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.
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.
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.
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.
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.
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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.
Free Margin – Your free margin represents your total equity minus any margin used for leveraged trades. For example, if your equity is $1,000 and your used margin is $100, your free margin would amount to $900. Following your free margin is extremely important, as it is used to withstand negative price fluctuations from your open trades and to open new leveraged trades. It’s important to understand that your free margin increases with profitable positions, but decreases with your losing positions. Once the free margin drops to zero or below, your broker will activate the so-called margin call and close all your open positions at the current market rate, in order to prevent your equity from falling below the required margin.

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The script is currently hardcoded to generate forex data for the entire month of January 2014. It uses the Python calendar library in order to ascertain business days (although I haven't excluded holidays yet) and then generates a set of files of the form BBBQQQ_YYYYMMDD.csv, where BBBQQQ will be the specified currency pair (e.g. GBPUSD) and YYYYMMDD is the specified date (e.g. 20140112).
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Why should you use Learn to Trade to learn Forex trading? That’s simple: we are a credible, well-established organisation that exists to educate people on Forex trading strategies. Our Forex trading training is second to none and will give you the foundation you need to achieve success in the high-opportunity world of currency trading. Every course is supported by our knowledgeable team of experts, who guide and support you throughout your learning experience. Not only that – you’ll get to take advantage of our live trading floor to get some hands-on experience. Getting a feel for what it’s like, while you have experienced traders around to help you, is a valuable opportunity that will help you get the confidence you need. Register now for your Forex trading tutorial and workshop!
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.
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In order to understand Forex trading better, one should know all they can about margins. Forex margin level is another important concept that you need to understand. The Forex margin level is the percentage value based on the amount of accessible usable margin versus used margin. In other words, it is the ratio of equity to margin, and is calculated in the following way:
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.
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:

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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.

The Federal Reserve Board and self-regulatory organizations (SROs), such as the New York Stock Exchange and FINRA, have clear rules regarding margin trading. In the United States, the Fed's Regulation T allows investors to borrow up to 50 percent of the price of the securities to be purchased on margin. The percentage of the purchase price of securities that an investor must pay for is called the initial margin. To buy securities on margin, the investor must first deposit enough cash or eligible securities with a broker to meet the initial margin requirement for that purchase.
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.
The Federal Reserve Board and self-regulatory organizations (SROs), such as the New York Stock Exchange and FINRA, have clear rules regarding margin trading. In the United States, the Fed's Regulation T allows investors to borrow up to 50 percent of the price of the securities to be purchased on margin. The percentage of the purchase price of securities that an investor must pay for is called the initial margin. To buy securities on margin, the investor must first deposit enough cash or eligible securities with a broker to meet the initial margin requirement for that purchase.
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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