Hi,

Using indicator in indicator is quite normal. I tried to build such a customer "indicator in indicator" built-in-factor but fialt. I got error as I run it:
" Runtime exception: AttributeError: type object 'MyTalibRSI' has no attribute 'rsi'
"

Below is my code

"""
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  
from quantopian.pipeline.filters.morningstar import Q1500US  
from quantopian.pipeline.factors import CustomFactor  
from collections import defaultdict  
from quantopian.pipeline.filters import StaticAssets

import numpy as np  
import pandas as pd  
import math  
import talib

class MyTalibRSI(CustomFactor):  
    inputs = (USEquityPricing.close,)  
    params = {'rsi_len' : 14,}  
    window_length = 60 # allow at least 4 times RSI len for proper smoothing  
    def compute(self, today, assets, out, close, rsi_len):  
        # TODO: figure out how this can be coded without a loop  
        rsi = []  
        for col in close.T:  
            try:  
                rsi.append(talib.RSI(col, timeperiod=rsi_len)[-1])  
            except:  
                rsi.append(np.nan)  
        out[:] = rsi

class MyDoubleRSI(CustomFactor):  
    inputs = (MyTalibRSI.rsi,)  
    outputs = ['rsi']  
    params = {'rsi_len' : 14,}  
    window_length = 60 # allow at least 4 times RSI len for proper smoothing  
    def compute(self, today, assets, out, rsi, rsi_len):  
        # TODO: figure out how this can be coded without a loop  
        rsi = []  
        for col in rsi.T:  
            try:  
                rsi.append(talib.RSI(col, timeperiod=rsi_len)[-1])  
            except:  
                rsi.append(np.nan)  
        out.rsi[:] = rsi  
def initialize(context):  
    """  
    Called once at the start of the algorithm.  
    """  
    context.stock = sid(8554) # SPY  
    # Create our dynamic stock selector.  
    attach_pipeline(make_pipeline(context), 'my_pipeline')  
def make_pipeline(context):  
    """  
    A function to create our dynamic stock selector (pipeline). Documentation on  
    pipeline can be found here: https://www.quantopian.com/help#pipeline-title  
    """  
    sec_mask = StaticAssets([context.stock])  
    my_mask = sec_mask  
    # Factor of yesterday's close price.  
    yesterday_close = USEquityPricing.close.latest

    rsi = MyTalibRSI(rsi_len=14, window_length = 80)  
    double_rsi = MyDoubleRSI(inputs=[MyTalibRSI.rsi], rsi_len=14, window_length = 80)  
    pipe = Pipeline(  
        screen = my_mask,  
        columns = {  
            'close': yesterday_close,  
            'rsi': rsi,  
            'double_rsi': double_rsi,  
        }  
    )  
    return pipe  
def before_trading_start(context, data):  
    """  
    Called every day before market open.  
    """  
    context.output = pipeline_output('my_pipeline').dropna()  
#    log.info("Length of DF:%d" %len(context.output))  
    log.info("Original DF:\n%s" %context.output)  
    # These are the securities that we are interested in trading each day.  
    context.security_list = context.output.index