def test_eia_release_analysis(self):
eikonkey = os.environ['EIKON_KEY']
ek.set_app_key(eikonkey)
d = stocks.eia_release_analysis()
self.assertTrue(isinstance(d, pd.DataFrame))
def __init__(self,
eikon_app_key,
data,
outputdir,
iscontinue=False,
pathtodone=None):
"""
data :: [Str] List with company rics
outputdir :: Str Path to outputdirname
iscontinue :: Bool If true, won't get
headlines for dates which already exist
pathtodone :: Str Path to textfile with done rics
dateto: :: Str | datetime Ending date, if Str form YYYY-MM-DD
datefrom :: Str | datetime Starting date, if Str form YYYY-MM-DD
"""
ek.set_app_key(eikon_app_key)
print('Eikon connected')
eventlet.monkey_patch()
self.ordered = ['A', 'O', 'B', 'K', 'PK'] #exchange types
self.rics = data
self.outputdir = outputdir
self.iscontinue = iscontinue
os.makedirs(self.outputdir, exist_ok=True)
self.done = pathtodone
if self.done is not None:
#READ ALL COMPS ALREADY DONE
with open(self.done, 'r') as f:
done_rics = set(f.read().strip().split('\n'))
self.rics -= done_rics
print('Updating %d companies' % len(self.rics))
def rtConn():
try:
TR_ID = '70650e2c881040408f6f95dea2bf3fa13e9f66fe'
ek.set_app_key(TR_ID)
except ek.eikonError.EikonError as e:
print(e)
return None
def reuters_eikon_data_scraper(instruments: list, fields: list, properties:dict, api_key:str):
df, err = None, None
ek.set_app_key(api_key)
try:
df, err = ek.get_data(instruments, fields, properties, field_name=True, raw_output=False)
if err is not None:
err = {'error_type': 'REUTERS', 'error_message': str(err), 'req_instruments': instruments, 'req_fields': fields, 'req_properties': properties}
else:
df.columns = [i.replace('.', '_') for i in df.columns.tolist()]
except Exception as error:
err = {'error_type': 'PyREUTERS', 'error_message': str(error).replace('\n', ';').encode(), 'req_instruments': instruments, 'req_fields': fields, 'req_properties':properties}
finally:
return df, err
def retrieve_monthly_assets_total_returns(date_from, date_to, assets_ric_list):
if date_from >= date_to:
raise Exception("Dates are wrong")
if len(assets_ric_list) == 0:
raise Exception("At least one RIC needed in the RICs list")
ek.set_app_key("94baceedc0c24ae8961456ae2b67bc72532e997f")
start_date = date_from
end_date = date_to
timeline = []
next_from_date = start_date.replace(day=1)
next_to_date = (start_date + rd.relativedelta(months=1)) + rd.relativedelta(days=-1)
next_time_slot = (next_from_date, next_to_date)
while next_time_slot[1] < end_date:
timeline.append(next_time_slot)
next_from_date = timeline[-1][1] + rd.relativedelta(days=1)
next_to_date = (next_from_date + rd.relativedelta(months=1)) + rd.relativedelta(days=-1)
next_time_slot = (next_from_date, next_to_date)
total_return_percentage_all = {}
for target_asset in assets_ric_list:
total_return_percentage_all[target_asset] = {}
total_return_percentage_all[target_asset]["timeline"] = []
for timeslot in timeline:
start_time = time.time()
i = timeline.index(timeslot)
tl = len(timeline)
f = dt.datetime.strftime(timeslot[0], "%Y-%m-%d")
t = dt.datetime.strftime(timeslot[1], "%Y-%m-%d")
o = ek.get_data(assets_ric_list, ['TR.TotalReturn'], {'SDate': f, 'EDate': t})
o_assets_list = o[0]["Instrument"].to_list()
for target_asset in assets_ric_list:
index_of_target_asset = o_assets_list.index(target_asset)
try:
to_store = ((f, t), float(o[0]["Total Return"][index_of_target_asset]) / 100)
except ValueError:
to_store = ((f, t), np.nan)
total_return_percentage_all[target_asset]["timeline"].append(to_store)
end_time = time.time()
msg = "Loading from Eikon ({:d} of {:d}, {:.2f}%) (remaining time: {:.2f} seconds)"
print(msg.format(i, tl, 100 * i / tl, (end_time - start_time)*(tl-i)))
return total_return_percentage_all
def init( app ):
cfg = json.load(
fp = open( os.path.join( os.getenv( 'FLASKEIKON' ), 'resource', 'config' ) )
)
eikon.set_app_key( cfg[ 'appid' ] )
handler = TimedRotatingFileHandler(
filename = cfg[ 'log' ], when = 'MIDNIGHT', encoding = 'utf-8'
)
handler.setFormatter( logging.Formatter(
'[%(processName)s[%(process)d]|%(threadName)s[%(thread)d]|%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s > %(message)s'
) )
app.logger.addHandler( handler )
app.logger.setLevel( logging.DEBUG )
app.config[ 'IP' ] = cfg[ 'ip' ]
app.config[ 'PORT' ] = cfg[ 'port' ]
from app.api import api as api_blueprint
app.register_blueprint( blueprint = api_blueprint, url_prefix = '/api/' )
def makeResponse(req):
if req.get("result").get("action") != "fetchNews":
return {}
result = req.get("result")
parameters = result.get("parameters")
ticker = parameters.get("ticker")
if ticker is None:
return None
tr.set_app_key('229dfa317f614c3c9cdfa2908c1ff66af4fdf1c6')
r = tr.get_news_headlines('ticker',
date_from='2020-03-09',
date_to='2020-03-10',
count=5)
r.head()
news = r("text")
print(news)
result = "The news on" + ticker + "are" + news
return {
"text": result,
}
def update_daily_data(stocks):
'''
Update the daily data for the stocks specified.
'''
s = sched.scheduler(time.time, time.sleep)
ek.set_app_key('66063f6d35e4453ebba0696f40307bc61e7172f2')
config = util.import_config()
stock_data_folder = config['STOCK_DATA_FOLDER']
# Get the current moment date in US
end_date = util.get_us_time()
for stock_code in stocks:
fname = os.path.join(stock_data_folder, stock_code + '.csv')
if os.path.isfile(fname):
stock_df = pd.read_csv(fname)
start_date = max(stock_df["Date"])
else:
stock_df = pd.DataFrame()
start_date = end_date
try:
new_df = ek.get_timeseries(stock_code,
start_date=start_date,
end_date=end_date).reset_index()
new_df["Date"] = new_df["Date"][0:10]
stock_df = stock_df.append(new_df)
stock_df = stock_df.reindex(columns=[
"Date", "HIGH", "CLOSE", "LOW", "OPEN", "COUNT", "VOLUME"
])
stock_df.drop_duplicates(["Date"], inplace=True)
stock_df.to_csv(fname, index=False)
#time.sleep(5.0 - ((time.time() - start_time) % 5.0))
print("Successfully updated daily date for", stock_code)
except Exception as e:
print("Error in acquiring ", stock_code, ":")
print(str(e))
def thomson_timeseries(
tickers,
fields,
corax,
start_date=datetime.today().strftime("%Y%m%d"),
end_date=datetime.today().strftime("%Y%m%d"),
):
""" 톰슨 로이터 시계열 데이터 소싱 """
ek.set_app_key("9c4370471d3c41ea9a2a8911d842812e941e06df")
try:
data = ek.get_timeseries(
tickers,
fields=fields,
start_date=start_date,
end_date=end_date,
interval="daily",
corax=corax,
)
print(data)
except:
raise ValueError("thomson data sourcing ERROR")
return data
def main():
# Excel
sheet = xw.Book.caller().sheets[0]
rng = sheet['A2'].expand()
pid_file = Path(__file__).resolve().parent / "pid"
if pid_file.exists():
# Stop streaming
with open(pid_file, 'r') as f:
pid = f.read()
try:
os.kill(int(pid), signal.SIGTERM)
pid_file.unlink()
except ProcessLookupError as e:
os.remove(pid_file)
finally:
rng[1:, 1:].clear_contents()
rng[0, 0].offset(row_offset=-1, column_offset=1).clear_contents()
else: # Start streaming
with open(pid_file, 'w') as f:
f.write(str(os.getpid()))
# Connect to Refinitiv Workspace
conf = ConfigParser()
conf.read(os.path.join(os.path.dirname(__file__), '..', 'eikon.conf'))
ek.set_app_key(conf['eikon']['APP_KEY'])
instruments, fields = rng[1:, 0].value, rng[0, 1:].value
streaming_prices = ek.StreamingPrices(instruments=instruments,
fields=fields)
streaming_prices.open()
while True:
rng[1, 1].value = streaming_prices.get_snapshot().to_numpy()[:, 1:]
rng[0, 0].offset(row_offset=-1,
column_offset=1).value = dt.datetime.now()
def thomson_sourcing(self):
ek.set_app_key("9c4370471d3c41ea9a2a8911d842812e941e06df")
tr_fields = [
"TR.CLOSEPRICE",
"TR.OPENPRICE(Adjusted=1)",
"TR.CLOSEPRICE(Adjusted=1)",
"TR.HIGHPRICE(Adjusted=1)",
"TR.LOWPRICE(Adjusted=1)",
"TR.IssueMarketCap",
]
sec = 4
interval = int(np.floor(len(self.tsTickers) / sec))
tot_data = pd.DataFrame()
for i in range(0, len(self.tsTickers), interval):
tsData = self.thomson(
self.tsTickers[i : i + interval], tr_fields
) # , parameters={'SDate':self.start, 'EDate':self.end})
tot_data = pd.concat([tot_data, tsData], axis=0)
time.sleep(3)
tot_data.columns = [
"Instrument",
"종가",
"수정시가",
"수정주가",
"수정고가",
"수정저가",
"시가총액",
"date",
]
tot_data.to_csv("{}/thomson.csv".format(self.today), encoding="cp949")
self.organize_data("thomson", tot_data)
import eikon as ek
import xlwings as xw
# Please read your Eikon APP_KEY from a config file or environment variable
ek.set_app_key('YOUR_APP_KEY')
def main():
# These parameters could also come from a config sheet in the template
instrument = '.DJI'
start_date = '2020-01-01'
end_date = '2020-01-31'
# Eikon queries
df = ek.get_timeseries(instrument,
fields='*',
start_date=start_date,
end_date=end_date)
summary, err = ek.get_data(instrument,
['TR.IndexName', 'TR.IndexCalculationCurrency'])
# Populate the Excel template with the data
wb = xw.Book.caller()
wb.sheets[0][
'A4'].value = f"{summary.loc[0, 'Index Name']} ({summary.loc[0, 'Calculation Currency']})"
wb.sheets[0]['A6'].value = df
if __name__ == '__main__':
xw.Book('sample2.xlsx').set_mock_caller()
import numpy
import pandas
import eikon
eikon.set_app_key('638fa3bbb90349d5a97dc60c1c0cc4b0b5646846')
eikon.get_news_headlines('R:LHAG.DE', date_from='2018-01-01T00:00:00', date_to='2018-12-13T18:00:00')
#df = eikon.get_timeseries("SPY",
# ['TR.IndexConstituentRIC',
# 'TR.IndexConstituentName',
# 'TR.IndexConstituentWeightPercent'],
data_grid, err = eikon.get_data("SPY",
['TR.IndexConstituentRIC',
{'TR.IndexConstituentWeightPercent':{'sort_dir':'desc'}}],
{'SDate':'2018-12-14'}
)
print(data_grid)
weight = data_grid.loc[:, "Weight percent"]
weight
import matplotlib.pyplot as plt
fig1, ax1 = plt.subplots()
ax1.pie(weight, shadow = True)
ax1.axis('equal')
plt.show()
NESN = eikon.get_timeseries(["NESN.S"], start_date="2014-01-06",
end_date="2018-12-20", interval="daily")
plt.plot(NESN.loc[:, "CLOSE"])
import eikon as ek # the Eikon Python wrapper package
import re
import time
import datetime
import os
import pandas as pd
import eventlet
eventlet.monkey_patch()
#set_app_id function being deprecated
eikon_app_id = 'fb1f0ff6cf794376af6ff9f29926d3fb17997843' #paste real app id
ek.set_app_key(eikon_app_id)
print('Eikon connected')
#range of dates
start = datetime.datetime.strptime("2018-02-28", "%Y-%m-%d")
end = datetime.datetime.strptime("2019-06-1", "%Y-%m-%d")
date_generated = [start + datetime.timedelta(days=x) for x in range((end-start).days)][::-1]
rics = set(pd.read_csv('constituents_matched.csv', sep=',')['reuters'])
# #find not done yet
with open('done.txt', 'r') as f:
notdone = set(f.read().strip().split('\n')[:-100])
rics -= notdone
print('Updating {} companies'.format(str(len(rics))))
def _handle_time(e, event):
if hasattr(e, 'message'):
import eikon as ek
import pandas as pd
ek.set_app_key('02251fexxxxxxxxxxxxxxxxxxxxxxxxxxxxxfd2c')
constituents, err = ek.get_data(".FTSE", "TR.IndexConstituentRIC")
for company in constituents['Constituent RIC']:
data, err = ek.get_data(company, ["TR.CompanyName", "TR.OfficerName(RNK=R1:R100)", "TR.OfficerTitle(RNK=R1:R100)"])
data.to_csv("data/"+company+".csv")
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 16 12:20:19 2019
@author: Admin
"""
import eikon as ek
ek.set_app_key('88728d9ceb094a57aaa16f1bdceb8d899dafab9a')
p1 = ek.get_timeseries(["JNJ.N"],
start_date="2010-02-01T15:04:05",
end_date="2017-02-05T15:04:05",
interval="tick")
import math
import eikon as ek # the Eikon Python wrapper package
ek.set_app_key('0ed6a35e0937415eab446d3375bca7cf671d6b4c')
import numpy as np # NumPy
import pandas as pd # pandas
import cufflinks as cf # Cufflinks
import configparser as cp
# import scipy.optimize as sco # optimization routines
cf.set_config_file(offline=True, theme="white")
#Font Setting
from matplotlib.font_manager import FontProperties
import sys
if sys.platform.startswith('win'):
FontPath = 'C:\\Windows\\Fonts\\meiryo.ttc'
elif sys.platform.startswith('darwin'):
FontPath = '/System/Library/Fonts/ヒラノギ角ゴシック W4.ttc'
elif sys.platform.startswith('linux'):
FontPath = '/usr/share/fonts/truetype/takao-gothic/TakaoExGothic.ttc'
jpfont = FontProperties(fname=FontPath)
import matplotlib.pyplot as plt
import matplotlib as mpl
import os
mpl_dirpath = os.path.dirname(mpl.__file__)
# デフォルトの設定ファイルのパス
default_config_path = os.path.join(mpl_dirpath, 'mpl-data', 'matplotlibrc')
# カスタム設定ファイルのパス
custom_config_path = os.path.join(mpl.get_configdir(), 'matplotlibrc')
import plotly.graph_objects as go
import chart_studio.plotly as py
import eikon as ek
ek.set_app_key('ba4e52456ba64a87be3b82001782535f159b564e')
print(
ek.get_news_headlines('R:IBM.N',
date_from='2019-09-27T09:00:00',
date_to='2019-09-27T10:00:00'))
import eikon as ek
import configparser as cp
import pandas as pd
# Set global variables.
cfg = cp.ConfigParser()
cfg.read('eikon_cfg.cfg')
ek.set_app_key(cfg['eikon']['app_id'])
def get_time_series(rics: list, fields: list, start_date,
end_date) -> pd.DataFrame:
"""
Retrieve time series data as Pandas DataFrame.
:param rics: List of RIC:s to get.
:param fields: List fields to get.
:param start_date: Start date of time series.
:param end_date: End date of time series.
:return: Pandas DataFrame.
"""
data = ek.get_timeseries(rics,
fields,
start_date=start_date,
calendar='native',
end_date=end_date,
interval='daily')
return data
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from datetime import datetime
from dateutil.relativedelta import relativedelta
import numpy as np
import pandas as pd
import eikon as ek
ek.set_app_key('bf38826c5e014c1cadf21425ee6e417d2b72fc9a')
Benchmark = '.FTSETWMC'
DATE = datetime.strptime('2020-12-31', r'%Y-%m-%d')
time_delta = relativedelta(months=-1)
RICs = list(pd.read_excel('data/TWMC100_RICs.xlsx', usecols='A')['RIC'])
df = pd.read_csv('data/TWMC100_20yr.csv', index_col='Date')
df2 = ek.get_timeseries(RICs + ['.TWII', Benchmark],
fields='CLOSE',
start_date='2020-10-31',
end_date=DATE,
interval='monthly',
corax='adjusted')
df2['Date'] = [DATE + 2 * time_delta, DATE + time_delta, DATE]
df2.set_index('Date', inplace=True)
df = df.append(df2)
df.to_csv('data/TWMC100_20yr.csv')
# check if all data are gotten!
elif contextAction[sender]['actionName'] == "gettimeseries":
resp_msg = '@' + sender + ' ' + gettimeseries(actionValue)
elif contextAction[sender]['actionName'] == "changeconfidencelevel":
confidence_level = float(actionValue)
resp_msg = 'Confidence Level is now adjusted to ' + actionValue
contextAction.pop(sender)
return resp_msg
# =============================== Main Process ========================================
# Running the tutorial
if __name__ == '__main__':
if use_data_from_eikon:
try:
# Set Eikon AppKey to reqeuest data from Eikon Desktop via Eikon Data API
ek.set_app_key(eikon_data_api_app_key)
except ek.EikonError as ex:
print("Eikon Initialize Error:{}".format(ex.message))
print('Load data and model for AHS Chatbot...')
loadmodel()
# Setting Python Logging
logging.basicConfig(format='%(levelname)s:%(name)s :%(message)s',
level=log_level)
print("ChatRoom is starting now. Type 'bye' to exit")
while is_running:
message = input("{} input:".format(userId))
print("Bot:{}\n".format(process_message(message, userId)))
def main():
# Files
template = xw.Book.caller()
report_path = os.path.join(os.path.dirname(__file__), 'report.xlsx')
# Eikon setup
conf = ConfigParser()
conf.read(os.path.join(os.path.dirname(__file__), '..', 'eikon.conf'))
ek.set_app_key(conf['eikon']['APP_KEY'])
# Configuration
date_format = template.sheets['Config']['date_format'].value
if date_format == 'UK':
fmt = '%e %b %Y'
elif date_format == 'US':
fmt = '%b %e, %Y'
else:
fmt = '%e %b %Y'
instrument = template.sheets['Config']['instrument'].value
start_date = dt.datetime(dt.datetime.now().year - 4, 1, 1)
# Prices
prices = ek.get_timeseries(instrument,
fields=['close'],
start_date=start_date,
end_date=dt.datetime.now(),
interval='weekly')
end_date = prices.index[-1]
# Summary
summary, err = ek.get_data(instrument, [
'TR.PriceClose', 'TR.Volume', 'TR.PriceLow', 'TR.PriceHigh',
'TR.IndexName', 'TR.IndexCalculationCurrency'
])
# Constituents
constituents, err = ek.get_data(
f'0#{instrument}',
fields=['TR.CommonName', 'TR.PriceClose', 'TR.TotalReturnYTD'])
constituents = constituents.set_index('Company Common Name')
# Add empty columns so it goes into the desired Excel cells
for i in range(0, 6):
constituents.insert(loc=i, column='merged' + str(i), value=np.nan)
constituents = constituents.drop(['Instrument'], axis=1)
constituents = constituents.rename(columns={"YTD Total Return": "YTD %"})
# Collect data
data = dict(perf_start_date=start_date.strftime(fmt),
perf_end_date=end_date.strftime(fmt),
index_name=summary.loc[0, 'Index Name'],
currency=summary.loc[0, 'Calculation Currency'],
reference_date=dt.date.today().strftime(fmt),
historical_perf=prices,
constituents=constituents,
price_close=float(summary['Price Close']),
volume=float(summary['Volume']),
price_low=float(summary['Price Low']),
price_high=float(summary['Price High']))
# Create the Excel report
wb = create_report(template.fullname, report_path, **data)
import os.path
import numpy as np
import pandas as pd
import eikon as ek
ek.set_app_key('48f17fdf21184b0ca9c4ea8913a840a92b338918')
from .future_root_factory import FutureRootFactory
from pandas import read_hdf
from pandas import HDFStore,DataFrame
import os
dirname = os.path.dirname(__file__)
#run through loop to get data for all, keeping in mind 5 year limit to history
def put_to_hdf(df):
hdf = HDFStore(dirname + "\_InstrumentData.5")
hdf.put('InstrumentData', df, format='table', data_columns=True)
hdf.close() # closes the file
columns =[
'exchange_symbol',
'root_symbol',
'instrument_name',
'underlying_name',
'underlying_asset_class_id',
'settle_start',
'settle_end',
'settle_method',
'settle_timezone',
'final_settle_start',
'final_settle_end',
def get_news_and_sentiments(key_id, ticker_and_params, ticker, start_date,
end_date):
ek.set_app_key(key_id)
Date_range = pd.date_range(start=start_date, end=end_date, freq='D')
date_range = []
for i in range(len(Date_range) - 1):
start_date = pd.to_datetime(Date_range)[i].strftime(
"%Y-%m-%dT%H:%M:%S")
end_date = pd.to_datetime(Date_range)[i +
1].strftime("%Y-%m-%dT%H:%M:%S")
date_range.append([])
for j in range(1):
date_range[i].append(start_date)
date_range[i].append(end_date)
example_data = pd.DataFrame()
for i in range(len(date_range)):
example_data = example_data.append(ek.get_news_headlines(
f'{ticker_and_params}',
count=10,
date_from=date_range[i][0],
date_to=date_range[i][1]),
ignore_index=False)
news = example_data
stories = []
for i, storyId in enumerate(news['storyId']):
try:
html = ek.get_news_story(storyId)
story = BeautifulSoup(html, 'html5lib').get_text()
stories.append(story)
except:
stories.append('')
news['story'] = stories
## Sentiment Over Time
sentiment = pd.DataFrame()
sid = SentimentIntensityAnalyzer()
for storyId in news['storyId']:
row = news[news['storyId'] == storyId]
scores = sid.polarity_scores(row['story'][0])
sentiment = sentiment.append(
pd.DataFrame(scores, index=[row['versionCreated'][0]]))
sentiment.index = pd.DatetimeIndex(sentiment.index)
sentiment.sort_index(inplace=True)
sentiment_list = list(sentiment['compound'])
news['sentiment'] = sentiment_list
#group by day
dates_normal = []
for i in range(len(news['versionCreated'])):
dates_normal.append(news['versionCreated'][i].strftime("%Y-%m-%d"))
dates_normal_2 = pd.to_datetime(dates_normal)
news['dates_normal'] = dates_normal_2
#save to csv
news.to_csv(f"{ticker}_news.csv")
daily_sentiments_listed = news.groupby(
pd.Grouper(key="dates_normal",
freq="D"))['sentiment'].apply(list).reset_index()
#Daily mood index
DMI = []
for i in range(len(daily_sentiments_listed['sentiment'])):
pos = 0
neg = 0
for j in range(len(daily_sentiments_listed['sentiment'][i])):
try:
if daily_sentiments_listed['sentiment'][i][j] > 0:
pos += 1
elif daily_sentiments_listed['sentiment'][i][j] < 0:
neg += 1
except:
pass
DMI.append(np.log((1 + pos) / (1 + neg)))
daily_sentiments_listed['DMI'] = DMI
#Average Sentiment
Average_S = []
for i in range(len(daily_sentiments_listed['sentiment'])):
Average_S.append(np.mean(daily_sentiments_listed['sentiment'][i]))
daily_sentiments_listed['Average_Sentiment'] = pd.DataFrame(
Average_S).fillna(0)
#save sentiments to csv
daily_sentiments_listed.to_csv(f"{ticker}_sentiment.csv", index=False)
return daily_sentiments_listed, news
import quandl
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
import eikon as ek
import pandas as pd
import matplotlib.pyplot as plt
ek.set_app_key('b47a7929747c40149d93089db6b3e895a3f8bca5')
df = ek.get_timeseries(["CHRS.O"], start_date = "2001-10-10", end_date = "2019-02-05")
print(df)
df = df[['CLOSE']]
print(df)
forecast_out = 30
df['Prediction'] = df[['CLOSE']].shift(-forecast_out)
print(df)
X = np.array(df.drop(['Prediction'],1))
X = X[:-forecast_out]
print(X)
y = np.array(df['Prediction'])
y = y[:-forecast_out]
print(y)
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_rbf.fit(x_train, y_train)
svm_confidence = svr_rbf.score(x_test, y_test)
print("svm confidence: ", svm_confidence)
lr = LinearRegression()
lr.fit(x_train, y_train)
lr_confidence = lr.score(x_test, y_test)
print("lr confidence: ", lr_confidence)
def logkey(n_clicks, value):
if n_clicks != None:
ek.set_app_key(value)
ek.set_timeout(60 * 5)
return 'Confirm: "{}"'.format(value)
def main():
# Files
template = xw.Book.caller()
report_path = Path(template.fullname).resolve().parent / 'report.xlsx'
# Eikon setup
ek.set_app_key(os.getenv('EIKON_APP_KEY'))
# Configuration
date_format = template.sheets['Config']['date_format'].value
if date_format == 'UK':
fmt = '%e %b %Y'
elif date_format == 'US':
fmt = '%b %e, %Y'
else:
fmt = '%e %b %Y'
instrument = template.sheets['Config']['instrument'].value
start_date = dt.datetime(dt.datetime.now().year - 4, 1, 1)
# Prices
prices = ek.get_timeseries(instrument,
fields=['close'],
start_date=start_date,
end_date=dt.datetime.now(),
interval='weekly')
end_date = prices.index[-1]
# Summary
summary, err = ek.get_data(instrument,
['TR.PriceClose', 'TR.Volume', 'TR.PriceLow', 'TR.PriceHigh',
'TR.IndexName', 'TR.IndexCalculationCurrency'])
# Constituents
constituents, err = ek.get_data(f'0#{instrument}',
fields=['TR.CommonName', 'TR.PriceClose', 'TR.TotalReturnYTD'])
constituents = constituents.set_index('Company Common Name')
# Add empty columns so it goes into the desired Excel cells
for i in range(6):
constituents.insert(loc=i, column='merged' + str(i), value=np.nan)
constituents = constituents.drop(['Instrument'], axis=1)
constituents = constituents.rename(columns={"YTD Total Return": "YTD %"})
constituents = constituents.sort_values("YTD %", ascending=False)
# Collect data
data = dict(
perf_start_date=start_date.strftime(fmt),
perf_end_date=end_date.strftime(fmt),
index_name=summary.loc[0, 'Index Name'],
currency=summary.loc[0, 'Calculation Currency'],
reference_date=dt.date.today().strftime(fmt),
historical_perf=prices,
constituents=constituents,
price_close=float(summary['Price Close']),
volume=float(summary['Volume']),
price_low=float(summary['Price Low']),
price_high=float(summary['Price High'])
)
# Create the Excel report
app = template.app
app.screen_updating = False
wb = create_report(template.fullname,
report_path, app=app, **data)
app.screen_updating = True
wb.sheets.active["A1"].select()
def CalcNetChart(row_ids, data, children, value):
if (row_ids != None) & (data != None):
if (len(row_ids) > 0) & (len(data) > 0):
ek.set_app_key(value)
ek.set_timeout(60 * 5)
data = pd.DataFrame(data)
data = data[data.index.isin(row_ids)]
data['Amount'] = data['Amount'].astype(float)
data['Price'] = data['Price'].astype(float)
data['Strike'] = data['Strike'].astype(float)
data["Day"] = pd.to_datetime(data["Day"])
data["Maturity"] = pd.to_datetime(data["Maturity"])
data["Financial"] = data['Amount'] * data['Price']
data["Financial"] = data["Financial"].round(2)
spot = pd.DataFrame(json.loads(json.loads(str(children))['spot']))
spot['TRDPRC_1'] = spot['TRDPRC_1'].astype(float)
spot["TRADE_DATE"] = pd.to_datetime(spot["TRADE_DATE"])
ssp = spot[
spot["Instrument"] != "BRSELICD=CBBR"].TRDPRC_1.values[0]
days = (data.apply(lambda row: businessDuration(
startdate=row['Day'], enddate=row['Maturity'], unit='day'),
axis=1)).values
days = list(set(days))
vola, err = ek.get_data([
spot[
spot["Instrument"] != "BRSELICD=CBBR"].Instrument.values[0]
], [
'TR.Volatility5D', 'TR.Volatility10D', "TR.Volatility20D",
"TR.Volatility30D", "TR.Volatility60D", "TR.Volatility90D"
])
volaV = (sum((
vola.T[0].values[1:] *
(abs(np.array([5, 10, 20, 30, 60, 90]) - 15) + 1) /
(sum(abs(np.array([5, 10, 20, 30, 60, 90]) - 15) + 1))))) / 100
vola.columns = ['Instrument', 5, 10, 20, 30, 60, 90]
vola[[5, 10, 20, 30, 60, 90]] = vola[[5, 10, 20, 30, 60, 90]] / 100
days = list(np.unique(days))
dists = []
vollist = []
for d in days:
y_interp = scipy.interpolate.interp1d(
vola.columns[1:].astype(float),
vola.T[0].values[1:].astype(float))
volaV = y_interp(d)
Distrib = 1 - np.random.normal(0,
(volaV / (252**0.5) * (d**0.5)),
size=5000)
Distrib = Distrib * ssp
vollist.append(volaV)
dists.append(Distrib)
Price = dists[pd.DataFrame(dists).max(1).argmax()]
Price.sort()
net = []
for k in range(len(data)):
if data['Amount'].iloc[k] >= 0:
if "C" in data['type'].iloc[k]:
Rt = -data['Strike'].iloc[k] - data['Price'].iloc[
k] + Price
Rt[Rt <=
-data['Price'].iloc[k]] = -data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
else:
Rt = data['Strike'].iloc[k] - data['Price'].iloc[
k] - Price
Rt[Rt <=
-data['Price'].iloc[k]] = -data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
else:
if "C" in data['type'].iloc[k]:
Rt = data['Strike'].iloc[k] + data['Price'].iloc[
k] - Price
Rt[Rt >= data['Price'].iloc[k]] = data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
else:
Rt = -data['Strike'].iloc[k] + data['Price'].iloc[
k] + Price
Rt[Rt >= data['Price'].iloc[k]] = data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
pay = pd.DataFrame(net)
pay.columns = Price
pays = pay
# print(pays.values)
pay = pay.sum()
tempTT = []
for j in range(len(set(days))):
tempT = []
for s in range(len(data["Strike"])):
temp = Func.OpitionsPrice(
Price, data["Strike"].values.astype(float)[s],
spot[spot["Instrument"] == "BRSELICD=CBBR"].TRDPRC_1.
values[0].astype(float) / 100,
np.array(data['ImpVol'].values[j]) / 100, days[j])
if "C" in data["type"].values[s]:
temp = list(
(temp.ValorC.values - data['Price'].values[s]) *
data['Amount'].values[s])
else:
temp = list(
(temp.ValorP.values - data['Price'].values[s]) *
data['Amount'].values[s])
tempT.append(temp)
tempTT.append(tempT)
fig = go.FigureWidget(
make_subplots(shared_xaxes=True,
specs=[[{
"secondary_y": True
}]],
print_grid=False))
trace3 = go.Scatter(name="0 line",
x=pay.index,
y=np.array([0 for i in Distrib]),
xaxis='x1',
yaxis='y2',
line=dict(color='black', width=2, dash='dash'))
fig.add_trace(trace3, secondary_y=False)
trace1 = go.Scatter(name="Payoff",
x=pay.index,
y=pay.values,
xaxis='x1',
yaxis='y2',
mode='lines',
fill='tozeroy')
fig.add_trace(trace1, secondary_y=False)
for i in range(len(tempTT)):
trace5 = go.Scatter(name="Price - " + str(days[i]) + ' Days',
x=pay.index,
y=pd.DataFrame(tempTT[i]).sum().values,
xaxis='x1',
yaxis='y2',
mode='lines')
fig.add_trace(trace5, secondary_y=False)
for lin, i in zip(pays.values, pays.index):
trace4 = go.Scatter(name=data.Instrument.values[i],
x=pay.index,
y=lin,
xaxis='x1',
yaxis='y2',
line=dict(width=2, dash='dash'))
fig.add_trace(trace4, secondary_y=False)
for i, j in zip(days, dists):
trace2 = ff.create_distplot([j],
[str(i) + " Days - Probabilidade"],
bin_size=.5,
curve_type='normal',
show_hist=False,
show_rug=False)
fig.add_trace(trace2['data'][0], secondary_y=True)
prob = round(sum(pay.values > 0) / len(Distrib) * 100, 2)
return [fig, html.H6("Profit probability: " + str(prob) + "%")]
else:
fig = go.FigureWidget(
make_subplots(shared_xaxes=True,
specs=[[{
"secondary_y": True
}]],
print_grid=False))
trace3 = go.Scatter(name="0 line",
x=[0],
y=[0],
xaxis='x1',
yaxis='y2',
line=dict(color='black', width=2, dash='dash'))
fig.add_trace(trace3, secondary_y=False)
return [fig, html.H6(str(0) + "%")]
else:
fig = go.FigureWidget(
make_subplots(shared_xaxes=True,
specs=[[{
"secondary_y": True
}]],
print_grid=False))
trace3 = go.Scatter(name="0 line",
x=[0],
y=[0],
xaxis='x1',
yaxis='y2',
line=dict(color='black', width=2, dash='dash'))
fig.add_trace(trace3, secondary_y=False)
return [fig, html.H6(str(0) + "%")]
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 29 15:14:04 2019
@author: Administrator
"""
import eikon as ek
ek.set_app_key('9559d6b91c084b348f50f9bcc0ef6a010b997405')
import pandas as pd
import eikon as ek
import numpy as np
ek.set_app_key('9559d6b91c084b348f50f9bcc0ef6a010b997405')
from datetime import date
start = '2004-1-1'
today = date.today().strftime('%Y-%m-%d')
firstday = '1999-12-30'
#reuters
start = '2004-1-1'
### TURKEY
turkey_yield_tickers = [
# Thomson Reuters Eikon API Implementation to track news
import os
from dotenv import load_dotenv
load_dotenv()
import eikon as ek
ek.set_app_key(os.getenv('EIKON_API_KEY'))
import pandas as pd
def get_news(topic, source, start_date, end_date, n_articles):
'''
To search a company, Append 'R:' in front of RIC (ticker followed by a dot and Echange ID (eg: TSLA.O))
The following is a list of news sources and their respective code from Mexico.
mexican_sources = {
'Mexico Ledger': 'MEXLED',
'El Financiero': 'ELMEX',
'El Economista': 'ELECOP',
'La Jornada': 'LAMEX',
'Mega News': 'MEGNEW',
'Milenio': 'MILMEX',
'El Nacional': 'ELNACI',
'Expansion': 'EXPSPB',
'Excelsior': 'EXCMEX',
'Mural': 'MURMEX',
'El Norte': 'ELNORT',
'Estrategia': 'ESTMEX',
'La I': 'LAIMEX',
'Al Chile': 'ALCHIL',