class_docstr: Shows how to create a simple FX CTA style strategy, using the 
  TradingModel abstract class (backtest_examples.py
  is a lower level way of doing this). Uses BBG total returns data.
class_docstr_tok: ["Shows","how","to","create","a","simple","FX","CTA","style","strategy",",","using","the","TradingModel","abstract","class","(","backtest_examples.py","is","a","lower","level","way","of","doing","this",")",".","Uses","BBG","total","returns","data","."]
code: def load_assets(self, br=None):
      ##### FILL IN WITH YOUR ASSET DATA
      from findatapy.util.loggermanager import LoggerManager
  
      logger = LoggerManager().getLogger(__name__)
  
      # For FX basket
      full_bkt = [
          "EURUSD",
          "USDJPY",
          "GBPUSD",
          "AUDUSD",
          "USDCAD",
          "NZDUSD",
          "USDCHF",
          "USDNOK",
          "USDSEK",
      ]
  
      basket_dict = {}
  
      for i in range(0, len(full_bkt)):
          basket_dict[full_bkt[i]] = [full_bkt[i]]
  
      basket_dict["FX trend"] = full_bkt
  
      br = self.load_parameters(br=br)
  
      logger.info("Loading asset data...")
  
      vendor_tickers = [x + "CR CMPN Curncy" for x in full_bkt]
  
      market_data_request = MarketDataRequest(
          start_date=br.start_date,  # start date
          finish_date=br.finish_date,  # finish date
          freq="daily",  # daily data
          data_source="bloomberg",  # use Quandl as data source
          tickers=full_bkt,  # ticker (Cuemacro)
          fields=["close"],  # which fields to download
          vendor_tickers=vendor_tickers,  # ticker (Quandl)
          vendor_fields=[
              "PX_LAST"
          ],  # which Bloomberg fields to download
          cache_algo="internet_load_return",
      )  # how to return data
  
      asset_df = self.market.fetch_market(market_data_request)
  
      # If web connection fails read from CSV
      if asset_df is None:
          import pandas
  
          asset_df = pandas.read_csv(
              "fxcta.csv",
              index_col=0,
              parse_dates=["Date"],
              date_parser=lambda x: pandas.datetime.strptime(
                  x, "%Y-%m-%d"
              ),
          )
  
      # Signalling variables
      spot_df = asset_df
      spot_df2 = None
  
      # asset_df
  
      return asset_df, spot_df, spot_df2, basket_dict
  
file_path: finmarketpy/finmarketpy_examples/tradingmodelfxtrend_bbg_example.py
imports: import datetime
  from findatapy.market import Market, MarketDataGenerator, MarketDataRequest
  from finmarketpy.backtest import TradingModel, BacktestRequest
  from finmarketpy.economics import TechIndicator
label_desc: Use image node_id 3 for calling the TradingModelFXTrend_BBG_Example obj's underlying member method code with example usage: obj.load_assets(br) and returns: asset_df,  spot_df,  spot_df2,  basket_dict