18182324

# Execute this cell to be sure to have a compatible TF (2.0) and TFP (0.8) version. 
# If you are bold you can skip this cell. 
try: #If running in colab 
  import google.colab
  !pip install tensorflow==2.0.0
  !pip install tensorflow_probability==0.8.0
except:
  print('Not running in colab')

try: #If running in colab 

18182324

#Solution for model assumption. Different number of dollar signs. 
from scipy.stats import binom
#Define the considered numbers of dollar signs on the die (zero to six):
ndollar = np.asarray(np.linspace(0,6,7), dtype='int')
#Calculate corresponding probability of 2 $-signs in 10 throws
pdollar = binom.pmf(k=2, n=10, p=ndollar/6)
plt.stem(ndollar, pdollar)
plt.xlabel('Number of dollar signs on the dice')
plt.ylabel('Probability observing 2 dollar signs in 10 throws')
plt.title('Unnormalized likelihoods')

18182324

try: #If running in colab 
    import google.colab
    IN_COLAB = True
    %tensorflow_version 2.x
except:
    IN_COLAB = False
 
import tensorflow as tf
if (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed
    print('Please install tensorflow 2.0 to run this notebook')

18182324

try: #If running in colab 
    import google.colab
    IN_COLAB = True
    %tensorflow_version 2.x
except:
    IN_COLAB = False
 
import tensorflow as tf
if (not tf.__version__.startswith('2')): #Checking if tf 2.0 is installed
    print('Please install tensorflow 2.0 to run this notebook')

18182324

i=0 
fitness = 0 
counter = 0 
while i < max_iter: 
   if counter > n_repeats: 
      STOP 
   Y = [ ]
   fit = [ ] 
   pairs = randompairsfrom (X) 
   for j in pairs: 

18182324

import math
 
increment = 0.1
startingPoint = [1, 1]
point1 = [1,5]
point2 = [6,4]
point3 = [5,2]
point4 = [2,1]
 
def distance(x1, y1, x2, y2):

18182324

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)

18182324

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:

18182324

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
            

18182324

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