Introduction

Algorithmic trading might seem like something only Wall Street pros, quant wizards, or high-frequency machines do—but Introduction to Algo Trading by Kevin J. Davey shows it’s for everyday traders too. Written clearly and based on real experience, this book helps retail traders bring structure, discipline, and logic to their trading. Kevin doesn’t just show you how to build strategies—he gets you thinking about your mindset, habits, and long-term goals.

What comes next is a chapter-by-chapter summary that’s more than just a recap. It tries to capture the real feel of the book—its honesty and practical advice. Whether you’re just starting out or already working on your own algo systems, these summaries will walk you through Kevin’s lessons, warnings, and insights in a clear, no-nonsense way.

I broke “Introduction to Algo Trading” into five parts, each covering a big theme in Davey’s approach. This way, it’s easier to follow the main ideas and helps you really get what algo trading is about—both the basics and the hands-on stuff. From mindset to choosing software and building strategies, every part shows Davey’s focus on clarity, realism, and helping traders succeed.

Part I: Foundations and Mindset

This opening section lays the psychological and philosophical groundwork for algorithmic trading. Kevin J. Davey introduces the concept of trading with discipline, consistency, and emotional detachment. By contrasting trading styles and offering a self-assessment of readiness, this part emphasizes that successful algorithmic trading begins not with code, but with mindset. Before building strategies, Davey urges traders to construct their internal architecture—clarity, patience, and self-awareness are the real entry signals.

Chapter 1: The Different Types of Trading

Speculation manifests in many guises, and this opening chapter, Kevin J. Davey maps the terrain with perspicuous clarity. He begins by demystifying terms like “algos” or “algorithmic trading,” which often evoke dystopian images of rogue automation and financial collapse. At its core, algorithmic trading is really just disciplined trading based on clear, written rules. Whether those rules are coded into a computer or written down on paper, the key is structure and consistency.

Davey breaks trading into three main types: discretionary, algorithmic, and hybrid. Discretionary traders rely on experience, intuition, and quick decisions—often without a fixed system—and emotions usually come into play. Algorithmic traders take emotion out of the equation by sticking to pre-set rules and algorithms. These rules, whether simple or complex, are set once and followed exactly.

Hybrid traders straddle both worlds—applying rules for entries or exits while reserving the right to intervene. They might halt trading during elections or override signals during market turmoil. While tempting, this approach poses a risk. Often, the most profitable trades are the most uncomfortable. Human interference, Davey warns, can erode the very framework meant to protect performance.

This chapter sets the tone for the book: those seeking consistency and aiming to compete with institutional titans must pursue a rule-based practice, not one led by emotion.

Chapter 2: Algo Trading Basics

Every trader, knowingly or not, uses rules. Whether it’s a gut feeling or a tip from a friend, there’s always a pattern beneath the surface. Kevin J. Davey explains that algorithmic trading simply formalizes these rules into a repeatable process. It’s not about genius-level math or mastering dense code—it’s about discipline and clarity. He defines the core elements of a trading algorithm: rules for entering and exiting both long and short positions, stop-loss protocols, profit targets, and position sizing.

From basic moving average crossovers to complex, multi-factor models, Davey shows how to translate concepts into executable systems. He also covers different types of orders and introduces automation, where software takes over execution based on your parameters.

Importantly, algo trading isn’t confined to one system. Skilled traders often run multiple strategies in parallel, managing their interplay like a conductor with an orchestra. For beginners, however, Davey urges simplicity. Treat each strategy as a standalone process. The key isn’t to revolutionize finance—it’s to understand your logic, encode it properly, and trust the system to do its job, even when your instincts say otherwise.

Chapter 3: Is Algo Trading For You?

This chapter takes a turn inward. Davey encourages readers to pause and ask the hard questions before diving in. Success in algorithmic trading depends less on technical ability and more on psychological makeup. Do you have analytical patience, emotional resilience, and the discipline to follow rules even when it hurts? These traits are essential. Davey shares stories of technically skilled traders who failed because they couldn’t stick to their systems.

Markets punish even the best code if the trader behind it can’t detach from fear or impatience. To help readers reflect, Davey offers a self-assessment based on ten essential traits—patience, emotional restraint, and adherence to rules among them. Those prone to perfectionism, impulsivity, or frustration may struggle, not due to lack of intellect, but because algorithmic trading demands a unique psychological profile.

But all is not lost. These traits can be developed over time. Davey advises traders not to rush in, seduced by excitement or the illusion of quick gains. Instead, he calls for internal preparation. The best algo traders aren’t just experts in systems—they’re experts in self-regulation. Before programming a machine, one must recalibrate the mind.

Chapter 4: The Many Advantages of Algo Trading

Davey opens this chapter with a core truth: algorithmic trading levels the playing field. For retail traders, it offers an escape from the chaos of discretionary decision-making by introducing structure, discipline, and clarity. One of its most significant advantages is the ability to backtest. Turning your ideas into code lets you test them objectively against historical data, cutting out guesswork and emotion.

This makes trading more like a science, improving your decisions and building confidence. Another big advantage is diversification. Algo trading lets you run multiple strategies across different markets and timeframes at once—lowering risk and smoothing out your results. Davey shows how experienced traders combine various systems into one balanced portfolio, with each strategy playing its part. Doing this by hand is tough, if not impossible.

Lastly, there’s freedom. Automated systems don’t need constant monitoring. Once coded and calibrated, they execute independently—freeing traders from screen-watching. You’re not giving up control—you design the system first, then step back. For those seeking consistency, scalability, and mental clarity, algorithmic trading provides a rare commodity: peace of mind amid market volatility.

Part II: Challenges and Realism

This second part confronts the inherent complexities and persistent myths surrounding algorithmic trading. Kevin J. Davey breaks down the myth of easy automation and emotionless trading, emphasizing that discipline and vigilance are essential. He warns that algo trading isn’t a quick path to wealth but a continuous process of careful review, adjustment, and accepting uncertainty.

Chapter 5: The Disadvantages and Misconceptions of Algo Trading

After establishing its virtues, Davey delivers a sobering counterpoint: although algorithmic trading is promising, it is not without its hazards. Chief among the misconceptions is the belief that automation extinguishes emotion. While machines may execute orders dispassionately, the trader behind them is still vulnerable—especially when real capital is at stake. Davey recounts his early experiences marked by obsessive screen-watching and nervous hesitation, despite the supposed comfort of automated execution.

Equally dangerous is the compulsion to tweak—a behavior that arises when traders continually adjust their strategies in response to short-term underperformance. While fine-tuning may appear productive, it often leads to overfitting —the trap of tailoring a system so perfectly to historical data that it fails in real-time markets. Davey urges readers to resist the urge to chase perfection, warning that strategy manipulation in the absence of robust logic undermines durability.

He also dispels the “set it and forget it” fallacy. Algorithmic trading is not a self-sustaining machine you leave unattended. Systems require ongoing oversight, periodic recalibration, and, at times, full suspension. Markets evolve. Infrastructure fails. Connectivity drops. Software misfires. Such risks are not rare—they’re part of the terrain. This chapter stands as a pivotal checkpoint: while the promise is real, so too are the demands.

For those seeking passive profit, disappointment awaits. For those prepared to treat trading as a disciplined craft, however, the potential endures.

Chapter 6: How to Begin Algo Trading On Your Own

With expectations recalibrated, Davey pivots to action. Now that readers understand what algorithmic trading entails—and what it doesn’t—it’s time to engage in practical applications. He outlines four primary methods to test a trading strategy: manual simulation, hiring a programmer, building a proprietary engine, or using retail software. Davey prefers retail platforms for independent traders, balancing power and ease of use.

These tools let you focus on strategy design instead of technical hassles. He shares his journey from paper notes to Excel tests and finally to professional platforms. It was only then that his systems began to flourish. Why?— Because robust software liberates the trader from execution mechanics and empowers them to concentrate on strategy development. Davey makes it clear: with today’s tools, failure to test is inexcusable.

Without testing, traders are flying blind—and in markets, blindness is perilous.

This chapter offers a pragmatic takeaway: you don’t need advanced degrees or institutional backing to enter the algorithmic trading space. What you do need is intellectual curiosity, a willingness to learn the tools, and the patience to build slowly. Davey advises against rushing to live markets. Start small. Develop basic systems. Observe how they perform under varied conditions. The path forward isn’t about quick wins—it’s about building a repeatable process that matures with experience and evolves with the trader.

Part III: Software and Platforms

Part three orients the reader toward the tools required to operate within the algorithmic domain. Kevin J. Davey guides traders through the process of selecting a suitable platform, evaluating essential features, and cultivating confidence in their chosen environment. Kevin pairs technical advice with practical wisdom: mastering software isn’t about complexity, but empowerment. A platform is a tool, not the goal—choosing wisely sets you up for clear, lasting strategy development.

Chapter 7: Selecting a Trading Software Platform

Selecting a trading platform is one of the first consequential choices for aspiring algorithmic traders. Davey stresses there’s no perfect platform—only the one that fits your goals, strategies, and skills. He offers a checklist of must-haves: solid charting, broker links, easy programming, reliable data, and strong automation support. The ideal platform should enable—not obstruct—the process of crafting and deploying strategies.

Sharing his experience, Davey favors TradeStation but respects rivals like NinjaTrader, MultiCharts, and MetaTrader. Each has strengths—some with deep customization, others easier to learn. A strong user community, clear docs, and good support are crucial. Isolation in times of difficulty can stall development—guidance is a critical asset.
Davey cautions new traders not to be seduced by overly complex features.

The goal is not to master every esoteric capability but to build and test reliable strategies. The platform should serve the trader’s logic, not distract from it. Mastery does not come from the platform’s sophistication but from the trader’s clarity in wielding it. The right software becomes a long-term ally—functional, intuitive, and aligned with the trader’s evolving goals.

Chapter 8: Popular Trading Platforms

With the selection criteria laid out, Davey now surveys the landscape of popular platforms, based on feedback from his own students and his own experience. He explores TradeStation, NinjaTrader, MultiCharts, MetaTrader, and other platforms, including AmiBroker and ThinkOrSwim. Each caters to a different trading archetype. TradeStation is favored for its ease of use and charting capabilities, NinjaTrader for its robust C# scripting, and MultiCharts for its compatibility with TradeStation.

Other platforms bring value to niche contexts—such as custom scripting, equities, or intraday trading. Davey doesn’t merely list features—he interlaces them with honest commentary and real-world application. He breaks down which platforms suit particular asset classes or trading styles, while stressing that the strength of a platform’s ecosystem—forums, user groups, learning content—is often more decisive than the core technology itself.

Importantly, Davey abstains from naming a “winner.” Instead, he urges readers to explore—download trial versions, test the workflow, and identify the platform that resonates with their process. Time and comfort with your tools matter. A cumbersome interface or fragmented documentation can cripple strategy development. Conversely, a platform that matches your pace and needs can unlock creative flow and accelerate your journey toward consistent execution.

Chapter 9: Trading Platform – Next Steps

With a platform chosen, Davey presses traders to go all in—not financially, but intellectually. Go beyond basics—explore the platform fully. Read manuals, watch tutorials, visit forums, and use all resources. Powerful features only matter if you know how to use them. Davey compares this to a child playing in a sandbox: learning through exploration is essential. Tinker with charts, modify settings, and run built-in strategies. Trial and error isn’t a setback; it’s the fastest route to internalization. Once the environment feels familiar, the next step is to develop or refine a strategy. The point is not to create brilliance from scratch but to begin translating rules and market hypotheses into working systems.

Here, Davey introduces a critical—and often overlooked—lesson: testing the test. He teaches traders how to “fool” their own backtest engine to expose weaknesses in logic or flaws in code. If you can generate a fake strategy that performs flawlessly, you learn to recognize dangerous illusions when they appear uninvited. This intellectual sharpening elevates the trader from an operator to an investigator—someone who doesn’t just build strategies but also interrogates them.

The real power, Davey insists, lies in your ability to spot what doesn’t work, even when the platform says it does.

Part IV: Building and Testing Strategies

Here, theory converges with practice. Kevin J. Davey guides readers through the creation of a straightforward breakout strategy, emphasizing the crucial role of backtesting—accounting for commissions and slippage—and the necessity of out-of-sample testing to ensure robustness. Rather than celebrating winning results, Davey celebrates the process itself, illustrating how simulated failures often pave the way for authentic learning. This section presents algorithmic design as a disciplined craft, where the lessons gleaned from testing far outweigh those from trading alone.

Chapter 10: Let’s Get Started – A Simple Sample Algo

Kevin bridges theory and application by guiding readers step-by-step through the process of building a foundational trading algorithm. The goal is not to deliver a market-beating system but to build confidence with the chosen platform and instill best practices. Using a daily soybean chart from 2007 to 2016, he introduces a straightforward breakout rule: enter long when the closing price exceeds the high of the previous “x” bars and the ADX indicator surpasses 20.

This simplicity is intentional—clarity is the best teacher for newcomers. Davey carefully walks through coding the strategy, performing optimizations, and interpreting performance metrics. He emphasizes the importance of factoring in commissions and slippage, warning against unrealistic and overly optimistic results. After optimization, the strategy undergoes out-of-sample testing—evaluated on data outside the calibration period—to simulate real market conditions.

If the strategy fails here, its live viability is doubtful.

Though the sample algo does not pass the out-of-sample test, Davey frames this as a victory. The real win is mastering the process: building, refining, testing, and automating a strategy from scratch. The chapter concludes by outlining how to move to live automation once a strategy proves sound.

Its actual value lies not in the specific rules but in ushering readers into the disciplined world of algorithmic trading—an essential rite of passage into rigorous, systematic speculation.

Part V: Final Advice and Mindset

The closing section delivers seasoned guidance for enduring success. Kevin J. Davey warns against emotional trading and unrealistic expectations, urging readers to approach algorithmic trading as a professional craft. Practical tips, psychological insights, and a call for humility underscore that lasting success depends on simplicity, realism, and adaptability. This part is both a conclusion and a fresh start—an invitation to cultivate not just systems, but a resilient trading mindset.

Chapter 11: Tips for Successful Algo Trading

Kevin shares hard-earned wisdom from years in the trenches. First and foremost: manage your expectations. Algo trading isn’t a shortcut to riches. He cautions against fantasies of turning $500 into a fortune overnight. Instead, focus on building solid habits, managing risk, and adopting a long-term outlook.

Realistic goals—not hype or hope—keep traders in the game. He emphasizes the technical and emotional discipline needed to thrive. Strategy development demands scientific rigor, including thorough testing over extended periods, consistent consideration of slippage and commissions, and skepticism toward backtests alone. Kevin champions walk-forward testing as a more honest predictor of future performance.

He advises keeping strategies simple and resisting the temptation to overoptimize, warning that “perfect” backtests rarely survive live markets. Success belongs to robust, durable systems—not dazzling ones. Kevin also tackles the human factor. Knowing when to walk away—from a failing strategy or trading entirely—is crucial. Emotional attachment can lead to endless tweaking and chasing losses. True professionals maintain exit plans not just for trades, but for their careers as well, when necessary.

This chapter is just as much about mindset as it is about mechanics. Trading challenges the psyche; those who meet it with humility, patience, and process-oriented focus have the best shot at lasting success.

Final Thoughts

Kevin J. Davey’s approach is refreshingly grounded. He offers no magic formulas or promises of overnight wealth—only practical tools, a fresh perspective, and encouragement. His journey is one of self-awareness, technical growth, and continuous learning. Above all, he reminds us that trading isn’t just about markets—it’s about mastering ourselves. If you finish this book—or these summaries—feeling more curious, cautious, and prepared, you’ve taken the first real step toward becoming not just an algo trader, but a thoughtful, resilient one.

And that, Kevin insists, is the true edge.