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).
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.
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.
This material does not contain and should not be construed as containing investment advice, investment recommendations, an offer of or solicitation for any transactions in financial instruments. Please note that such trading analysis is not a reliable indicator for any current or future performance, as circumstances may change over time. Before making any investment decisions, you should seek advice from independent financial advisors to ensure you understand the risks.
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.
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.
I post this to let you know, as the title mentions it, that I made a trading diary, with google documents tool. This a generic spreadsheet which allows any trader to manage his trading (his risk, his pnl, his opened position, the orders...) with a trding diary. Every trader,should have one, and I mad mine with google docs. At least you must have an account to acces this spreadsheet.
×