import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from toollib.Data.TA.TA_indicator import TA
from toollib.Data.info import Info
SYMBOL = ''
SnPList = Info(pathname='data/constituents.csv').get_symbol_list()
stocks_bundle = 'custom-stocks-csvdir-bundle'
currency_bundle = 'custom-currency-csvdir-bundle'
start = pd.to_datetime('2009-01-01').tz_localize('US/Eastern')
end = pd.to_datetime('2018-12-01').tz_localize('US/Eastern')
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 11
context.orders_submitted = False
large_num = 9999999
least_num = 0
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from toollib.Data.info import Info
import csv
pathname = 'src/constituents.csv'
symbol_list = Info(pathname).get_symbol_list()
test_string = ['BAC', 'INTC', 'SPLS', 'PFE', 'HPQ', 'JPM', 'IPG', 'TROW']
model_list = ['SVM','KNeighbors','DecisionTree','RandomForest','LogisticRegression']
def readCSV(pathname,index_):
n = 0
myList = []
with open(pathname, mode='r') as infile:
reader = csv.reader(infile)
for rows in reader:
if n==0:
pos = 0
for ele in rows:
if ele == index_:
break
else:
pos = pos +1
if not n == 0:
myList.append(rows[pos])
else:
n = 1
return myList
import urllib
import urllib.request
import os
import csv
import progressbar
from time import sleep
from datetime import datetime
from toollib.Data.info import Info
SnP500 = Info(pathname='data/constituents.csv').get_symbol_list()
ASX200 = Info(pathname='data/asx200.csv').get_symbol_list()
DT_FORMAT = "%Y-%m-%d"
min_size = 200000
max_size = 300000
QUERY_URL_CSV = "https://www.alphavantage.co/query?function={REQUEST_TYPE}&outputsize=full&datatype=csv&apikey={KEY}&symbol={SYMBOL}"
API_KEY = "VKNYIAEYDFJGF1RS"
root_path = 'csv/stocks/daily/'
def _request_csv(symbol, req_type):
try:
urllib.request.urlretrieve(
QUERY_URL_CSV.format(REQUEST_TYPE=req_type,
KEY=API_KEY,
SYMBOL=symbol), root_path + symbol + '.csv')
except urllib.error.HTTPError as ex:
print('Problem:', ex)
def initialize(context):
"""
Called once at the start of the algorithm.
"""
feature_num = 14
context.orders_submitted = False
large_num = 9999999
least_num = 0
context.n_components = 11
context.SP500_symbol = ['AAPL', 'ABT', 'ABBV', 'ACN', 'ACE', 'ADBE', 'ADT', 'AAP', 'AES', 'AET', 'AFL',
'AMG', 'A', 'GAS', 'ARE', 'APD', 'AKAM', 'AA', 'AGN', 'ALXN', 'ALLE', 'ADS', 'ALL', 'ALTR', 'MO',
'AMZN', 'AEE', 'AAL', 'AEP', 'AXP', 'AIG', 'AMT', 'AMP', 'ABC', 'AME', 'AMGN', 'APH', 'APC', 'ADI',
'AON', 'APA', 'AIV', 'AMAT', 'ADM', 'AIZ', 'T', 'ADSK', 'ADP', 'AN', 'AZO', 'AVGO', 'AVB', 'AVY',
'BHI', 'BLL', 'BAC', 'BK', 'BCR', 'BXLT', 'BAX', 'BBT', 'BDX', 'BBBY', 'BRK.B', 'BBY', 'BLX', 'HRB',
'BA', 'BWA', 'BXP', 'BSX', 'BMY', 'BRCM', 'BF.B', 'CHRW', 'CA', 'CVC', 'COG', 'CAM', 'CPB', 'COF',
'CAH', 'HSIC', 'KMX', 'CCL', 'CAT', 'CBG', 'CBS', 'CELG', 'CNP', 'CTL', 'CERN', 'CF', 'SCHW', 'CHK',
'CVX', 'CMG', 'CB', 'CI', 'XEC', 'CINF', 'CTAS', 'CSCO', 'C', 'CTXS', 'CLX', 'CME', 'CMS', 'COH',
'KO', 'CCE', 'CTSH', 'CL', 'CMCSA', 'CMA', 'CSC', 'CAG', 'COP', 'CNX', 'ED', 'STZ', 'GLW', 'COST',
'CCI', 'CSX', 'CMI', 'CVS', 'DHI', 'DHR', 'DRI', 'DVA', 'DE', 'DLPH', 'DAL', 'XRAY', 'DVN', 'DO',
'DTV', 'DFS', 'DISCA', 'DISCK', 'DG', 'DLTR', 'D', 'DOV', 'DOW', 'DPS', 'DTE', 'DD', 'DUK', 'DNB',
'ETFC', 'EMN', 'ETN', 'EBAY', 'ECL', 'EIX', 'EW', 'EA', 'EMC', 'EMR', 'ENDP', 'ESV', 'ETR', 'EOG',
'EQT', 'EFX', 'EQIX', 'EQR', 'ESS', 'EL', 'ES', 'EXC', 'EXPE', 'EXPD', 'ESRX', 'XOM', 'FFIV', 'FB',
'FAST', 'FDX', 'FIS', 'FITB', 'FSLR', 'FE', 'FISV', 'FLIR', 'FLS', 'FLR', 'FMC', 'FTI', 'F', 'FOSL',
'BEN', 'FCX', 'FTR', 'GME', 'GPS', 'GRMN', 'GD', 'GE', 'GGP', 'GIS', 'GM', 'GPC', 'GNW', 'GILD',
'GS', 'GT', 'GOOGL', 'GOOG', 'GWW', 'HAL', 'HBI', 'HOG', 'HAR', 'HRS', 'HIG', 'HAS', 'HCA', 'HCP',
'HCN', 'HP', 'HES', 'HPQ', 'HD', 'HON', 'HRL', 'HSP', 'HST', 'HCBK', 'HUM', 'HBAN', 'ITW', 'IR',
'INTC', 'ICE', 'IBM', 'IP', 'IPG', 'IFF', 'INTU', 'ISRG', 'IVZ', 'IRM', 'JEC', 'JBHT', 'JNJ', 'JCI',
'JOY', 'JPM', 'JNPR', 'KSU', 'K', 'KEY', 'GMCR', 'KMB', 'KIM', 'KMI', 'KLAC', 'KSS', 'KRFT', 'KR',
'LB', 'LLL', 'LH', 'LRCX', 'LM', 'LEG', 'LEN', 'LVLT', 'LUK', 'LLY', 'LNC', 'LLTC', 'LMT', 'L',
'LOW', 'LYB', 'MTB', 'MAC', 'M', 'MNK', 'MRO', 'MPC', 'MAR', 'MMC', 'MLM', 'MAS', 'MA', 'MAT',
'MKC', 'MCD', 'MCK', 'MJN', 'MMV', 'MDT', 'MRK', 'MET', 'KORS', 'MCHP', 'MU', 'MSFT', 'MHK', 'TAP',
'MDLZ', 'MON', 'MNST', 'MCO', 'MS', 'MOS', 'MSI', 'MUR', 'MYL', 'NDAQ', 'NOV', 'NAVI', 'NTAP',
'NFLX', 'NWL', 'NFX', 'NEM', 'NWSA', 'NEE', 'NLSN', 'NKE', 'NI', 'NE', 'NBL', 'JWN', 'NSC', 'NTRS',
'NOC', 'NRG', 'NUE', 'NVDA', 'ORLY', 'OXY', 'OMC', 'OKE', 'ORCL', 'OI', 'PCAR', 'PLL', 'PH', 'PDCO',
'PAYX', 'PNR', 'PBCT', 'POM', 'PEP', 'PKI', 'PRGO', 'PFE', 'PCG', 'PM', 'PSX', 'PNW', 'PXD', 'PBI',
'PCL', 'PNC', 'RL', 'PPG', 'PPL', 'PX', 'PCP', 'PCLN', 'PFG', 'PG', 'PGR', 'PLD', 'PRU', 'PEG',
'PSA', 'PHM', 'PVH', 'QRVO', 'PWR', 'QCOM', 'DGX', 'RRC', 'RTN', 'O', 'RHT', 'REGN', 'RF', 'RSG',
'RAI', 'RHI', 'ROK', 'COL', 'ROP', 'ROST', 'RLD', 'R', 'CRM', 'SNDK', 'SCG', 'SLB', 'SNI', 'STX',
'SEE', 'SRE', 'SHW', 'SPG', 'SWKS', 'SLG', 'SJM', 'SNA', 'SO', 'LUV', 'SWN', 'SE', 'STJ', 'SWK',
'SPLS', 'SBUX', 'HOT', 'STT', 'SRCL', 'SYK', 'STI', 'SYMC', 'SYY', 'TROW', 'TGT', 'TEL', 'TE',
'TGNA', 'THC', 'TDC', 'TSO', 'TXN', 'TXT', 'HSY', 'TRV', 'TMO', 'TIF', 'TWX', 'TWC', 'TJX', 'TMK',
'TSS', 'TSCO', 'RIG', 'TRIP', 'FOXA', 'TSN', 'TYC', 'UA', 'UNP', 'UNH', 'UPS', 'URI', 'UTX', 'UHS',
'UNM', 'URBN', 'VFC', 'VLO', 'VAR', 'VTR', 'VRSN', 'VZ', 'VRTX', 'VIAB', 'V', 'VNO', 'VMC', 'WMT',
'WBA', 'DIS', 'WM', 'WAT', 'ANTM', 'WFC', 'WDC', 'WU', 'WY', 'WHR', 'WFM', 'WMB', 'WEC', 'WYN',
'WYNN', 'XEL', 'XRX', 'XLNX', 'XL', 'XYL', 'YHOO', 'YUM', 'ZBH', 'ZION', 'ZTS']
context.model2 = SVC(kernel='rbf', tol=1e-3, random_state=0, gamma=0.2, C=10.0, verbose=True) # 8.05 for SVM model
context.model3 = KNeighborsClassifier(n_neighbors=feature_num, p=3, metric='minkowski') # 7.05 for model
context.model5 = DecisionTreeClassifier(criterion='entropy', max_depth=feature_num, random_state=0)
context.model4 = RandomForestClassifier(criterion='entropy', n_estimators=feature_num, random_state=1,
n_jobs=2) # 5.2 for randomforest
context.model1 = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')
#context.model = KMeans(n_clusters=8, init='k-means++', max_iter=300, tol=1e-4, random_state=0)
context.model = DBSCAN(eps=0.2,min_samples=3,metric='euclidean')
context.sector = (Info(pathname)).info
context.lookback = 350 # Look back 62 days
context.history_range = 350 # Only consider the past 400 days' history
context.threshold = 4.05
context.longprices = large_num
context.shortprices = least_num
context.times = 0
set_long_only()
# Generate a new model every week
schedule_function(create_model, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Generate a new model every week
schedule_function(create_model1, date_rules.week_end(), time_rules.market_close(minutes=10))
"""
# Trade at the start of every day
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open(minutes=1))