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.

All currency trading is done in pairs. Unlike the stock market, where you can buy or sell a single stock, you have to buy one currency and sell another currency in the forex market. Next, nearly all currencies are priced out to the fourth decimal point. A pip or percentage in point is the smallest increment of trade. One pip typically equals 1/100 of 1 percent.
How can you avoid this unanticipated surprise? Margin calls can be effectively avoided by carefully monitoring your account balance on a regular basis, and by using stop-loss orders on every position to minimise the risk. Another smart action to consider is to implement risk management within your trading. By managing your the potential risks effectively, you will be more aware of them, and you should also be able to anticipate them and potentially avoid them altogether.
Margins are a hotly debated topic. Some traders argue that too much margin is very dangerous, however it all depends on trading style and the amount of trading experience one has. If you are going to trade on a margin account, it is important that you know what your broker's policies are on margin accounts, and that you fully understand and are comfortable with the risks involved. Be careful to avoid a Forex margin call.
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 […]
You could ask yourself, why wouldn’t you use the highest leverage ratio available in order to decrease your margin requirements and get an extremely high market exposure? The answer is rather simple and deals with Forex risk management. While leverage magnifies your potential profits, it also magnifies your potential losses. Trading on high leverage increases your risk in trading.
As we've already stated, trading on margin is trading on money borrowed from your broker. Each time you open a trade on margin, your broker automatically allocates the required margin from your existing funds in the trading account in order to back the margin trade. The precise amount of allocated funds depends on the leverage ratio used on your account.
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!
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 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 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.

For those of you who are new to source version control you will probably want to read up on how git (and version control in general) works with the fantastic free ebook Pro Git. It is worth spending some time learning about source control as it will save you a huge amount of future headache if you spend a lot of time programming and updating projects!


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.
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.
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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