3catz

oversampler = MulticlassOversampling(sv.TRIM_SMOTE(proportion = 0.1))
warnings.filterwarnings("ignore")
Scores1 = []
cmatrices1 = []
cmatrices2 = []
Scores2 = [] 
for i in range(50):
  print("Trial {}".format(i))
  print("-----------------------------")
  scores1 = [] 

3catz

from bayes_opt import BayesianOptimization

def RF_opt(n_estimators, max_depth): 
    
  global rskf 
  reg = RandomForestClassifier(verbose = 0,
                          n_estimators = int(n_estimators),
                          #min_samples_split = int(min_samples_split),
                          #min_samples_leaf = int(min_samples_leaf),
                          max_depth = int(max_depth),

3catz

from catboost import Pool, cv, CatBoostClassifier
from bayes_opt import BayesianOptimization
from sklearn.model_selection import * 
from sklearn.metrics import *


def CB_opt(n_estimators, depth, learning_rate, max_bin,
             subsample, num_leaves, l2_leaf_reg, model_size_reg): 
  scores = []
  skf = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 1944)

3catz

D = reconstruct(s, dim = 22 * 2 + 5, tau = 1)
Xs = []
Ys = [] 
for choice in np.repeat("random",3):
  X = D[:,:22 * 2] ; Y = D[:,-5:]

  if choice == 'random':
    import random
    y = []
    for i in range(len(Y)):

3catz

trainstock = yf.Ticker("SPY")
start = "2009-01-01"
end = "2016-01-01"

st = trainstock.history(start = start,end = end)
st = st[["Close","Open","Volume","High","Low"]]

D = reconstruct(st["Close"].values, dim = 45, tau = 1)
win = D[:,:-1] ; s = D[:,-1]
std = np.std(win, axis = -1)

3catz

3catz

#!/usr/bin/env python
# encoding: utf-8

import tweepy #https://github.com/tweepy/tweepy
import csv

#Twitter API credentials
consumer_key = ""
consumer_secret = ""
access_key = ""