Trading on margin refers to trading on money borrowed from your broker in order to substantially increase your market exposure. When opening a margin trade, your broker lends you a certain sum of money depending on the leverage ratio used, and allocates a small portion of your trading account as the collateral, or margin for that trade. The remaining funds in your trading account will act as your free margin, which can be used to withstand negative price fluctuations from your existing leveraged positions, or to open new leveraged trades. The relation between your free margin and other important elements of your trading account, such as your balance and equity, will be explained later. For now, it’s important to understand the meaning of margin in Forex.
Popular leverage ratios in Forex trading include 1:10, 1:50, 1:100, 1:200, or even higher. Simply put, the leverage ratio determines the position size you’re allowed to take based on the size of your trading account. For example, a 1:100 leverage allows you to open a position 10 times higher than your trading account size, i.e., if you have $1,000 in your account, you can open a position worth $10,000. Similarly, a  leverage ratio of 1:100 allows you to open a position size 100 times larger than your trading account size. With $1,000 in your trading account, you could open a position worth $100,000!
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These articles, on the other hand, discuss currency trading as buying and selling currency on the foreign exchange (or "Forex") market with the intent to make money, often called "speculative forex trading". XE does not offer speculative forex trading, nor do we recommend any firms that offer this service. These articles are provided for general information only.
Robust Strategies - I have only demonstrated some simple random signal generating "toy" strategies to date. Now that we are beginning to create a reliable intraday forex trading system, we should start carrying out some more interesting strategies. Future diary entries will concentrate on strategies drawn from a mixture of "technical" indicators/filters as well as time series models and machine learning techniques.

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.
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.
Note also that when we begin storing our trades in a relational database (as described above in the roadmap) we will need to make sure we once again use the correct data-type. PostgreSQL and MySQL support a decimal representation. It is vital that we utilise these data-types when we create our database schema, otherwise we will run into rounding errors that are extremely difficult to diagnose!
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 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.
I post this to let you know, as the title mentions it, that I made a trading diary, with google documents tool. This a generic spreadsheet which allows any trader to manage his trading (his risk, his pnl, his opened position, the orders...) with a trding diary. Every trader,should have one, and I mad mine with google docs. At least you must have an account to acces this spreadsheet.
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