这是一个很长的阅读，但我查看了StackOverflow上创建函数以迭代DataFrames等的许多示例，并且无法找到符合我需求的任何内容。我一般只使用python和编码大约2个月，所以如果不清楚我会道歉。
我有一个包含每日价格历史记录的数据框，我正在尝试根据此策略创建买入信号的回测：
我们首先寻找收盘价 前一天和后一天的收盘价的日子。让我们称之为＆＃34;基准日。＆＃34;
为了发出我们的买入信号，我们等待收盘价回到基准日以上的一天。＆＃34;我们现在有一个空缺职位。
我们保持这个位置，直到我们得到一个卖出信号，这与我们的买入信号正在寻找的相反。 （即收盘价低于前一天和前一天的价格较高）
我只希望一次买入活动，直到我们得到卖出信号，然后流程重新开始。
下面是一个示例数据框，其中包含我正在查看的一小部分数据
import pandas as pd
data = {
'date': [1/3/2000,1/4/2000,1/5/2000,1/6/2000,1/7/2000,1/10/2000,1/11/2000,1/12/2000,1/13/2000,1/14/2000,1/18/2000,1/19/2000,1/20/2000,1/21/2000,1/24/2000,1/25/2000,1/26/2000,1/27/2000,1/28/2000,1/31/2000,2/1/2000,2/2/2000,2/3/2000,2/4/2000,2/7/2000,2/8/2000,2/9/2000,2/10/2000,2/11/2000,2/14/2000,2/15/2000,2/16/2000,2/17/2000,2/18/2000,2/22/2000,2/23/2000,2/24/2000,2/25/2000,2/28/2000,2/29/2000],
'close': [308.3,315.3,314.4,307.5,309.8,313.4,310.7,324.2,332.5,348.8,351.1,348.2,348.7,343.5,343,343.3,342.4,343,334.4,334.6,336,333.8,331.6,332.8,335.9,341.2,338.4,342.1,343.2,339.5,346.9,342,339.6,337.4,335,330.8,331.3,331.1,332.6,335.1]}
df = pd.DataFrame(data)
## Create columns to compare price to day before and day after
df['prev_close'] = df['close'].shift(1)
df['next_close'] = df['close'].shift(-1)
## BOOLEAN TO RETURN IF PRICE IS LOWER THAN PREVIOUS AND NEXT DAY
df['high_high'] = ((df['prev_close']) > df['close']) & ((df['next_close']) > df['close'])
## BOOLEAN TO RETURN TRUE IF PRICE IS GREATER THAN PREVIOUS AND NEXT DAY
df['low_low'] = ((df['prev_close']) < df['close']) & ((df['next_close']) < df['close'])
## RETURN PRICE OF MOST RECENT true IN low_low
df['comp_price'] = df['close'].where(df['low_low'] == True)
## FILL IN BLANKS WITH PREVIOUS VALUE TO KEEP COMPARISON PRICE ACTIVE
df['comp_price'].fillna(method='pad',inplace=True)
## CREATE SELL COMPARISON DATE TO REFERENCE WHEN CLOSING POSITION
df['sell_comp'] = df['close'].where(df['high_high'] == True)
df['sell_comp'].fillna(method='pad',inplace=True)
## CREATE BUY SIGNAL
df['buy_sig'] = df['close'] > df['comp_price']
## DESIGNATE FIRST INSTANCE OF BUY SIGNAL AS DAY TO OPEN POSITION
df['open_pos'] = (df['buy_sig'] == 1) & (df['buy_sig'].shift(1) != 1)
df['take_signal'] = (df['buy_sig'] == 1) & (df['open_pos'] == True)
df['open_pos_price'] = df['close'].where(df['take_signal'] == True)
df['open_pos_price'].fillna(method='pad',inplace=True)
## CREATE SELL SIGNAL
df['sell_sig'] = df['close'] < df['sell_comp']
## DESIGNATE FIRST INSTANCE OF SELL AS DAY TO CLOSE POSITION
df['close_pos'] = (df['sell_sig'] == True) & (df['sell_sig'].shift(1) == False)
## CREATE COLUMNS THAT ORGANIZE WHEN POSITION WAS OPENED
df['open_pos_date'] = df['date'].where((df['open_pos'] == True)&(df['take_signal'] == True))
df['open_pos_date'].fillna(method='pad',inplace=True)
## CREATE COLUMNS SHOW DATE AND PRICE OF CLOSING POSITION
df['close_pos_price'] = df['close'].where(df['close_pos'] == True)
df['close_pos_date'] = df['date'].where((df['close_pos'] == True))
## CALCULATE GAIN FOR TRADE
df['gain'] = (df['close_pos_price'] - df['open_pos_price']).where((df['close_pos_price'] > 0)& (df['open_pos_price'] > 0))
然后我创建了另一个数据框，当我获得卖出信号时显示结果，以便稍后我可以将结果转换为元组并迭代以增加交易成本等，以完成图表目的。
strat_df = df.loc[(df['close_pos'] == True)&(df['sell_sig'] == True), ['open_pos_date','open_pos_price', 'close_pos_date','close_pos_price','gain']]
我看到同一个open_pos_date的多个实例具有不同的close_pos_date值。在某个地方，我允许多个未结头寸工作。
我想把我的第一个买入信号作为我唯一的位置，忽略所有其他买入信号，直到我得到卖出信号。那时我想寻找一个新的买入信号并持有这个位置，直到我获得新的卖出。
我可能创造了比必要更多的列，但我找不到一种方法来获取一个独特的信号来获取一个位置，然后将价格与我获得卖出信号时的价格进行比较。如果有人可以推荐一种更干净的方式来做到这一点，我很乐意废弃我的第一次尝试并尝试一下。