We also apply a concentrated margining requirement to Margin accounts. An account's two largest positions and their underlying derivatives will be re-valued using the worst case scenario within a +/- 30% scanning range. The remaining positions will be re-valued based upon a move of +/-5%. If the concentrated margining requirement exceeds that of the standard rules based margin required, then the newly calculated concentrated margin requirement will be applied to the account.
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).
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 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.
As we've already stated, trading on margin is trading on money borrowed from your broker. Each time you open a trade on margin, your broker automatically allocates the required margin from your existing funds in the trading account in order to back the margin trade. The precise amount of allocated funds depends on the leverage ratio used on your account.
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.

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.

Forex margin is a good faith deposit that a trader puts up as collateral to initiate a trade. Essentially, it is the minimum amount that a trader needs in the trading account to open a new position. This is usually communicated as a percentage of the notional value (trade size) of the forex trade. The difference between the deposit and the full value of the trade is “borrowed” from the broker.

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.


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.
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).
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.
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.
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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