Update: 新增指定量分钟价格计算

Specified_Price.py

@@ -0,0 +1,33 @@
+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']]
+    
+    

__init__.py

@@ -2,4 +2,5 @@ from account import Account
 from trader import Trader
 from spread_backtest import Spread_Backtest
 
-__all__ = ['Account', 'Trader', 'Spread_Backtest']
+
+__all__ = ['Account', 'Trader', 'Spread_Backtest', 'Specified_Price']