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.
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Trading foreign exchange on margin carries a high level of risk, and may not be suitable for everyone. Before deciding to trade foreign exchange you should carefully consider your investment objectives, level of experience, and risk appetite. Remember, you could sustain a loss of some or all of your initial investment, which means that you should not invest money that you cannot afford to lose. If you have any doubts, it is advisable to seek advice from an independent financial advisor.
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.
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.
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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!
The market then wants to trigger one of your pending orders but you may not have enough Forex free margin in your account. That pending order will either not be triggered or will be cancelled automatically. This can cause some traders to think that their broker failed to carry out their orders. Of course in this instance, this just isn't true. It's simply because the trader didn't have enough free margin in their trading account.
Margin requirements for futures and futures options are established by each exchange through a calculation algorithm known as SPAN margining. SPAN (Standard Portfolio Analysis of Risk) evaluates overall portfolio risk by calculating the worst possible loss that a portfolio of derivative and physical instruments might reasonably incur over a specified time period (typically one trading day.) This is done by computing the gains and losses that the portfolio would incur under different market conditions. The most important part of the SPAN methodology is the SPAN risk array, a set of numeric values that indicate how a particular contract will gain or lose value under various conditions. Each condition is called a risk scenario. The numeric value for each risk scenario represents the gain or loss that that particular contract will experience for a particular combination of price (or underlying price) change, volatility change, and decrease in time to expiration.
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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.