--- title: "How I Generate and Sort Trading Algorithms" description: "How I Generate and Sort Trading Algorithms: An Introduction to the Process Most traders start with a market and try to force a strategy onto it. I do the opposite. I start with a broad research proces..." url: https://quantmonitor.net/how-i-generate-and-sort-trading-algorithms/ date: 2026-08-08 modified: 2026-08-10 author: "Lukas" image: https://quantmonitor.net/wp-content/uploads/2026/08/ChatGPT-Image-8.-8.-2026-13_42_11.webp categories: ["Nezařazené"] type: post lang: en --- # How I Generate and Sort Trading Algorithms # How I Generate and Sort Trading Algorithms: An Introduction to the Process Most traders start with a market and try to force a strategy onto it. I do the opposite. I start with a broad research process, then let the data show me which markets, templates, and timeframes deserve deeper work. **Algorithm sorting is the process of building many strategy candidates, filtering them, and keeping only robust groups.** That is the focus of this introduction. In the next publications, I will go into the exact settings for specific instruments, the way I configure builds with our templates, and the retest process in StrategyQuant. ![Overview of the nine premium breakout templates used in the initial QuantMonitor research process.](https://quantmonitor.net/wp-content/uploads/2026/08/ChatGPT-Image-8.-8.-2026-13_20_57-1.webp) *The starting point: nine premium breakout templates used as the base for the first full research cycle.* ## Why I Start Wide If you want robust trading systems, you should not begin by falling in love with one market, one idea, or one equity curve. You need breadth first. My goal is not to find one lucky strategy. My goal is to find repeatable logic that works across a group of strategies, across validation tests, and ideally across other markets too. That is why I began with a cross-sector universe of roughly 15 instruments. Instead of trying to optimise one market in isolation, I tested a broad set of candidates and let the results narrow the field. - About **15 instruments** across multiple sectors - **9 premium templates** focused on breakout system generation - All templates used in their **base configuration** - Each build limited to **3,000 strategies in the databank** - Builds run on **M15** and **H1**, while **BTCUSD** was tested on **H4** and **D1** This is a practical compromise. It is broad enough to reveal patterns, but constrained enough to finish in a realistic amount of time. ## The Test Structure The logic of the first research cycle was simple. 1. Select a broad watchlist of instruments from different sectors. 2. Run each market through all 9 premium breakout templates. 3. Use two timeframes or build variants per market-template combination. 4. Record how many strategies pass the filters in each build. 5. Save the best candidates for follow-up validation. That gave me a total of **15 x 9 x 2 = 270 builds**. Each of those builds produced a different profile. Some generated a healthy number of passing strategies. Others produced very little. Some looked good at first glance but weakened during later validation. Others were much more stable than their raw top-line result initially suggested. ![QuantMonitor algorithm generation and sorting research workflow.](https://quantmonitor.net/wp-content/uploads/2026/08/ChatGPT-Image-8.-8.-2026-13_24_43-1.webp) *Research workflow: a wide universe, nine breakout templates, 270 total builds, and a structured filtering process before deeper validation.* ## What I Measured from Each Build I was not interested in a single headline result. A top strategy alone tells you very little. What matters is the quality of the entire build. For each build, I noted: - the **number of strategies** that passed the filter set, - the **depth and quality** of the passing group, - whether the results were concentrated in one narrow corner or spread across a broader area, - which candidates were worth saving for deeper tests, and - how the build compared with other template-market-timeframe combinations. This is an important shift in mindset. I do not sort markets by asking, “Which single strategy made the most money?” I sort them by asking, “Where do I see a healthy population of strategies with similar logic and stable behaviour?” ## What Counts as a Good Result A good result is not one beautiful backtest. A good result is a **cluster**. I want to see a group of strategies that behave similarly and remain stable when pushed through tougher validation. That includes walk-forward optimisation, follow-up robustness checks, and cross-market testing. The target is not perfection. The target is a **functional band**. By that, I mean an area where strategy performance remains broadly similar even when key variables move by around **plus or minus 30%**. If a strategy only works at one tiny parameter point, I do not trust it. If a family of strategies produces similar outcomes across a wider parameter neighbourhood, that is much more interesting. In practice, this means I am looking for: - more than one good strategy, - similar logic across multiple candidates, - stable performance across parameter variation, - resilience in walk-forward testing, and - evidence that the concept can survive tests on other markets as well. ![Research funnel from a broad market universe to a shortlist of robust trading system combinations.](https://quantmonitor.net/wp-content/uploads/2026/08/ChatGPT-Image-8.-8.-2026-13_35_42-1.webp) *The selection funnel: the goal is not a single winner, but a shortlist of market-template-timeframe combinations that produce a durable group of candidates.* ## The Outcome of the First Research Cycle This first large sorting test took roughly **one year** to complete. That is the real cost of doing this properly. Wide research takes time, especially when you want more than a cosmetic answer. The result of that year of work was a much tighter shortlist: **5 markets, 5 timeframes, and 5 templates** stood out as the most promising combinations. That does not mean the other combinations were useless. It means these five gave me the strongest evidence of robustness. They were the combinations most likely to produce not just one isolated result, but a whole group of strategies with consistent behaviour. This is the real purpose of sorting. It helps me identify where deeper custom work is worth doing. Once I know which combinations repeatedly show strength, I can invest more time into tuning, retesting, and building custom projects around them. ## Why This Matters Without this sorting process, it is very easy to waste months on weak markets or weak template combinations. You can optimise endlessly and still end up with fragile systems. A wide, structured selection process reduces that risk. It also gives context. When a market performs well, I do not want to know only that it performed well. I want to know *why* it made the shortlist, how broad the opportunity set was, and whether the result came from genuine structure or a narrow optimisation accident. Same rules. Same logic. Different markets. That is where the real insight starts. ![Robustness concept showing a functional parameter band with stable performance across plus or minus 30 percent variation.](https://quantmonitor.net/wp-content/uploads/2026/08/ChatGPT-Image-8.-8.-2026-13_36_49.webp) *Robustness matters more than precision: the goal is a functional parameter band where walk-forward and follow-up tests remain broadly consistent.* ## What Comes Next This article is only the introduction to the framework. In the next publications, I will break the process down into practical parts. - **Specific settings for specific instruments** — which markets made the cut and why - **Build configuration with our templates** — how I move from default settings to more focused custom projects - **Retests in StrategyQuant** — how I validate the candidates after the initial build phase - **Custom project values** — the exact numbers and ranges I use when taking an idea further The key idea is simple: I do not start by optimising a favourite market. I start by building broadly, measuring honestly, and narrowing down only after the evidence is strong enough. ## Your Move: Test a Market the Same Way Reading about robust strategy selection is useful. Running the process yourself is where the real progress begins. Pick a market, apply a structured template workflow, and see whether it produces one isolated result or a real cluster of opportunity. **[Start free. Open a free QuantMonitor account, explore the free research and strategies, and see how evidence-first algorithm development works.](https://quantmonitor.net/)** **[Tool up. Use the MetaTrader ToolBox to reduce the repetitive StrategyQuant-to-MetaTrader workload, so more of your time goes into research and less into manual tasks.](https://quantmonitor.net/pricing)** **[Go all in. Premium is the full kit I use in real research: templates, workflows, StrategyQuant PRO, the deployer toolbox, and the video course — and it keeps evolving as the research evolves.](https://quantmonitor.net/pricing)** Your next durable strategy idea is not one backtest away. It is one disciplined research process away. --- *Disclaimer: This content is for educational and research purposes only. Trading and investing involve substantial risk. Any strategy examples, backtests, or hypothetical results discussed here have limitations and do not guarantee future performance. Past performance is not necessarily indicative of future results.*