【发布时间】：2018-05-10 20:25:25
【问题描述】：
这是一篇长篇文章，但我查看了 StackOverflow 上创建函数以迭代 DataFrame 等的许多示例，但找不到任何适合我需要的东西。我也只使用了大约 2 个月的 python 和编码，所以如果有不清楚的地方，我很抱歉。
我有一个包含每日价格历史记录的数据框，我正在尝试基于此策略为买入信号创建回测：
我们首先寻找收盘价大于前一天和后一天的收盘价的一天。让我们称之为“基准日”。
为了发出我们的买入信号，我们等待收盘价回到“基准日”上方的那一天。我们现在有一个空缺职位。
我们持有该头寸，直到我们收到与我们所寻找的买入信号相反的卖出信号。 （即收盘价低于前一天和后一天较高的前一天）
我只希望一次激活一次买入，直到我们收到卖出信号，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，当我收到卖出信号时显示结果，以便我以后可以将结果转换为元组并迭代以添加交易成本等，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']]
我看到相同 open_pos_date 的多个实例具有不同的 close_pos_date 值。在此过程中，我允许多个空缺职位工作。
我想将我的第一个买入信号作为我唯一的头寸，忽略所有其他买入信号，直到我收到卖出信号。那时我想寻找一个新的买入信号并持有那个头寸直到我得到一个新的卖出。
我创建的列可能比需要的多得多，但我很难找到一种方法来获得一个独特的信号来建立头寸，然后将价格与我收到卖出信号时的价格进行比较。如果有人可以推荐一种更清洁的方法来做到这一点，我很乐意放弃我的第一次尝试并试一试。
【问题讨论】：
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请将此解释和代码简化为minimal reproducible example，乍一看非常难以理解您的问题。
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恭喜您提出第一个问题。欢迎来到 StackOverflow！ stackoverflow.blog/2018/04/26/…
标签： python pandas dataframe trading