In order to understand Forex trading better, one should know all they can about margins. Forex margin level is another important concept that you need to understand. The Forex margin level is the percentage value based on the amount of accessible usable margin versus used margin. In other words, it is the ratio of equity to margin, and is calculated in the following way:
In a margin account, the broker uses the $1,000 as a security deposit of sorts. If the investor's position worsens and his or her losses approach $1,000, the broker may initiate a margin call. When this occurs, the broker will usually instruct the investor to either deposit more money into the account or to close out the position to limit the risk to both parties.
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Foreign exchange (forex) or FX trading involves trading the prices of global currencies, and at City Index it is possible to trade on the prices of a huge range of global currencies. Currency trading allows you to speculate on the movement of one currency against another, and is traded in pairs, for example the Euro against the US Dollar (EUR/USD).
The "philosophy" of the forex trading system, as with the rest of the QuantStart site, is to try and mimic real-life trading as much as possible in our backtesting. This means including the details that are often excluded from more "research oriented" backtesting situations. Latency, server outages, automation, monitoring, realistic transaction costs will all be included within the models to give us a good idea of how well a strategy is likely to perform.
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.
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.
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.