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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 market conditions and order flow. It requires a bit more setup and understanding of its architecture, but the payoff is a powerful and flexible testing environment.

  • backtestpy, on the other hand, is a more lightweight and perhaps more immediately intuitive library for quick strategy evaluations. It's excellent for rapid prototyping and getting a good overview of how a strategy performs with minimal fuss. It shines when you want to quickly see the equity curve and key performance metrics.

I'm still in the process of thoroughly evaluating which one will be the primary workhorse for my strategy. The goal is to ensure I can rigorously test my news straddle strategy's logic, identify its strengths and weaknesses, and optimize its parameters to death before even thinking about live trading. This is where I'll uncover if my assumptions about news-driven volatility actually translate into profitable trading signals.

The Linux Deployment Puzzle: A Personal Conundrum

Beyond just proving the strategy works on historical data, there's the even bigger question: how do I actually run this thing live? This is where my personal computing setup introduces a unique challenge.

I'm a steadfast Linux user. My entire development environment, from my coding IDEs to my data analysis tools, runs on Linux. I don't personally own a Windows computer, and frankly, I'd prefer not to. This puts me in a bit of a pickle when it comes to deploying an algo trading bot.

Many trading platforms, brokerage APIs, or even prop firm interfaces often assume a Windows environment. This creates a potential "bridge that gap" scenario for me. I'm actively thinking through the various ways I might need to overcome this:

  • Native Linux Support: This is the dream scenario. If a brokerage or platform offers a robust API that works seamlessly on Linux, that's a huge win. I'm always on the lookout for these.
  • Virtualization: A common workaround is running a lightweight Windows Virtual Machine (VM) on my Linux machine. This allows me to use Windows-specific software or APIs within a contained environment. While it adds a layer of complexity and resource overhead, it's a viable option.
  • Cloud-Based Solutions: Another path is to deploy my bot on a cloud server. This could involve renting a virtual private server (VPS) running Windows or even a Linux VPS if I can find a broker or platform with a compatible API. This removes the need for my local machine to be constantly running, offering reliability, but it also introduces ongoing costs and its own set of deployment intricacies.

The deployment phase often gets less attention in the beginner stages, but it's crucial. Having a finely tuned strategy is useless if you can't actually execute trades reliably. This Linux deployment dilemma is a real-world hurdle I'm trying to anticipate and solve now, rather than scrambling when my strategy is "ready."

Are any of you out there fellow Linux algo traders? How have you tackled the deployment challenge? I'd love to hear your experiences and any insights you might have!

Happy (algo) trading,

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