为什么我的 python 测试循环没有完成？

Question

我正在尝试开发一个股票回测器。 顺序如下：

1. 遍历 pandas dataframe。
2. 当买入信号 == 1 时，从账户中扣除平仓费用。
3. 为每个 position 添加日志返回，直到...
4. 卖出信号 == 1
5. 从该观察中记入结算成本
6. 停止对日志返回求和
7. 重复步骤 1 - 6，直到数据框结束。

这是我使用的代码：

def back_test(df):
    """
    Parameters
    ----------
    df : dataframe
    starting_value : portfolio value
    pct_cash : percentage you want to remain in cash

    Returns
    -------
    None.

    """

    account_balance_strategy = 0
    

    return_strategy = 0
    
    df  = df.copy()
    df.dropna(inplace=True)
    
    condition = [(df['predicted_RSI_indicator'] > .94)]
    replace = [1]
    df['buy_open'] = np.select(condition,replace, default = 0)
    
    buy_open_signal = (df['buy_open'] == 1) & (df['buy_open'].shift(1) == 0)
    
    condition = [(df['predicted_RSI_indicator'] <= .94)]
    replace = [1]
    df['sell_close'] = np.select(condition,replace, default = 0)
    
    buy_open_signal = (df['buy_open'] == 1) & (df['buy_open'].shift(1)==0)
    sell_close_signal = (df['sell_close'] == 1) & (df['sell_close'].shift(1)==0)
                                              
    position = 0
    for ts, row in df.iterrows():
        print(position, ts)
        while position == 0:
            if (row['buy_open'] == 1) and (position == 0):
                account_balance_strategy -= row['adjusted_close']
                postion = 1
                return_strategy += row['log_return']
        while position == 1:
            print(position, ts)
            return_strategy += row['log_return']
            if (row['predicted_RSI_indicator'] < .95):
                account_balance += row['adjusted_close']
                position = 0
                
    print(return_strategy, account_balance_strategy)

此代码打印第一个时间帧索引和 position。 然后它运行直到我取消它。 所以某事没有在某处关闭。 有人看到我的循环错误吗？ 还是有比循环更好的方法来完成这些步骤？

更新/答案：

这是while循环导致迭代器永远不会超过第一行。 这是最终的工作。

# -*- coding: utf-8 -*-
"""
Created on Mon May 10 14:50:27 2021

@author: Jordan Howell
"""
import pandas as pd
import numpy as np




def back_test(df):
    """
    Parameters
    ----------
    df : dataframe
    starting_value : portfolio value
    pct_cash : percentage you want to remain in cash

    Returns
    -------
    None.

    """
    account_balance_hold = 0
    account_balance_strategy = 0
    
    return_hold = 0
    return_strategy = 0
    
    df  = df.copy()
    df.dropna(inplace=True)
    
    condition = [(df['predicted_RSI_indicator'] > .94)]
    replace = [1]
    df['buy_open'] = np.select(condition,replace, default = 0)
    
    buy_open_signal = (df['buy_open'] == 1) & (df['buy_open'].shift(1) == 0)
    
    condition = [(df['predicted_RSI_indicator'] <= .94)]
    replace = [1]
    df['sell_close'] = np.select(condition,replace, default = 0)
    
    buy_open_signal = (df['buy_open'] == 1) & (df['buy_open'].shift(1)==0)
    sell_close_signal = (df['sell_close'] == 1) & (df['sell_close'].shift(1)==0)
                                               
    # condition =[(df['predicted_RSI_indicator'] > 17.85) & (df['predicted_RSI_indicator'] <36.7)]
    # replace =[1]
    # df['sell_open'] = np.select(condition,replace, default = 0)
    # sell_open_signal = (df['sell_open'] == 1) & (df['sell_open'].shift(1) == 0)
    
    
    position = 0
    for ts, row in df.iterrows():
        print(position, ts)
        position == 0
        if (row['buy_open'] != 1) and (position == 0):
            print(ts, row['buy_open'])
            
        elif (row['buy_open'] == 1) and (position == 0) :
            position = 1
            account_balance_strategy -= row['adjusted_close']
            print(position, ts, row['predicted_RSI_indicator'],
                  account_balance_hold)
            
            
        elif (position == 1):
            return_strategy += row['log_return']
            print(position, ts, row['predicted_RSI_indicator'],
                  return_strategy, account_balance_hold)
            
        elif (position == 1) & (row['predicted_RSI_indicator'] < .95):
            account_balance_strategy += row['adjusted_close']
            
            print(position, ts, row['predicted_RSI_indicator'],
                  return_strategy, account_balance_hold)
            break
    print(return_strategy, account_balance_strategy)