All -

Pardon my 'nube'-ness for coding. I am having trouble implementing a multiple algo strategy. Trying to utilize MA and RSI to implement a long/short strategy. I'm not a great coder...but know what I want it to do. Any help would be immensely appreciated..I know this code is probably pretty sloppy. My apologies. Thanks all.

Goal is to initialize a long position when price, OR 50MA, crosses above 100MA AND RSI > 50, and to sell when price, OR 50MA, crosses below 100MA and RSI<50

Put any initialization logic here. The context object will be passed to

the other methods in your algorithm.

import pandas as pd
import numpy
import pytz
import datetime
import talib
import math

def initialize(context):
# Algorithm will call myfunc every day 15 minutes after the market opens
schedule_function(
myfunc,
date_rules.every_day(),
time_rules.market_open(minutes=1)
)

context.stock = sid(26578)

def myfunc(context,data):
pass

def handle_data(context, data):

stock = context.stock  


#current and historic data from Google  
stock_data = data[stock]  

#moving average price data  
mavg_short_buy = stock_data.mavg(50)  
mavg_long_buy = stock_data.mavg(200)  
mavg_short_sell = stock_data.mavg(1)  
mavg_long_sell = stock_data.mavg(3)  

# current price of the stock  
current_price = stock_data.price  
# how much cash in portfolio  
cash = context.portfolio.cash


stock = context.stock  


#RSI  
#variables  
lookback = 200  
lookback2 = 200  #period to lookback  
charttype = '1d' #day or minute chart  
percent_baseline = .5#RSI retracement amount  
low = 30#RSI low value  
RSI_timeperiod = 3  
divergence_strength = 0  


prices_close = history(lookback, charttype, 'close_price')  

prices = history(lookback2,charttype, 'close_price')[context.stock]


prices_close_day = history(10, '1d', 'close_price')  
prices_close_day = list(prices_close_day.values.flatten())  
prices_close_day = prices_close_day[-2]  

rsi = talib.RSI(prices, timeperiod=RSI_timeperiod)  

#rsi_prices = create_rsi_price_array(rsi,prices_close)  





num_shares = math.floor(context.max_notional / data[context.stock].close_price)  


# This runs through each stock.  
for stock in context.stock:  
   if mavg_short_buy > mavg_long_buy and cash > current_price:  

    # how many shares we can buy  
    number_of_shares = int(cash/current_price)  
    # int() helps us get a integer value, we can't buy half a share  

    # place the buy order  
    order(stock, number_of_shares)  

   elif mavg_short_sell < mavg_long_sell:  
    #how many shares we have  
    number_of_shares = context.portfolio.positions[stock.sid].amount  

    #sell all shares  
    order(stock, -number_of_shares)  



# This runs through each stock again.  
for stock in context.stock:  

   if data[context.stock].price < context.stop_price:  
        order_target(context.stock, 0)  
        context.stop_price = 0  

        currentshares = context.portfolio.positions[context.stock].amount  

        divergence = bullish_divergence(rsi_prices,percent_baseline,low)


   if divergence != None and divergence[1] > 1 and currentshares == 0:  
     troughs = divergence[0]  
     trough_diff = troughs[1] - troughs[0]#used for calculate strength of divergence  

   if trough_diff > divergence_strength:  
        order_target_percent(context.stock, 1.0)  
        print 'buying '+str(num_shares)


   record(price=data[context.stock].price, stop=context.stop_price)