Quantifiable Alpha: Exploiting market inefficiencies for quantifiable alpha. Crypto | Stocks | ETFs | Futures

Joined May 2025
Got a nice looking backtest? What can you REALLY expect from it in live trading? - from Kevin Davey @kjtrading
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🧵 Nick Radge: Trading Lessons Hidden Between the Lines A wonderful chat on the podcast with @thechartist here: algoadvantage.io/podcast/044… For serious system traders only. Subtle, but powerful. Don't skim this. Nick builds structural edges and lets time do the heavy lifting. What looks simple is often deeply layered. Here's what you might miss if you don't look at how he trades. 👇 Diversify by strategy archetype, not just asset class. Radge combines: * Absolute momentum (trend-following) * Relative momentum (rotation) * Mean reversion (short-term swing) * All Weather Tactical Asset Allocation with ETFs He's creating anti-correlation in the equity curve at every step, trading US and AUS markets in their own way. This is key, listen in on the pod. There are multiple layers of diversification we discuss. Avoid the beginner cycle. Most traders system-hop after 3–6 months of underperformance. Radge? He’s traded some models for decades. Through multiple flat periods. That’s not stubbornness. That’s expectation alignment. You must map out a strategy’s likely cold streak before you fund it. Psych capital > trade capital. In 2008, his ASX trend system went to cash. Clients panicked. “Why are we paying to do nothing?” Then the market dropped 50%. His strategy preserved not just capital—but confidence to re-engage in 2009. That’s a rarely discussed edge: emotional solvency. System timing is a fool’s errand. Build systems with purpose to round out your portfolio. Beware of trying to time a system. His solution? Strategy stacking. Not rotation. Simultaneous deployment. Mean reversion draws down? Trend-following carries the torch. He doesn’t over optimize. Build based on the simple mathematics of non-normal distributions. Markets must trend. (See why in the pod). The edge isn’t in prediction—it’s in asymmetry. Winners will overrun. Losers get cut. Edge = right tail exposure + small loss certainty. Low touch, not low effort. Radge runs longer term models now. Push a button. Done. But it took 30 years to design it that way. Simplicity is not “easy.” It’s compression of complexity by knowing how to ensure robustness & durability. Final thought: Nick's success isn’t from flashy models or endless curve fitting. It’s from consistency, structural robustness, and psychological realism. It's treating trading as a business, staying focused. If you're hunting alpha through novelty, you’ll miss him. But if you're building longevity—you’ll see him as a blueprint. Let it run for a couple of years and be pleasantly surprised by how much you can out-perform. Thanks Nick for an excellent chat! algoadvantage.io/podcast/044…
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Quantifiable Alpha retweeted
Same system, three markets. You don't need to create new systems from scratch all the time. Momentum exists on most markets. Once you figure out how to harvest it once, it doesn't take much extra work to figure out how to harvest it on other markets too. CAGR: 17.34% Max Drawdown: 24.96% Max Drawdown Length: 570 days Sharpe: 1.48 Volatility: 10.93%
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📈Post Earnings Announcement Drift Trade Re-Cap 🚀 Let's take a look at last week's trades. Interesting trades from the ML enhanced ranking. #SystematicTrading #AlgoTrading
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Want to make money in systematic trading? Start simple! 💹Let’s test a simple momentum system using IBD Relative Strength Factor and see what how a few tweaks affect performance 📊 1/4 Here's the concept: focus on simplicity. The system buys the top 10 strongest positions and rebalances monthly.
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Great article from @slicknet on a persona-based approach to coding with AI. Use AI as a team of specialists (product manager, architect, etc.) - and he's got recommendations on which model is best for each. humanwhocodes.com/blog/2025/…
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🧵 THREAD: How we solved the "Missing Data Hell" in quantitative finance Most ML models fail in finance because they treat missing data like it's just... missing. But financial data isn't random - it's temporal and semantic. Here's how we cracked the code 👇 1/8
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The full MCP experience is now in VS Code! 🚀 From secure auth to prompts, resources, and sampling, MCP is now a complete foundation for building AI agents. @digitarald @TylerLeonhardt @ConnorPeet share all the details on the VS Code blog. Read the full post: code.visualstudio.com/blogs/…
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OpenAI, Google, and Anthropic just published guides on: • Prompt engineering • Building agents • AI in business • 601 AI use cases 9 of the best guides you can't miss:
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Cursor v1.0 is out you sure don't wanna miss this summarised everything in this thread 🧵👇
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It feels natural to write "If X, then Y" when giving an instruction. For a language model, that pattern is unreliable — and it leads to subtle failures you might not notice at first. Here’s why it happens, and how to build prompts that actually work 🧵👇
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CodeLLM Introduces Chat Mode! - chat with all the LLMs directly from your desktop code editor! - use MCP to automate anything - set coding rules to accelerate agents
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*RBA CUTS CASH RATE TARGET BY 25BPS TO 3.85%; EST. 3.85%
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My new favorite workflow is to combine Replit with cursor so that I can not worry about keys or deployment and just build and get stuff out quickly. Check it out youtu.be/v5thUgPLlSM?si=jkpz…
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