我试图找出如何以动态的方式使用np.where，我选择一些预定义的值，将它们传递给一个函数，让它们创建条件。理想情况下，我希望用几个逻辑运算符创建长条件。
在下面的代码中，我愚蠢地尝试使用字符串：
cond_arr[1]['cond']作为一个逻辑运算符，来说明我想要的，因为我不知道如何进行这个？
有没有一个优雅的（或只是工作）的方式来创造这些动态的条件？

import numpy as np
import pandas as pd
import random
from datetime import datetime, timedelta

data = {
    'open': [random.uniform(50, 100) for _ in range(30)],
    'high': [random.uniform(100, 150) for _ in range(30)],
    'low': [random.uniform(25, 50) for _ in range(30)],
    'close': [random.uniform(50, 100) for _ in range(30)],
    'volume': [random.randint(1000, 10000) for _ in range(30)],
    'datetime': [datetime(2023, 10, 1, 0, 0) + timedelta(hours=i) for i in range(30)]
}

df = pd.DataFrame(data)

# The meat and potatoes

indicators = [{"ind": "open"},{"ind": "rsi"}, {"ind": "macd"}]
conds = [{"cond": "<"}, {"cond": ">"}, {"cond": "=="}, {"cond": "and"}, {"cond": "or"}]
values = [{"val": 10}, {"val": 100}, {"val": 15}, {"val": 17}, {"val": 18}, {"val": 7}]

def create_condition(cond_array):
    print(f'{cond_array[0]["ind"]}')  # Use double curly braces to escape
    
    #df["signal"] = np.where(df["open"] > 10, 1, -1) <-- what i want to do below
    df["signal"] = np.where(df[f'{cond_array[0]["ind"]}'] cond_arr[1]['cond']  df[f'{cond_array[2 ["val"]}'], 1, -1)

selected_conds = [indicators[0],conds[0],values[0]]

create_condition(selected_conds)

字符串