The majority of the volume in currency trading is confined to only 18 currency pairs compared to the thousands of stocks that are available in the global equity markets. Although there are other traded pairs outside of the 18, the eight currencies most often traded are the U.S. dollar (USD), Canadian dollar (CAD), euro (EUR), British pound (GBP), Swiss franc (CHF), New Zealand dollar (NZD), Australian dollar (AUD) and the Japanese yen (JPY). Although nobody would say that currency trading is easy, having far fewer trading options makes trade and portfolio management an easier task.
Just like securities, commodities have required initial and maintenance margins. These are typically set by the individual exchanges as a percentage of the current value of a futures contract, based on the volatility and price of the contract. The initial margin requirement for a futures contract is the amount of money you must put up as collateral to open position on the contract. To be able to buy a futures contract, you must meet the initial margin requirement, which means that you must deposit or already have that amount of money in your account.
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).
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.