Let’s cover this with an example. If you have $1,000 in your trading account and use a leverage of 1:100 you could theoretically open a position size of $100,000. However, by doing so, your entire trading account would be allocated as the required margin for the trade, and even a single price tick against you would lead to a margin call. There would be no free margin to withstand any negative price fluctuation.
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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.

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Trading on margin can be a profitable Forex strategy, but it is important to understand all the possible risks. You should make sure you know how your margin account operates, and be sure to read the margin agreement between you and your selected broker. If there is anything you are unclear about in your agreement, ask questions and make sure everything is clear.
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.
So, for an investor who wants to trade $100,000, a 1% margin would mean that $1,000 needs to be deposited into the account. The remaining 99% is provided by the broker. No interest is paid directly on this borrowed amount, but if the investor does not close their position before the delivery date, it will have to be rolled over. In that case, interest may be charged depending on the investor's position (long or short) and the short-term interest rates of the underlying currencies.
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.

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