Dawn in Lujiazui was roused by the invisible tides of capital markets. Before the first rays of morning light had fully dispersed the mist over the Huangpu River, Mozi was already in his silent trading room. The great wall of screens lit up one by one; indices, exchange rates, commodity prices from major global financial markets stirred like waking giants, beginning to swallow and spew vast data streams, weaving a complex, dynamic map that mirrored the pulse of the global economy.
His [Oscillation Model] had performed well over the past few weeks, like a tireless precision pendulum, steadily capturing minuscule profits within gold market's narrow fluctuation band. The snowball of capital grew quietly, rolled by near‑silent compound interest. This kind of stable profitability, based on algorithms and probability, brought him not excitement but a cool satisfaction that everything was under control. This was a temporary victory of order over chaos.
Yet the nature of the market is chaos. Order is always local and transient.
This morning, shortly after the Asian session opened, a faint anomaly appeared in the gold market. A vaguely sourced piece of news about subtle political shifts in a Middle Eastern oil‑producing country acted like a pebble tossed into a calm lake, sending ripples. In just a few minutes, gold price surged rapidly; the price curve broke upward through the upper rail of the previous oscillation range at a somewhat abrupt angle.
On the screen, the indicator light representing the [Oscillation Model]'s position status switched instantly from the pale green of no position to the warning yellow of a triggered short signal. Based on its trained pattern recognition, the model judged this breakout as a high‑probability "false breakout" opportunity for reversion to the mean; following preset logic, it established a short position.
Mozi sat upright in his command chair, his body unmoving, but his gaze sharpened like a hawk's. His eyes did not linger on that abruptly rising candlestick; instead, they swept rapidly over a dozen auxiliary monitoring windows—the US Dollar Index, US Treasury futures, the VIX fear index, movements of related energy products, and a real‑time‑crawled, NLP‑analyzed global‑news sentiment heatmap.
Anomaly.
The dollar's reaction was tepid, the bond market calm; the fear index rose only slightly. Most crucially, the source of the news was of extremely low authority, unconfirmed by follow‑ups in other mainstream media; sentiment analysis showed that the market's overall attention and emotional fluctuation fell far short of the strength needed to drive gold price through a key resistance level in a valid breakout.
This looked more like a "noise pulse"—a typical **false breakout** triggered by resonance between a few momentum‑following orders and algorithmic trades, lacking solid foundation.
In the world of quantitative trading, **overfitting** was a ghost that haunted every model developer, an insidious specter that had to be vigilantly guarded against at all times. It arose from mining historical data too deeply, too finely, such that the model learned not only the general "signals" with predictive power existing in the data, but also over‑learned the specific, accidental "noise" belonging to that particular historical dataset.
It was like a student who, to cope with an exam, memorized not only the theorems and formulas from the textbook (signal), but also the specific solution steps—even printing flaws (noise)—of every problem in a particular exercise book. In the exam, if he encountered questions highly similar to those in the exercise book, he might score high; but once the question format changed slightly, or a new type appeared, he would be at a loss, his performance plummeting.
In Mozi's model, **overfitting** might manifest specifically as: the model becoming too "clever" at recognizing and memorizing certain specific forms of "false breakout" patterns in historical data (for instance, a rapid surge accompanied by a certain combination of technical indicators and a certain news‑sentiment score), and assigning excessively high win‑rate expectations to such patterns. Yet the market evolved dynamically; the combinations of factors driving prices were endless. Out of a hundred similar patterns in history, ninety‑five might have quickly reversed (the "regularity" the model learned), but the remaining five might, due to some deeper structural changes not captured by the model, develop into genuine, robust trend beginnings.
If a model fell into **overfitting**, it might show stunning, nearly perfect return curves and Sharpe ratios in historical backtesting. But once deployed live, facing new, unseen market data, its performance would deteriorate sharply, because it could not effectively distinguish, in the new market environment, which were noteworthy "signals" and which were meaningless "noise." It would treat historical noise as signals to act upon, leading to frequent erroneous trades and massive losses. The model's **generalization ability**—its capacity to adapt and predict in new data, new environments—would become extremely fragile.
At this moment, Mozi strongly suspected that his [Oscillation Model] might have a certain degree of overfitting to the present "low‑drive breakout" pattern. Based on historical experience, the model arbitrarily judged this breakout as most likely "false." But what if… this time was different? What if behind this seemingly weak pulse lurked the germ of some real driving force that the model had not yet sensed?
Market noise stemmed not only from interference by invalid information, but even more from the model's own "memory" and misinterpretation of historical noise.
Decision was a matter of instants.
Holding the short position: if correct, price would quickly retreat, and the model would capture another successful mean‑reversion profit. But if wrong, if this breakout was valid—even weakly valid—and gold price continued upward even a small amount, triggering the model's stop‑loss, the resulting loss would be something that the sum of many previous tiny profits could not compensate. In the weighing of probability and payoff, in the discrimination between signal and noise, sometimes one had to trust one's own macro judgment and cross‑market insight, rather than rely entirely on the model's automated decisions.
"Intervene." Mozi uttered softly, his voice clear in the quiet room.
His hands flew over the custom keyboard; a few keystrokes called up the model's risk‑control back‑end. He did not modify the model's core logic, but activated the highest‑authority "manual override" function, forcibly closing the short position just established, and temporarily reducing the trading weight of that specific pattern signal to zero. This meant that for a period to come, the model would "ignore" trading opportunities of this type, until he confirmed the market environment was stable or the model had been recalibrated.
Almost within seconds after he completed the operation, the vaguely sourced Middle Eastern news was confirmed by several authoritative agencies as exaggerated. Gold price, as if its support had been pulled, swiftly turned downward, not only erasing all gains but even falling through the lower rail of the previous oscillation range.
The price curve on the screen traced a textbook "false breakout" reversal pattern.
If he had not intervened, the model would now be enjoying substantial profits.
But Mozi, looking at that long upper shadow and the subsequent plunge, felt a fine layer of cold sweat break out on his back. No regret whatsoever, only a survivor's relief. This time, he had gambled correctly. The market proved this was indeed a false breakout under noise interference. Yet his intervention was not based on conclusive evidence, but on vigilance against the potential "overfitting" risk of the model, and a comprehensive judgment of the current macro‑market atmosphere. This was a kind of "fuzzy correctness" in a probabilistic world—underpinned by a deep recognition of the model's limitations.
This incident sounded an alarm for him. The [Oscillation Model] needed a thorough "check‑up" and recalibration. He had to introduce stricter regularization methods (a technique in machine learning to prevent overfitting, by adding penalty terms for model complexity, forcing it to learn more general, simpler patterns); he might need to adjust the thresholds for feature selection, to improve its **generalization ability**, ensuring that in future variable markets it would not be misled by historical ghosts (noise), but truly capture those enduring, universal "fragments of order."
He leaned back in his chair, slowly exhaled a deep breath. The mental fatigue brought by high‑intensity mental calculation and decision‑making began to surface. He rubbed his temples; his gaze inadvertently swept across a secondary monitor screen scrolling AI‑filtered, tech‑related news summaries.
A rather inconspicuous brief caught his eye: "[Industry Update] Former ASML senior lithography engineer Xiuxiu confirmed to join a domestic lithography‑machine R&D team."
Xiuxiu? The name was unfamiliar. But the combination of keywords—"ASML," "lithography engineer," "returned to join"—acted like a faint electric current, instantly piercing through nerves slightly numbed by financial‑market battles.
Lithography machines. Of course he knew what they were—the crown jewel of the semiconductor industry, the cornerstone of the modern information society, and one of the most fiercely contested domains in current international tech rivalry. A senior engineer working at a pinnacle like ASML, choosing to return at this moment…
He instinctively clicked on the brief. The content was sparse, no photo, only the most basic professional background and movement description. Yet for some reason, Mozi stared at those few lines for a long time.