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Merge branch 'pr-1' into dev

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# Conflicts:
#	stock_analyzer.py
#	templates/index.html
This commit is contained in:
兰志宏
2025-03-05 17:21:41 +08:00
parent 25601b9bd6 cdd2728e71
commit 5dacc9f528
4 changed files with 379 additions and 51 deletions
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185
.gitignore vendored
View File

@@ -1,2 +1,183 @@
logs/*
.*
# log
logs/
*.log
# Custom
.vs/
.vscode/
.idea/
.conda/
.vs/
.vscode/
.idea/
AppData/
output/
dist/
main.build/
main.dist/
main.onefile-build/
build_upload.log
*report.xml
*.spec
*.zip
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

195
stock_analyzer.py
View File

@@ -47,8 +47,25 @@ class StockAnalyzer:
end_date = datetime.now().strftime('%Y%m%d')
try:
# 根据市场类型获取数据
# 验证股票代码格式
if market_type == 'A':
# 上海证券交易所股票代码以6开头
# 深圳证券交易所股票代码以0或3开头
# 科创板股票代码以688开头
# 北京证券交易所股票代码以8开头
valid_prefixes = ['0', '3', '6', '688', '8']
valid_format = False
for prefix in valid_prefixes:
if stock_code.startswith(prefix):
valid_format = True
break
if not valid_format:
error_msg = f"无效的A股股票代码格式: {stock_code}。A股代码应以0、3、6、688或8开头"
logger.error(f"[股票代码格式错误] {error_msg}")
raise ValueError(error_msg)
df = ak.stock_zh_a_hist(
symbol=stock_code,
start_date=start_date,
@@ -108,8 +125,13 @@ class StockAnalyzer:
return df.sort_values('date')
# except ValueError as ve:
# # 捕获格式验证错误
# logger.error(f"[股票代码格式错误] {str(ve)}")
# raise Exception(f"股票代码格式错误: {str(ve)}")
except Exception as e:
raise Exception(f"获取股票数据失败: {str(e)}")
logger.error(f"[获取数据失败] {str(e)}")
raise Exception(f"获取数据失败: {str(e)}")
def calculate_ema(self, series, period):
"""计算指数移动平均线"""
@@ -192,7 +214,7 @@ class StockAnalyzer:
raise
def calculate_score(self, df):
"""计算股票评分"""
"""计算评分"""
try:
score = 0
latest = df.iloc[-1]
@@ -325,12 +347,12 @@ class StockAnalyzer:
# 检查API配置
if not self.API_URL:
error_msg = "API URL未配置，无法进行AI分析"
logger.error(error_msg)
logger.error(f"[API配置错误] {error_msg}")
return error_msg if not stream else (yield json.dumps({"error": error_msg}))
if not self.API_KEY:
error_msg = "API Key未配置，无法进行AI分析"
logger.error(error_msg)
logger.error(f"[API配置错误] {error_msg}")
return error_msg if not stream else (yield json.dumps({"error": error_msg}))
# 标准化API URL
@@ -379,12 +401,12 @@ class StockAnalyzer:
error_text = response.text[:500] if response.text else "无响应内容"
error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}"
logger.error(error_msg)
logger.error(f"[API请求失败] {error_msg}")
yield json.dumps({"stock_code": stock_code, "error": error_msg})
except Exception as e:
error_msg = f"流式API请求异常: {str(e)}"
logger.error(error_msg)
logger.error(f"[流式API异常] {error_msg}")
logger.exception(e)
yield json.dumps({"stock_code": stock_code, "error": error_msg})
else:
@@ -415,18 +437,18 @@ class StockAnalyzer:
error_text = response.text[:500] if response.text else "无响应内容"
error_msg = f"API请求失败: 状态码 {response.status_code}, 响应: {error_text}"
logger.error(error_msg)
logger.error(f"[API请求失败] {error_msg}")
return error_msg
except Exception as e:
error_msg = f"非流式API请求异常: {str(e)}"
logger.error(error_msg)
logger.error(f"[非流式API异常] {error_msg}")
logger.exception(e)
return error_msg
except Exception as e:
error_msg = f"AI 分析过程中发生错误: {str(e)}"
logger.error(error_msg)
logger.error(f"[AI分析异常] {error_msg}")
logger.exception(e)
if stream:
@@ -495,7 +517,7 @@ class StockAnalyzer:
})
yield chunk_json
except json.JSONDecodeError as e:
logger.error(f"JSON解析错误: {str(e)}, 行内容: {data_content}")
logger.error(f"[JSON解析错误] {str(e)}, 行内容: {data_content}")
# 忽略无法解析的JSON
pass
else:
@@ -510,7 +532,7 @@ class StockAnalyzer:
except Exception as e:
error_msg = f"处理AI流式响应时出错: {str(e)}"
logger.error(error_msg)
logger.error(f"[流式响应异常] {error_msg}")
logger.exception(e)
yield json.dumps({"stock_code": stock_code, "error": error_msg})
@@ -530,13 +552,48 @@ class StockAnalyzer:
return '强烈建议卖出'
def analyze_stock(self, stock_code, market_type='A', stream=False):
"""分析股票或基金"""
try:
logger.info(f"开始分析: {stock_code}, 市场: {market_type}, 流式模式: {stream}")
"""分析单只"""
logger.info(f"开始分析 {stock_code}, 市场类型: {market_type}, 流式模式: {stream}")
# 获取数据
logger.debug(f"获取 {stock_code} 数据")
df = self.get_stock_data(stock_code, market_type)
try:
# 获取股票数据
try:
df = self.get_stock_data(stock_code, market_type)
except Exception as e:
# 捕获股票数据获取异常
error_msg = str(e)
logger.error(f"[数据获取异常] {error_msg}")
# 格式化错误响应
error_response = {
'stock_code': stock_code,
'error': error_msg,
'status': 'error',
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
if stream:
return (yield json.dumps(error_response))
else:
return error_response
# 检查数据是否为空
if df.empty:
error_msg = f" {stock_code} 数据为空"
logger.error(f"[空数据] {error_msg}")
# 格式化错误响应
error_response = {
'stock_code': stock_code,
'error': error_msg,
'status': 'error',
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
if stream:
return (yield json.dumps(error_response))
else:
return error_response
# 计算技术指标
logger.debug(f"计算 {stock_code} 技术指标")
@@ -592,61 +649,109 @@ class StockAnalyzer:
return report
except Exception as e:
error_msg = f"分析 {stock_code} 时出错: {str(e)}"
logger.error(error_msg)
error_msg = f"分析 {stock_code} 时出错: {str(e)}\n"
logger.error(f"[分析异常] {error_msg}")
logger.exception(e)
if stream:
error_json = json.dumps({'stock_code': stock_code, 'error': error_msg})
logger.info(f"流式错误输出: {error_json}")
yield error_json
else:
raise
# 格式化错误响应
error_response = {
'stock_code': stock_code,
'error': error_msg,
'status': 'error',
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
def scan_market(self, stock_list, min_score=60, market_type='A', stream=False):
"""扫描市场，寻找符合条件的"""
logger.info(f"开始扫描市场，数量: {len(stock_list)}, 最低分数: {min_score}, 市场: {market_type}, 流式模式: {stream}")
if stream:
return (yield json.dumps(error_response))
else:
return error_response
def scan_stocks(self, stock_codes, market_type='A', min_score=60, stream=False):
"""扫描多只"""
logger.info(f"开始扫描 {len(stock_codes)} 只, 市场类型: {market_type}, 最低评分: {min_score}, 流式模式: {stream}")
if not stream:
recommendations = []
# 非流式模式
recommended_stocks = []
stock_count = 0
error_count = 0
for stock_code in stock_codes:
stock_count += 1
logger.info(f"扫描进度: {stock_count}/{len(stock_codes)}, 当前: {stock_code}")
for stock_code in stock_list:
try:
logger.debug(f"分析: {stock_code}")
report = self.analyze_stock(stock_code, market_type)
# 检查是否有错误
if isinstance(report, dict) and 'error' in report:
error_count += 1
logger.warning(f"[扫描错误] {stock_code}: {report['error']}")
continue
# 检查评分是否达到最低要求
if report['score'] >= min_score:
logger.info(f" {stock_code} 评分 {report['score']} >= {min_score}，添加到推荐列表")
recommendations.append(report)
recommended_stocks.append(report)
else:
logger.debug(f" {stock_code} 评分 {report['score']} < {min_score}，不添加到推荐列表")
except Exception as e:
logger.error(f"分析 {stock_code} 时出错: {str(e)}")
error_count += 1
error_msg = f"分析 {stock_code} 时出错: {str(e)}"
logger.error(f"[扫描异常] {error_msg}")
logger.exception(e)
# 添加错误信息到推荐列表，确保前端能看到错误
error_response = {
'stock_code': stock_code,
'error': error_msg,
'status': 'error',
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
recommended_stocks.append(error_response)
continue
# 按得分排序
recommendations.sort(key=lambda x: x['score'], reverse=True)
logger.info(f"扫描完成，找到 {len(recommendations)} 个推荐股票")
return recommendations
logger.info(f"扫描完成，共 {stock_count} 只，{error_count} 只出错，{len(recommended_stocks)} 只推荐")
return recommended_stocks
else:
# 流式处理每个股票
logger.info(f"开始流式扫描 {len(stock_list)} 只股票")
# 流式模式
stock_count = 0
for stock_code in stock_list:
error_count = 0
for stock_code in stock_codes:
stock_count += 1
logger.debug(f"流式分析 {stock_code} ({stock_count}/{len(stock_list)})")
logger.info(f"流式扫描进度: {stock_count}/{len(stock_codes)}, 当前: {stock_code}")
try:
# 分析单只股票并获取流式结果
chunk_count = 0
for chunk in self.analyze_stock(stock_code, market_type, stream=True):
chunk_count += 1
# 检查是否有错误信息
try:
chunk_data = json.loads(chunk)
if 'error' in chunk_data:
error_count += 1
logger.warning(f"[流式扫描错误] {stock_code}: {chunk_data['error']}")
except:
pass
yield chunk
logger.debug(f" {stock_code} 流式分析完成，共 {chunk_count} 个块")
except Exception as e:
error_count += 1
error_msg = f"分析 {stock_code} 时出错: {str(e)}"
logger.error(error_msg)
logger.error(f"[流式扫描异常] {error_msg}")
logger.exception(e)
error_json = json.dumps({'stock_code': stock_code, 'error': error_msg})
# 格式化错误响应
error_response = {
'stock_code': stock_code,
'error': error_msg,
'status': 'error',
'timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
error_json = json.dumps(error_response)
logger.info(f"流式错误输出: {error_json}")
yield error_json
logger.info(f"流式扫描完成，处理了 {stock_count} 只股票")
logger.info(f"流式扫描完成，共处理 {stock_count} ，{error_count} 只出错")

48
templates/index.html
View File

@@ -724,14 +724,56 @@
if (chunk.error) {
// 添加或更新显示错误的卡片
let errorCard = document.getElementById(`error-${stockCode}`);
// 处理错误信息，提取关键部分
let errorMessage = chunk.error;
// 移除多余的错误前缀
errorMessage = errorMessage.replace(/获取股票数据失败: /g, '');
// 格式化错误信息
let formattedError = errorMessage;
// 针对特定类型的错误提供更友好的提示
if (errorMessage.includes('无效的A股股票代码格式')) {
formattedError = `<strong>股票代码格式错误</strong>: ${stockCode} 不是有效的A股代码<br>
<small>A股代码应以0、3、6、688或8开头</small>`;
} else if (errorMessage.includes('股票代码格式错误')) {
formattedError = `<strong>股票代码格式错误</strong>: ${errorMessage.split(':')[1] || errorMessage}`;
} else if (errorMessage.includes('数据为空')) {
formattedError = `<strong>数据获取失败</strong>: 未找到股票 ${stockCode} 的交易数据`;
}
if (!errorCard) {
errorCard = document.createElement('div');
errorCard.id = `error-${stockCode}`;
errorCard.className = 'bg-red-50 p-4 rounded-lg text-red-600';
errorCard.innerHTML = `分析 ${stockCode} 出错: ${chunk.error}`;
errorCard.className = 'bg-red-50 p-4 rounded-lg text-red-600 mb-4 flex items-start';
// 添加警告图标
errorCard.innerHTML = `
<div class="mr-3 flex-shrink-0">
<svg class="h-5 w-5 text-red-500" fill="currentColor" viewBox="0 0 20 20">
<path fill-rule="evenodd" d="M10 18a8 8 0 100-16 8 8 0 000 16zM8.707 7.293a1 1 0 00-1.414 1.414L8.586 10l-1.293 1.293a1 1 0 101.414 1.414L10 11.414l1.293 1.293a1 1 0 001.414-1.414L11.414 10l1.293-1.293a1 1 0 00-1.414-1.414L10 8.586 8.707 7.293z" clip-rule="evenodd"></path>
</svg>
</div>
<div>
<p class="text-sm font-medium">股票 ${stockCode} 分析失败</p>
<p class="mt-1 text-sm">${formattedError}</p>
</div>
`;
container.appendChild(errorCard);
} else {
errorCard.innerHTML = `分析 ${stockCode} 出错: ${chunk.error}`;
errorCard.innerHTML = `
<div class="mr-3 flex-shrink-0">
<svg class="h-5 w-5 text-red-500" fill="currentColor" viewBox="0 0 20 20">
<path fill-rule="evenodd" d="M10 18a8 8 0 100-16 8 8 0 000 16zM8.707 7.293a1 1 0 00-1.414 1.414L8.586 10l-1.293 1.293a1 1 0 101.414 1.414L10 11.414l1.293 1.293a1 1 0 001.414-1.414L11.414 10l1.293-1.293a1 1 0 00-1.414-1.414L10 8.586 8.707 7.293z" clip-rule="evenodd"></path>
</svg>
</div>
<div>
<p class="text-sm font-medium">股票 ${stockCode} 分析失败</p>
<p class="mt-1 text-sm">${formattedError}</p>
</div>
`;
}
return;
}

2
web_server.py
View File

@@ -90,7 +90,7 @@ def analyze():
logger.debug(f"开始处理批量股票的流式响应")
chunk_count = 0
for chunk in custom_analyzer.scan_market(
for chunk in custom_analyzer.scan_stocks(
[code.strip() for code in stock_codes],
min_score=0,
market_type=market_type,