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).
If you believe that a currency pair such as the Australian dollar will rise against the US Dollar you can place a buy trade on AUD/USD. If the prices rises, you will make a profit for every point that AUD appreciates against the USD. If the market falls, then you will make a loss for every point the price moves against you. Our trading platform tells you in real-time how much profit or loss you are making.
In particular I would like to make the system a lot faster, since it will allow parameter searches to be carried out in a reasonable time. While Python is a great tool, it's one drawback is that it is relatively slow when compared to C/C++. Hence I will be carrying out a lot of profiling to try and improve the execution speed of both the backtest and the performance calculations.
A Portfolio Margin account can provide lower margin requirements than a Margin account. However, for a portfolio with concentrated risk, the requirements under Portfolio Margin may be greater than those under Margin, as the true economic risk behind the portfolio may not be adequately accounted for under the static Reg T calculations used for Margin accounts. Customers can compare their current Reg T margin requirements for their portfolio with those current projected under Portfolio Margin rules by clicking the Try PM button from the Account Window in Trader Workstation (demo or customer account).
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.