Two arenas, one platform
QuantArenas operates two complementary research modules. The Strategy Arena covers systematic stock strategies scored and ranked across multiple instruments and time windows using a consistent methodology. The EA Arena provides AI-analyzed MQL5 Expert Advisors sourced from the MQL5 CodeBase, each with bilingual descriptions, parameter documentation, and category classification (trading vs. utility).
QuantArenas is not a signal service, brokerage, or automated trading system. It does not issue buy or sell alerts, provide personalized investment recommendations, or promise results. Strategies and EAs here are research models meant to be inspected and stress tested by the reader. The goal is to make strategy behavior visible, not to offer a turnkey product or a performance guarantee.
EA Arena: MQL5 analysis pipeline
The EA Arena analyzes MQL5 Expert Advisors using an automated pipeline powered by NVIDIA GLM-5.1. Each EA is extracted from the MQL5 CodeBase, parsed for strategy logic, and documented with bilingual (CN/EN) descriptions, parameter explanations, and category tags. Trading EAs are separated from utility indicators, scripts, and panel tools so researchers can focus on actionable strategies.
For auto-convertible EAs, the platform generates Python backtrader code from the extracted logic, backtests it on one year of hourly data for EUR/USD, GBP/USD, USD/JPY and gold, and publishes the results on each EA's page. Backtest metrics include total trades, win rate, net P&L, maximum drawdown and recovery factor, and every trading EA gets a blow-up risk label. This LLM-assisted MQ5→Python pipeline makes it possible to evaluate EAs at scale without a live MetaTrader environment.
Strategy Arena: stock strategy scoring
Stock strategies are scored with the Arena Score (v2, since November 2026). Every strategy is compared with all other strategies on the same market and time window, and five components are turned into percentiles: return quality (Sharpe and Sortino, 30%), drawdown control (Calmar and Ulcer index, 25%), consistency (share of positive months and time under water, 20%), robustness (the worst Sharpe across the 1Y, 3Y, 5Y and full windows, 15%) and statistical significance (the probabilistic Sharpe ratio, 10%). Because each component is a rank, a single extreme value cannot dominate, and strategies no longer tie at the top. Strategies that were in the market less than 5% of the time in a window are not ranked.
The system also compares strategy behavior across multiple time windows. A model that only looks strong in the last few months but collapses on longer windows will rank lower than a steadier approach. Parameters are re-selected every six months by walk-forward optimization, using only data available at the time, and the walk-forward result is the one shown. Until 3 November 2026 the default parameters were kept whenever they scored better over the whole history; because that choice looked at the full sample, it made about one in five results slightly optimistic, so it was dropped.
Data sources and transparency
Stock strategy data uses historical daily bars from public sources, collected through the yfinance interface with adjusted closes for splits and dividends. EA backtests use the most recent twelve months of hourly (H1) bars for EUR/USD, GBP/USD, USD/JPY and gold, downloaded from free public sources (yfinance, with Dukascopy as a fallback) and re-run every month. All data sources are documented, and the same instrument set is applied consistently within each module so that rankings reflect strategy differences, not data inconsistencies.
Because the project is static and reproducible, both pipelines prioritize determinism. Missing sessions are left as gaps, and strategies that require data not available in public feeds are excluded. The data is not a substitute for institutional feeds and may include survivorship or proxy biases. These limitations are documented so readers can interpret results as research signals rather than trading grade performance.
Research goals and engineering
QuantArenas is built to support structured research. Stock strategies include readable pseudocode, tags, and suggested comparisons. EA analyses include signal logic breakdowns, parameter descriptions, and backtest performance with equity curves. The platform complements academic papers and practitioner notes by giving a standardized output for exploration and comparison.
From an engineering perspective, the platform emphasizes deterministic pipelines, limited external dependencies, and open inspection of assumptions. The EA analysis pipeline uses GLM-5.1 for MQL5 parsing and backtrader for execution, both running in a reproducible, script-driven workflow. The site also serves as a log of iterations, showing how strategies and EA analyses evolve as new evidence is added.
How to use the platform responsibly
Treat the rankings and backtest results as a research index, not a buy list. Use the results to narrow down ideas, then read the strategy description, inspect the chart behavior, and understand what the signal is actually doing. A strong rank or profitable backtest does not replace independent validation, and a weak rank does not mean the idea is useless. The point is to reveal trade offs and to surface questions worth testing.
If you decide to explore a strategy or EA further, rebuild it with your own data, add realistic costs, and stress test across multiple regimes. Consider position sizing, liquidity, and risk controls that are not modeled here. QuantArenas gives a clean baseline, but responsible use requires additional work and a clear understanding of the risks.
Keep notes on why a strategy ranks well or an EA backtest shows profit, and test whether that reason is still plausible today. A simple checklist such as data assumptions, regime fit, and failure mode helps avoid copying strategies blindly. The site is designed to be a starting point for that checklist, not the final answer, and it rewards careful reading over fast decisions.
Risk and usage disclaimer
All information on this site is for educational and research purposes only. It is not financial advice, and it should not be construed as an offer to buy or sell any security, derivative, or cryptocurrency. Past performance does not guarantee future results, and every strategy can experience prolonged losses or underperformance. EA backtests are conducted on limited historical data and do not account for slippage, spread, or commission costs.
Use this material at your own risk. If you act on any idea presented here, you are responsible for evaluating whether it is appropriate for your risk tolerance, time horizon, and regulatory environment. QuantArenas makes no warranties about the accuracy or completeness of the data, and it does not provide tax, legal, or investment guidance.
Last updated: 2026-11-03