Could Somebody Help Me Please

Hi, I'm fairly new to quantopian and I'm experimenting with a mean reversion type algorithm. However, I'm seeing runtime errors and can't figure out what could be causing them. I'm trying to use pipeline to filter in those companies from the Q500US with common shares and EBITDA . If anyone could help me figure out where I'm going wrong and perhaps some provide any general pointers I would be very grateful. I've included my workings below.

import quantopian.algorithm as algo
from quantopian.pipeline import Pipeline
from quantopian.pipeline import CustomFactor
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.data import morningstar
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline.filters.morningstar import Q500US

Strategy

Prices that deviate from the mean eventually move back to the mean

Overpriced stocks will Fall

Underpriced stocks will rise

If 10d SMA is below 30d SMA = Price will go up/Open long position

If 10d SMA is above 30d SMA = Price will go down/Open short position

The bigger the difference between 10d and 30d SMA = The more likely stock will return to mean/Open bigger position

def initialize(context):

 ## Rebalance once a week, Begining of week, At market Open  
 ##...................................................................................  
 schedule_function(rebalance, date_rules.week_start(), time_rules.market_open(hours=1))  


 ## Record every day, At market close  
 ##..................................................................................  
 schedule_function(record_vars, date_rules.every_day(), time_rules.market_close(hours=1))  


 attach_pipeline(make_pipeline(), 'my_pipeline')

def make_pipeline():

# Create a pipeline object.  
pipe = Pipeline()  

## Get latest Revenue Value  
##..............................................................................  
ebitda = morningstar.income_statement.ebitda.latest  
pipe.add(ebitda, 'ebitda')  

## Get share class (If we only want primary shares)  
##..............................................................................  
share_class = morningstar.share_class_reference.is_primary_share.latest  

## Rank the companies based on (Revenue)  
ranked_ebitda = ebitda.rank(ascending=False)  
ranked_ebitda = ebitda > 1  
pipe.add(ranked_ebitda, 'ebitda_rank')  

## Rank the companies based on (primary shares)  
ranked_shares = share_class  
pipe.add(ranked_shares, 'primary_shares')  

## Create a sfilter to narrow down securities which are given back to pipe (shareclass=boolean)  
pipe = pipe.set_screen(Q500US & (ebitda > 1) & (share_class==True))  

return pipe

def before_trading_start(context, data):

## Pipeline_output returns a pandas DataFrame with the results of our factors  
## and filters.  
context.output = pipeline_output('my_pipeline')

# Keep a list reference and a set reference to all of our pipeline securities  
# (set has much faster lookup)  
context.security_list = context.output  
context.security_set = set(context.security_list)

Decide whether to take long or short position

Decide size of the position

def compute_weights(context, data):

## Get 30 day window of close prices  
hist = data.history(context.security_list, 'price', 30, '1d')  

## Store price history  
prices_10 = hist[-10:]  
prices_30 = hist[-30:]  

## Store Avg prices  
sma_10 = prices_10.mean()  
sma_30 = prices_30.mean()  

## Define relative difference between 10d and 30d SMA  
raw_weights = (sma_10 - sma_30) / sma_30  

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

return normalized_weights

Places Orders

def rebalance(context, data):

weights = compute_weights(context, data)  

for security in context.security_list:  
    if data.can_trade(security):  
        order_target_percent(security, weights[security])

Count how many Long and Short positions each day

def record_vars(context, data):

longs = shorts = 0  

positions = context.portfolio.positions  

long_count = 0  
short_count = 0  

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

## Record leverage, number of songs, number of shorts  
record(leverage = context.account.leverage, long_count=longs, short_count = shorts)

from quantopian.pipeline import factors, filters, classifiers, Pipeline, CustomFactor from quantopian.algorithm import attach_pipeline, pipeline_output from quantopian.pipeline.data import Fundamentals def initialize(context): schedule_function(rebalance, date_rules.week_start(), time_rules.market_open(hours = 1)) stock_set = filters.Q500US() ebitda = Fundamentals.ebitda.latest attach_pipeline(Pipeline(screen = (ebitda > 1.0) & stock_set), 'my_pipe') def rebalance(context, data): if get_open_orders(): return output = pipeline_output('my_pipe') security_list = output.index mavg_f = data.history(security_list, 'price', 10, '1d').mean() mavg_s = data.history(security_list, 'price', 30, '1d').mean() raw_weights = mavg_s / mavg_f - 1.0 wt = raw_weights / raw_weights.abs().sum() for sec in context.portfolio.positions: if data.can_trade(sec) and sec not in security_list: order_target(sec, 0) for sec in security_list: if data.can_trade(sec): order_target_percent(sec, wt[sec]) def before_trading_start(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(leverage = context.account.leverage, long_count = longs, short_count = shorts)