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.
GUI Control and Reporting - Right now the system is completely console/command line based. At the very least we will need some basic charting to display backtest results. A more sophisticated system will incorporate summary statistics of trades, strategy-level performance metrics as well as overall portfolio performance. This GUI could be implemented using a cross-platform windowing system such as Qt or Tkinter. It could also be presented using a web-based front-end, utilising a web-framework such as Django.

In order to understand Forex trading better, one should know all they can about margins. Forex margin level is another important concept that you need to understand. The Forex margin level is the percentage value based on the amount of accessible usable margin versus used margin. In other words, it is the ratio of equity to margin, and is calculated in the following way:
If you sell a security short, you must have sufficient equity in your account to cover any fees associated with borrowing the security. If you borrow the security through us, we will borrow the security on your behalf and your account must have sufficient collateral to cover the margin requirements of the short sale. To cover administrative fees and stock borrowing fees, we must post 102% of the value of the security borrowed as collateral with the lender. In instances in which the security shorted is hard to borrow, borrowing fees charged by the lender may be so high (greater than the interest earned) that the short seller must pay additional interest for the privilege of borrowing a security. Customers may view the indicative short stock interest rates for a specific stock through the Short Stock (SLB) Availability tool located in the Tools section of their Account Management page. For more information concerning shorting stocks and associated fees, visit our Stock Shorting page.
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.
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.
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.
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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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 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.
Trading on margin refers to trading on money borrowed from your broker in order to substantially increase your market exposure. When opening a margin trade, your broker lends you a certain sum of money depending on the leverage ratio used, and allocates a small portion of your trading account as the collateral, or margin for that trade. The remaining funds in your trading account will act as your free margin, which can be used to withstand negative price fluctuations from your existing leveraged positions, or to open new leveraged trades. The relation between your free margin and other important elements of your trading account, such as your balance and equity, will be explained later. For now, it’s important to understand the meaning of margin in Forex.
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