My 20-Year Trading Journey & Building Passive Income

The Realities of Building Passive Income

What Passive Income Really Means: It’s not about money magically dropping into your bank account. Instead, it’s about uncoupling your income from your daily working hours. Picture writing a book or developing a piece of software once and continuing to earn from it long after it’s completed.

The Two Main Paths:

1. Investing Your Money — A straightforward but often slower route. You place capital in assets like stocks, bonds, real estate, or high-interest savings, waiting for returns to accumulate over time.
2. Creating Something of Value — More hands-on initially, but potentially more rewarding. Examples include developing digital products, producing engaging content, or designing automated services. The key is to build systems that largely run on their own.

The Success Formula: Many effective passive income strategies share three elements:

1. Leverage Your Strengths — Start with skills you already have and build from there.
2. Offer Real Value — Ensure your idea solves a genuine problem or provides a tangible benefit.
3. Scale Efficiently — Create something you can replicate and deliver without substantial additional effort (e.g., an online course that sells repeatedly).

The J-Curve Reality: Passive income often involves active effort at the start. Think of it as a “J” shape: you invest time and money upfront, remain in the negative for a while, and eventually climb to profitable returns.

Common Pitfalls:

1. Perfection Paralysis — Waiting for the ideal plan can prevent you from ever beginning.
2. Lack of Consistency — Steady, repeated actions usually outperform short bursts of brilliance.
3. Misaligned Focus — Building something you love that no one else wants is a quick way to fail.

A Smarter Approach: Dive in where you already have knowledge. Test quickly, fail quickly, and refine. Lay down processes early for seamless growth.

Conclusion: Passive income isn’t a shortcut; it’s about working intelligently now so that later, you can enjoy more freedom. If you’re ready to invest in yourself—by building, automating, and refining—there’s no better time to start than right now.

My 20-Year Trading Journey

Unfiltered Truths About Trading and Automation

Let’s start with this fundamental truth: you are always in someone else’s trend, no matter how hard you try to convince yourself otherwise. The markets are vast and interconnected, and while you might catch a wave or two, the reality is that you’re always navigating a trend that someone else initiated or influenced. Recognizing this can free you from chasing illusions of total control and help you focus on what truly matters: adapting to the reality in front of you.

And let’s be crystal clear—no one, absolutely no one, on this planet can predict prices. Ignore the slick marketing, the self-proclaimed “gurus,” and the AI-driven hype machines. Predicting price movements with certainty is a fantasy, no matter how convincing the pitch. If someone tells you otherwise, they’re selling you a dream, not a strategy. True trading is about probabilities, not promises.

Speaking of hype, AI in this space right now is worse than garbage. Sure, it looks shiny on a generic landing page with buzzwords plastered everywhere, but when you dig into the results, it’s clear these tools are more about selling subscriptions than delivering meaningful outcomes. I’m not here to sell you anything. In fact, I’ll give you more insight for free than those “shitfarm merchants” ever will—just ask. This is simply part of my portfolio of work, built on real experience, not marketing fluff.

Now, is automating trading profitable? Hell yeah, it is! Automation eliminates emotional decision-making, ensures consistency, and lets you execute complex strategies that would be impossible manually. But don’t be fooled into thinking it’s some magic button for success. It takes effort, testing, and refinement to create systems that work. Automation is a tool—a damn powerful one—but it’s only as good as the person wielding it.

The trading world is full of noise and half-truths. My goal is to cut through that and share the raw, unfiltered realities of this space. No sales pitch, no sugarcoating—just lessons from the trenches. Whether it’s understanding trends, embracing uncertainty, or mastering automation, trading is a constant journey of learning, adapting, and improving. And if you’re ready to learn, I’m here to share what I know.

Over two decades ago, I took my very first live trade. At the time, I was fresh out of university with a background in Software Engineering and a burning desire to become a programmer. I’d always been fascinated by how technology could reshape the trading world, so when I discovered the IG Index API, it felt like a natural fit. Tinkering with that API not only propelled my coding journey forward but also served as the perfect launchpad for my personal and professional development. Since then, I’ve tried almost every mainstream indicator—MACD, Bollinger Bands, Moving Averages—but found myself hungering for something less conventional. Enter Alpha Sutte and Vortex, backed by solid research that let me adapt quickly to fast-paced markets.

Being a highly visual person, I like to imagine the market as a chaotic cattle auction, with someone like Del Boy in the middle shouting prices and swinging deals. Beneath the polished suits and complicated jargon, global financial markets are driven by the same impulses: panic, greed, hope, and desperation. The real trick is filtering out all that commotion and committing to a single, well-defined strategy. For me, automation is what makes that possible. Once my rules are set in code, there’s no second-guessing—no overthinking in the heat of the moment. It’s execution without emotional baggage, a steady hand in a storm of volatility.

Across these two decades, I’ve seen it all: catastrophic failures that made me question everything, and triumphant wins that reaffirmed my faith in data-driven approaches. If you’re reading this, you might have one of two goals—chasing financial independence or craving something more tangible like an Aston Martin. But here’s the truth: there’s no magical recipe for instant success. It took me years of trial, error, and relentless coding to get comfortable with my current setups. While I won’t sugarcoat how tough trading can be, I will say this: if you stay curious, refine your methods, and keep pace with the markets, you’ll carve out a trading style that works for you.

Early on, I realized my biggest roadblock wasn’t a lack of information—it was my own emotions. Fear, greed, and indecision can unravel even the best trade ideas. By automating my strategies, I could eliminate snap judgments and bias from the equation. That first live trade more than a decade ago was intimidating, but it taught me an essential lesson: disciplined execution outperforms gut feelings every time. Coding my own algorithms turned out to be the perfect vehicle for imposing structure on an otherwise chaotic environment.

In 2024, I upgraded my trading system countless times—not because it wasn’t effective, but because I’m relentless about improving year after year. Markets never stand still, and neither do I. While some strategies produced outstanding returns and others fell short of expectations, every tweak and adjustment brought new insights into market behavior and how I could respond more effectively.

Insights on Hedging and RSI

Hedging is often hailed as a strategy for risk management, but in my experience, it simply doesn’t work. I’ve tried it repeatedly, revisiting the idea every couple of years to see if I could make it viable, but the results have always been disappointing. Even with advanced automation, the complexity of managing hedged positions outweighs any potential benefits. It introduces too many variables, often leading to unintended consequences rather than the intended risk mitigation. For me, the added layers of decision-making and execution made hedging more of a distraction than a solution.

On the other hand, RSI has proven to be an exceptionally reliable indicator. When paired with the StochRSI, it becomes one of the most accurate tools I’ve ever used for identifying market conditions. The clarity and precision it offers are unmatched, making it an indispensable part of my trading strategies. Any limitations RSI might have, such as false signals or overreactions, can typically be resolved by shifting to a higher timeframe. Zooming out smooths out the noise and provides a clearer perspective, reinforcing RSI’s effectiveness in both short-term and long-term market analysis.

Writing software for my strategies wasn’t just about crafting solutions; it honed my Python skills and instilled a mindset of adaptability. In a world where stagnation equals complacency, I focused on building a fully autonomous algorithm—a system capable of reacting dynamically to market conditions and staying consistently profitable. The ultimate goal was always clear: maintain the edge.

With the rise of generative AI, I could develop and deploy scripts faster than ever, enabling a level of productivity over the past three years that easily surpassed the previous two decades. I also scaled my infrastructure with unprecedented ease, laying the foundation for future growth.

Beyond trading, my drive to optimize pushed me to explore Kubernetes, squeezing every ounce of performance from my servers. This isn’t just about staying competitive; it’s about pushing boundaries and striving for excellence in every facet of my systems and strategies.

Backtesting has remained the cornerstone of my development. Early experiments involved rudimentary scripts to get a feel for how a concept might play out in real conditions. I used demo accounts to confirm trades were firing correctly, flagging any API quirks back to IG Index. Over time, I moved from simple Cron jobs triggering Python scripts to a fully automated CI/CD pipeline via Jenkins—where each commit triggers a build, runs tests, and deploys new code if everything checks out. It’s a far cry from my earliest days, but this continuous integration has allowed me to push small improvements rapidly, staying nimble as the market shifts. Ultimately, it’s this blend of technology, creativity, and resilience that keeps me hooked on trading after all these years.

Jenkins Pipeline in Action:

While spread betting in the UK (via IG Index) remains my mainstay, I’m planning to test my theories with Coinbase and various cryptocurrencies. Part of me is reluctant; crypto can be wild. Yet, the allure of filling knowledge gaps is too strong to ignore. If you share that unquenchable thirst for improvement, you’ll appreciate how these small leaps can lead to enormous breakthroughs.

Ultimately, I want this to stand as a record of what I’ve done—and hopefully, it helps someone else on their journey. I know from reading forums and boards that many new traders could avoid heartache if they had the right guidance. If that describes you, feel free to reach out. Trading may be a solitary pursuit, but it doesn’t have to be lonely.

Resources & Links

Here’s a curated selection of links that have been vital to my growth, from basic spread betting to more advanced algorithmic concepts.

Data Analytics & Quick Visualisations

With the rise of generative AI, it has never been easier to whip up fast, illustrative graphs to explore performance metrics—like profits, losses, and drawdowns.

• data-analytics on GitHub — github.com/tg12/data-analytics
  This repo contains some rough-around-the-edges code and a few basic (sometimes terrible!) graphs. Still, it highlights how quickly GenAI can transform raw trading data into visual insights.

Screenshots

Below are some placeholder images representing my Jenkins pipeline in action and a sample trading graph.

Trading Performance Graph:

Trade Data & Backtest Reports

Below you’ll find some additional resources that dive deeper into my trades and backtesting methodology.

• Trades 2024 (CSV)
  This CSV file contains a detailed list of all trades placed throughout 2024. It includes entry and exit timestamps, position sizes, profit/loss figures, and any relevant indicators used at the time. It’s a great way to see the raw data behind the performance graphs.
• Backtest Report (PDF)
  A breakdown of my backtesting process, focusing on the Alpha Sutte indicator and its interaction with Vortex and RSI. The report shows the longs and the short entry and exit positions,

Disclaimer

The information in this post is intended for educational purposes only and does not constitute financial advice. Trading carries inherent risks, and you should only trade with capital you can afford to lose. Always perform your own due diligence and consider seeking professional advice before making any investment decisions.