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.
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).
Not all securities can be bought on margin. Buying on margin is a double-edged sword that can translate into bigger gains or bigger losses. In volatile markets, investors who borrowed from their brokers may need to provide additional cash if the price of a stock drops too much for those who bought on margin or rallies too much for those who shorted a stock. In such cases, brokers are also allowed to liquidate a position, even without informing the investor. Real-time position monitoring is a crucial tool when buying on margin or shorting a stock.
Note also that when we begin storing our trades in a relational database (as described above in the roadmap) we will need to make sure we once again use the correct data-type. PostgreSQL and MySQL support a decimal representation. It is vital that we utilise these data-types when we create our database schema, otherwise we will run into rounding errors that are extremely difficult to diagnose!
As we've already stated, trading on margin is trading on money borrowed from your broker. Each time you open a trade on margin, your broker automatically allocates the required margin from your existing funds in the trading account in order to back the margin trade. The precise amount of allocated funds depends on the leverage ratio used on your account.
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.
Popular leverage ratios in Forex trading include 1:10, 1:50, 1:100, 1:200, or even higher. Simply put, the leverage ratio determines the position size you’re allowed to take based on the size of your trading account. For example, a 1:100 leverage allows you to open a position 10 times higher than your trading account size, i.e., if you have $1,000 in your account, you can open a position worth $10,000. Similarly, a leverage ratio of 1:100 allows you to open a position size 100 times larger than your trading account size. With $1,000 in your trading account, you could open a position worth $100,000!
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Often, closing one losing position will take the margin level Forex higher than 5%, as it will release the margin of that position, so the total used margin will decrease and consequently the margin level will increase. The system often takes the margin level higher than 5%, by closing the biggest position first. If your other losing positions continue losing and the margin level reaches 5% once more, the system will just close another losing position.
Now, let’s say you open a trade worth $50,000 with the same trading account size and leverage ratio. Your required margin for this trade would be $500 (1% of your position size), and your free margin would now also amount to $500. In other words, you could withstand a negative price fluctuation of $500 until your free margin falls to zero and causes a margin call. Your position size of $50,000 could only fall to $49,500 – this would be the largest loss your trading account could withstand.
Multiple Currency Pairs - Similarly we need to support the major currency pairs beyond "Cable" (GBP/USD). There are two aspects to this. The first is to correctly handle the calculations when neither the base or quote of a currency pair is equal to the account denomination currency. The second aspect is to support multiple positions so that we can trade a portfolio of currency pairs.
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.
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.