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.
Trading on a margin can have varying consequences. It can influence your trading experience both positively and negatively, with both profits and losses potentially being seriously augmented. Your broker takes your margin deposit and then pools it with someone else's margin Forex deposits. Brokers do this in order to be able to place trades within the whole interbank network.
The Federal Reserve Board and self-regulatory organizations (SROs), such as the New York Stock Exchange and FINRA, have clear rules regarding margin trading. In the United States, the Fed's Regulation T allows investors to borrow up to 50 percent of the price of the securities to be purchased on margin. The percentage of the purchase price of securities that an investor must pay for is called the initial margin. To buy securities on margin, the investor must first deposit enough cash or eligible securities with a broker to meet the initial margin requirement for that purchase.

Free margin in Forex is the amount of money that is not involved in any trade. You can use it to take more positions, however, that isn't all - as the free margin is the difference between equity and margin. If your open positions make you money, the more they achieve profit, the greater the equity you will have, so you will have more free margin as a result. There may be a situation when you have some open positions and also some pending orders simultaneously.
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.
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.
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 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.
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.
Equity – Your equity is simply the total amount of funds you have in your trading account. Your equity will change and float each time you open a new trading position, in such a way that all your unrealised profits and losses will be added to or deducted from your total equity. For example, if your trading account size is $1,000 and your open positions are $50 in profit, your equity will amount to $1,050.
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.
If traders are positive on the prospects for the Yen, they would expect the number on the right to go down – i.e. the Yen would be getting stronger against the Dollar. Traders would be buying less Yen with a Dollar as the Yen got stronger. Similarly, if the Yen was expected to weaken, forex traders would expect the Yen number to go up, reflecting the fact that the dollar could buy more yen.