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.
The market then wants to trigger one of your pending orders but you may not have enough Forex free margin in your account. That pending order will either not be triggered or will be cancelled automatically. This can cause some traders to think that their broker failed to carry out their orders. Of course in this instance, this just isn't true. It's simply because the trader didn't have enough free margin in their trading account.
Systems that derive risk-based margin requirements deliver adequate assessments of the risk for complex derivative portfolios under small/moderate move scenarios. Such systems are less comprehensive when considering large moves in the price of the underlying stock or future. We have enhanced the basic exchange margin models with algorithms that consider the portfolio impact of larger moves up 30% (or even higher for extremely volatile stocks). This 'Extreme Margin Model' may increase the margin requirement for portfolios with net short options positions, and is particularly sensitive to short positions in far out-of-the-money options.
In a margin account, the broker uses the $1,000 as a security deposit of sorts. If the investor's position worsens and his or her losses approach $1,000, the broker may initiate a margin call. When this occurs, the broker will usually instruct the investor to either deposit more money into the account or to close out the position to limit the risk to both parties.

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.
Unit Tests for Position/Portfolio - While I've not mentioned it directly in diary entries #1 and #2, I've actually been writing some unit tests for the Portfolio and Position objects. Since these are so crucial to the calculations of the strategy, one must be extremely confident that they perform as expected. An additional benefit of such tests is that they allow the underlying calculation to be modified, such that if all tests still pass, we can be confident that the overall system will continue to behave as expected.

All currency trading is done in pairs. Unlike the stock market, where you can buy or sell a single stock, you have to buy one currency and sell another currency in the forex market. Next, nearly all currencies are priced out to the fourth decimal point. A pip or percentage in point is the smallest increment of trade. One pip typically equals 1/100 of 1 percent.
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.
Multiple Broker/FIX Integration - At the moment we are strongly coupled to the OANDA broker. As I said this is simply because I came across their API and found it to be a modern offering. There are plenty of other brokers out there, many of which support the FIX protocol. Adding a FIX capability would increase the number of brokers that could be used with the system.

In particular we will need strategy level metrics, including common risk/reward ratios such as the Sharpe Ratio, Information Ratio and Sortino Ratio. We will also need drawdown statistics including the distribution of the drawdowns, as well as descriptive stats such as maximum drawdown. Other useful metrics include the Compound Annual Growth Rate (CAGR) and total return.

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