Hey, so I am having trouble with finishing the, for loop in the algorithm. Also the log.info.
The basic premise of the script it to have an evaluated price for a company and if it goes above that it shorts the company, and if it goes below it longs.
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import AverageDollarVolume
from quantopian.pipeline.filters.morningstar import Q1500US
import pandas as pd
import numpy as np

security_list = [sid(5061), sid(24), sid(9883), sid(19725), sid(16649)]

def initialize(context):
set_commission(commission.PerShare(cost=0.000, min_trade_cost=0))
context.security_list = [sid(5061), sid(24), sid(9883), sid(19725), sid(16649)]
"""
msf = sid(5061)
AAPL = sid(24)
ATVI = sid(9883)
NVDA = sid(19725)
LOGI = sid(16649)
"""
schedule_function(rebalance,
date_rules.week_start(days_offset=0),
time_rules.market_open())
schedule_function(record_vars,
date_rules.every_day(),
time_rules.market_close())
def compute_weights(context, data):
"""
hist = data.history(sid(24), 'price', 10, '1d')

esta = context.security_list = [sid(5061), sid(24), sid(9883), sid(19725), sid(16649)]  
"""  

context.security_list[0] = '70'  
context.security_list[1] = '150'  
context.security_list[2] = '60'  
context.security_list[3] = '150'  
context.security_list[4] = '36'  

weights =  150 - data.current(sid(24), 'price')  



longp = weights > 0  

shortp = weights < 0  

'''  
normalized_weghts = raw_weights / raw_weights.abs().sum()  

short = data.current(context.security_list, 'price') > context.security_list  

longp = data.current('price').security_list < context.security_list  

short = normalized_weghts.index[normalized_weghts > context.security_list]  

longp = normalized_weghts.index[normalized_weghts < context.security_list]  
'''  
"""  
log.info("This week's longs: " + ", ".join([lon.symbol for lon in longp]))  
log.info("This week's shorts: " + ", ".join([short.symbol for short in shortp]))  

price_10 = hist[-10:]  
price_30 = hist  

sma_10 = price_10.mean()  
sma_30 = price_30.mean()  

raw_weights = (sma_30 - sma_10) / sma_30  

normalized_weights = raw_weights / raw_weights.abs().sum()  

short_secs = normalized_weights.index[normalized_weights < 0]  
long_secs = normalized_weights.index[normalized_weights > 0]

log.info("This week's longs: " + ", ".join([long_.symbol for long_ in long_secs]))  
log.info("This week's shorts: " + ", ".join([short_.symbol for short_ in short_secs]))  

return normalized_weights  
"""  
return longp , shortp

def rebalance(context, data):
compute_weights(context, data)
"""
This function is called according to our schedule_function settings and calls
order_target_percent() on every security in weights.
"""
"""
weights = compute_weights(context, data)

for security in context.security_list:  
    if data.can_trade(security.sid):  
        order_target_percent(security, weights[security])  
"""  
for security in context.security_list:  
    if data.can_trade(security): #and compute_weights(security, security_list.index) > 0:                    <_______________________ problem  
        order_target_percent(context.security_list.index, 0.05)  
        order_target_percent(context.shortp.sid, -0.05)  

""" for security in context.security_list:
if data.current('price').security_list < security.security_list and data.can_trade(security):
order_target_percent(security, 0.05)
elif data.current('price') > context.security_list and data.can_trade(security):
order_target_percent(security, -0.05)
"""
def record_vars(context, data):

longs = shorts = 0
for position in context.portfolio.positions.itervalues():
if position.amount > 0:
longs += 1
elif position.amount < 0:
shorts += 1

# Record our variables.
record(leverage=context.account.leverage, long_count=longs, short_count=shorts)