I am getting the following error on line 108, and am not sure why it is happening and what to fix. If anyone knows I would appreciate feedback!
Thank you!

import quantopian.algorithm as algo
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.filters.morningstar import Q1500US
from quantopian.algorithm import order_optimal_portfolio
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline.factors import SimpleMovingAverage, VWAP, RSI, AverageDollarVolume
import quantopian.optimize as opt
from datetime import datetime
from quantopian.pipeline.data.psychsignal import stocktwits
from quantopian.pipeline.experimental import risk_loading_pipeline

def initialize(context):

context.max_leverage = 1.0  
context.max_pos_size = 0.015  
context.max_turnover = 0.95  

algo.schedule_function(  
    rebalance,  
    algo.date_rules.week_start(), # Schedules frequency of rebalance  
    algo.time_rules.market_open(),# Hours offset  
)  

sentiment_score = SimpleMovingAverage(  
    inputs=[stocktwits.bull_minus_bear],  
    window_length=3,  

)  

algo.attach_pipeline(  
    risk_loading_pipeline(),  
    'risk'  
)  

"All stocks must be part of the Q500US Universe"  
base_universe = Q1500US()  

"Last minutes closing price"  
last_close_price = USEquityPricing.close.latest

"Ensures that price of stock is over $1"  
last_open_price = USEquityPricing.open.latest  
pricefilter = (last_open_price > 1) & (last_open_price < 50)  

"Calculates SMA5 for all Stocks in Q500US"  
sma_5 = SimpleMovingAverage(  
    inputs=[USEquityPricing.close],  
    window_length=5  
)  

"Makes sure that LAST MINUTES BAR CLOSED ABOVE sma5, and OPENS OVER sma5"  
sma5filter = (last_close_price > sma_5) & (last_open_price > sma_5)

"Calculates VWAP and Makes sure that VWAP is ABOVE CURRENT Price"  
vwap = VWAP(window_length=14)  
vwapfilter = (vwap > last_open_price)  

"Calculates RSI14 and Makes Sure that RSI is below 60"  
rsi = RSI(window_length=14)  
rsifilter = rsi < 60  

requirements = (rsifilter &  
                vwapfilter &  
                sma5filter &  
                base_universe &  
                pricefilter)  

log.info("{}: ...".format(get_datetime('US/Eastern').time()), ...)  



# Now create an instance of the pipeline and add the desired columns you want returned in the resulting output  
pipe=Pipeline()

pipe.add(requirements, 'Confirmation')  

pipe.add(sentiment_score, 'Sentiment')

attach_pipeline(pipe,'my_pipeline')  

pipe.set_screen(requirements & sentiment_score.notnull())  

def before_trading_start(context,data):
# Run the pipeline we previously defined. It will return the columns of data we requested.
context.output = algo.pipeline_output('my_pipeline')

context.risk_factor_betas = algo.pipeline_output(  
  'risk'  
)  

context.pipeline_data = algo.pipeline_output(  
    'my_pipeline'  
)  

print(context.output)

def rebalance(context, data):

log.info(context.pipeline_data.head(10))  

alpha = context.pipeline_data.sentiment_score

if not alpha.empty:  
  objective = opt.MaximizeAlpha(alpha)  

# Create position size constraint  
  constrain_pos_size = opt.PositionConcentration.with_equal_bounds(  
      -context.max_pos_size,  
      context.max_pos_size  
  )

  # Constrain target portfolio's leverage  
  max_leverage = opt.MaxGrossExposure(context.max_leverage)

  # Ensure long and short books  
  # are roughly the same size  
  dollar_neutral = opt.DollarNeutral()

  # Constrain portfolio turnover  
  max_turnover = opt.MaxTurnover(context.max_turnover)

  # Rebalance portfolio using objective  
  # and list of constraints  
  algo.order_optimal_portfolio(  
      objective=objective,  
      constraints=[  
          constrain_pos_size,  
          max_leverage,  
          dollar_neutral,  
          max_turnover,  
      ]  
  )