {e}")
# 2. 获取利润表（tushare未获取到时回退akshare）
if financial_data["income_statement"] is None:
try:
income_statement = ak.stock_financial_abstract_ths(symbol=symbol, indicator="利润表")
if income_statement is not None and not income_statement.empty:
financial_data["income_statement"] = income_statement.head(8).to_dict('records')
except Exception as e:
print(f"获取利润表失败: {e}")
# 4. 获取主要财务指标
try:
financial_abstract = ak.stock_financial_abstract(symbol=symbol)
if financial_abstract is not None and not financial_abstract.empty:
# 提取关键财务指标
key_indicators = [
'净资产收益率(ROE)', '总资产报酬率(ROA)', '销售毛利率', '销售净利率',
'资产负债率', '流动比率', '速动比率', '存货周转率', '应收账款周转率',
'总资产周转率', '营业收入同比增长', '净利润同比增长'
]
# 3. 获取现金流量表（tushare未获取到时回退akshare）
if financial_data["cash_flow"] is None:
try:
cash_flow = ak.stock_financial_abstract_ths(symbol=symbol, indicator="现金流量表")
if cash_flow is not None and not cash_flow.empty:
financial_data["cash_flow"] = cash_flow.head(8).to_dict('records')
except Exception as e:
print(f"获取现金流量表失败: {e}")
# 筛选出包含关键指标的行
indicator_rows = financial_abstract[financial_abstract['指标'].isin(key_indicators)]
# 4. 获取主要财务指标（tushare未获取到时回退akshare）
if not financial_data["financial_ratios"]:
try:
financial_abstract = ak.stock_financial_abstract(symbol=symbol)
if financial_abstract is not None and not financial_abstract.empty:
# 提取关键财务指标
key_indicators = [
'净资产收益率(ROE)', '总资产报酬率(ROA)', '销售毛利率', '销售净利率',
'资产负债率', '流动比率', '速动比率', '存货周转率', '应收账款周转率',
'总资产周转率', '营业收入同比增长', '净利润同比增长'
]
if not indicator_rows.empty:
# 获取最新的报告期数据（第一列日期）
date_columns = [col for col in financial_abstract.columns if col not in ['选项', '指标']]
if date_columns:
latest_date = date_columns[0] # 最新日期列
# 筛选出包含关键指标的行
indicator_rows = financial_abstract[financial_abstract['指标'].isin(key_indicators)]
# 构建财务比率字典
financial_ratios = {"报告期": latest_date}
if not indicator_rows.empty:
# 获取最新的报告期数据（第一列日期）
date_columns = [col for col in financial_abstract.columns if col not in ['选项', '指标']]
if date_columns:
latest_date = date_columns[0] # 最新日期列
# 提取每个指标的最新值
for _, row in indicator_rows.iterrows():
indicator_name = row['指标']
value = row.get(latest_date, 'N/A')
if value is not None and not (isinstance(value, float) and pd.isna(value)):
try:
financial_ratios[indicator_name] = str(value)
except:
# 构建财务比率字典
financial_ratios = {"报告期": latest_date}
# 提取每个指标的最新值
for _, row in indicator_rows.iterrows():
indicator_name = row['指标']
value = row.get(latest_date, 'N/A')
if value is not None and not (isinstance(value, float) and pd.isna(value)):
try:
financial_ratios[indicator_name] = str(value)
except:
financial_ratios[indicator_name] = "N/A"
else:
financial_ratios[indicator_name] = "N/A"
else:
financial_ratios[indicator_name] = "N/A"
financial_data["financial_ratios"] = financial_ratios
except Exception as e:
print(f"获取财务指标失败: {e}")
financial_data["financial_ratios"] = financial_ratios
except Exception as e:
print(f"获取财务指标失败: {e}")
# 注意：季报数据现在由 quarterly_report_data.py 模块使用 akshare 获取（8期完整季报）
# 不再使用问财获取季报，避免重复
test_tdx_api.py
+1 -1
View File
@@ -14,7 +14,7 @@ from dotenv import load_dotenv
load_dotenv()
# 获取TDX API URL
TDX_API_URL = os.getenv('TDX_BASE_URL', 'http://127.0.0.1:5000')
TDX_API_URL = os.getenv('TDX_BASE_URL', 'https://tdx.javagood.top')
print("=" * 60)
print("TDX API配置测试")
value_stock_strategy.py
+4 -5
View File
@@ -6,10 +6,10 @@
"""
import pandas as pd
import akshare as ak
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import logging
from data_source_manager import data_source_manager
class ValueStockStrategy:
@@ -146,10 +146,9 @@ class ValueStockStrategy:
RSI值 或 None
"""
try:
# 获取近60天日线数据
df = ak.stock_zh_a_hist(
# 获取近60天日线数据（tushare优先，akshare备用）
df = data_source_manager.get_stock_hist_data(
symbol=stock_code,
period="daily",
start_date=(datetime.now() - timedelta(days=90)).strftime("%Y%m%d"),
end_date=datetime.now().strftime("%Y%m%d"),
adjust="qfq"
@@ -159,7 +158,7 @@ class ValueStockStrategy:
return None
# 计算RSI
close = df['收盘'].astype(float)
close = df['close'].astype(float)
delta = close.diff()
gain = delta.where(delta > 0, 0)
loss = (-delta).where(delta < 0, 0)
verify_data_sources.py
+109
View File
@@ -0,0 +1,109 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
数据源改造实盘验证脚本
请在【有网络】的环境运行（例如在 PyCharm 的 aiagents-stock 解释器中执行），
它会逐项调用各模块的数据获取接口，并打印 PASS/FAIL 与实际使用的数据源日志。
"""
import os
import sys
PROJECT = os.environ.get("AIAGENTS_STOCK_HOME", "/Users/songzhuoyuan/Desktop/code/python/aiagents-stock")
if not os.path.isdir(PROJECT):
PROJECT = input("请输入项目绝对路径: ").strip()
sys.path.insert(0, PROJECT)
os.chdir(PROJECT)
print("=" * 60)
print("数据源改造验证 - 股票代码统一使用 600637")
print("=" * 60)
from data_source_manager import data_source_manager
print("Tushare Token 已配置:", bool(data_source_manager.tushare_token))
print("Tushare 可用:", data_source_manager.tushare_available)
print()
passed = 0
failed = 0
def test(name, fn):
global passed, failed
try:
result = fn()
if result:
passed += 1
print(f"PASS | {name}")
else:
failed += 1
print(f"FAIL | {name}（返回空/失败）")
except Exception as e:
failed += 1
print(f"FAIL | {name}: {type(e).__name__}: {str(e)[:120]}")
# 1. 数据源管理器（核心）
test("历史数据(1年,前复权)", lambda: data_source_manager.get_stock_hist_data("600637", start_date="20250801", end_date="20260810", adjust="qfq") is not None)
test("基本信息", lambda: data_source_manager.get_stock_basic_info("600637").get("name") != "未知")
test("实时行情", lambda: bool(data_source_manager.get_realtime_quotes("600637")))
test("财务数据(利润表)", lambda: data_source_manager.get_financial_data("600637", "income") is not None)
# 2. 资金流向
def fund_flow_test():
from fund_flow_akshare import FundFlowAkshareDataFetcher
return FundFlowAkshareDataFetcher().get_fund_flow_data("600637").get("data_success")
test("资金流向", fund_flow_test)
# 3. 季报
def quarterly_test():
from quarterly_report_data import QuarterlyReportDataFetcher
return QuarterlyReportDataFetcher().get_quarterly_reports("600637").get("data_success")
test("季报(三表+指标)", quarterly_test)
# 4. 新闻
def news_test():
from qstock_news_data import QStockNewsDataFetcher
return QStockNewsDataFetcher().get_stock_news("600637").get("data_success")
test("个股新闻", news_test)
# 5. 市场情绪
def sentiment_tests():
from market_sentiment_data import MarketSentimentDataFetcher
f = MarketSentimentDataFetcher()
return (f._get_turnover_rate("600637") is not None and
f._get_market_index_sentiment() is not None)
test("情绪-换手率/大盘指数", sentiment_tests)
def limit_test():
from market_sentiment_data import MarketSentimentDataFetcher
return MarketSentimentDataFetcher()._get_limit_up_down_stats() is not None
test("情绪-涨跌停统计", limit_test)
def margin_test():
from market_sentiment_data import MarketSentimentDataFetcher
return MarketSentimentDataFetcher()._get_margin_trading_data("600637") is not None
test("情绪-融资融券", margin_test)
# 6. 综合股票数据（主分析链路）
def stock_data_test():
from stock_data import StockDataFetcher
f = StockDataFetcher()
info = f.get_stock_info("600637")
data = f.get_stock_data("600637", "1y")
fin = f.get_financial_data("600637")
return (isinstance(data, dict) and "error" not in data) and len(fin) > 0
test("主分析链路(信息/行情/财务)", stock_data_test)
# 7. 港股（可选，若积分不足会自动回退akshare）
def hk_test():
from stock_data import StockDataFetcher
f = StockDataFetcher()
data = f.get_stock_data("00700", "1mo")
return isinstance(data, dict) and "error" not in data
test("港股日线(00700)", hk_test)
print()
print("=" * 60)
print(f"验证完成：通过 {passed} 项，失败 {failed} 项")
if failed:
print("注意：失败项通常表示对应 tushare 接口积分不足或网络问题，程序会自动回退 akshare，不影响使用。")
print("=" * 60)