Discussing the article: "Implementation of the Quantum Reservoir Computing (QRC) circuit"
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Check out the new article: Implementation of the Quantum Reservoir Computing (QRC) circuit.
Modern traders face a fundamental dilemma: the more complex a trading system becomes, the more prone it is to overfitting. Classical machine learning algorithms demonstrate brilliant results on historical data, but fail catastrophically in real trading. Neural networks trained on thousands of examples suddenly become useless at the slightest change in market conditions. Support Vector Machines show 85% accuracy in backtests, but incur losses within the first week of live trading.
The root of the problem goes beyond technical nuances. Financial markets are adaptive systems where each participant constantly changes their behavior in response to the actions of others. Any strategy based on static patterns of the past is doomed to failure in a dynamically changing environment. Traditional approaches to machine learning assume stationarity of data — an assumption that, in the context of financial markets, is not just inaccurate, but fundamentally flawed.
Moreover, most trading algorithms suffer from "catastrophic forgetting" – when trained on new data, they completely lose previously acquired knowledge. This creates a vicious cycle: the system either remains static and becomes obsolete, or constantly retrains and becomes unstable. Traders are forced to choose between the reliability of past patterns and adaptability to new conditions.
Author: Yevgeniy Koshtenko