The central question is: Is there evidence that a trading robot, tested under rigorous methods such as backtesting, cross-optimization, or walk-forward, can be consistent and profitable over time? Or are good results just coincidence and spurious correlations? After obtaining benevolent generalities on the subject when asking ChatGPT, I insisted on a direct and evidence-based response. Its response was: “There is no solid evidence that a retail robot based on classical technical analysis can consistently outperform randomness in the long term.” What follows is the explanation it provided before reaching this conclusion.
1. Empirical Evidence
- There are very few trading robots that maintain consistent profitability across all markets and periods. Academic studies and practical experiences show that most automated systems deliver inconsistent results over the long term.
- Some hedge funds and quantitative traders achieve consistency, but they employ more complex approaches (e.g., dynamically adaptive market models) and have access to data and technology far beyond retail tools.
2. Reasons Behind the Inconsistency
- The market is non-stationary: Market conditions change (trends, volatility, liquidity structure), rendering rules optimized for one period invalid for another.
- Market noise: The market is dominated by noise rather than consistent patterns. This causes many systems to achieve profitable historical results by mere chance.
- Competition: Simple robots using technical analysis compete against advanced systems, reducing opportunities for statistical edge.
3. Spurious Correlations and Random Results
- Backtesting results can be misleading. Even with rigorous optimization, a system may appear profitable due to statistical coincidences in historical data.
- Robustness tests like Monte Carlo or simulations with synthetic data often reveal that systems which seem promising in backtesting fail in forward testing.
4. Is It Random?
- For systems relying exclusively on classical technical analysis, yes, it largely is random. There is no solid evidence that strategies based solely on indicator patterns are consistently better than a random approach, especially in liquid markets like Forex.
Conclusion
Optimizing and testing trading robots for simple systems (classical technical analysis) often leads to spurious correlations. While more advanced approaches may be profitable (such as machine learning, adaptive analysis, or integrating external data), a retail system like the one described is limited by market nature and the tools available.
In summary: there is no solid evidence that a retail robot based on classical technical analysis can consistently outperform randomness in the long term. This does not mean it is impossible to make money with algorithmic trading, but relying solely on backtesting and optimization is unlikely to achieve consistency.
When asked about the sources used to craft the above, this was its response:
Evidence on the consistent profitability of trading robots based solely on technical analysis is limited, and in many cases, it suggests that these systems fail to sustain gains over time. A study titled GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading indicates that while it is possible to design trading algorithms that generate positive returns in certain periods, the noisy and volatile nature of the market makes consistency and profitability challenging for these systems.
Additionally, works like Diseño de un robot de trading que involucra técnicas de machine learning para la predicción del precio de Bitcoin suggest that combining technical analysis with advanced techniques like machine learning can improve predictive capability and profitability. However, these approaches require constant adaptation to changing market conditions and careful handling of overfitting to avoid spurious correlations.
In summary, while there are studies exploring the effectiveness of trading robots based on technical analysis, the evidence points out that without incorporating more sophisticated techniques and continuous adaptation, it is unlikely for these systems to maintain consistent profitability in the long run.
Help enrich this discussion. Have you had experiences with trading robots that contradict or support these conclusions? What strategies or approaches do you consider effective in achieving consistent profitability over time? If you have studies, evidence, or additional references that complement this perspective, please feel free to share them in the comments.