Hi Isaac,

Quantopian backtesting and live trading effectively run on a minute-period clock, so you won't be able to run an algorithm at the second timescale. Would your algo work running on minute bars?

Grant

Yes, in a sense where i would compare the signal value from the previous minute with the current signal.
Do you know the best method/library that would allow me to do this

You might consider using the 'history' method (see the help page) and then applying .pct_change() to the resulting Pandas DataFrame (see http://pandas.pydata.org/pandas-docs/stable)./computation.html).

def initialize(context):  
    context.stocks = [sid(8554),sid(33652)]

def handle_data(context, data):  
    price_change = history(390,'1m','price').pct_change()  
    print price_change.tail(3)  

Okay a couple points:
1. Im only checking signals for one stock and i am just comparing the signal for that one stock for the current minute to the previous minute.
2. Also, I'm not sure how to check for change in percentage.
Essentially, I am checking to see if there is a sudden signal change in a small time period and then buy or sell to take advantage of that volatility.
I tried something like this pretty quickly:
if price_change >= .10 and notional < (context.max_notional - context.target):
order_value(context.stock, context.target)
But im not sure if i did the right thing for the percentage.
Isaac

Isaac,

Here's an example. Is this what your are looking to do?

Grant

def initialize(context):  
    context.stock = sid(8554)  
    # for development, turn off commissions & slippage  
    set_commission(commission.PerTrade(cost=0.0))  
    set_slippage(slippage.FixedSlippage(spread=0.00))

def handle_data(context, data):  
    price_change = history(2,'1m','price').pct_change().iloc[-1]  
    if price_change > 0.001:  
        order_target_percent(context.stock,1)  
    elif price_change < -0.001:  
        order_target_percent(context.stock,0)  

Hi Grant,

I know the above code is old and API change but I am trying to get my head around why the following line of code is failing:
if price_change > 0.001:

with the following exception message ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

The price_change is of type float when I debug it

Thanks

@JohnSam - The price_changeas defined in line 10 is a pandas.Series of length 1. Replace it with float(price_change) in lines 12 and 14.