"""
This is a template algorithm on Quantopian for you to adapt and fill in.
"""
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, SimpleMovingAverage, RSI, BollingerBands, MovingAverageConvergenceDivergenceSignal
from quantopian.pipeline.filters.morningstar import Q500US, Q1500US

import talib
import numpy  as np
import pandas as pd

def initialize(context):
    """
    Called once at the start of the algorithm.
    """
    context.toTradeLong  = []
    context.toTradeShort = []
    # Rebalance every day, 1 hour after market open.
    schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(minutes=10))

    # Record tracking variables at the end of each day.
    #schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())

    # Create our dynamic stock selector.
    attach_pipeline(make_pipeline(), 'my_pipeline')

def make_pipeline():
    """
    A function to create our dynamic stock selector (pipeline). Documentation on
    pipeline can be found here: https://www.quantopian.com/help#pipeline-title
    """

    # Base universe set to the Q500US
    base_universe = Q1500US()

    # Factor of yesterday's close price.
    lower, middle, upper = BollingerBands(mask=base_universe, window_length=22, k=3)

    bbScreen = USEquityPricing.close.latest > upper or USEquityPricing.close.latest < lower

    upperFilter = (USEquityPricing.close.latest > upper)

    pipe = Pipeline(
        screen = bbScreen,
        columns = {
            'upper': upper,
            'lower': lower,
            'upFilter': upperFilter
        }
    )
    return pipe

def before_trading_start(context, data):
    """
    Called every day before market open.
    """
    context.output = pipeline_output('my_pipeline')

    # These are the securities that we are interested in trading each day.
    context.security_list = context.output.index
    i = 0
    for index in context.security_list:
        if context.output[index]['upFilter'] == True:
            i = i+1

    print i

def my_assign_weights(context, data):
    """
    Assign weights to securities that we want to order.
    """
    pass

def my_rebalance(context,data):
    """
    Execute orders according to our schedule_function() timing.
    """
    pass

def my_record_vars(context, data):
    """
    Plot variables at the end of each day.
    """
    pass

def handle_data(context,data):
    """
    Called every minute.
    """
    pass