Hello Haydn,

I also want to know about the VWAP but for a hour and day time points, do you know how to do this?

Justin

Hi Justin,

I have been using the data.history object in order to return my results then filter on todays date ("today" being the day of the backtest). This means every minute this handle_data function is called the *hist variable gets bigger by each minute of data collected, this will probably work the same for hours in your case.

Then once I have this I use the vwap calculation below in order to work out the price.

    # Get only todays pricing data.  
    today = get_datetime()  
                                                  # 60mins * 6.5hrs market open = ~400  
    hist = data.history(context.security_list, ['price', 'high', 'low', 'volume'], 400, '1m')  
    todays_data = hist.filter(like=str(today.date()), axis=1)  
    p = todays_data.get('price')  
    v = todays_data.get('volume')  
    h = todays_data.get('high')  
    l = todays_data.get('low')

    vwap = (v * (h+l)/2 ).cumsum() / v.cumsum()  

I would be interested to know your thoughts?

Thanks, Haydn

Hi Haydn,

I can't figure out how to make your vwap go in my code...

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 SimpleMovingAverage  
from quantopian.pipeline.filters.morningstar import Q1500US  
from quantopian.pipeline.factors import VWAP

def initialize(context):  
    # Schedule our rebalance function to run at the start of each week.  
    schedule_function(my_rebalance, date_rules.week_start(), time_rules.market_open(hours=1))

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

    # Create our pipeline and attach it to our algorithm.  
    my_pipe = make_pipeline()  
    attach_pipeline(my_pipe, 'my_pipeline')

def make_pipeline():  
    """  
    Create our pipeline.  
    """

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

    # 10-day close price average.  
    mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10, mask=base_universe)

    # 30-day close price average.  
    mean_30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30, mask=base_universe)

    percent_difference = (mean_10 - mean_30) / mean_30  
   # Filter to select securities to long.  
    longs = percent_difference.bottom(25)

    # Filter for all securities that we want to trade.  
    securities_to_trade = (longs)

    return Pipeline(  
        columns={  
            'longs': longs,  
           },  
        screen=(securities_to_trade),  
    )

def my_compute_weights(context):  
    """  
    Compute ordering weights.  
    """  
    # Compute even target weights for our long positions and short positions.  
    long_weight = 0.5 / len(context.longs)  
    return long_weight

def before_trading_start(context, data):  
    # Gets our pipeline output every day.  
    context.output = pipeline_output('my_pipeline')

    # Go long in securities for which the 'longs' value is True.  
    context.longs = context.output[context.output['longs']].index.tolist()

    context.long_weight = my_compute_weights(context)  
def handle_data(data, context):

    # Get only todays pricing data.  
    today = get_datetime()  
                                                  # 60mins * 6.5hrs market open = ~400  
hist = data.history(['price', 'high', 'low', 'volume'], 400, '1m')  
todays_data = hist.filter(like=str(today.date()), axis=1)  
p = todays_data.get('price')  
v = todays_data.get('volume')  
h = todays_data.get('high')  
l = todays_data.get('low')

vwap = (v * (h+l)/2 ).cumsum() / v.cumsum()  


def my_rebalance(context, data):  
    """  
    Rebalance weekly.  
    """  
    for security in context.portfolio.positions:  
        if security not in context.longs and security and data.can_trade(security):  
            order_target_percent(security, 0)

    for security in context.longs:  
        if data.can_trade(security):  
            order_target_percent(security, context.long_weight)  


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

Thanks for your help...
Justin

Justin, having a quick look at your code:

1. You're passing context and data to handle_data in the wrong order ( "def handle_data(context, data):" )
   
2. In data.history the first argument should be a security from either your pipeline or maybe a value stored in context.variable_name and the list of field names second.

I recommend having a read of the API docs if this is your problem.

Thanks, Haydn

FYI - This can also be done with Pipeline

from quantopian.pipeline.factors import VWAP

def make_pipeline():  
    vwap = VWAP(inputs=[USEquityPricing.close, USEquityPricing.volume], window_length=10)  
    return Pipeline(  
        columns={  
            'VWAP': vwap,  
        }  
    )

my_pipe = make_pipeline()  
result = run_pipeline(my_pipe, '2015-05-05', '2015-05-05')  
result.head()  

Put this into a notebook and see what you get out.