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.
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. 

Not all securities can be bought on margin. Buying on margin is a double-edged sword that can translate into bigger gains or bigger losses. In volatile markets, investors who borrowed from their brokers may need to provide additional cash if the price of a stock drops too much for those who bought on margin or rallies too much for those who shorted a stock. In such cases, brokers are also allowed to liquidate a position, even without informing the investor. Real-time position monitoring is a crucial tool when buying on margin or shorting a stock.
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.

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Not all securities can be bought on margin. Buying on margin is a double-edged sword that can translate into bigger gains or bigger losses. In volatile markets, investors who borrowed from their brokers may need to provide additional cash if the price of a stock drops too much for those who bought on margin or rallies too much for those who shorted a stock. In such cases, brokers are also allowed to liquidate a position, even without informing the investor. Real-time position monitoring is a crucial tool when buying on margin or shorting a stock.


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.
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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.
Monitoring and High Availability - Since we are considering a high-frequency intraday system, we must put comprehensive monitoring and high availability redundancy in place. This means reporting on CPU usage, disk usage, network I/O, latency and checking that any periodic scripts are set to keep running. In addition we need a backup and restore strategy. Ask yourself what backup plans you would have in place if you had large open positions, in a volatile market, and your server suddenly died. Believe me, it happens!

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.


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.
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.

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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