GUI Control and Reporting - Right now the system is completely console/command line based. At the very least we will need some basic charting to display backtest results. A more sophisticated system will incorporate summary statistics of trades, strategy-level performance metrics as well as overall portfolio performance. This GUI could be implemented using a cross-platform windowing system such as Qt or Tkinter. It could also be presented using a web-based front-end, utilising a web-framework such as Django.
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.
Equity – Your equity is simply the total amount of funds you have in your trading account. Your equity will change and float each time you open a new trading position, in such a way that all your unrealised profits and losses will be added to or deducted from your total equity. For example, if your trading account size is $1,000 and your open positions are $50 in profit, your equity will amount to $1,050.
Imagine that you have $10,000 on your account account, and you have a losing position with a margin evaluated at $1,000. If your position goes against you, and it goes to a $9,000 loss, the equity will be $1,000 (i.e $10,000 - $9,000), which equals the margin. Thus, the margin level will be 100%. Again, if the margin level reaches the rate of 100%, you can't take any new positions, unless the market suddenly turns around and your equity level turns out to be greater than the margin.
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Just like securities, commodities have required initial and maintenance margins. These are typically set by the individual exchanges as a percentage of the current value of a futures contract, based on the volatility and price of the contract. The initial margin requirement for a futures contract is the amount of money you must put up as collateral to open position on the contract. To be able to buy a futures contract, you must meet the initial margin requirement, which means that you must deposit or already have that amount of money in your account.
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.
Forex margin is a good faith deposit that a trader puts up as collateral to initiate a trade. Essentially, it is the minimum amount that a trader needs in the trading account to open a new position. This is usually communicated as a percentage of the notional value (trade size) of the forex trade. The difference between the deposit and the full value of the trade is “borrowed” from the broker.
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.
Currency markets are important to a broad range of participants, from banks, brokers, hedge funds and investor traders who trade FX. Any company that operates or has customers overseas will need to trade currency. Central banks can also be active in currency markets, as they seek to keep the currency they are responsible for trading within a specific range.
Unit Tests for Position/Portfolio - While I've not mentioned it directly in diary entries #1 and #2, I've actually been writing some unit tests for the Portfolio and Position objects. Since these are so crucial to the calculations of the strategy, one must be extremely confident that they perform as expected. An additional benefit of such tests is that they allow the underlying calculation to be modified, such that if all tests still pass, we can be confident that the overall system will continue to behave as expected.
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.