My Rocky Road
Four years in the markets, and I’ve made every rookie mistake in the book, strategy hopping, style-switching, you name it. My biggest mistake? Ignoring how little time I could realistically commit. I was day trading on my phone while grinding through a demanding engineering job, splitting my focus between screens and work. Looking back, I don’t even want to think about the opportunity cost! A honest chat with myself could’ve screamed, “Swing trade”! It would’ve saved time, money, and a lot of headaches. But, as they say, every mistake leads you somewhere. So here I am…
The Day Trading Trap and a Swing Trading Epiphany
Trying to day trade with a full-time job was painful and doomed to fail. No wonder my results were all over the place. Then it hit me: swing trading fits my life. It’s slower, deliberate, and lets me breathe. But the real game-changer? We’re in an era where AI and cloud computing that can 10x our output. With discipline, we can learn any skill for next to nothing. The catch? Most retail traders, myself included, underuse these tools, sticking to inconsistent, discretionary trading hyped up on social media.
Where Retail Traders Go Wrong
Retail traders, myself included, often obsess over one market—like forex majors tied to the U.S. dollar. Problem is, they’re correlated. If the DXY rallies alongside crude oil, CAD might stall; same with AUD when gold’s . If you miss a setup one two of the other currencies like Euro or GBP? You’re waiting weeks for another, which fuels frustration. I’ve been there, closing a Euro short just because I was anxious, not because the trade was wrong!
The fix? Think like an institution. Treat your account as a portfolio, not a single bet. Diversify with uncorrelated strategies across asset classes to boost returns and cut risk. If one strategy is in a drawdown? Another might be killing it. Just don’t underestimate the time this takes to develop and deploy.
The Power of Algorithms and Data
Executing multiple strategies consistently is impossible. Even one’s tough to execute consistently. The answer? Algorithms and machine learning. But here’s the caveat: these need rigorous backtesting across long time horizons, stress-tested for scenarios like the dot-com bubble, 2008 crash, or Covid’s oil plunge.
Algorithms also tackle recency bias—our tendency to overweigh recent events, like a market dip, while ignoring long-term trends. A solid backtested strategy, grounded in historical data, prepares you for shocks and builds a resilient portfolio. Data gives you confidence; confidence kills emotional trading.
Building a Data-Driven Edge
To match real-world markets, I want to use a mix of analyses:
- Time Series Analysis: Tracks daily data and structural shocks.
- Historical Data Analysis: Shows how a strategy would’ve performed.
- Statistical Analysis: Validates predictive power and robustness.
- Risk Analysis: Quantifies potential downsides.
- Out-of-Sample Testing: Ensures the model works on unseen data.
Institutions lean on these tools daily, yet retail traders rarely do. I’ve seen consistency in my swing trading since adopting a discretionary approach but what if I leant on a data-driven & automated approach? but I’m missing a robust dataset. Manually collecting 10 years of data? That’s a decade of missed trades and distractions. Why wait 10 years when a computer can 10x my output and get this data set in a few hours?
Why Retail Traders Need to Embrace Tech
As traders, we love leverage, but we’re ignoring the biggest one: technology. AI, algorithms, and cloud computing let us backtest strategies, eliminate bias, and execute with precision. Discretion belongs in research, not daily trading. My goal? Build a portfolio of systematic strategies across asset classes, backed by stats, to hunt alpha.
Join me as I dive deeper into data-driven trading and share insights to help you find your edge. Let’s skip the retail traps and build something exceptional.
Speak soon,
Liam
I Trade Global 
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