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.
If you sell a security short, you must have sufficient equity in your account to cover any fees associated with borrowing the security. If you borrow the security through us, we will borrow the security on your behalf and your account must have sufficient collateral to cover the margin requirements of the short sale. To cover administrative fees and stock borrowing fees, we must post 102% of the value of the security borrowed as collateral with the lender. In instances in which the security shorted is hard to borrow, borrowing fees charged by the lender may be so high (greater than the interest earned) that the short seller must pay additional interest for the privilege of borrowing a security. Customers may view the indicative short stock interest rates for a specific stock through the Short Stock (SLB) Availability tool located in the Tools section of their Account Management page. For more information concerning shorting stocks and associated fees, visit our Stock Shorting page.
tweet at 3:44pm: [RTRS] - U.S. CDC DIRECTOR REDFIELD SAYS RISK TO U.S. PUBLIC FROM CORONAVIRUS OUTBREAK IS LOW tweet at 3:46pm: REDFIELD SAYS THERE ARE 191 INDIVIDUALS UNDER INVESTIGATION IN U.S. AMID CORONAVIRUS OUTBREAK tweet at 3:45pm: US CDC Director Redfield: This is a significant global situation. https://t.co/Ao1Ci2OEfi tweet at 3:52pm: US declares the coronavirus a public health emergency, implementing special temporary measures $SPX
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.
Foreign exchange (forex) or FX trading involves trading the prices of global currencies, and at City Index it is possible to trade on the prices of a huge range of global currencies. Currency trading allows you to speculate on the movement of one currency against another, and is traded in pairs, for example the Euro against the US Dollar (EUR/USD).
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:
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.
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!
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).
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.
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.
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.