Systems that derive risk-based margin requirements deliver adequate assessments of the risk for complex derivative portfolios under small/moderate move scenarios. Such systems are less comprehensive when considering large moves in the price of the underlying stock or future. We have enhanced the basic exchange margin models with algorithms that consider the portfolio impact of larger moves up 30% (or even higher for extremely volatile stocks). This 'Extreme Margin Model' may increase the margin requirement for portfolios with net short options positions, and is particularly sensitive to short positions in far out-of-the-money options.
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.
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.
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.
76% of retail accounts lose money when trading CFDs with this provider. CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. 76% of retail investor accounts lose money when trading CFDs with this provider. You should consider whether you understand how CFDs work and whether you can afford to take the high risk of losing your money.
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.