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.
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.
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.
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.
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.

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.
Maintenance margin for commodities is the amount that you must maintain in your account to support the futures contract and represents the lowest level to which your account can drop before you must deposit additional funds. Commodities positions are marked to market daily, with your account adjusted for any profit or loss that occurs. Because the price of underlying commodities fluctuates, it is possible that the value of the commodity may decline to the point at which your account balance falls below the required maintenance margin. If this happens, brokers typically make a margin call, which means you must deposit additional funds to meet the margin requirement.
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.
If traders are positive on the prospects for the Yen, they would expect the number on the right to go down – i.e. the Yen would be getting stronger against the Dollar. Traders would be buying less Yen with a Dollar as the Yen got stronger. Similarly, if the Yen was expected to weaken, forex traders would expect the Yen number to go up, reflecting the fact that the dollar could buy more yen.
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