In Search of Alpha: Navigating the Risks and Rewards in Algorithmic Trading

A Personal Quest for Robust, Uncorrelated Trading Systems

Section 1: An Evolving Trading Approach

Lately, I've found myself veering away from conventional methods, more intently seeking an elusive edge in the trading world. This pursuit, however, has made me confront an uncomfortable reality: no matter how painstakingly I construct these edges, a good number of them will inevitably crumble in the live environment. This realization has left me grappling with two critical questions:

1. How can I more swiftly recognize when they're underperforming?

2. How can I avoid becoming overly reliant on a handful of strategies that might falter?

Section 2: The Comfort of Time-Tested Strategies

Entering into strategies with a well-worn track record—think typical trend-following and mean reversion strategies—gives traders a certain sense of comfort. These strategies have proven their mettle over decades, and many of the largest funds continue to bank on these trading styles. But for me, the issue arises precisely because I'm building all of these strategies from a data-first perspective.

Section 3: The Data-First Perspective

Essentially, this means I'm diving headfirst into the data, trying to unravel what transpired in the past. I add my own biases and apply my own rigorous stress testing, but there's a nagging doubt that these efforts might not suffice. Nor do I want to squander precious time determining this. The aim here is to cycle through strategies rapidly.

Section 4: The Quest for Cash Flow

Additionally, I'm striving to amp up the "cash flow" in my business, seeking independence from positions that stay exposed to the market for months on end, hoping to ride the upward trend. While these trend-following strategies are perfectly acceptable, they do lack a certain cash flow element. If I can create a strategy offering a more substantial cash flow, it would be an excellent way to diversify my portfolio. These are the concepts I'm currently refining, uncertain yet excited to see how they'll pan out.

Section 5: The Balance Between Trade Volume and Robustness

In my journey towards that elusive edge, I've noticed that strategies with a high volume of individual trades tend to lack the robustness of their less frequently traded counterparts. This leaves me with two options:

1. Embrace the lack of robustness and deploy these higher-frequency systems.

2. Assemble a collection of low-frequency systems that are uncorrelated with one another.

Section 6: The Pursuit of Robust, Uncorrelated Systems

At present, I find myself leaning more towards the latter option. I favor having a portfolio brimming with robust, uncorrelated systems over one with a few high-frequency, low-robustness systems.

Section 7: Looking Ahead

So, that's the plan for the upcoming months. I've been meticulously recording all of my trading activity on the dashboard I built, running non-stop on an AWS server. My goal is to grow a modest account there, demonstrating to everyone how to harness the power of edge for optimal growth as a retail trader.

Section 8: The Advantage of the Retail Trader

Despite common perceptions, being a retail trader doesn't have to be a hindrance to anyone looking to launch an algo system. In fact, you have a distinct advantage over large funds: you aren't shackled by the stringent regulations they face when managing other people's money. Nor do you need the colossal liquidity levels they do to trade, as you aren't managing billions of dollars. Make the most of these advantages. Venture where they can't, in your quest for alpha. That's exactly what I'm working on, and I'm excited to share my journey live for all of my audience to witness.