Foreign exchange (forex) or FX trading involves trading the prices of global currencies, and at City Index it is possible to trade on the prices of a huge range of global currencies. Currency trading allows you to speculate on the movement of one currency against another, and is traded in pairs, for example the Euro against the US Dollar (EUR/USD).
Now, let’s say you open a trade worth $50,000 with the same trading account size and leverage ratio. Your required margin for this trade would be $500 (1% of your position size), and your free margin would now also amount to $500. In other words, you could withstand a negative price fluctuation of $500 until your free margin falls to zero and causes a margin call. Your position size of $50,000 could only fall to $49,500 – this would be the largest loss your trading account could withstand.
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.