fabervaale.official

I've pointed this out on YouTube and Instagram, so it's worth pinning here as well. Let me be clear, once and for all: 1. I don't sell algos, expert advisors, bots, or passive income schemes. 2. I don't run Telegram or Discord signal services. 3. I don't sell investment packages, capital management schemes, or fixed-return products. 4. I will never DM you first to pitch you anything. 5. I don't propose, imply, or advertise guaranteed results, nor do I offer copy trading designed to replicate what I do. 6. I don't have a pre-recorded course for sale. What I do have is a YouTube channel flooded with free content. 7. I've never used and never will use watches, supercars, rented lifestyles, or emotional manipulation to sell a service. You'll never hear me use words like "guaranteed" to lure people in. If you receive messages offering any of the above while pretending to be me, it's not me. The only philosophy I try to promote is simple: • Look beyond the surface and study deeper layers of data, such as Order Flow and Option Flow. • Consume live material whenever possible. In hindsight and pre recorded content, everyone looks like a wizard. • Stop wasting time arguing on social media about which strategy is superior. Test it, collect data, and follow the evidence. Stay safe.

I’ve spent years studying, 7 figures in research, failed test, assembling the team, and testing almost every kind of edge that came through my hands. For the first time, I feel ready. 2027 is the year I want to take the next step and build the fund. I’ll continue sharing research, insights, and as much free value as I can. That won’t change. But I probably won’t be as present on social media as I’ve been in the past. The next chapter requires my full focus.

I used to be a hardcore MMORPG gamer. The type obsessed with customizing a class down to every stat, skill and synergy. Options trading gives me the same feeling. Direction is just one variable. You can engineer around volatility, time, skew, convexity and Greeks, then adapt the structure as the market changes. I’m far from an options expert, but studying directly from ex market makers and family office managers definitely helped me skip a few steps. I’ve built a few quant models around it, and I’m genuinely falling in love with the complexity. It feels like building the perfect class for each market environment and creating synergy between them for the final boss fight. Yes, I’ll always be a nerd.

If you could go back to your first year of trading, knowing everything you know today... What advice would you give your younger self? One sentence. One lesson.

If I were entering the 2027 Futures World Cup, I'd start from a premise most competitors miss: Identify the objective through a game theory lens. The objective isn't riskadjusted returns. It's asymmetrical to the hedge fund approach. A hedge fund maximizes return per unit of risk. A trading competition rewards only one metric: Net Return. Different game. Different optimization function. For the first quarter, I wouldn't focus on profits. I'd focus on information. Three independent models tested on three sequential accounts: • Momentum • Mean Reversion • Pure Trend Following Why? Because no backtest can fully replicate: • Live execution • Slippage • Liquidity constraints • Competition commission structure The first quarter is an intelligence gathering phase focused on regime identification and OSS live condition testing. At the same time, I would monitor the average performance emerging on the leaderboard and use that information to calibrate risk allocation for the future podium account. The objective is to reduce uncertainty and identify which model possesses the highest live expectancy under the exact conditions of the competition while remaining robust under high execution frequency and elevated risk. Then comes the Deployment phase. Open a dedicated podium account. Select the model that, based on both OSS testing and current Regime detection, demonstrates the highest expectancy. One model. One framework. The remaining nine months are dedicated to deploying the snowball effect: Use a portion of accumulated profits to scale performance, progressively increase risk as equity grows, and continuously benchmark against the return threshold required to reach the podium.

Next Wednesday I'll drop a YouTube video covering my 5 favourite trading edges. No course link in bio. No email optin. No Discord funnel. No mentorship pitch. No attempt to convince you that everything you've learned is wrong. Just 5 edges supported by academic research and validated through institutional implementation. Hypothesis. Data. Validation. Take what is useful. You're welcome.

One of the most valuable lessons I learned from the FTX collapse wasn't about crypto. It was about Overconfidence. Not just in finance. In assumptions. Some famous last words I've said over the years: "FTX is too big to fail. The withdrawal ''pending'' delay is probably a temporary technical issue. Tomorrow it will be processed." "**** It's a major bank. They would never force clients into an unfavorable currency conversion to collect fees." "The UAE is one of the safest places on earth. Geopolitical tensions would never reach here." "This model has been robust for four years. It's impossible for it to materially underperform, I don't need other models." Every single statement had the same hidden flaw: Overconfidence in a single point of failure. Today I diversify: • Brokers • Banks • Jurisdictions • Strategies • Markets Because robustness is not built by being right. It's built by surviving when you're wrong.

One of the most expensive mistakes in systematic trading is selecting a model before identifying the regime. A Momentum strategy and a mean reverting strategy can both exhibit positive expectancy, attractive Sharpe ratios, and robust OOS results. Yet over a single quarter, their performance distributions can diverge dramatically. Why? Because alpha is often regimedependent. A volatility expansion environment rewards momentum persistence. A volatility compression environment rewards mean reversion. The model didn't change. The market structure did. Before asking whether a strategy works, ask: Under which conditions was that edge created? Regime first. Model second. This is why many large funds allocate simultaneously to: Trend Following Mean Reversion Global Macro Volatility Arbitrage Relative Value Market Neutral Event Driven

The biggest productivity boost of my life wasn't a new business, a new strategy, or a new hire. It was building an AI that doesn't care about my feelings. It organizes my nutrition. It audits my trading. It monitors my health. It plans my business. It challenges my excuses. It rewards execution. No sugarcoating. No motivation speeches. No "you got this." Just brutal accountability and objective feedback. I use it as an operating accountability system. The cyborg era it's here, without any chip implanted in your brain.

Value Drop #1 Research → Hypothesis → Validation → Out-of-Sample Testing → Live Deployment. The best edges I have tested that survived this framework: • US Equities Drift with Volume Confirmation | IVB, ORB or IB Volatility Breakout | Timing Improvement using Orderflow Data • Earnings Surprise Drift Effect | Paired with Sector Analysis Congressional Trades Portfolio | Information Asymmetry • Option Premium Harvesting | Implied Volatility > Realized Volatility • Blockchain Intelligence Statistical Models | Mathematical DCA These are not models I use because I like them. They are supported by decades of academic research, validated across multiple markets and countries, tested by thousands of researchers and practitioners, and have demonstrated persistence across different market regimes and economic cycles. PEAD, momentum, earnings drift, and related anomalies have been documented, replicated, challenged, and studied extensively for decades across global financial markets.

Structural edge exists. It just doesn’t usually arrive wearing a rented supercar, a Richard Mille, and a “DM me freedom” caption. A few boring examples: Regulatory filings from informed individuals. Earnings surprise drift. U.S. equities open-session momentum, when paired with actual orderflow. Blockchain intelligence for cycle positioning. Institutions consistently overpaying for downside protection in options. The tragedy? These edges require capital, patience, research, execution, and the emotional maturity to not need a dopamine hit every 14 seconds. So no, they don’t sell well to people looking for a shortcut. They are boring. They are structural. They compound. Which is exactly why most people will ignore them and move to the "DM me Wifi Cash" guru.

Sen. Fetterman’s household buys Micron. Micron is tied to U.S. semiconductor policy. Fetterman sits on the Senate Commerce Committee. Micron gets CHIPS Act money. Then $MU casually goes vertical. But don’t worry guys, retail just needs better moving averages. That’s not insider trading! That’s democracy with an informational edge.

The game is rigged. But I know how to put a straw in their profit cocktail. Politicians, lawmakers, presidents they all sit closer to the information flow than the average investor. That’s exactly why they’re forced to disclose their positions. But disclosure doesn’t mean neutrality. If they’re heavily positioned in a sector, do you really think they have zero incentive to push the agenda in that direction? I’ve been milking the market with my long term positioning by combining Post Earnings Announcement Drift with Congress positioning. Because when Nancy Pelosi somehow outperforms Warren Buffett and Ray Dalio… you stop calling it luck. You call it informational asymmetry. And then you monetize it.

Billion dollar business idea for the builders: Orderflow gives traders an insane data layer: L1, L2, MBO, thousand and thousand of Terabyte of data for thousand of asset class Pattern that a human cannot spot But the market is still missing the real weapon: A software that turns orderflow into quantifiable market microstructure edge. I mean a Claude Code for traders with proper process audit: able to ingest market microstructure data able to test hypotheses fast able to research patterns statistically able to transform discretionary observations into models able to move from data → research → validation → deployment It is an institutional research environment where traders can convert raw orderflow into deployable live market strategies. Data → model → validation → execution. That’s the missing bridge. And whoever builds it properly will own a massive category B2B.

"Do you really think you're the best scalper in the world?" Fellas, come on! There are people out there like Paul Rotter, Ed seykota, Lewis Borsellino. People that started Scalping while I was playing with Pokémon cards, I'm 30 years old, give me time to deserve this title.

To be deployed at institutional standard, a model needs: • A clearly defined hypothesis • A repeatable decision framework • In-sample research • Out-of-sample validation • Walk-forward testing • Regime stress testing • Slippage and execution-cost assumptions • Liquidity and capacity analysis • Risk sizing rules • Drawdown and tail-risk limits • Independent review Audit • Live paper-trading observation • Kill-switch criteria Because in a hedge fund, the question is not: “Can this trader make money?” The question is: “Can this process survive scale, pressure, volatility, liquidity constraints, execution friction, and regime change?”.

My next operational goal is to open a hedge fund. Not because I want another screen in my bedroom. Because the next level is not built in isolation, it’s built around a table. Researchers, quants, operators, risk minds, execution specialists. Different lenses. One mandate. Every model independently Audited and tested outside its comfort zone. Every edge forced to survive scrutiny. Every risk profile engineered before capital touches the market. This is not about being the best performer from your room. It’s about building a machine that can keep performing when talent alone is no longer enough. 36 months. Time to enter the big league.