Arch Physicist

"Compra S&P500. Bolsa sempre sobe." Buffett (e Munger) está com a maior posição de caixa da história da Berkshire. Benjamin Graham, o pai do value investing, estaria fora do mercado. Falta margem de segurança. John Templeton estaria migrando capital para fora dos EUA. Peter Lynch estaria comprando, mas ações específicas — nunca o índice cegamente. Joel Greenblatt estaria ignorando o índice e garimpando small caps esquecidas. Stanley Druckenmiller declarou publicamente em fevereiro: está comprado em cobre, ouro e vendido em dólar e Treasuries. IA não é mais o tema central. Ray Dalio projeta retorno de longo prazo do S&P500 em 4,7% ao ano — abaixo dos próprios títulos do Tesouro americano. Howard Marks, um dos maiores estudiosos de ciclos de mercado da história, está operando com cautela máxima. Os maiores investidores de todos os tempos estão, cada um da sua maneira, evitando o índice. Mas compra S&P. Bolsa sempre sobe. Confia.

It’s wild to see copper hitting record highs while still trading near historical lows when priced in gold terms. The copper-to-gold ratio remains nearly 80% below its 2006 peak. More importantly: Periods where copper became this cheap relative to gold have historically not lasted very long. tavicosta.substack.com/p/copper-at-re…

@joao_wedson @Alphractal It apparently always happens in parallel with some kind of Bitcoin rally.

A small altcoin season is happening right now! But unfortunately, over the last 2 years, every time some altcoins started gaining an advantage over Bitcoin, the market cooled down shortly after, and in many cases, everything ended up dropping.

@IT_Tech_PL @Alphractal Probably a data display error. It remains decreasing in the database. It is already with support.

@Alphractal Sorry, what is it?

Since March 30th.. 10 Stocks Drove 69% of the $SPX 16% Rally via McElligott (Nomura)

@rothbard_brasil @Fhoer_here Não tanko a tradução de covenant communities como democracia privada... Milei é o cara que põe um pouco de ordem na bagunça socialista, para a farra voltar com tudo depois. Era o esperado de qualquer político de direita.

Title: The Quantum Threshold and the Strategic Defense of Bitcoin Abstract: This report analyzes the risk posed by quantum computation to legacy encryption and evaluates the strategic responses within the Bitcoin ecosystem. It examines the technical utility of BIP 361 in securing vulnerable assets through protocol level freezing and introduces a Transparent Proof of Knowledge recovery system. These mechanisms ensure a secure transition to modern standards while maintaining the fundamental principles of individual property sovereignty and ledger immutability. Free Report: app.alphractal.com/research/detai…

Holy shit. The biggest unsolved problem in AI agents isn't reasoning it's memory. Your agent forgets everything between sessions. MemFactory just open-sourced the first unified framework for training agents to manage their own memory via reinforcement learning. Extract. Update. Retrieve. All trainable. All modular. Runs on one GPU. Every AI agent built today is amnesiac by design. It can reason. It can plan. It can use tools. But the moment a session ends, everything it learned about you, your preferences, your context, and your history disappears. The next conversation starts from zero. This is not a minor inconvenience it is the fundamental barrier between AI assistants and AI agents that actually work over days, weeks, and months. The field has known this for years. The solutions have been fragmented, task-specific, and impossible to combine. Memory-R1 handles structured CRUD operations on a memory bank. MemAgent compresses history into a fixed-length recurrent state. RMM optimizes retrieval through retrospective reflection. Each works. None can be combined. Each lives in its own repository with its own data format, its own training pipeline, and its own set of assumptions. MemFactory ends that fragmentation. The core insight is that memory management is a decision problem, not a retrieval problem. Current systems treat memory as a database store things, look things up. MemFactory treats memory as a policy an agent that learns when to extract new information, when to update existing memories, when to delete contradicted facts, and what to retrieve for any given query. That policy is trained via reinforcement learning, specifically Group Relative Policy Optimization, which eliminates the need for a separate critic model and cuts training memory requirements in half. This matters because memory-augmented agents already have saturated context windows from dialogue history and retrieved content. The last thing they need is a training algorithm that doubles the memory footprint. The architecture is four layers that compose like Lego blocks. The Module Layer decomposes memory into atomic operations: > Extractor parses raw conversations into structured memory entries, > Updater decides whether each new piece of information should be added, modify an existing entry, delete a contradiction, or left alone, > Retriever fetches relevant memories using semantic search or LLM-based reranking. The Agent Layer assembles these modules into a complete memory policy and executes rollout trajectories during training. The Environment Layer standardizes any dataset into the format the agent needs and computes reward signals format rewards for structural compliance, LLM-as-a-judge scores for quality. The Trainer Layer runs GRPO to update the memory policy based on those rewards. Every module plugs into every other module through standardized interfaces. You can swap the retriever in Memory-R1 for an LLM-based reranker without touching anything else. The results from training a MemAgent-style architecture through MemFactory on two base models: → Qwen3-1.7B base: average score 0.3118 across three evaluation sets → Qwen3-1.7B after MemFactory RL: 0.3581 14.8% relative improvement → Qwen3-4B-Instruct base: average score 0.6146 → Qwen3-4B-Instruct after MemFactory RL: 0.6595 7.3% relative improvement → 4B model gains hold on out-of-distribution benchmarks the memory policy transfers to unseen tasks → Entire training and evaluation pipeline runs on a single NVIDIA A800 80GB GPU → 250 training steps on simplified long-context data no massive compute cluster required → Three ready-to-use agent architectures out of the box: MemoryR1Agent, MemoryAgent, MemoryRMMAgent > The out-of-distribution result is the one that matters most. The 1.7B model improved on in-domain tasks but slightly degraded on the OOD benchmark the learned policy was too specific to the training distribution. The 4B model improved on both. This is the capability threshold at which a memory policy becomes genuinely general: large enough to abstract principles about what information is worth keeping, not just pattern-match on training examples. A memory agent that only remembers the right things in familiar situations is not much better than no memory at all. The 4B result suggests that threshold is reachable with models that fit on a single consumer GPU. > The fragmentation problem MemFactory solves is deeper than it looks. When every memory implementation has its own pipeline, researchers cannot compare approaches fairly. Two systems that nominally differ by one design choice say, CRUD operations versus recurrent state compression actually differ simultaneously in data format, reward structure, training algorithm, and evaluation protocol. Nobody knows which choice caused which outcome. MemFactory puts all three major paradigms under the same training loop, the same reward computation, and the same evaluation framework. Now you can actually isolate what matters. Your agent forgets everything. This is the infrastructure to fix that.

How to build a Custom Dashboard that works as a decision system. 5 steps. Step 1: Click "+ New Dashboard" A blank panel opens. Name it after its objective. Good examples: → "Market Overview" → "Top & Bottom Timing" → "Short-Term Setup" The name sets the intention. If the name is vague, the dashboard will be too. Step 2: Define the objective before adding anything One question before you touch a single chart: "What decisions is this dashboard going to help me make?" → Is the market overpriced or underpriced? → Is a trend starting or ending? → Should I increase or reduce exposure? If you can't answer that clearly — you're building a collection, not a system. Step 3: Add charts that complement, not duplicate Less is more. A solid starting structure: → Risk / cycle metric ex: Market Temperature identifies excess optimism or pessimism → On-chain behavior accumulation or distribution signals → Price + context aligns on-chain data with price action If two metrics tell you the same thing with different names, remove one. Step 4: Organize the visual flow Drag charts into a logical reading order: → Top: market regime metrics (macro / cycle) → Middle: confirmation metrics (on-chain, sentiment) → Bottom: execution metrics (price, momentum) Top to bottom = context to decision. You don't look at timing until cycle and on-chain confirm. Step 5: Use it as a daily checklist Open the same dashboard every day. Read it in the same sequence. Over time, patterns become visible. When Market Temperature enters an elevated zone while other metrics decelerate — asymmetric risk becomes clear before price confirms it. That's the edge a structured dashboard builds.

Which past year does Bitcoin’s 2026 resemble? Starting with bear market years like 2014, 2018, and 2022. See more in the thread:

STHRP and Active Realized Price are primary indicators for identifying Bitcoin market cycle transitions. Historical price action from 2018 and 2022 shows that STHRP functions as a global ceiling, while its -1.5 SD serves as a support floor. The recent crossover in March 2026 suggests the onset of a new bearish phase. Although this is an initial signal, it is a key development for cycle analysis. I’ve released a detailed report on this for @Alphractal Standard Plan subscribers. app.alphractal.com/research/detai…

The biggest launch in Alphractal’s history is now officially live! 🎉 After months of rebuilding the platform from the ground up, the new Alphractal experience is officially here. This release goes far beyond interface changes. It restructures how data is organized, consumed, and acted upon. What’s included in the update: • New UX Architecture — redesigned navigation logic, reduced cognitive load, optimized information hierarchy • Fully Customizable Dashboard — modular widgets, metric pinning, layout control, multi-chart structuring • Smart Alert System — condition-based triggers across on-chain, derivatives, and macro metrics • Alpha AI — contextual interpretation of charts, liquidity conditions, positioning data, and cycle structure • Performance Optimization — faster data rendering, improved chart responsiveness, streamlined workflow • High-Performance Data Engine — faster database architecture, lower latency queries, improved real-time processing • Advanced Variation Selectors — dynamic statistical filters, multi-timeframe deviation analysis, volatility-adjusted views • TradingView Beta Integration — professional-grade charting embedded directly into the Alphractal workflow • New Alpha Metrics — proprietary cycle, liquidity and positioning indicators built for macro-aware accumulation The objective remains simple: More signal density. Less operational friction. Higher decision efficiency. The next evolution of Alphractal starts now. Explore the update 👇

1. Before any indicator, the basics: A cryptocurrency is built on three layers: → A token with market value → A network infrastructure (own blockchain or third-party) → A community and real adoption Concrete examples: Bitcoin: decentralized money project that replicates the characteristics of physical currency (divisibility, fungibility, transportability, etc.) with the objective of achieving the three fundamental functions of money: store of value, medium of exchange and unit of account. Ethereum: smart contract platform (software) that runs on a blockchain.Hyperliquid: decentralized exchange (DEX) project whose blockchain is the order book itself. AAVE: decentralized liquidity protocol (DApp) that does not have its own blockchain, operating through smart contracts on networks such as Ethereum, Polygon and Avalanche. Its functioning is based on collateralized loans, where the user must deposit assets as collateral (guarantee) to access liquidity. Understanding a project's architecture is the prerequisite for any serious fundamental analysis.

This meme never gets old. You never know the exact bottom — and it’s not worth having the hubris to think you do. I’ll keep scaling in at what I see as cheap, historically oversold levels. No need to follow my approach — I know many prefer to buy at $5,500 instead.

I’ve written a report on this for @Alphractal Standard Plan subscribers, evaluating this metric for every UTXO-based cryptocurrency we cover. Predicting Cycle Tops and Bottoms: The Power of Optimized CVDD This report explores the evolution of on-chain analysis by shifting from traditional time-based metrics to block-level dynamics within UTXO-based blockchains. While standard Cumulative Value-Days Destroyed (CVDD) relies on arbitrary adjustment factors to identify market extremes, we introduce an optimized model that calculates progressive and regressive curves using only raw, daily blockchain data. By accounting for the unique "mining eras" of various networks, this methodology provides a robust framework for identifying price tops and bottoms across Bitcoin, Zcash, Bitcoin Cash, and Dogecoin. The results demonstrate that objective, data-driven metrics offer a superior alternative to visual fitting, providing traders with a more reliable support and resistance channel while identifying clear areas for future research in assets like Dash and Litecoin. app.alphractal.com/research/detai…

This is why so many people lose money in crypto. This chart explains crypto better than most headlines ever could. Every cycle begins with disbelief. Then comes confirmation. Then momentum. Then excess. In crypto, the greatest opportunities usually appear when fear is high, volume is still low, and conviction is scarce. That is the phase where smart money is positioning quietly, long before the crowd feels comfortable. The worst decisions usually happen later, when momentum looks unstoppable, sentiment turns euphoric, and everyone suddenly becomes a market expert. What feels safe in that moment is often where risk is already much higher. Accumulation happens in silence. Distribution happens in excitement. If you want to survive this market, stop chasing narratives and start understanding cycle structure, liquidity, sentiment, and crowd behavior. Always use data to make informed decisions and reduce herd behavior. Data > Narratives. @Alphractal

Every time the STH NUPL (QoQ) turned positive, it marked a selling opportunity for BTC since the all-time high. You found a great signal @0xFelp, congratulations!