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.
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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.
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:
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.