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.
The currency exchange rate is the rate at which one currency can be exchanged for another. It is always quoted in pairs like the EUR/USD (the Euro and the US Dollar). Exchange rates fluctuate based on economic factors like inflation, industrial production and geopolitical events. These factors will influence whether you buy or sell a currency pair.
An extremely important requested feature for QSForex has been the ability to backtest over multiple days. Previously the system only supported backtesting via a single file. This was not a scalable solution as such a file must be read into memory and subsequently into a Pandas DataFrame. While the tick data files produced are not huge (roughly 3.5Mb each), they do add up quickly if we consider multiple pairs over periods of months or more.
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All currency trading is done in pairs. Unlike the stock market, where you can buy or sell a single stock, you have to buy one currency and sell another currency in the forex market. Next, nearly all currencies are priced out to the fourth decimal point. A pip or percentage in point is the smallest increment of trade. One pip typically equals 1/100 of 1 percent.
The "philosophy" of the forex trading system, as with the rest of the QuantStart site, is to try and mimic real-life trading as much as possible in our backtesting. This means including the details that are often excluded from more "research oriented" backtesting situations. Latency, server outages, automation, monitoring, realistic transaction costs will all be included within the models to give us a good idea of how well a strategy is likely to perform.
Monitoring and High Availability - Since we are considering a high-frequency intraday system, we must put comprehensive monitoring and high availability redundancy in place. This means reporting on CPU usage, disk usage, network I/O, latency and checking that any periodic scripts are set to keep running. In addition we need a backup and restore strategy. Ask yourself what backup plans you would have in place if you had large open positions, in a volatile market, and your server suddenly died. Believe me, it happens!
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!
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To get started, investors interested in trading in the forex markets must first sign up with either a regular broker or an online forex discount broker. Once an investor finds a proper broker, a margin account must be set up. A forex margin account is very similar to an equities margin account – the investor is taking a short-term loan from the broker. The loan is equal to the amount of leverage taken on by the investor.
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.
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.
Trading on a margin can have varying consequences. It can influence your trading experience both positively and negatively, with both profits and losses potentially being seriously augmented. Your broker takes your margin deposit and then pools it with someone else's margin Forex deposits. Brokers do this in order to be able to place trades within the whole interbank network.
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.
For securities, the definition of margin includes three important concepts: the Margin Loan, the Margin Deposit and the Margin Requirement. The Margin Loan is the amount of money that an investor borrows from his broker to buy securities. The Margin Deposit is the amount of equity contributed by the investor toward the purchase of securities in a margin account. The Margin Requirement is the minimum amount that a customer must deposit and it is commonly expressed as a percent of the current market value. The Margin Deposit can be greater than or equal to the Margin Requirement. We can express this as an equation: