My Path to Python- Exploring Fractal Markets and Building Trading Tools

Well, How Did I Get Here and Where Am I?

Initially, the allure of financial markets for me was the prospect of making millions. For many, the concept of buying and holding over the long term inherently makes sense. Over time, this strategy typically yields positive results, culminating in a comfortable nest egg by retirement. However, I believe we can achieve even more.

Gaining Education

Growing up in a quaint town in rural Oregon, my background was far removed from the world of markets and trading. My first foray into this realm was through buying and holding a mutual fund, which yielded mixed results. Yet, observing a chart depicting the market's consistent upward trajectory, albeit with its fluctuations, left a lasting impression. Although I initially explored architecture and design, the allure of the markets eventually steered me towards a degree in economics. "It'll be beneficial for understanding the markets," I was advised.

While college offers a glimpse into potential knowledge, it doesn't quite capture the vastness of what can be learned — a fact often omitted in university brochures. Like many, I navigated somewhat aimlessly, seeking a consistent trading approach. My passion for understanding stock trading led me to experiment with Excel sheets, pulling stock data and ranking them based on rudimentary criteria. Without professional experience, I was essentially trying to apply academic theories to the markets, which unsurprisingly yielded modest results. Fundamental analysis seemed ill-suited for predicting short-term market movements.

Real-World Experience is Key

The excitement was palpable when I secured my first post-college job as a Personal Banker and Investment Representative at Chase Bank. I eagerly anticipated the training and certification associated with the esteemed Series 6. The initial months were dedicated solely to preparing for this exam. However, the Series 6, with its emphasis on rulebook memorization, was a far cry from the trading panacea I had envisioned. Instead, it paved the way for a sales-centric career, diverging from my true interests. Despite my aspirations to ascend the corporate ladder, my efforts went largely unnoticed in such a vast corporation. While this didn't propel me closer to my goal of deeper market involvement, it did grant me access to company-provided training.

The Quest Persists

In my spare time, I delved into every course available on the company intranet that resonated with my aspirations. As machine learning began gaining traction in mainstream media, I contemplated its potential applications in stock markets. After immersing myself in online courses covering regression, Markov models, tipping points, and more, it became evident that Excel wasn't the optimal tool for comprehensive analysis and automation. My search for a more robust solution led me to Python, unveiling a realm of possibilities.

Overcoming Learning Curves

The frequent rains in my Oregon hometown meant ample indoor time, allowing me to familiarize myself with computers from a young age. With this foundation, I embarked on my Python learning journey, starting with the basics. However, mastering the fundamentals wasn't enough. I chanced upon two invaluable courses by EdHec Business School on Coursera:

These courses introduced innovative perspectives on market analysis and portfolio construction, insights I plan to revisit and apply in the future.

The Role of Podcasts

My 45-minute work commute presented an ideal opportunity to consume valuable content. However, finding quality material was challenging. Most available content offered qualitative, non-measurable insights, often echoing the sales-driven narrative of the industry. Amidst the overwhelming amount of content, a few podcasts stood out, offering genuine insights into the markets. One such podcast introduced me to the team at Hedgeye (hedgeye.com), whose pragmatic, emotion-free approach to markets resonated deeply with me.

The Appeal of Fractal Markets

Keith McCullough, the founder of Hedgeye, devised a fractal risk range product, a tool signaling market buy or sell opportunities. Drawing from calculus, statistics, and insights from luminaries like Benoit Mandelbrot and Harold Edwin Hurst, this approach offers a captivating model for asset prices and time series data. My background in architecture and design had already introduced me to the universe's fractal nature, making Keith's mathematically grounded approach particularly appealing.

In the upcoming series of posts, I aim to delve into the creation of a similar model, capable of predicting an asset's price range. As a trader, my goal is to input an asset symbol and receive a probable trading range, aiding more informed portfolio decisions.

I hope this offers a clear context and roadmap for the journey ahead. I intend to detail my explorations and provide a resource for others with similar aspirations in the realm of market trading tools.

Warm regards,