如何分离收盘价以仅分析其百分位？并筛选收盘价小数位为0.001-0.009的情况以关联成交量分析

Hey there, let's tackle your two requirements with practical, actionable steps. I'll break this down clearly so you can implement it easily.

1. 分离收盘价数据，提取小数部分用于百分位分析 #

First, to isolate the decimal portion of your closing prices (for percentile analysis), you have a couple of reliable options depending on your data type:

• Math-based extraction (for numeric data):

  • Grab the integer part with int(close_price) or math.floor(close_price) (safe since closing prices are positive).
  • Calculate the decimal part using close_price - int(close_price).
  • If you need to focus specifically on the hundredth percentile (two decimal places), round the decimal part to two digits: round(close_price % 1, 2). This makes it easy to group and analyze distributions across different percentile ranges.

• String-based extraction (avoids floating-point precision issues):

  • Convert the price to a string and split on the decimal point: parts = str(close_price).split('.')
  • If there's no decimal point, the decimal part is 0.0. Otherwise, take the substring after the dot and convert it back to a float (e.g., float('.' + parts[1])).

2. 筛选小数部分在0.001-0.009区间的记录并分析成交量 #

This is the trickier part because floating-point precision can trip you up. Here are two robust methods to identify those $12.001-style prices, followed by how to analyze their corresponding volumes:

Method 1: Use Decimal type for precise comparisons #

Floating-point numbers can have hidden precision errors (e.g., 12.001 might be stored as 12.000999999999998). Using Python's Decimal type eliminates this:

from decimal import Decimal
import pandas as pd

# Assume your data is in a DataFrame with columns 'close' and 'volume'
df = pd.read_csv('your_trading_data.csv')

def matches_target_range(price):
    # Convert to Decimal to avoid precision issues
    price_dec = Decimal(str(price))
    decimal_part = price_dec % Decimal('1')
    # Check if decimal part is between 0.001 and 0.009 inclusive
    return Decimal('0.001') <= decimal_part <= Decimal('0.009')

# Filter the target records
target_records = df[df['close'].apply(matches_target_range)]

Method 2: String parsing (直观且精准) #

Parsing the price as a string lets you directly inspect the decimal digits:

def matches_target_range_str(price):
    price_str = str(price)
    if '.' not in price_str:
        return False  # No decimal part, skip
    decimal_segment = price_str.split('.')[1]
    # Pad with zeros to ensure we have at least 3 digits to check
    padded_decimal = decimal_segment.ljust(3, '0')[:3]
    # Convert the first three decimal digits to an integer
    dec_int = int(padded_decimal)
    # Check if it's between 1 and 9 (corresponds to 0.001-0.009)
    return 1 <= dec_int <= 9

target_records_str = df[df['close'].apply(matches_target_range_str)]

Analyze the filtered volumes #

Once you have your target records, you can run standard volume analyses:

• Calculate key stats: target_records['volume'].agg(['mean', 'median', 'sum', 'std'])
• Compare volume distributions against non-target records (e.g., using histograms or boxplots)
• Look for trends over time (e.g., group by date and calculate daily average volume for target records)

内容的提问来源于stack exchange，提问作者Jose Mozqueda