caiobvilar

# -*- coding: utf-8 -*-
"""
Created on Mon Dec  9 23:03:35 2019

@author: caiob
"""
import pandas as pd
import numpy as np
import io
from sklearn.model_selection import train_test_split

caiobvilar

# -*- coding: utf-8 -*-
"""
Created on Mon Dec  9 23:03:35 2019

@author: caiob
"""
import pandas as pd
import numpy as np
import io
from sklearn.model_selection import train_test_split

caiobvilar

xgb_pipeline = Pipeline([("st_scaler", StandardScaler()),("xgb_model",xgb.XGBRegressor())])

gbm_param_grid = {
                  'xgb_model__subsample': np.arange(.05, 1, 0.05),
                  'xgb_model__max_depth': np.arange(5,50,5),
                  'xgb_model__colsample_bytree': np.arange(.1,1.05,.05)
                  }

randomized_neg_mse = RandomizedSearchCV(estimator=xgb_pipeline, 
                      param_distributions=gbm_param_grid, n_iter=10,

caiobvilar

cv_params = {"objective":"reg:squarederror", "max_depth":4}
cv_results = xgb.cv(dtrain=DM_train, params=cv_params,nfold=10,num_boost_round=5, metrics="rmse",as_pandas=True,seed=42)

caiobvilar

X,y = df_hist.iloc[:,1:],df_hist.iloc[:,1]
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)
DM_train = xgb.DMatrix(data=X_train, label=y_train)
DM_test = xgb.DMatrix(data=X_test, label=y_test)
params = {"booster":"gblinear", "objective":"reg:linear"}
xg_reg = xgb.train(params=params, dtrain=DM_train, num_boost_round=10)
preds = xg_reg.predict(DM_test)
rmse = np.sqrt(mean_squared_error(y_test,preds))

caiobvilar

df_hist.to_csv('LMT_stock.csv')
df_hist[['Year','Month','Day']] = df_hist['Date'].str.split('-',3,expand=True)
df_hist = df_hist.drop('Date',axis=1)
df_hist = df_hist.apply(pd.to_numeric)

caiobvilar

wtd = WorldTradingData(token)
optional_params = {'output':'csv'}
LMT_history = wtd.history('LMT',optional_params)
df_hist = pd.read_csv(io.StringIO(LMT_history))
df_hist.info()

caiobvilar

import requests
from worldtradingdata import WorldTradingData
import pandas as pd
import io
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

caiobvilar

import requests
from worldtradingdata import WorldTradingData
import pandas as pd
import io
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

caiobvilar

import pandas as pd

income_data = pd.read_csv("income.csv")
income.head()