To date, we've been experimenting with the OANDA Rest API in order to see how it compared to the API provided by Interactive Brokers. We've also seen how to add in a basic portfolio replication element as the first step towards a proper event-driven backtesting system. I've also had some helpful comments on both previous articles (#1 and #2), which suggests that many of you are keen on changing and extending the code yourselves.
Imagine that you have $10,000 on your account account, and you have a losing position with a margin evaluated at $1,000. If your position goes against you, and it goes to a $9,000 loss, the equity will be $1,000 (i.e $10,000 - $9,000), which equals the margin. Thus, the margin level will be 100%. Again, if the margin level reaches the rate of 100%, you can't take any new positions, unless the market suddenly turns around and your equity level turns out to be greater than the margin.
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.
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.
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.

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.
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.
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 that we have discussed the longer term plan I want to present some of the changes I have made to the code since diary entry #2. In particular, I want to describe how I modified the code to handle the Decimal data-type instead of using floating point storage. This is an extremely important change as floating point representations are a substantial source of long-term error in portfolio and order management systems.
The Forex market is one of a number of financial markets that offer trading on margin through a Forex margin account. Many traders are attracted to the Forex market because of the relatively high leverage that Forex brokers offer to new traders. But, what are leverage and margin, how are they related, and what do you need to know when trading on margin? This and more will be covered in the following lines.
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).
Free Margin – Your free margin represents your total equity minus any margin used for leveraged trades. For example, if your equity is $1,000 and your used margin is $100, your free margin would amount to $900. Following your free margin is extremely important, as it is used to withstand negative price fluctuations from your open trades and to open new leveraged trades. It’s important to understand that your free margin increases with profitable positions, but decreases with your losing positions. Once the free margin drops to zero or below, your broker will activate the so-called margin call and close all your open positions at the current market rate, in order to prevent your equity from falling below the required margin.
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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