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632 lines (520 loc) · 18.7 KB
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"""
工具函数模块，提供各种辅助功能
"""
import os
import json
import csv
import time
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
import config
from logger import get_logger
# 获取logger
logger = get_logger("utils")
def format_number(number, decimal_places=2):
"""
格式化数字，保留指定小数位
参数:
number (float): 要格式化的数字
decimal_places (int): 保留的小数位数
返回:
str: 格式化后的字符串
"""
if number is None:
return "N/A"
try:
return f"{float(number):.{decimal_places}f}"
except (ValueError, TypeError):
return "N/A"
def format_percentage(number, decimal_places=2):
"""
格式化百分比，保留指定小数位
参数:
number (float): 要格式化的数字
decimal_places (int): 保留的小数位数
返回:
str: 格式化后的字符串
"""
if number is None:
return "N/A"
try:
return f"{float(number) * 100:.{decimal_places}f}%"
except (ValueError, TypeError):
return "N/A"
def format_datetime(dt, format_str="%Y-%m-%d %H:%M:%S"):
"""
格式化日期时间
参数:
dt (datetime或str): 要格式化的日期时间
format_str (str): 格式化字符串
返回:
str: 格式化后的字符串
"""
if dt is None:
return "N/A"
try:
if isinstance(dt, str):
dt = pd.to_datetime(dt)
return dt.strftime(format_str)
except:
return "N/A"
def is_valid_stock_code(stock_code):
"""
检查股票代码是否有效
参数:
stock_code (str): 股票代码
返回:
bool: 是否有效
"""
if not stock_code:
return False
# 股票代码格式检查
parts = stock_code.split('.')
if len(parts) != 2:
return False
code, market = parts
# 检查市场代码
if market not in ['SH', 'SZ']:
return False
# 检查股票代码
if not code.isdigit():
return False
# 上交所：6开头，6位数
if market == 'SH' and (not code.startswith('6') or len(code) != 6):
return False
# 深交所：0或3开头，6位数
if market == 'SZ' and (not (code.startswith('0') or code.startswith('3')) or len(code) != 6):
return False
return True
def calculate_trade_metrics(trades_df):
"""
计算交易指标
参数:
trades_df (pandas.DataFrame): 交易记录
返回:
dict: 交易指标
"""
if trades_df.empty:
return {
'total_trades': 0,
'win_trades': 0,
'lose_trades': 0,
'win_rate': 0,
'avg_profit': 0,
'avg_loss': 0,
'profit_factor': 0,
'max_profit': 0,
'max_loss': 0,
'total_profit': 0,
'total_commission': 0
}
# 计算每笔交易的盈亏
trades_df = trades_df.sort_values('trade_time')
trades_df['profit'] = 0
# 按股票代码和交易时间分组，计算盈亏
for stock_code, group in trades_df.groupby('stock_code'):
buy_records = group[group['trade_type'] == 'BUY']
sell_records = group[group['trade_type'] == 'SELL']
if buy_records.empty or sell_records.empty:
continue
# 简单计算：假设先买后卖
avg_buy_price = buy_records['price'].mean()
avg_sell_price = sell_records['price'].mean()
sell_volume = sell_records['volume'].sum()
# 计算盈亏
profit = (avg_sell_price - avg_buy_price) * sell_volume
# 更新盈亏
for idx in sell_records.index:
trades_df.loc[idx, 'profit'] = profit * (trades_df.loc[idx, 'volume'] / sell_volume)
# 计算交易指标
win_trades = trades_df[trades_df['profit'] > 0]
lose_trades = trades_df[trades_df['profit'] < 0]
metrics = {
'total_trades': len(trades_df),
'win_trades': len(win_trades),
'lose_trades': len(lose_trades),
'win_rate': len(win_trades) / len(trades_df) if len(trades_df) > 0 else 0,
'avg_profit': win_trades['profit'].mean() if len(win_trades) > 0 else 0,
'avg_loss': lose_trades['profit'].mean() if len(lose_trades) > 0 else 0,
'profit_factor': abs(win_trades['profit'].sum() / lose_trades['profit'].sum()) if len(lose_trades) > 0 and lose_trades['profit'].sum() != 0 else 0,
'max_profit': win_trades['profit'].max() if len(win_trades) > 0 else 0,
'max_loss': lose_trades['profit'].min() if len(lose_trades) > 0 else 0,
'total_profit': trades_df['profit'].sum(),
'total_commission': trades_df['commission'].sum()
}
return metrics
def export_trades_to_csv(trades_df, filename=None):
"""
导出交易记录到CSV文件
参数:
trades_df (pandas.DataFrame): 交易记录
filename (str): 文件名，如果为None则使用日期时间生成文件名
返回:
str: 文件路径
"""
if trades_df.empty:
logger.warning("没有交易记录可导出")
return None
# 创建导出目录
export_dir = os.path.join(config.DATA_DIR, 'exports')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
# 生成文件名
if filename is None:
filename = f"trades_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
file_path = os.path.join(export_dir, filename)
try:
# 导出到CSV
trades_df.to_csv(file_path, index=False, encoding='utf-8-sig')
logger.info(f"交易记录已导出到 {file_path}")
return file_path
except Exception as e:
logger.error(f"导出交易记录到CSV时出错: {str(e)}")
return None
def export_positions_to_csv(positions_df, filename=None):
"""
导出持仓记录到CSV文件
参数:
positions_df (pandas.DataFrame): 持仓记录
filename (str): 文件名，如果为None则使用日期时间生成文件名
返回:
str: 文件路径
"""
if positions_df.empty:
logger.warning("没有持仓记录可导出")
return None
# 创建导出目录
export_dir = os.path.join(config.DATA_DIR, 'exports')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
# 生成文件名
if filename is None:
filename = f"positions_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
file_path = os.path.join(export_dir, filename)
try:
# 导出到CSV
positions_df.to_csv(file_path, index=False, encoding='utf-8-sig')
logger.info(f"持仓记录已导出到 {file_path}")
return file_path
except Exception as e:
logger.error(f"导出持仓记录到CSV时出错: {str(e)}")
return None
def load_stock_pool_from_csv(file_path):
"""
从CSV文件加载股票池
参数:
file_path (str): CSV文件路径
返回:
list: 股票代码列表
"""
if not os.path.exists(file_path):
logger.error(f"文件 {file_path} 不存在")
return []
try:
stock_codes = []
with open(file_path, 'r', encoding='utf-8-sig') as f:
csv_reader = csv.reader(f)
for row in csv_reader:
if row and row[0]:
stock_code = row[0].strip()
if is_valid_stock_code(stock_code):
stock_codes.append(stock_code)
logger.info(f"从 {file_path} 加载了 {len(stock_codes)} 只股票")
return stock_codes
except Exception as e:
logger.error(f"从CSV加载股票池时出错: {str(e)}")
return []
def save_stock_pool_to_json(stock_codes, file_path=None):
"""
保存股票池到JSON文件
参数:
stock_codes (list): 股票代码列表
file_path (str): JSON文件路径，如果为None则使用默认路径
返回:
bool: 是否保存成功
"""
if file_path is None:
file_path = "stock_pool.json"
try:
with open(file_path, 'w') as f:
json.dump(stock_codes, f)
logger.info(f"股票池已保存到 {file_path}")
return True
except Exception as e:
logger.error(f"保存股票池到JSON时出错: {str(e)}")
return False
def calculate_position_metrics(positions_df):
"""
计算持仓指标
参数:
positions_df (pandas.DataFrame): 持仓记录
返回:
dict: 持仓指标
"""
if positions_df.empty:
return {
'total_positions': 0,
'total_market_value': 0,
'total_cost': 0,
'total_profit': 0,
'profit_ratio': 0,
'win_positions': 0,
'lose_positions': 0,
'win_ratio': 0,
'max_profit_ratio': 0,
'max_loss_ratio': 0
}
# 计算总市值和总成本
total_market_value = positions_df['market_value'].sum()
total_cost = (positions_df['cost_price'] * positions_df['volume']).sum()
# 计算总盈亏
total_profit = total_market_value - total_cost
profit_ratio = total_profit / total_cost if total_cost > 0 else 0
# 计算盈亏持仓数量
win_positions = positions_df[positions_df['profit_ratio'] > 0]
lose_positions = positions_df[positions_df['profit_ratio'] < 0]
metrics = {
'total_positions': len(positions_df),
'total_market_value': total_market_value,
'total_cost': total_cost,
'total_profit': total_profit,
'profit_ratio': profit_ratio,
'win_positions': len(win_positions),
'lose_positions': len(lose_positions),
'win_ratio': len(win_positions) / len(positions_df) if len(positions_df) > 0 else 0,
'max_profit_ratio': positions_df['profit_ratio'].max() if len(positions_df) > 0 else 0,
'max_loss_ratio': positions_df['profit_ratio'].min() if len(positions_df) > 0 else 0
}
return metrics
def convert_volume_to_chinese(volume):
"""
将数量转换为中文表示（万、亿）
参数:
volume (float): 数量
返回:
str: 中文表示
"""
if volume is None:
return "N/A"
try:
volume = float(volume)
if volume >= 100000000: # 亿
return f"{volume / 100000000:.2f}亿"
elif volume >= 10000: # 万
return f"{volume / 10000:.2f}万"
else:
return f"{volume:.0f}"
except (ValueError, TypeError):
return "N/A"
def convert_amount_to_chinese(amount):
"""
将金额转换为中文表示（万、亿）
参数:
amount (float): 金额
返回:
str: 中文表示
"""
if amount is None:
return "N/A"
try:
amount = float(amount)
if amount >= 100000000: # 亿
return f"{amount / 100000000:.2f}亿"
elif amount >= 10000: # 万
return f"{amount / 10000:.2f}万"
else:
return f"{amount:.2f}"
except (ValueError, TypeError):
return "N/A"
def get_trading_days(start_date, end_date=None):
"""
获取交易日列表
参数:
start_date (str): 开始日期，格式 'YYYY-MM-DD'
end_date (str): 结束日期，格式 'YYYY-MM-DD'，如果为None则使用当前日期
返回:
list: 交易日列表
"""
# 由于迅投API没有提供交易日历接口，这里简单处理，忽略了节假日
if end_date is None:
end_date = datetime.now().strftime('%Y-%m-%d')
start = pd.to_datetime(start_date)
end = pd.to_datetime(end_date)
all_days = pd.date_range(start=start, end=end)
# 周末过滤
trading_days = [day.strftime('%Y-%m-%d') for day in all_days if day.weekday() < 5]
return trading_days
def _memory_usage_windows():
"""未安装 psutil 时，通过 Win32 API 获取当前进程内存占用。
口径与 psutil 保持一致：WorkingSetSize->rss, PagefileUsage->vms。
"""
import ctypes
from ctypes import wintypes
class _ProcessMemoryCounters(ctypes.Structure):
_fields_ = [
("cb", ctypes.c_ulong),
("PageFaultCount", ctypes.c_ulong),
("PeakWorkingSetSize", ctypes.c_size_t),
("WorkingSetSize", ctypes.c_size_t),
("QuotaPeakPagedPoolUsage", ctypes.c_size_t),
("QuotaPagedPoolUsage", ctypes.c_size_t),
("QuotaPeakNonPagedPoolUsage", ctypes.c_size_t),
("QuotaNonPagedPoolUsage", ctypes.c_size_t),
("PagefileUsage", ctypes.c_size_t),
("PeakPagefileUsage", ctypes.c_size_t),
]
kernel32 = ctypes.WinDLL("kernel32")
# 必须显式声明句柄类型：64 位下默认 c_int 会截断伪句柄，导致调用失败
kernel32.GetCurrentProcess.restype = wintypes.HANDLE
get_memory_info = kernel32.K32GetProcessMemoryInfo
get_memory_info.argtypes = [
wintypes.HANDLE, ctypes.POINTER(_ProcessMemoryCounters), ctypes.c_ulong
]
get_memory_info.restype = wintypes.BOOL
counters = _ProcessMemoryCounters()
counters.cb = ctypes.sizeof(_ProcessMemoryCounters)
if not get_memory_info(
kernel32.GetCurrentProcess(), ctypes.byref(counters), counters.cb
):
return None
return {
'rss': counters.WorkingSetSize,
'vms': counters.PagefileUsage,
'rss_mb': counters.WorkingSetSize / (1024 * 1024),
'vms_mb': counters.PagefileUsage / (1024 * 1024)
}
def process_resource_stats():
"""当前进程的 OS 级资源计数：真实线程数与句柄数（Windows）。
threading.active_count() 只统计 Python threading 模块创建的 Thread 对象，
看不到 xtquant / QMT SDK 等 C 扩展创建的原生线程——实测本系统两者
分别为 18 与 104，相差 86 条。诊断 can't start new thread 或句柄耗尽
必须看 OS 口径，Python 口径会漏掉真正在增长的那部分。
返回:
dict: {'os_threads': int, 'handles': int}，取不到时返回 None
"""
import ctypes
from ctypes import wintypes
class _ThreadEntry32(ctypes.Structure):
_fields_ = [
("dwSize", wintypes.DWORD),
("cntUsage", wintypes.DWORD),
("th32ThreadID", wintypes.DWORD),
("th32OwnerProcessID", wintypes.DWORD),
("tpBasePri", ctypes.c_long),
("tpDeltaPri", ctypes.c_long),
("dwFlags", wintypes.DWORD),
]
TH32CS_SNAPTHREAD = 0x00000004
INVALID_HANDLE_VALUE = ctypes.c_void_p(-1).value
kernel32 = ctypes.WinDLL("kernel32")
kernel32.GetCurrentProcess.restype = wintypes.HANDLE
kernel32.GetCurrentProcessId.restype = wintypes.DWORD
kernel32.CreateToolhelp32Snapshot.argtypes = [wintypes.DWORD, wintypes.DWORD]
kernel32.CreateToolhelp32Snapshot.restype = wintypes.HANDLE
kernel32.Thread32First.argtypes = [wintypes.HANDLE, ctypes.POINTER(_ThreadEntry32)]
kernel32.Thread32First.restype = wintypes.BOOL
kernel32.Thread32Next.argtypes = [wintypes.HANDLE, ctypes.POINTER(_ThreadEntry32)]
kernel32.Thread32Next.restype = wintypes.BOOL
kernel32.GetProcessHandleCount.argtypes = [
wintypes.HANDLE, ctypes.POINTER(wintypes.DWORD)
]
kernel32.GetProcessHandleCount.restype = wintypes.BOOL
handle_count = wintypes.DWORD()
if not kernel32.GetProcessHandleCount(
kernel32.GetCurrentProcess(), ctypes.byref(handle_count)
):
return None
# 线程快照是全系统的，需按 OwnerProcessID 过滤。实测扫描约 3600 条耗时 70ms，
# 每 30 分钟一次的心跳可以接受。
snapshot = kernel32.CreateToolhelp32Snapshot(TH32CS_SNAPTHREAD, 0)
if snapshot == INVALID_HANDLE_VALUE:
return None
try:
pid = kernel32.GetCurrentProcessId()
entry = _ThreadEntry32()
entry.dwSize = ctypes.sizeof(_ThreadEntry32)
os_threads = 0
if kernel32.Thread32First(snapshot, ctypes.byref(entry)):
while True:
if entry.th32OwnerProcessID == pid:
os_threads += 1
if not kernel32.Thread32Next(snapshot, ctypes.byref(entry)):
break
finally:
kernel32.CloseHandle(snapshot)
return {'os_threads': os_threads, 'handles': handle_count.value}
def memory_usage():
"""
获取当前进程的内存使用情况
返回:
dict: 内存使用情况
"""
try:
import psutil
process = psutil.Process(os.getpid())
memory_info = process.memory_info()
return {
'rss': memory_info.rss, # 常驻内存
'vms': memory_info.vms, # 虚拟内存
'rss_mb': memory_info.rss / (1024 * 1024), # MB
'vms_mb': memory_info.vms / (1024 * 1024) # MB
}
except ImportError:
# psutil 非必装依赖，Windows 下回退到 Win32 API
try:
return _memory_usage_windows()
except Exception as e:
logger.warning(f"未安装psutil模块，Win32回退也失败，无法获取内存使用情况: {str(e)}")
return None
except Exception as e:
logger.error(f"获取内存使用情况时出错: {str(e)}")
return None
def disk_usage(path=None):
"""
获取磁盘使用情况
参数:
path (str): 路径，如果为None则使用当前目录
返回:
dict: 磁盘使用情况
"""
if path is None:
path = '.'
try:
import psutil
usage = psutil.disk_usage(path)
return {
'total': usage.total,
'used': usage.used,
'free': usage.free,
'percent': usage.percent,
'total_gb': usage.total / (1024 * 1024 * 1024), # GB
'used_gb': usage.used / (1024 * 1024 * 1024), # GB
'free_gb': usage.free / (1024 * 1024 * 1024) # GB
}
except ImportError:
logger.warning("未安装psutil模块，无法获取磁盘使用情况")
return None
except Exception as e:
logger.error(f"获取磁盘使用情况时出错: {str(e)}")
return None
def system_info():
"""
获取系统信息
返回:
dict: 系统信息
"""
try:
import platform
return {
'system': platform.system(),
'node': platform.node(),
'release': platform.release(),
'version': platform.version(),
'machine': platform.machine(),
'processor': platform.processor(),
'python_version': platform.python_version()
}
except Exception as e:
logger.error(f"获取系统信息时出错: {str(e)}")
return None