Let's presume that the market keeps on going against you. In this case, the broker will simply have no choice but to shut down all your losing positions. This limit is referred to as a stop out level. For example, when the stop out level is established at 5% by a broker, the trading platform will start closing your losing positions automatically if your margin level reaches 5%. It is important to note that it starts closing from the biggest losing position.
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).
In particular I've made the interface for beginning a new backtest a lot simpler by encapsulating a lot of the "boilerplate" code into a new Backtest class. I've also modified the system to be fully workable with multiple currency pairs. In this article I'll describe the new interface and show the usual Moving Average Crossover example on both GBP/USD and EUR/USD.
In particular we need to modify -every- value that appears in a Position calculation to a Decimal data-type. This includes the units, exposure, pips, profit and percentage profit. This ensures we are in full control of how rounding issues are handled when dealing with currency representations that have two decimal places of precision. In particular we need to choose the method of rounding. Python supports a few different types, but we are going to go with ROUND_HALF_DOWN, which rounds to the nearest integer with ties going towards zero.
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.