import copy as copy
import pylab
# Own graphical library
from graph_lib import gl
import graph_tsa as grtsa
# Data Structures Data
import CTimeData as CTD
# Import functions independent of DataStructure
import basicMathlib as bMA
import utilities_lib as ul
import KalmanFilter as KF
plt.close("all") # Close all previous Windows
######## SELECT DATASET, SYMBOLS AND PERIODS ########
dataSource = "GCI" # Hanseatic FxPro GCI Yahoo
[storage_folder, info_folder,
updates_folder] = ul.get_foldersData(source = dataSource)
#symbols = ["XAUUSD","Mad.ITX", "EURUSD"]
symbols = ["Alcoa_Inc"]
#symbols = ["Amazon"]
periods = [1440]
######## SELECT DATE LIMITS ###########
sdate_str = "01-01-2016"
edate_str = "2-1-2017"
sdate = dt.datetime.strptime(sdate_str, "%d-%m-%Y")
edate = dt.datetime.strptime(edate_str, "%d-%m-%Y")
######## CREATE THE OBJECT AND LOAD THE DATA ##########
# Tell which company and which period we want
timeData = CTD.CTimeData(symbols[0],periods[0])
timeData.set_csv(storage_folder) # Load the data into the model
timeData.set_interval(sdate,edate) # Set the interval period to be analysed
preprocess_data = 1 perform_EM = 1 perform_HMM_after_EM = 0 final_clusters_graph = 1 responsibility_graph = 1 ########################################################################## ################# DATA OBTAINING ###################################### ########################################################################## ######## SELECT SOURCE ######## dataSource = "Google" # Hanseatic FxPro GCI Yahoo [storage_folder, info_folder, updates_folder] = ul.get_foldersData(source = dataSource) folder_images = "../pics/Trapying/EM_trading/" ul.create_folder_if_needed(folder_images) ######## SELECT SYMBOLS AND PERIODS ######## symbols = ["XAUUSD","Mad.ITX", "EURUSD"] symbols = ["Alcoa_Inc"] symbols = ["GooG", "Alcoa_Inc"] periods = [5] # Create porfolio and load data cmplist = DBl.read_NASDAQ_companies(whole_path = "../storage/Google/companylist.csv") cmplist.sort_values(by = ['MarketCap'], ascending=[0],inplace = True) symbolIDs = cmplist.iloc[0:30]["Symbol"].tolist() symbol_ID_indx1 = 0
from graph_lib import gl
# For statistics. Requires statsmodels 5.0 or more
from statsmodels.formula.api import ols
# Analysis of Variance (ANOVA) on linear models
from statsmodels.stats.anova import anova_lm
import pandas as pd
from sklearn import linear_model
import basicMathlib as bMA
import indicators_lib as indl
plt.close("all")
######## SELECT DATASET, SYMBOLS AND PERIODS ########
# Hanseatic FxPro GCI Yahoo
[storage_folder, info_folder,
updates_folder] = ul.get_foldersData(source="GCI")
################## Date info ###################
sdate_str = "01-8-2014"
edate_str = "21-12-2016"
sdate = dt.datetime.strptime(sdate_str, "%d-%m-%Y")
edate = dt.datetime.strptime(edate_str, "%d-%m-%Y")
####################################################
#### Load the info about the available symbols #####
####################################################
load_info = 0
if (load_info == 1):
Symbol_info = CSy.load_symbols_info(info_folder)
Symbol_names = Symbol_info["Symbol_name"].tolist()
Nsym = len(Symbol_names)
# Change main directory to the main folder and import folders
import os
os.chdir("../")
import import_folders
import CTimeData as CTD
import utilities_lib as ul
######## SELECT DATASET, SYMBOLS AND PERIODS ########
dataSource = "GCI" # Hanseatic FxPro GCI Yahoo Google
[storage_folder, info_folder, updates_folder] = \
ul.get_foldersData(source = dataSource)
symbol = "Amazon"
period = 1440
######## CREATE THE OBJECT AND LOAD THE DATA ##########
timeData = CTD.CTimeData(symbol,period) # Set main properties
timeData.set_csv(storage_folder) # Load the data into the model
import datetime as dt
sdate = dt.datetime.strptime("01-01-2016", "%d-%m-%Y")
edate = dt.datetime.strptime("15-05-2016", "%d-%m-%Y")
timeData.set_interval(sdate,edate) # Set the interval period to be analysed
mask = timeData.time_mask
dates = timeData.get_dates()
import numpy as np
import CSymbol as CSy
import copy as copy
import CPortfolio as CPfl
import gc
import utilities_lib as ul
import DDBB_lib as DBl
### CODE TO EXECUTE FROM TIME TO TIME TO DOWNLOAD A LOT OF DATA FROM MT4 OR
### THE INTERNET
######## SELECT SOURCE ########
dataSource = "Google" # Hanseatic FxPro GCI Yahoo Google
[storage_folder, info_folder,
updates_folder] = ul.get_foldersData(source = dataSource, symbol_info_list = "Current")
## Select the Symbol Names and periods we want to download
periods = [15]
#periods = [60,1440]
#periods = [1,15]
### UPDATE ALL SYMBOLS
if ((dataSource != "Google") and (dataSource != "Yahoo")):
# Update from the CSVs downloaded already
Symbol_info = CSy.load_symbols_info(info_folder)
Symbol_names = Symbol_info["Symbol_name"].tolist()
for period in periods:
Cartera = CPfl.Portfolio("BEST_PF", Symbol_names, [period])
from graph_lib import gl
# For statistics. Requires statsmodels 5.0 or more
from statsmodels.formula.api import ols
# Analysis of Variance (ANOVA) on linear models
from statsmodels.stats.anova import anova_lm
import pandas as pd
from sklearn import linear_model
import basicMathlib as bMA
import indicators_lib as indl
plt.close("all")
######## SELECT DATASET, SYMBOLS AND PERIODS ########
# Hanseatic FxPro GCI Yahoo
[storage_folder, info_folder,
updates_folder] = ul.get_foldersData(source = "GCI")
################## Date info ###################
sdate_str = "01-8-2014"
edate_str = "21-12-2016"
sdate = dt.datetime.strptime(sdate_str, "%d-%m-%Y")
edate = dt.datetime.strptime(edate_str, "%d-%m-%Y")
####################################################
#### Load the info about the available symbols #####
####################################################
load_info = 0
if (load_info == 1):
Symbol_info = CSy.load_symbols_info(info_folder)
Symbol_names = Symbol_info["Symbol_name"].tolist()
Nsym = len(Symbol_names)
# Change main directory to the main folder and import folders
import os
os.chdir("../")
import import_folders
import CTimeData as CTD
import utilities_lib as ul
######## SELECT DATASET, SYMBOLS AND PERIODS ########
dataSource = "GCI" # Hanseatic FxPro GCI Yahoo Google
[storage_folder, info_folder, updates_folder] = \
ul.get_foldersData(source = dataSource)
symbol = "Amazon"
period = 1440
######## CREATE THE OBJECT AND LOAD THE DATA ##########
timeData = CTD.CTimeData(symbol, period) # Set main properties
timeData.set_csv(storage_folder) # Load the data into the model
import datetime as dt
sdate = dt.datetime.strptime("01-01-2016", "%d-%m-%Y")
edate = dt.datetime.strptime("15-05-2016", "%d-%m-%Y")
timeData.set_interval(sdate, edate) # Set the interval period to be analysed
mask = timeData.time_mask
dates = timeData.get_dates()
