BugVanquisher

import threading

class RateLimiter:
    """
    Simple rate limiter using token bucket algorithm
    
    Args:
        max_calls: Maximum number of calls allowed
        time_window: Time window in seconds
    """

BugVanquisher

import os
import tempfile
from contextlib import contextmanager

@contextmanager
def temporary_file(suffix='', prefix='tmp', dir=None):
    """
    Context manager for creating and automatically cleaning up temporary files
    
    Usage:

BugVanquisher

def batch_process(items, batch_size=100):
    """
    Process items in batches using a generator
    
    Args:
        items: Iterable of items to process
        batch_size: Number of items per batch
        
    Yields:
        Lists of items with length up to batch_size

BugVanquisher

def memoize(func):
    """Simple memoization decorator for functions with hashable arguments"""
    cache = {}
    
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        key = (args, tuple(sorted(kwargs.items())))
        if key not in cache:
            cache[key] = func(*args, **kwargs)
        return cache[key]

BugVanquisher

def timer(func):
    """Decorator to measure function execution time"""
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        start = time.perf_counter()
        result = func(*args, **kwargs)
        end = time.perf_counter()
        print(f"{func.__name__} took {end - start:.4f} seconds")
        return result
    return wrapper

BugVanquisher

import time
import functools

def retry(max_attempts=3, delay=1, backoff=2, exceptions=(Exception,)):
    """
    Retry decorator with exponential backoff
    
    Args:
        max_attempts: Maximum number of retry attempts
        delay: Initial delay between retries in seconds

BugVanquisher

def split_files(df):
    '''
    Split files by TICKER. If the corresponding csv file doesn't exist, create one;
    otherwise append new data to current csv file
    :param df: dataframe
    :return: None
    '''
    grouped = df.groupby('TICKER')
    # loop through the grouped dataframe
    for ticker, group in grouped: