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.
Now, let’s say you open a trade worth $50,000 with the same trading account size and leverage ratio. Your required margin for this trade would be $500 (1% of your position size), and your free margin would now also amount to $500. In other words, you could withstand a negative price fluctuation of $500 until your free margin falls to zero and causes a margin call. Your position size of $50,000 could only fall to $49,500 – this would be the largest loss your trading account could withstand.

Have you always dreamed of financial freedom? Maybe you want to start your own business and need a way to supplement the income it brings in. It doesn’t matter what your goals are – Forex trading may be the solution you have been looking for. This high-reward, high-risk market has plenty of opportunities for the patient, insightful investor. You do not need to spend all day researching and watching the market; currency trading only requires you to dedicate a small portion of each day to it, leaving you with more time to spend following your dreams!


Margin requirements for futures and futures options are established by each exchange through a calculation algorithm known as SPAN margining. SPAN (Standard Portfolio Analysis of Risk) evaluates overall portfolio risk by calculating the worst possible loss that a portfolio of derivative and physical instruments might reasonably incur over a specified time period (typically one trading day.) This is done by computing the gains and losses that the portfolio would incur under different market conditions. The most important part of the SPAN methodology is the SPAN risk array, a set of numeric values that indicate how a particular contract will gain or lose value under various conditions. Each condition is called a risk scenario. The numeric value for each risk scenario represents the gain or loss that that particular contract will experience for a particular combination of price (or underlying price) change, volatility change, and decrease in time to expiration.
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.
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.
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.
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.

Risk Management - Many "research" backtests completely ignore risk management. Unfortunately this is generally necessary for brevity in describing the rules of a strategy. In reality we -must- use a risk overlay when trading, otherwise it is extremely likely that we will suffer a substantial loss at some stage. This is not to say that risk management can prevent this entirely, but it certainly makes it less likely!


Slippage Handling - The system is currently generating a lot of slippage due to the high-frequency nature of the tick data provided from OANDA. This means that the portfolio balance calculated locally is not reflecting the balance calculated by OANDA. Until correct event-handling and slippage adjustment is carried out, this will mean that a backtest will not correctly reflect reality.
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.
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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