Hi All,

I'm having a bit of an issue but I'm unsure about why this error is occurring. Could anyone help me understand and fix this error? The error is within the rebalance method where I have written moving_average = linear[stock].

Thanks,

Rohit

import numpy as np
from scipy import stats
from quantopian.pipeline import Pipeline
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import AverageDollarVolume
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.filters.morningstar import IsPrimaryShare

def initialize(context):

# Set benchmark security  
set_benchmark(sid(8554))  

# Sechedule ordering function to order once a day at market open  
schedule_function(process_data,  
                  date_rules.every_day(),  
                  time_rules.market_open(hours = 0, minutes = 1))  

# Sechedule selling function to sell once a day at market open  
schedule_function(rebalance,  
                  date_rules.every_day(),  
                  time_rules.market_open(hours = 0, minutes = 1))  

# Record tracking variables at the end of each day  
schedule_function(plot,  
                  date_rules.every_day(),  
                  time_rules.market_close(hours = 0, minutes = 1))  

attach_pipeline(make_pipeline(context), 'tradable_securities')  

def make_pipeline(context):

# top 500 by marketcap  
top_500 = morningstar.valuation.market_cap.latest.top(500)  

# Not when-issued equities  
not_wi = ~morningstar.share_class_reference.symbol.latest.endswith('.WI')  

# Filter for primary share equities  
primary_share = IsPrimaryShare()  

# Equities listed as common stock  
common_stock = morningstar.share_class_reference.security_type.latest.eq('ST00000001')  

# Equities not trading OTC  
not_otc = ~morningstar.share_class_reference.exchange_id.latest.startswith('OTC')  

# Construct a 100-day average dollar volume factor and add it to the pipeline.  
dollar_volume = AverageDollarVolume(window_length=1)

#Create high dollar-volume filter to be the top 1% of stocks by dollar volume.  
high_dollar_volume = dollar_volume.percentile_between(99, 100)  

# Set the screen on the pipelines to filter out securities.  
#pipe.set_screen(high_dollar_volume)

tradable_securities = {  
    'top_500':top_500, # increase market cap and compare data  
    'not_wi':not_wi,  
    'primary_share':primary_share,  
    'common_stock':common_stock,  
    'not_otc':not_otc # put in OTC and compare data  
    }  

# Create, register and name a pipeline  
pipe = Pipeline(columns=tradable_securities, screen=high_dollar_volume)  

return pipe  

def before_trading_start(context, data):

# Pipeline_output returns the constructed dataframe  
output = pipeline_output('tradable_securities')  

# Assign variable to output  
context.my_securities = output.index

def plot(context, data):

"""  
This function is called at the end of each day and plots certain variables.  
"""

# Check number of long and short positions  
longs = shorts = 0  
for position in context.portfolio.positions.itervalues():  
    if position.amount > 0:  
        longs += 1  
    if position.amount < 0:  
        shorts += 1

# Record and plot the leverage of our portfolio over time as well as the  
# number of long and short positions. Even in minute mode, only the end-of-day  
# leverage is plotted.  
record(leverage = context.account.leverage, long_count=longs, short_count=shorts)  

def process_data(context, data):

"""  
Enter long position if price closes 2.5 standard deviations below 20 period moving average           and enter short position if price closes 2.5 standard deviations above 20 period moving average  
"""  

linear = get_linear(context, data)  

for stock in context.my_securities:  

    close = data.current(stock, "close")  
    stock_price = data.current(stock, "price")  
    moving_average = linear[stock]  
    stddev_history = data.history(stock, "price", 20, "1d")[:-1]  
    moving_dev = stddev_history.std()  
    upper_band = moving_average + 2.5*moving_dev  
    lower_band = moving_average - 2.5*moving_dev  

    if close < lower_band:  
        go_long(context, data, stock, stock_price)  
    if close > upper_band:  
        short(context, data, stock, stock_price)  

def go_long(context, data, stock, stock_price):

long_weight = 0.6 / len(context.my_securities)  

order_target_percent(stock, long_weight, style=StopOrder(stock_price - (stock_price * 0.10)))  

def short(context, data, stock, stock_price):

short_weight = -0.4 / len(context.my_securities)  

order_target_percent(stock, short_weight, style=StopOrder(stock_price + (stock_price * 0.10)))

def rebalance(context, data):

"""  
Sell positions when price is within 0.5 standard deviations of the mean  
"""  

linear = get_linear(context, data)  

for stock in context.portfolio.positions:  

    moving_average = linear[stock]  
    stddev_history = data.history(stock, "price", 20, "1d")[:-1]  
    moving_dev = stddev_history.std()  
    upper_band_one = moving_average + 0.5*moving_dev  
    lower_band_one = moving_average - 0.5*moving_dev  

    stock_price = data.current(stock, "price")  

    if stock_price >= lower_band_one and stock_price <= upper_band_one:  
        order_target_percent(stock, 0)

20 period moving average based on a linear regression curve

def get_linear(context, data):

days = [i for i in range(1,21)]  
stocks = {}  
for stock in context.my_securities:  
    linear = stats.linregress(days, data.history(stock, "price", 20, "1d"))[1]  
    stocks[stock] = linear  
return stocks