Skip to main content

Welcome

 Hello world

Welcome to Quant Quest, my brand new journal documenting my foray into the fascinating and sometimes daunting world of algorithmic forex trading. As a relative beginner in the realm of automated strategies and quantitative analysis, this blog will serve as my logbook, my testing ground, hopefully, my chronicle of progress (and perhaps a few entertaining missteps along the way) but more importantly, my accountability.

In this blog, you can expect to find:

  • My learning journey: The concepts I’m grappling with, the resources I’m exploring, and the “aha!” (and “oh no!”) moments I encounter.
  • Algorithm development: Snippets of code (when I dare to share!), the logic behind my strategies, and the evolution of my bots.
  • Backtesting results: The cold, hard numbers (and my attempts to interpret them!).
  • Live trading experiments: The nervous excitement (and potential heartbreak) of deploying my creations in the real market.
  • My thoughts and reflections: The psychological aspects of trusting a machine with my capital, and the lessons I learn along the way.

I’m excited (and a little terrified!) about this new chapter in my trading journey. I believe that the future of trading lies in leveraging data and automation, and I’m eager to learn and grow in this space.

So, join me on this Quant Quest! Whether you’re a seasoned algo trader, a fellow beginner, or just curious about the intersection of finance and technology, I hope you’ll find something of interest here. Let the quest begin!

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...