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Day Eight

 Hey everyone,

It's been a bit of a frustrating week on the algo trading front, and it highlights a common dilemma for beginners like myself: the constant tug-of-war between actually building the strategy and getting bogged down in infrastructure and deployment issues.

I've realized I've been spending a disproportionate amount of time and mental energy lately worrying about how I'm going to deploy my news straddle strategy once it's ready. We talked about my Linux concerns in a previous post, and that's been a persistent background hum. While it's crucial to think about deployment eventually, I'm starting to feel like it's becoming a significant distraction from the core task: making the strategy itself robust and profitable.

Wasting Effort on "How" Instead of "What"

My main focus right now should be on refining my strategy's logic, optimizing parameters, and thoroughly backtesting it. That's the engine of the whole operation. But instead, I find myself researching cloud VPS options, debating virtual machine setups, and sifting through forum posts about Linux compatibility for various trading APIs.

It's a classic case of getting ahead of myself. A brilliant deployment solution is useless if the strategy it's running isn't profitable. I need to remind myself to prioritize the what (the strategy) over the how (the deployment) for now. The "how" will come, and there will be solutions, but the strategy needs to be sound first.

Investpy Woes: Another Data Hiccup

Adding to the frustration, I've run into a specific technical roadblock with investpy. For those unfamiliar, investpy is a Python library that allows you to retrieve financial data from Investing.com. It's a fantastic resource for getting historical data on stocks, indices, currencies, and more, which is crucial for backtesting and analysis.

However, lately, I've been hitting persistent 403 Forbidden errors when trying to use it. After a quick dive into online forums and GitHub issues, it seems I'm not alone. It appears Investing.com may have implemented some new anti-scraping measures, or perhaps there's a change in how investpy interacts with their site, causing it to break.

This is a real pain, as investpy was a convenient source for some of the historical data I needed. It means I now have to spend time looking for alternative data sources, figuring out how to get clean data, and potentially adapting my data ingestion pipelines. It's yet another example of an "infrastructure" problem diverting attention from strategy development.

Back to Basics (Literally)

So, moving forward, my new mantra is: Strategy First. I'm going to deliberately pull back from the deployment rabbit hole for a while. The Linux challenges and data sourcing issues are real, but they can wait until I have a strategy that's actually worth deploying.

My focus will be entirely on:

  • Refining my news straddle strategy's entry/exit logic.
  • Thoroughly backtesting it with the libraries I'm exploring (QStrader/backtestpy).
  • Finding and implementing reliable alternative data sources for my backtests.

It's a tough lesson to learn, but sometimes, the best way forward is to simplify and prioritize. Wish me luck in staying disciplined!

Have you ever found yourself getting distracted by deployment issues before your strategy was ready? Or run into similar frustrating data source problems? Share your experiences in the comments!

Happy (and focused) algo trading,

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