Leveraged trading in foreign currency or off-exchange products on margin carries significant risk and may not be suitable for all investors. We advise you to carefully consider whether trading is appropriate for you based on your personal circumstances. Forex trading involves risk. Losses can exceed deposits. We recommend that you seek independent advice and ensure you fully understand the risks involved before trading.
As you may now come to understand, FX margins are one of the key aspects of Forex trading that must not be overlooked, as they can potentially lead to unpleasant outcomes. In order to avoid them, you should understand the theory concerning margins, margin levels and margin calls, and apply your trading experience to create a viable Forex strategy. Indeed a well developed approach will undoubtedly lead you to trading success in the end.
The script is currently hardcoded to generate forex data for the entire month of January 2014. It uses the Python calendar library in order to ascertain business days (although I haven't excluded holidays yet) and then generates a set of files of the form BBBQQQ_YYYYMMDD.csv, where BBBQQQ will be the specified currency pair (e.g. GBPUSD) and YYYYMMDD is the specified date (e.g. 20140112).
An extremely important requested feature for QSForex has been the ability to backtest over multiple days. Previously the system only supported backtesting via a single file. This was not a scalable solution as such a file must be read into memory and subsequently into a Pandas DataFrame. While the tick data files produced are not huge (roughly 3.5Mb each), they do add up quickly if we consider multiple pairs over periods of months or more.
Local Portfolio Handling - In my opinion carrying out a backtest that inflates strategy performance due to unrealistic assumptions is annoying at best and extremely unprofitable at worst! Introducing a local portfolio object that replicates the OANDA calculations means that we can check our internal calculations while carrying out practice trading, which gives us greater confidence when we later use this same portfolio object for backtesting on historical data.