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def backtest(parameters, price1,init_capital, max_capital_deploy, buy_margin, sell_margin):
    longwindow = int(parameters[0])
    shortwindow = int(parameters[1])
    if (longwindow < shortwindow) or (shortwindow < 1) or (init_capital <= 0) or (max_capital_deploy <=0) or (buy_margin < 0) or (sell_margin < 0)  :
        return 0
    short_moving_avg = data.ewm(span=5).mean()
    long_moving_avg =  data.ewm(span=60).mean()
    
    #simulate the trading 
    capital=init_capital
    qty1 = 0
    p1 = 0
    pos = 0
    margin_blocked = 0
    trade_pnl = []
    mtm_pl = []
    for i in range(len(data)):
        if capital <= 0:
            break
        if pos == 0:  
            if short_moving_avg [i] > long_moving_avg[i]:
                pos=-1
                p1 = price1[i]
                margin_blocked = capital * max_capital_deploy
                qty1 = -(margin_blocked / 2) // (p1 * sell_margin)
                if -qty1 < 1:
                    break
            elif short_moving_avg [i] < long_moving_avg[i]:
                pos = 1
                p1 = price1[i]
                margin_blocked = capital * max_capital_deploy
                qty1 = (margin_blocked / 2) // (p1 * buy_margin)
                if qty1 < 1:
                    break
            elif pos < 0: 
                if short_moving_avg [i] > long_moving_avg[i]:
                    pnl = qty1 * (price1[i] - p1) 
                if not fixedcapital:
                    capital += pnl
                
                trade_pnl = np.append(trade_pnl, pnl)
                margin_blocked = 0
                qty1 = 0
                pos = 0
        else:
                pnl = qty1 * (price1[i] - p1)
                mtm_pl = np.append(mtm_pl, pnl)

        elif pos > 0: 
             if short_moving_avg [i] < long_moving_avg[i]:
                pnl = qty1 * (price1[i] - p1) 
                trade_pnl = np.append(trade_pnl, pnl)
                margin_blocked = 0
                qty1 = 0
                pos = 0
        else:
                pnl = qty1 * (price1[i] - p1)
                mtm_pl = np.append(mtm_pl, pnl)
            else: 
                
                if  short_moving_avg [i] > long_moving_avg[i]:
                    pnl = qty1 * (price1[i] - p1)
                trade_pnl = np.append(trade_pnl, pnl)
                margin_blocked = 0
                qty1 = 0
                pos = 0
            return capital, trade_pnl, mtm_pl

I m getting error in indentation but I am not sure this is indentation error or code error can you please help to correct the error and get the trade pnl mtm pnl capital...I want to pass it to the backtest function

Please, always post the full traceback you get in error tags.
There is inconsistent indentation - e.g. lines 46-47, 57-58.
Also there are huge problems with your nested if/elif/else blocks

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

data = pd.read_csv('aapl.csv')
data1 = data[['Date']]

data1 = data1.set_index('Date')

data = data[["Adj Close"]]
data.head()
data.columns = ["price"]
data.head()


x = [i for i in range(len(data))]
n = len(data)

short_period = 5
long_period = 10

for i in range(n):
    short_moving_avg = data.ewm(span=5).mean()
    long_moving_avg = data.ewm(span=60).mean()

initial part of code is this

---

how can I add whole trace book can you sugest

---

Error:
File "<ipython-input-1-2e5b5ffb66bd>", line 75
    elif pos > 0:
       ^
SyntaxError: invalid syntax

pos=[]
for i in range(len(data)):
       
        if pos == 0:  
            if data[:"short_moving_avg"]  > data["long_moving_avg"]:
                pos=-1
                
        elif data["short_moving_avg"]  < data["long_moving_avg"]:
                pos = 1
               
        elif pos < 0: 
                if data["short_moving_avg"]  < data["long_moving_avg"]:
                    
                      pos = 0     
        elif pos > 0: 
             if data["short_moving_avg"]  > data["long_moving_avg"]:
                pos = 0    
                    

Error:
ValueError                                Traceback (most recent call last)
<ipython-input-29-467a46c71805> in <module>()
      6                 pos=-1
      7 
----> 8         elif data["short_moving_avg"]  < data["long_moving_avg"]:
      9                 pos = 1
     10 

~\Anaconda3\lib\site-packages\pandas\core\generic.py in __nonzero__(self)
   1571         raise ValueError("The truth value of a {0} is ambiguous. "
   1572                          "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
-> 1573                          .format(self.__class__.__name__))
   1574 
   1575     __bool__ = __nonzero__

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

I don't no what does this error mean..how I solve this error

def backtest(inCapital):
    ohp = 0
    ocp = 0
    hp = 0
    cp = 0
    capital = 1000
    rCapital = 0
    pnl = 0
    pa = 0
    
    columns = ['hp','cp', 'adj.close','pnl','capital']
    df = pd.DataFrame(index= data.set_index(pd.to_datetime(data['Date']), inplace=True), columns=columns)
    
for i in range(len(data)):
    if data['short_moving_emavg'][i] > data['long_moving_emavg'][i]:
        if  hp==0:
            hp=1
            cp=1
            pa = data['Adj Close'][i]
            rcapital=cap+pnl
            pnl=0   
        pdb_set_trace()
                  
        if (hp==1):
            hp=-1
            cp=-1
            pa = data['Adj Close'][i]
            pnl = (data['Adj Close'][i] - pa)
        
              
    elif data['short_moving_emavg'][i] < data['long_moving_emavg'][i]:
        if (hp==0):
            hp=-1
            cp=-1
            pa = data['Adj Close'][i]
            rcapital=cap+pnl
            pnl=0
        if (hp==-1):
            hp=1
            cp=-1
            pa = data['Adj Close'][i]
            rcapital=cap+pnl
            pnl=0
                               

Error:
[error]
NameError                                 Traceback (most recent call last)
<ipython-input-5-4d163fc15f0e> in <module>()
     14 for i in range(len(data)):
     15     if data['short_moving_emavg'][i] > data['long_moving_emavg'][i]:
---> 16         if  hp==0:
     17             hp=1
     18             cp=1

NameError: name 'hp' is not defined

[/error]