How to use the finmarketpy.util.marketconstants.MarketConstants function in finmarketpy

from findatapy.util import SwimPool
from findatapy.util import LoggerManager

import pickle
#import zlib
#import lz4framed    # conda install -c conda-forge py-lz4framed

# make blosc optional (only when trying to run backtests in parallel)
try:
    import blosc
except:
    pass

import pickle

market_constants = MarketConstants()


class Backtest(object):
    """Conducts backtest for strategies trading assets. Assumes we have an input of total returns. Reports historical return statistics
    and returns time series.

    """

    def __init__(self):
        self._pnl = None
        self._portfolio = None
        return

    def calculate_diagnostic_trading_PnL(self, asset_a_df, signal_df, further_df=[], further_df_labels=[]):
        """Calculates P&L table which can be used for debugging purposes,

import datetime

# import matplotlib
# import matplotlib.pyplot as plt
import pandas
import copy

from chartpy import Chart, Style, ChartConstants
from findatapy.timeseries import Calculations, Timezone
from findatapy.util.loggermanager import LoggerManager
from finmarketpy.backtest import Backtest

from finmarketpy.util.marketconstants import MarketConstants
from findatapy.util.swimpool import SwimPool

market_constants = MarketConstants()


class TradeAnalysis(object):
    """Applies some basic trade analysis for a trading strategy (as defined by TradingModel). Use PyFolio to create some
    basic trading statistics. Also allows you test multiple parameters for a specific strategy (like TC).

    """

    def __init__(self, engine=ChartConstants().chartfactory_default_engine):
        # self.logger = LoggerManager().getLogger(__name__)
        self.DUMP_PATH = 'output_data/' + datetime.date.today().strftime("%Y%m%d") + ' '
        self.DEFAULT_PLOT_ENGINE = engine
        self.chart = Chart(engine=self.DEFAULT_PLOT_ENGINE)

        return

USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-high	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-low	            xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-high.1	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.number-observations	xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.first-revision	        185
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.first-revision-date	20030307
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.release-dt	            20030207
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.release-date-time-full	08/01/1999 13:30

    """

    # _econ_data_frame = None

    # where your HDF5 file is stored with economic data
    # TODO integrate with on the fly downloading!
    _hdf5_file_econ_file = MarketConstants().hdf5_file_econ_file
    _db_database_econ_file = MarketConstants().db_database_econ_file

    ### manual offset for certain events where Bloomberg/data vendor displays the wrong date (usually because of time differences)
    _offset_events = {'AUD-Australia Labor Force Employment Change SA.release-dt' : 1}

    def __init__(self, df = None):
        super(EventStudy, self).__init__()

        self.config = ConfigManager()
        self.logger = LoggerManager().getLogger(__name__)
        self.filter = Filter()
        self.io_engine = IOEngine()
        self.speed_cache = SpeedCache()

        if df is not None:
            self._econ_data_frame = df

import datetime
from datetime import timedelta

import numpy

from finmarketpy.util.marketconstants import MarketConstants
from findatapy.market import IOEngine
from findatapy.util import ConfigManager
from findatapy.market import SpeedCache

try:
    from numbapro import autojit
except: pass

marketconstants = MarketConstants()

class EventsFactory(EventStudy):
    """Provides methods to fetch data on economic data events and to perform basic event studies for market data around
    these events. Note, requires a file of input of the following (transposed as columns!) - we give an example for
    NFP released on 7 Feb 2003 (note, that release-date-time-full, need not be fully aligned by row).

    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.Date	                31/01/2003 00:00
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.close	                xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.actual-release	        143
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-median	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-average	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-high	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-low	            xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-high.1	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.number-observations	xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.first-revision	        185

USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-low	            xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.survey-high.1	        xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.number-observations	xyz
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.first-revision	        185
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.first-revision-date	20030307
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.release-dt	            20030207
    USD-US Employees on Nonfarm Payrolls Total MoM Net Change SA.release-date-time-full	08/01/1999 13:30

    """

    # _econ_data_frame = None

    # where your HDF5 file is stored with economic data
    # TODO integrate with on the fly downloading!
    _hdf5_file_econ_file = MarketConstants().hdf5_file_econ_file
    _db_database_econ_file = MarketConstants().db_database_econ_file

    ### manual offset for certain events where Bloomberg/data vendor displays the wrong date (usually because of time differences)
    _offset_events = {'AUD-Australia Labor Force Employment Change SA.release-dt' : 1}

    def __init__(self, df = None):
        super(EventStudy, self).__init__()

        self.config = ConfigManager()
        self.logger = LoggerManager().getLogger(__name__)
        self.filter = Filter()
        self.io_engine = IOEngine()
        self.speed_cache = SpeedCache()

        if df is not None:
            self._econ_data_frame = df
        else: