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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);

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}

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

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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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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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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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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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class NetCurrentAssetValue(QCAlgorithm):
 
    def Initialize(self):
        #rebalancing should occur in July
        self.SetStartDate(2007,5,15)  #Set Start Date
        self.SetEndDate(2018,7,15)    #Set End Date
        self.SetCash(1000000)           #Set Strategy Cash
        self.UniverseSettings.Resolution = Resolution.Daily
        self.previous_fine = None
        self.filtered_fine = None

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def CalculateAccruals(self, current, previous):
    accruals = []
    for stock_data in current:
        try:
            prev_data = None
            for x in previous:
                if x.Symbol == stock_data.Symbol:
                    prev_data = x
                    break
            

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def CoarseSelectionFunction(self, coarse):
        if self.yearly_rebalance:
            self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)
                                                            and (x.Market == "usa")]
            return self.filtered_coarse
        else: 
            return []      
    
def FineSelectionFunction(self, fine):
    if self.yearly_rebalance:

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model = RandomForestRegressor(n_jobs=-1, random_state=42, verbose=2)
 
grid = {'n_estimators': [10, 13, 18, 25, 33, 45, 60, 81, 110, 148, 200],
        'max_features': [0.05, 0.07, 0.09, 0.11, 0.13, 0.15, 0.17, 0.19, 0.21, 0.23, 0.25],
        'min_samples_split': [2, 3, 5, 8, 13, 20, 32, 50, 80, 126, 200]}
 
rf_gridsearch = GridSearchCV(estimator=model, param_grid=grid, n_jobs=4, 
        cv=cv, verbose=2, return_train_score=True)
 
rf_gridsearch.fit(X1, y1)