In particular I've made the interface for beginning a new backtest a lot simpler by encapsulating a lot of the "boilerplate" code into a new Backtest class. I've also modified the system to be fully workable with multiple currency pairs. In this article I'll describe the new interface and show the usual Moving Average Crossover example on both GBP/USD and EUR/USD.

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.


Trading on margin is extremely popular among retail Forex traders. It allows you to open a much larger position than your initial trading account would otherwise allow, by allocating only a small portion of your trading account as the margin, or collateral for the trade. Trading on margin also carries certain risks, as both your profits and losses are magnified.
In addition, I've had some comments from people suggesting that they'd like to see more varied order types than the simple Market Order. For carrying out proper HFT strategies against OANDA we are going to need to use Limit Orders. This will probably require a reworking of how the system currently executes trades, but it will allow a much bigger universe of trading strategies to be carried out.
Now that we have discussed the longer term plan I want to present some of the changes I have made to the code since diary entry #2. In particular, I want to describe how I modified the code to handle the Decimal data-type instead of using floating point storage. This is an extremely important change as floating point representations are a substantial source of long-term error in portfolio and order management systems.

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Each time you open a new trade, calculate how much free margin you would need to use if the trade drops to its stop loss level. In other words, if your free margin is currently $500, but your potential losses of a trade are $700 (if the trade hits stop loss), you could be in trouble. In these situations, either close some of your open positions, or decrease your position sizes in order to free up additional free margin.
We use real-time margining to allow you to see your trading risk at any moment of the day. Our real-time margin system applies margin requirements throughout the day to new trades and trades already on the books and enforces initial margin requirements at the end of the day, with real-time liquidation of positions instead of delayed margin calls. This system allows us to maintain our low commissions because we do not have to spread the cost of credit losses to customers in the form of higher costs.
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Often, closing one losing position will take the margin level Forex higher than 5%, as it will release the margin of that position, so the total used margin will decrease and consequently the margin level will increase. The system often takes the margin level higher than 5%, by closing the biggest position first. If your other losing positions continue losing and the margin level reaches 5% once more, the system will just close another losing position.
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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.

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.
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.
Risk Management - Many "research" backtests completely ignore risk management. Unfortunately this is generally necessary for brevity in describing the rules of a strategy. In reality we -must- use a risk overlay when trading, otherwise it is extremely likely that we will suffer a substantial loss at some stage. This is not to say that risk management can prevent this entirely, but it certainly makes it less likely!
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.
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.

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 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.
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