import matplotlib
matplotlib.use("TkAgg")
import datetime as dt
import pandas
import numpy as np
import os
import matplotlib.pyplot as plt
import order_execution
import time as time
import risk_budget
import utils
import AllocationDaily

#################################################### DATA LOAD #######################
#set time zone, otherwise dates are screwed
os.putenv("TZ","America/New_York")

#LOCAL TEST

path=""
#load data
cp=pandas.load("adj_close_px_all.bintest")
'''
############################################## Symbol set up ######################################

#market column (later on, can add 'Market' column and use it if SPY is not working)
market='SPY'

#exclude market column and those for which there is no sentiment data
#excluded_tickers=[market,'EEM','EWJ','FAS','FAZ','GLD','IWM','NOK','QQQ','RIMM','SLV','TLT','UNG','USO','XLE','XLF','LVLT','SIRI']
excluded_tickers=[market,'EEM','EWJ','FAS','FAZ','GLD','IWM','QQQ','SLV','TLT','UNG','USO','XLE','XLF','LVLT','SIRI']
tickers=cp.columns
for symbol in excluded_tickers:
    tickers=tickers[tickers!=symbol]

'''
tickers=cp.columns     
############################################### Day count set up   ###################################

#get all the start of days for which you have data skip the first day as it used to generate initial parameters
day_length=389 #389 minutes in a day
days_range=[]
for day in np.arange(389,len(cp.index),day_length):
    days_range.append(cp.index[day])

trade_freq=10 #minutes
risk=AllocationDaily.make(tickers, 20, 25, trade_freq, 1000)

tickers=cp.columns    
#go day by day 
for day in days_range: #allow at least b_window days to calculate betas
    #go minute by minute
    for trade_min in pandas.DateRange(dt.datetime.combine(day.date(),dt.time(10,0)),dt.datetime.combine(day.date(),dt.time(15,58)),offset=pandas.datetools.Minute()*trade_freq):
        returns=cp.ix[trade_min,tickers]/cp.ix[trade_min-pandas.DateOffset(minutes=trade_freq),tickers]-1
        
        #returns[np.random.randint(0,4)]=0
        #returns[np.random.randint(0,4)]=0
        #returns['SPY']=0
        shares_to_trade=risk.get_allocation_sharpe(returns)