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from pandas import *
from datetime import *
import pdb as pdb

df = DataFrame.from_csv('aapl_1-2012_5min.csv')
dayCount=0
rangeHigh = -1
rangeLow = 9999
openDayRangeDict = {}
getRange = 1

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from sklearn import linear_model
import numpy as np
import pandas as pd
from scipy import stats
from math import floor
from datetime import timedelta


class PairsTradingAlgorithm(QCAlgorithm):
    

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import numpy as np
from scipy import stats
from statsmodels.distributions.empirical_distribution import ECDF
from scipy.stats import kendalltau, pearsonr, spearmanr
from scipy.optimize import minimize
from scipy.integrate import quad
import sys
from collections import deque

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INPUT:
MAXFLDUR(15), //Max Flag Duration
FLAGMIN(2.5), // Max Atr in lowest point in flag 
PX(23), //Max Pole Duration.
UPT1BARS(70), // Bars for Uptrend leading to flag
POLEMIN(5.5), //Min  ATR Height of the pole
LBF(50), // Min distance between flags
ATRmin(5),// Min volatility change 
K(1.2), //Profit Target constant
timeexit(100), //Time exit bars 

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}

variables:
	Event( false ),
	FuturePrice( 0 ),
	j( 0 ),
	CG( 0 ),
	Denom( 0 ) ;
arrays:
	PredictBin[100]( 0 );

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EasyLanguage Code To TesT The Predictability Of An Event
Vars:
	Event(false),
	FuturePrice(0),
	I(0),
	CG(0),
	Denom(0);

Arrays:
	PredictBin[100](0);