)
print(str(e))
from jinja2 import Environment, FileSystemLoader
from chartpy import Canvas, Chart, Style
import plotly.graph_objs as go
from tcapy.conf.constants import Constants
from tcapy.util.utilfunc import UtilFunc
# compatible with Python 2 *and* 3:
ABC = abc.ABCMeta('ABC', (object, ), {'__slots__': ()})
constants = Constants()
class Renderer(object):
"""Creates HTML pages based on a dictionary input full of charts and can also convert them to PDF. Can both write
HTML/PDF to disk or return as a string, which is easier to consume by certain callers such as a webserver.
"""
def __init__(self):
self._util_func = UtilFunc()
@abc.abstractmethod
def render_elements(self,
elements_to_render_dict,
title=None,
output_filename=None,
#
# Copyright 2018 Cuemacro
#
# See the License for the specific language governing permissions and limitations under the License.
#
import os
from tcapy.conf.constants import Constants
from tcapy.util.loggermanager import LoggerManager
from tcapy.data.databasesource import DatabaseSourceCSV
from tcapy.data.databasesource import DatabaseSourceArctic, DatabaseSourcePyStore, DatabaseSourceInfluxDB, DatabaseSourceKDB
constants = Constants()
if __name__ == '__main__':
logger = LoggerManager.getLogger(__name__)
PLOT_BACK_DATA = False
data_vendor = 'ncfx' # 'dukascopy' or 'ncfx'
# Either use 'arctic' or 'pystore' or 'influxdb' or 'kdb' to store market tick data
market_data_store = 'arctic'
logger.info("About to upload data to " + market_data_store)
## YOU WILL NEED TO CHANGE THE BELOW LINES #########################################################################
from tcapy.analysis.tcarequest import MarketRequest
from tcapy.data.databasesource import DatabaseSourceCSVBinary as DatabaseSourceCSV
from tcapy.data.databasesource import \
DatabaseSourceMSSQLServer, DatabaseSourceMySQL, DatabaseSourceSQLite, \
DatabaseSourceArctic, DatabaseSourceKDB, DatabaseSourceInfluxDB, DatabaseSourcePyStore
import datetime
from collections import OrderedDict
from tcapy.util.customexceptions import *
logger = LoggerManager().getLogger(__name__)
constants = Constants()
from tcapy.util.mediator import Mediator
tcapy_version = constants.tcapy_version
# check that your database has market and trade data for these before running the test
logger.info('Make sure you have created folder ' + constants.csv_folder +
' & ' + constants.temp_data_folder + ' otherwise tests will fail')
########################################################################################################################
# YOU MAY NEED TO CHANGE THESE
start_date = '26 Apr 2017'
finish_date = '05 Jun 2017'
# data_test_creator.fetch_test_database()
# Load up market data from CSV and dump into SQL database
ticker = ['EURUSD', 'GBPUSD', 'AUDUSD', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDNOK', 'USDSEK', 'EURNOK', 'EURSEK',
'USDTRY', 'USDJPY']
# Trades for these tickers
ticker_trade = ['EURUSD', 'GBPUSD', 'AUDUSD', 'NZDUSD', 'USDCAD', 'USDCHF', 'EURNOK', 'EURSEK',
'USDJPY', 'AUDJPY', 'NZDCAD', 'EURJPY']
# folder = '/ext_data/tcapy_data/'
folder = None
if folder is None:
folder = Constants().test_data_folder
# Copy market data from flat files (can be either .csv or .h5 file - much quicker to use .h5 files)
# to Arctic market database
# This relies on you have market data stored in H5 files already (eg. by downloading from DukasCopy)
# note: whilst free FX data can be used for testing (in particular for generating randomised trades),
# we recommend using higher quality data for actual benchmark
if COPY_MARKET_CSV_DATA :
csv_file = ['test_market_' + x + '.h5' for x in ticker]
csv_market_data = [os.path.join(folder, x) for x in csv_file]
data_test_creator.populate_test_database_with_csv(csv_market_data = csv_market_data, ticker = ticker,
csv_trade_data = None,
if_exists_table = 'append', if_exists_ticker = 'replace')
