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.
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.
Once an investor has started buying a stock on margin, the NYSE and FINRA require that a minimum amount of equity be maintained in the investor's margin account. These rules require investors to have at least 25% of the total market value of the securities they own in their margin account. This is called the maintenance margin. For market participants identified as pattern day traders, the maintenance margin requirement is a minimum of $25,000 (or 25% of the total market value of the securities, whichever is higher).
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.
Forex margin is a good faith deposit that a trader puts up as collateral to initiate a trade. Essentially, it is the minimum amount that a trader needs in the trading account to open a new position. This is usually communicated as a percentage of the notional value (trade size) of the forex trade. The difference between the deposit and the full value of the trade is “borrowed” from the broker.
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.

One of the unique features of TradingDiary Pro which you cannot find in any trading journal software is the options strategy support. TradingDiary Pro is the perfect solution for an options trading journal and tracking your stock and futures options strategies. What is an options strategy? Options strategy is simultaneously buying or selling one or […]
Currency markets are important to a broad range of participants, from banks, brokers, hedge funds and investor traders who trade FX. Any company that operates or has customers overseas will need to trade currency. Central banks can also be active in currency markets, as they seek to keep the currency they are responsible for trading within a specific range.
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.
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.

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.