import pandas as pd
import numpy as np


def amount_specified(
    min_data: pd.DataFrame,
    post_adj_factor: pd.DataFrame,
    min_amount: float=0.
):
    """
    计算指定金额下的平均后复权价格
    
    Args:
    min_data (pd.DataFrame):        指定日期分钟数据，需至少包含代码(stock_code)、分钟(Time)、价格(price)、成交量(vol)、成交金额(amount)列
    post_adj_factor (pd.DataFrame): 后复权因子数据，需至少包含股票、后复权因子
    min_amount (float):             指定最小金额下的平均价
    """
    
    # 按照指定最小量获取平均价格
    stock_amt = min_data.pivot_table(index='Time', columns='stock_code', values='amount').cumsum()
    stock_vol = min_data.pivot_table(index='Time', columns='stock_code', values='vol').cumsum()
    amount_price = stock_amt / stock_vol
    amount_price.iloc[1:] = amount_price.iloc[1:].where(stock_amt <= min_amount, np.nan)
    amount_price = amount_price.unstack().reset_index()
    amount_price.columns = ['stock_code', 'time', 'price']
    amount_price = amount_price.dropna(subset=['price']).drop_duplicates(subset='stock_code', keep='last')
    # 计算后复权价格
    amount_price = amount_price.merge(post_adj_factor, on=['stock_code'], how='left').dropna(subset=['factor'])
    amount_price['open_post'] = amount_price['price'] * amount_price['factor']

    return amount_price[['stock_code','open_post']]