The financial world is at a crossroads. As algorithms and artificial intelligence reshape how securities are bought and sold, a vital question emerges: Can markets thrive without human involvement? Daniel Schlaepfer, a veteran trader and founder of Select Vantage Inc., tackles this issue head-on in his latest work, “Real Trading: Why Stock Markets Need a Human Touch.” Published by Forbes Books, the volume arrives at a moment when high-frequency trading accounts for a significant share of daily volume and AI-driven strategies promise ever-greater speed and efficiency. Yet, Schlaepfer argues that the core of trading remains irreducibly human, drawing on decades of experience in global equities to warn against an over-reliance on automation. His perspective is not a nostalgic call to turn back the clock but a pragmatic warning that ignoring the human element risks destabilizing the very markets we seek to optimize. This introduction sets the stage for a deeper exploration of why judgment, intuition, and experience continue to matter in an age of algorithms.
Over the past decade, algorithmic trading has moved from a niche activity to the dominant force in many equity markets. Estimates suggest that over 60% of trading volume in major US equities now originates from automated systems, with high-frequency traders executing trades in microseconds to exploit fleeting price discrepancies. This shift has brought undeniable benefits: tighter bid-ask spreads, lower transaction costs, and greater access for institutional investors seeking to execute large orders efficiently. However, the same speed and scale that powers these advantages also introduces fragility. Markets have become more interconnected, and a glitch in one algorithm can cascade across venues in milliseconds, as seen in episodes like the 2010 flash crash. Moreover, the proliferation of similar models can lead to crowded trades, where dozens of funds react to the same signal simultaneously, amplifying volatility. Beyond mechanical risks, the opacity of proprietary algorithms makes it difficult for regulators and participants to anticipate how these systems will behave under stress. Thus, while automation has undoubtedly made markets more efficient in calm conditions, it has also introduced new sources of systemic risk that demand careful oversight.
The belief that markets can be fully automated rests on a flawed assumption: that historical patterns will always repeat and that machines can anticipate every possible scenario. In reality, financial markets are driven by human emotions, geopolitical shocks, and unexpected events that defy simple statistical modeling. When faced with novel situations — such as a sudden pandemic, a geopolitical crisis, or an unexpected central bank announcement — algorithms trained on past data may produce erratic or harmful outputs. During the March 2020 market turmoil, for instance, many quantitative strategies exacerbated selling pressure as they liquidated positions to meet risk limits, contributing to a vicious cycle of declining prices and rising volatility. In such moments, liquidity can evaporate not because there are no buyers or sellers but because automated systems withdraw simultaneously, leaving a vacuum that only human market makers can fill by stepping in with capital and willingness to take the other side. Furthermore, algorithms lack the ability to exercise judgment about fairness or market health; they will execute a trade as long as it meets predefined criteria, even if the trade risks destabilizing the broader market. This limitation underscores why a purely automated approach is insufficient for maintaining orderly markets, especially when confidence is most needed.
One of the most enduring strengths of human traders lies in their ability to interpret information beyond raw numbers. While algorithms excel at processing vast datasets and identifying statistical patterns, they often struggle with nuance, context, and the subtle signals that precede major market moves. A seasoned trader might read a central banker’s speech and detect a shift in tone that hints at future policy changes, or notice a subtle change in the wording of an economic report that suggests a turning point. Similarly, human analysts can assess the credibility of a news source, weigh conflicting reports, and integrate non-quantifiable factors such as leadership changes, regulatory shifts, or social movements into their outlook. This capacity for qualitative judgment becomes particularly valuable during periods of uncertainty, when data is scarce or conflicting. For example, during the early stages of the COVID-19 pandemic, traditional economic indicators offered little guidance, but traders who paid attention to supply chain disruptions, consumer behavior shifts, and government responses were better positioned to anticipate market reactions. By blending quantitative analysis with qualitative insight, humans can adapt to evolving circumstances in ways that rigid models cannot, providing a crucial counterbalance to the speed of automation.
Risk management is another domain where human judgment proves indispensable. Quantitative risk models — such as value-at-risk (VaR) or stress-testing frameworks — rely on assumptions about market behavior that may break down under extreme conditions. These models typically assume normal distributions, stable correlations, and predictable liquidity, yet real-world crises often violate these premises dramatically. During the 2008 financial crisis, for instance, many institutions discovered that their VaR models vastly underestimated potential losses because correlations that were assumed to be modest spiked to near unity, turning seemingly diversified portfolios into concentrated bets. Human risk managers, by contrast, can draw on experience and intuition to question model outputs, adjust assumptions, and implement ad-hoc safeguards when standard procedures appear inadequate. They might decide to reduce leverage preemptively based on a gut feeling about mounting tensions, or increase cash reserves after noticing unusual patterns in market depth. This flexibility allows humans to act as a shock absorber, preventing the kind of cascading failures that can occur when automated systems follow flawed models to the letter. Effective risk management thus requires a hybrid approach: using models as a starting point but supplementing them with human oversight to catch the unknown unknowns that no model can fully capture.
Liquidity — the ease with which assets can be bought or sold without moving the price — is a cornerstone of healthy markets, and it is precisely where human traders often outperform their automated counterparts. In calm markets, algorithms can provide liquidity efficiently by continuously posting bids and offers and adjusting them in response to order flow. However, when volatility spikes and uncertainty rises, many automated systems are programmed to retreat, widening spreads or pulling quotes entirely to limit exposure. This behavior can exacerbate price swings and create a feedback loop where declining liquidity fuels further volatility. Human market makers, by contrast, possess the capacity to assess the broader context and make judgments about when to provide liquidity even at short-term cost. A seasoned dealer might recognize that a temporary imbalance is likely to reverse and choose to absorb some selling pressure in anticipation of future order flow, thereby acting as a stabilizing force. Their willingness to take on risk when others flee is rooted in experience and a professional ethos that values market functioning over immediate profit. Moreover, humans can communicate and coordinate implicitly, such as through the unspoken understanding among specialists on a trading floor, to provide liquidity in a coordinated manner that algorithms struggle to replicate without explicit programming. This human ability to act as a shock absorber is vital for preventing disorderly markets and maintaining confidence during turbulent periods.
As trading becomes increasingly dominated by proprietary algorithms, concerns about transparency and trust have grown among investors, regulators, and the public. Black-box systems, by their nature, offer little insight into how decisions are made, making it difficult to assess whether they are acting in the best interest of market health or merely pursuing short-term profits. This opacity can erode trust, particularly when unexpected events occur and market participants struggle to understand why prices moved in a certain direction. Human oversight introduces a layer of accountability that can mitigate these concerns. When traders are required to justify their actions — whether through internal compliance reviews, regulatory reporting, or public disclosures — they must articulate the reasoning behind their decisions, making the process more transparent. Furthermore, human managers can enforce ethical standards and ensure that trading practices align with broader market integrity goals, something that is challenging to encode into an algorithm without risking unintended consequences. For example, a human trader might refuse to exploit a temporary glitch that would be profitable but unfair, whereas an algorithm lacking such ethical constraints might execute the trade without hesitation. By combining the efficiency of automation with the accountability of human judgment, markets can achieve both innovation and trust, ensuring that technological advances serve the broader good rather than undermining it.
Recognizing that neither pure human intuition nor full automation offers the ideal solution, Schlaepfer proposes the Human Insight Model as a pragmatic framework for integrating the strengths of both. This model does not advocate for rejecting technology but rather for using it as a tool that augments human capabilities. In practice, this might involve leveraging algorithms for tasks such as data collection, pattern recognition, and trade execution while reserving final judgment for human traders who can interpret the output in context, apply ethical considerations, and adjust strategies based on evolving circumstances. For instance, an algorithm might flag a potential arbitrage opportunity based on statistical anomalies, but a human trader would assess whether the opportunity is genuine or the result of a data error, consider the liquidity conditions required to exploit it, and decide whether the risk aligns with the portfolio’s objectives. The model also emphasizes continuous learning, where humans refine their intuition by studying algorithmic outputs and algorithms are updated based on human feedback. By creating a feedback loop between human insight and machine efficiency, the Human Insight Model aims to capture the best of both worlds: the speed and scalability of automation paired with the adaptability, judgment, and responsibility of human expertise. This approach not only enhances performance but also builds resilience against the kinds of failures that can arise when either element operates in isolation.
While the rise of commission-free trading apps and fractional shares has been hailed as a democratizing force in finance, Schlaepfer challenges the notion that markets have become truly open and equitable. Despite the influx of retail participants, influence remains heavily concentrated among a small number of large institutions, high-frequency trading firms, and sophisticated proprietary shops. These entities possess advantages that are difficult for individual investors to match: access to cutting-edge technology, co-location services, direct market access, and vast amounts of capital that allow them to withstand periods of volatility and execute complex strategies at scale. Retail traders, by contrast, often face limitations such as slower execution speeds, higher effective costs due to slippage and spreads, and limited access to sophisticated research tools. Moreover, the very design of some retail platforms — such as gamified interfaces or push notifications encouraging frequent trading — can lead to behaviors that benefit the platform more than the investor, such as excessive trading or chasing trends. Thus, while the barriers to entry have lowered, the playing field remains uneven, and the promise of democratization risks being overshadowed by the realities of market structure. Addressing this imbalance requires not only better education and tools for retail investors but also regulatory measures that ensure fair access and prevent exploitative practices.
For retail traders, the evolving market landscape presents both exciting opportunities and significant pitfalls. On the positive side, lower costs, easy access to markets, and the availability of educational resources have empowered more people than ever to participate in wealth-building activities. However, the same accessibility can lead to overconfidence, particularly when novices mistake short-term gains for skill or fail to appreciate the risks inherent in leveraged products or volatile assets. Many retail participants enter the market without a solid grasp of fundamentals such as risk management, diversification, or the importance of a long-term perspective, making them vulnerable to emotional decision-making and herd behavior. Platforms that prioritize education and responsible trading — such as the Real Trading initiative mentioned in Schlaepfer’s work — aim to bridge this gap by offering structured learning, mentorship, and tools that emphasize risk control and disciplined decision-making. By providing direct market access coupled with embedded safeguards (like maximum loss limits or mandatory cooling-off periods), such platforms help newcomers develop sound habits while still allowing them to experience the realities of live trading. The goal is not to eliminate risk but to ensure that retail traders approach the market with a clear understanding of the trade-offs involved and the tools needed to manage those risks effectively. This approach not only protects individuals but also contributes to healthier markets by reducing the likelihood of destabilizing flows driven by uninformed speculation.
For market participants seeking to navigate the automated landscape effectively, several practical steps can help harness the strengths of both human insight and technology. First, traders should view algorithms as aids rather than replacements, using them to handle repetitive tasks such as scanning for opportunities, monitoring price levels, or executing predefined orders while retaining final authority over strategy and risk limits. Second, maintaining a disciplined routine that includes regular review of both quantitative signals and qualitative factors — such as news, sentiment, and macroeconomic developments — ensures that decisions remain grounded in a holistic view of the market. Third, setting clear risk parameters and adhering to them strictly can prevent the kind of emotional or model-driven overreach that leads to losses; humans excel at exercising judgment when rules are well-defined, so combining strict limits with flexible interpretation within those bounds can be powerful. Fourth, investing in continuous education — through reading, mentorship, or formal training — helps keep intuition sharp and informed by evolving market dynamics. Finally, fostering a mindset of responsibility — recognizing that trading decisions impact not only personal outcomes but also market health — encourages traders to consider the broader implications of their actions, such as avoiding excessively aggressive strategies that could harm liquidity or confidence. By following these practices, traders can create a synergistic relationship where technology amplifies human capabilities without supplanting the essential role of judgment, experience, and ethical consideration.
The evidence is clear: while algorithms and AI have transformed the mechanics of trading, the human element remains indispensable for ensuring market stability, transparency, and trust. Schlaepfer’s “Real Trading” serves as both a timely warning and a constructive roadmap, reminding us that technology should serve human goals rather than the other way around. For individual investors, the takeaway is to approach trading with humility and a commitment to lifelong learning, leveraging tools for efficiency but never outsourcing judgment entirely. For professionals and institutions, the message is to invest in hybrid systems that pair sophisticated algorithms with skilled human oversight, creating feedback loops that continuously improve both the models and the intuitions that guide them. Regulators should encourage transparency in algorithmic trading and consider measures that ensure human accountability remains a core component of market infrastructure. Ultimately, the future of trading lies not in choosing between humans and machines but in finding the optimal blend that captures the efficiency of automation while preserving the adaptability, ethics, and responsibility that only humans can provide. By embracing this balanced approach, markets can continue to innovate without sacrificing the trust and resilience that have long been their foundation.