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You could ask yourself, why wouldn’t you use the highest leverage ratio available in order to decrease your margin requirements and get an extremely high market exposure? The answer is rather simple and deals with Forex risk management. While leverage magnifies your potential profits, it also magnifies your potential losses. Trading on high leverage increases your risk in trading.
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Robust Strategies - I have only demonstrated some simple random signal generating "toy" strategies to date. Now that we are beginning to create a reliable intraday forex trading system, we should start carrying out some more interesting strategies. Future diary entries will concentrate on strategies drawn from a mixture of "technical" indicators/filters as well as time series models and machine learning techniques.
Trading on margin is extremely popular among retail Forex traders. It allows you to open a much larger position than your initial trading account would otherwise allow, by allocating only a small portion of your trading account as the margin, or collateral for the trade. Trading on margin also carries certain risks, as both your profits and losses are magnified.
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.

As you may now come to understand, FX margins are one of the key aspects of Forex trading that must not be overlooked, as they can potentially lead to unpleasant outcomes. In order to avoid them, you should understand the theory concerning margins, margin levels and margin calls, and apply your trading experience to create a viable Forex strategy. Indeed a well developed approach will undoubtedly lead you to trading success in the end.


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. 

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.
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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