ΞVLVΞ.hl
6K posts

ΞVLVΞ.hl
@EvolveWeb3
You wasted $150,000 on an education you coulda got for $1.50 in late fees at the public library.





// HYPE Still one of the strongest in our market. Poking above and attacking the $36 resistance again. Clear close above and we’ve got a good case for higher prices. Let's see if we can get that close above.











You’re staring at a P&L that looks like an EKG in a horror movie. We’ve all been there. It’s not just frustrating; it’s exhausting. 🛑 But here’s the cold truth: The "secret" isn't a magical indicator. The real edge lies in knowing which trades to skip. The Trading Floor: SMB Capital Podcast sat down with Dave Mabe, who’s been automating since 2005. He treats backtesting like a superpower—not because it predicts the future, but because it challenges his ego. If your backtest doesn't hurt your feelings and tell you you're wrong, you aren't doing it right. Stop hunting for the "perfect" entry. Start building a "Column Library" that filters for trading edge while casting a wide net. The market can reward systematic traders who trade like machines but remain curious like students. Your breakthrough just might be one rigorous backtest away. Let’s get to work. 📈 Executive Summary for Systematic Traders Core Objective: Shifting from "Guess and Check" loops to a robust, scalable optimization process that treats indicators as a library and backtesting as a source of truth. What you will learn: 1. The First Blanket Backtest: Why your initial test should "cast a wide net" with weak thresholds to generate the maximum amount of data for optimization. 2. Column Library Architecture: The power of building a reusable library of 300+ indicators/columns that can be applied across all future strategies. 3. Ownership of Edge: Why you cannot "borrow" confidence; you must do the work to understand the drawdown profile to survive live trading. 4. Strategic Collaboration: Treating networking as a trading skill and being a "default giver" to find synergies with other quants. Systematic Trader Notes: The Breakdown Subject: Heuristics of Systematic Strategy Optimization (Dave Mabe Case Study) I. The Thesis: The "Skipping" Alpha The Problem: Discretionary traders and hobbyists often seek the "perfect signal." This leads to overfitting and low trade counts. The Systematic Solution: Professional edge is often found in the optimization phase—identifying which subsets of a common signal (e.g., Opening Range Breakouts) are statistically inferior and "skipping" them. The "Ivy" Insight: In a zero-sum environment, your advantage isn't what you know; it's the mechanical discipline to avoid the negative expected value ($EV-$) trades that others take out of boredom or bias. II. Technical Framework: Casting the Wide Net Threshold Dilution: Instead of testing a 5% gap, test a 3% gap. This increases the N-count, providing more "surface area" for the optimization engine to identify meaningful correlations. Universe vs. Predictive Filters: * Universe Filters: "Fencing in" the tradable assets (volume, ATR, price). Predictive Filters: The specific indicators (yesterday's range, position in range) that actually generate the alpha. The Column Library: Treat trading code like a software library. A "bug" or "feature" found in one strategy should be able to optimize all other strategies in the portfolio simultaneously. III. Behavioral Finance & Heuristics The "Gun to the Head" Experiment: If forced to trade the opposite side of your favorite signal, how would you do it? This prevents confirmation bias and often uncovers powerful counter-trend strategies. The Partial Profit Trap: Scaling out for "psychological comfort" is mathematically inferior for the majority of trending strategies. Traders often trade off P&L for "feel-good" moments, destroying their long-term expectancy. Confidence Ownership: Confidence is the byproduct of the backtesting process. If you haven't built the strategy, you will abandon it during the inevitable first drawdown. IV. Key Takeaway for Discretionary Traders using Tech Quantifying Intuition: If you "feel" like Tesla is a good buy, find a way to measure that feeling as a column (indicator). If you can't measure it, you can't manage it. Backtesting as a Mirror: Use the data to challenge your "truisms." If the data shows a "dead" strategy still works, the market is signaling an inefficiency. Conclusion: Professional systematic trading is less about "being right" and more about being the most rigorous scientist in the room. Success is the inevitable result of a refined process applied to a massive library of data points. Your Trading Strategy Will Fail Until You Understand This ONE Process youtu.be/pPIPvyticq4?si… via @YouTube @davemabe @GarrettDrinon @smbcapital





Our team is still gathering all relevant data and will share a full report shortly. We appreciate your patience and fully understand how frustrating this situation has been for everyone.

@danrobinson You still didn’t read the theoretical results (skill issue?), are acting like an asshole and pretending you’re totally right without more than a little substance


$ZEC KOL distribution completed 🫡

$AAPL - APPLE CUTS JOBS ACROSS ITS SALES ORGANIZATION IN RARE LAYOFF




