This might be your central problem. How can you be sure that the coder is writing code that is - efficient - truely doing what you want (and in the long term: not using the code/the strategy behind if it turns out to be profitable?)
I review the roles/formulas we have put and compare to the log out put. However, i cannot be sure 100% that he did or didnot.
You are welcome. Unless the system has particularly complex logic, you should be able to code the backtest in excel. OK, you will be able to test the system this way on just thousands, not millions of data points but that should be enough to figure out if anything goes wrong. Splice your tick data so that you have one tick every 30 seconds or even every few minutes. Use these in excel, run teh backtest on exactly the same sample. You would expect the trade times and prices shopuld match exactly. If they don't something is different between what the developer coded and your spreadsheet. Regarding efficiency, you should have a ballpark estimate what computational complexity is involved. Given an interpreted laguage is involved direct benchmarking is more difficult though (unless you can code a simple test, like a large cycle every tick, with the same complexity in the same MQL).
That's very bad advice. I specialize in highly parameterized strategies and optimization frameworks that are pretty good at rapidly solving 100+ dimension parameter spaces. Of course I also rely on intuition and experience. Its not that difficult actually. The thought of deliberately over parameterizing a strategy for sake of obscurity is a very flawed concept. Simple intuition and statistical analysis will fill in many parameters. Aggressive optimization will do the rest.
1) Purchase Matlab and devote a solid 3 months to learning to program in it. Why Matlab? Matlab is designed for scientists, engineers, data exploration, rapid model/algorithm development. It has the best and most complete math and visualization capabilities out-of-the-box. That's why it costs $$$. Matlab is also fast to learn because it is very high level and avoids the complexity/verbosity that application developers demand in lower-level languages like Java and C++. If Matlab is too expensive there is also R, Gauss, Octave and SciLab, but many quant/algo developers consider these to be a "poor man's Matlab" because many capabilities are missing, slow or buggy. 2) Collaborate with others in your same situation. Learn from them. You will make mistakes. If you overcome your mistakes and financial losses you may become a successful quant/algo developer. Many of us started with quant/algo trading, lost a lot of money, got discouraged, but came back to it stronger. 3) Start trading your system or shop your systems around. Do not give out your code or even a parametrized version, except under written contract. Try to maintain some advantage. Like keep your best system private but share a less profitable system to test the trust in your partnership.