Skip to main content

Day Eleven

 Hey everyone,

Just wanted to check in and let you know: I'm still here! Lol. After the sobering, albeit necessary, reality check of my news straddle strategy's recent backtesting performance (or lack thereof!), you might have expected me to throw in the towel. But nope, that's not how we roll on this algo trading journey.

If anything, those clear, undeniable losses have only intensified my resolve. It's frustrating, yes, but it's also incredibly motivating to figure out why it's not working and how to fix it.

Back to the Drawing Board: News Straddle Re-Evaluation

So, my primary focus remains the re-evaluation of my news straddle strategy. As I mentioned, the manual backtesting revealed a painful consistency in hitting stops. This means I need to go deeper than just tweaking parameters. I'm looking at fundamental questions:

  • Are my entry conditions too broad or too precise?
  • Is the 10-pip loss simply too tight for the post-news volatility?
  • Am I missing a crucial filtering mechanism to avoid whipsaws?
  • Perhaps the type of news events I'm targeting needs adjustment.

It's a process of dissecting every single component, analyzing the market behavior around those failed trades, and trying to identify the core weakness. It's challenging, but this deep dive is where the real learning happens.

Learning from the Best: "Six Figure From Scratch" Insights

While I'm wrestling with my own strategy's shortcomings, I've also been dedicating time to learning from those who have walked this path successfully. I've been absolutely glued to the very insightful chapter readings of the "Six Figure From Scratch" book from The Trading Cafe.

You might remember I recently mentioned starting to read the TradingCafe book. Well, these chapter readings are taking it to another level. Hearing the concepts explained, seeing the examples walked through – it's incredibly helpful for cementing complex ideas. It's like having a guided tour through the book, providing additional context and emphasis on the most critical points.

This book seems to offer a practical, no-nonsense approach to building trading systems, which is exactly what I need right now. It's helping me to:

  • Think more systematically: Breaking down strategy development into clear, manageable steps.
  • Identify potential pitfalls: Learning from others' mistakes and avoiding common traps.
  • Develop a more robust framework: Understanding the broader principles of building a reliable trading business, not just a single strategy.

It's a fantastic complement to the hands-on (and sometimes painful!) lessons I'm getting from my own backtesting. The combination of practical application and theoretical learning is truly powerful.

So, the journey continues! My news straddle strategy is currently taking a beating, but my commitment to figuring this out is stronger than ever, especially with these new educational resources lighting the way.

What resources are currently inspiring your trading or algo development journey? Share your recommendations below!

Happy (and persistent) less algo trading, more learning,

Comments

Popular posts from this blog

Day Seventeen

 Hey everyone, After the eye-opening (and somewhat humbling!) experiences with my manual backtesting, and having re-prioritized my learning journey, I've come to a crucial realization: one of the most fundamental skills I need to truly master is proper backtesting methodology. It's not enough to just scroll charts and count pips. To truly validate a strategy and understand its edge, you need a systematic, unbiased, and thorough approach to historical data. This is where many aspiring traders, myself included, often fall short. Why Proper Backtesting is My Next Big Focus You might recall my previous posts about the pain of manual backtesting and the frustration of realizing my news straddle strategy "sucked." While those were valuable lessons, they also highlighted a gap in my knowledge: how to design and execute a truly effective backtest. That's why my immediate next step is to learn how to backtest properly via The Trading Cafe's dedicated course on the sub...

Day Four

 Sometimes, the most productive days in algo trading aren't spent coding or backtesting, but simply absorbing knowledge. Today was one of those quieter days for me, and I finally carved out some dedicated time to dive into a new resource. Diving into TradingCafe I've finally cracked open the TradingCafe book ! I've heard a lot about it in various trading circles, and I'm excited to start digging into its insights. It's easy to get caught up in the technical aspects of building bots and analysing data, but understanding the underlying market dynamics and trading psychology is just as crucial. I'm hoping to gain some fresh perspectives and perhaps even discover new strategy ideas from its pages. It's a reminder that continuous learning is paramount in this ever-evolving field. Babypips School of Pipsology: A Game-Changer! On another exciting note, I made a fantastic discovery today: the Babypips School of Pipsology is available in audio format! Seriously, wha...

Day Five

 Hello fellow aspiring algorithmic traders! Today, I want to talk about two critical, yet often overlooked, aspects of developing an algo trading strategy: backtesting libraries and the looming question of deployment , especially when you're a dedicated Linux user like me. Diving into Backtesting: QStrader vs. backtestpy So, my current focus is really on nailing down the backtesting of my news straddle strategy. It's one thing to have a great idea; it's another entirely to prove it stands a chance against historical market data. For this, I've been getting my hands dirty with a couple of prominent Python backtesting libraries: QStrader and backtestpy . Both libraries offer fantastic functionalities, but they approach the problem from slightly different angles. QStrader is a comprehensive framework that provides a more structured and object-oriented approach. It's great for building sophisticated event-driven backtesting systems, which can accurately simulate ma...