I am trying to create a slow stochastic model from scratch using the pipeline for learning purposes (I understand that talib offers a library function for such a computation), however I am running into the following error at line 48 (labeled below) --> TypeError: 'lazyval' object is not iterable. Below is the algorithm I have constructed thus far.

Right now, my intention is to plot both K and D using the record() function to ensure that I am computing the values properly. If anyone could provide some insight, I would really appreciate it, thanks!

from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline, CustomFactor  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.factors import SimpleMovingAverage  
from quantopian.pipeline.filters import StaticSids  
import numpy as np

class K(CustomFactor):  
    window_safe = True  
    inputs = [USEquityPricing.high, USEquityPricing.low, USEquityPricing.close]  
    def compute(self, today, asset_ids, out, high, low, close):  
        h14 = np.max(high, axis=0)  
        l14 = np.min(low, axis=0)  
        c = close[-1]  
        out[:] = 100*(c - l14)/(h14 - l14)

def initialize(context):  
    context.stock = sid(19920)  
    set_benchmark(context.stock) 

    # Only trade longs, not shorts  
    set_long_only()

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

def make_pipeline(context):  
    in_security_list = StaticSids([context.stock])

    # the current market rate for the currency pair  
    k = K(window_length=14)

    # 3-period moving average of k  
    d = SimpleMovingAverage(inputs=[K], window_length=3)

    pipe = Pipeline(  
        screen = (in_security_list),  
        columns = {  
            'K': k,  
            'D': d,  
        }  
    )  
    return pipe

def before_trading_start(context, data):  
    # Pipeline output after applying the screen  
    context.output = pipeline_output('my_pipeline')

    # These are the securities that we are interested in trading each day.  
    context.security_list = context.output.index # LINE 48

    k_val = context.output.get_value(context.stock, 'K')  
    d_val = context.output.get_value(context.stock, 'D')

    # plot stochastic indicator  
    record(K=k_val,D=d_val,mid=50,top=80,btm=20)

def handle_data(context, data):  
    pass