Looking for Best Practices in EA Refinement Before Requesting Improvements - page 2

 
Fernando Carreiro #:

You keep hailing Renko as a "miracle", but it is only as good as the size that you set for it. Selecting a size can be just as arbitrary as selecting the period of a moving average.

It requires just as much (if not more) optimisation passes to find the best fit and is highly symbol (and broker) dependant and can shift over time.

It is not a "miracle" solution that fits all. Like all methods and strategies, it requires the right market conditions and just the right brick size for it to be any good.

It is simply only one of the many, many methods—no better, no worse!

Really? Which of my posts are you quoting "miracle" from?

I never claimed to be a miracle worker so providing proof would be... a miracle.

By the way, "dependant" is actually spelled as "dependent." 

[Deleted]  
Ryan L Johnson #Really? Which of my posts are you quoting "miracle" from? I never claimed to be a miracle worker so providing proof would be... a miracle.

Ok, maybe I overreacted given the number of times you keep mentioning it in your posts! 👍

Ryan L Johnson #By the way, "dependant" is actually spelled as "dependent." 

Yes, it was a typo! They are both valid words, just with different meanings.

 
Fernando Carreiro #:
Ok, maybe I overreacted given the number of times you keep mentioning it in your posts! 👍
Oh, please believe me when I say that I'm well aware that exaggeration is the basis of most comedy. I know what you meant. Just having some fun.👍
 
Ryan L Johnson #:

Really? Which of my posts are you quoting "miracle" from?

I never claimed to be a miracle worker so providing proof would be... a miracle.

By the way, "dependant" is actually spelled as "dependent." 

I have renko strategies but I do not run them on fx pairs or cfds I only use them on actual CME products because it makes me feel better and CME prices all have to go through Chicago.
 
Csquared #:
I have renko strategies but I do not run them on fx pairs or cfds I only use them on actual CME products because it makes me feel better and CME prices all have to go through Chicago.
Yeah, Renko charts inherently require volatility. They're great on GC, but ok on GBPJPY too.
 
marcelocolladojr:

Hello everyone,

I have an Expert Advisor that operates based on trend conditions; it opens trades when the market starts to move in a clear direction (based on indicators). However, during sideways or ranging periods, it tends to accumulate losses or end up in break-even situations according to my backtests.

Before opening a job request to improve this EA, I’d like to better understand what experienced developers consider good practices when refining an EA’s logic and performance. My goal is to know exactly what to ask for — avoiding requests that are either impractical or not aligned with how optimization should ideally be approached in MQL5.

Currently, the main improvement I’m considering is a lateral market filter — something capable of identifying and avoiding trades when the price is consolidating. From what I’ve researched, this type of filter is relatively common, but I’m interested in learning what kind of implementation is considered robust and efficient in real-time conditions (not just in backtests).

The second part — and the one I find most intriguing — is something I’ve seen my former instructor use. He referred to it as a “Quant” environment, which seems to be a combination of software and infrastructure beyond the standard MT5 backtesting system. From what I understood, it runs on a high-performance VPS, equipped with top-tier CPU cores that dramatically speed up backtesting and optimization cycles.

This “Quant” setup not only handles intensive computation but also tests strategies on a reliable data source (Darwinex). It performs extensive iterations across parameter combinations and produces the most profitable configuration (“sets”) for each symbol and timeframe. It also shows the developer points to be enhanced on code; things that would make the solution more profitable.

I don’t have access to that specific system, but I’d like to explore how something similar could be applied. Or, thinking on a good hypothesis, find someone with the exactly technology that I could hire. For that, I'd need the name of it.

So, my questions are:

1 - What do you consider best practices when refining or optimizing an existing EA?

2- When it comes to filtering out non-trending periods, what’s the best approach to make it both lightweight and reliable?

3- For large-scale optimization or “Quant-like” testing, are there tools, frameworks, techniques etc. that I could hire?

And finally, based on your experience, what would be a reasonable cost or time estimate for implementing these improvements — both the lateral market filter and the advanced optimization structure?

Any guidance, references, or examples from experienced programmers would be extremely valuable before I move forward with development requests.

Thanks in advance for your insights,


— Marcelo

Hi Marcelo,

Good questions. I'll address each one based on my experience:

1. Best practices for EA refinement:

- Walk-Forward Optimization is essential. Split your data 60% in-sample / 40% out-of-sample. Optimize on IS, validate on OOS. If performance degrades significantly OOS, you're overfitting.
- Test on multiple symbols (at least 5-8 correlated pairs). A robust strategy should work across similar instruments.
- Monte Carlo simulation (1000+ permutations) helps verify your results aren't due to luck.
- Keep parameters to a minimum. Each additional parameter increases overfitting risk.

2. Lateral market filter:

The most reliable lightweight approach is ADX (Average Directional Index):
- ADX < 20-25 = ranging market → disable entries
- ADX > 25 = trending → allow trades

Alternative: Bollinger Bands width. Narrow bands = low volatility = ranging.

3. Quant-like testing tools:

What your instructor likely uses is a combination of:
- Python with vectorbt, backtrader, or zipline for fast backtesting
- Walk-Forward libraries for proper validation
- High-quality tick data (Dukascopy offers free historical data)

For MQL5 specifically, the built-in Strategy Tester with "Complete Algorithm" genetic optimization is decent, but Python gives you more control over statistical validation.

Cost estimate: A lateral market filter implementation is straightforward ($50-150). The full "Quant" optimization infrastructure is more complex — either you build it yourself in Python, or hire someone to create a custom validation pipeline ($300-800 depending on scope).