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It is essential that traders understand the margin close out rule specified by the broker in order to avoid the liquidation of current positions. When an account is placed on margin call, the account will need to be funded immediately to avoid the liquidation of current open positions. Brokers do this in order to bring the account equity back up to an acceptable level.

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Borrowing money to purchase securities is known as "buying on margin". When an investor borrows money from his broker to buy a stock, he must open a margin account with his broker, sign a related agreement and abide by the broker's margin requirements. The loan in the account is collateralized by investor's securities and cash. If the value of the stock drops too much, the investor must deposit more cash in his account, or sell a portion of the stock.
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.

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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.
If you believe that a currency pair such as the Australian dollar will rise against the US Dollar you can place a buy trade on AUD/USD. If the prices rises, you will make a profit for every point that AUD appreciates against the USD. If the market falls, then you will make a loss for every point the price moves against you. Our trading platform tells you in real-time how much profit or loss you are making.
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.
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.
Simply download the latest version from the Software page and after installation follow the initial wizard or click on the help/start trial menu. Please note that the software periodically communicates with the license servers to validate your trial. After the trial period you can use the software in read only mode which means you cannot modify your TradingDiary Pro database anymore.
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Margin calls are mechanisms put in place by your Forex broker in order to keep your used margin secure. Remember, your used margin is allocated by your broker as the collateral for funds borrowed from your broker. A margin call happens when your free margin falls to zero, and all you have left in your trading account is your used, or required margin. When this happens, your broker will automatically close all open positions at current market rates.
An extremely important requested feature for QSForex has been the ability to backtest over multiple days. Previously the system only supported backtesting via a single file. This was not a scalable solution as such a file must be read into memory and subsequently into a Pandas DataFrame. While the tick data files produced are not huge (roughly 3.5Mb each), they do add up quickly if we consider multiple pairs over periods of months or more.
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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