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ETF FLOWS: US SPOT CRYPTO ETFs FLOWS DATA UPDATE (13-07-2026) YESTERDAY 🟥 Bitcoin ETFs: -6,846 $BTC (-$424.66M) 🟥 Ethereum ETFs: -8,720 $ETH (-$15.41M) 🟥 HYPE ETFs: -61.75K $HYPE (-$3.93M) 🟩 $DOGE, $LINK, $BNB, $AVAX, $DOT, $HBAR , $LTC, $XRP, $SOL Flows Was Zero. TOTAL US SPOT CRYPTO ETFs OUTFLOW: ≈ -$444M U.S. BITCOIN ETFs SOLD ~6,846 BTC Worth $424.66M 🇺🇸 BlackRock ETF Has SOLD ~2,990 BTC for $185.50M And 9,050 ETH for $16.20M 🇺🇸 Fidelity ETF Has SOLD 3,960 ETH for $245.62M And 8,720 ETH for $15.41M 🇺🇸 Grayscale ETF Has BOUGHT ~5 BTC for $320K 🇺🇸 VanEck ETF Has BOUGHT ~99 BTC for $6.14M Fact: U.S. Spot Bitcoin ETFs SOLD Nearly 15 Days Mined BITCOIN Supply Yesterday.

Log vs Linear: Why Your "Broken" Support Never Actually Broke LOG vs LINEAR: THE ADVANCED BREAKDOWN (Premium Edition - for traders who actually draw levels for a living) Most people treat this as a cosmetic toggle. It isn't. Your scale choice silently changes your trendlines, your Fibs, your moving averages, and your backtest results. Here's the deep version. 👇 🔰 THE CORE MATH: 🔹 Linear scale: equal vertical distance = equal absolute price change (Δ$). 🔹 Log scale: equal vertical distance = equal proportional change (Δ ln P). On a log chart, a straight line is not constant price growth, it is constant percentage growth. A rising straight line on log = compounding at a fixed rate. That single fact is why log is the natural home for any asset with exponential history. Crypto is the most exponential asset class in existence. $BTC has moved roughly 8 orders of magnitude. Displaying that on linear is a rendering error, not analysis. 🔰 WHERE LOG IS NON-NEGOTIABLE ✔️ HTF structure - Weekly, Monthly, and multi-cycle views ✔️ Long-term trendlines and channels (semi-log channels only) ✔️ Power law / log regression bands, rainbow models, diminishing returns curves ✔️ Cycle-to-cycle comparison: 2013 vs 2017 vs 2021 vs 2024–25 tops ✔️ Ratio charts (ETH/BTC, TOTAL2/BTC.D, alt/BTC pairs): ratios are inherently multiplicative ✔️ Any asset with 10x+ range on screen: majors, low-caps, memecoins ✔️ Comparative performance overlays between two assets with different price magnitudes ⚠️ The Trap: a multi-year trendline that reads as a clean breakdown on linear is frequently untouched on log. Every cycle, a chunk of the market capitulates into a support that never actually broke. Check the log view before you post a breakdown call. 🔰 WHERE LINEAR IS THE CORRECT TOOL ✔️ Execution timeframes: 1m through 4H ✔️ Order Blocks, FVGs, breaker blocks, liquidity pools, equal highs/lows ✔️ Precise entry, invalidation, and R:R measurement ✔️ Range-bound and compressed price action ✔️ Anything where your position sizing is denominated in absolute dollar risk ✔️ Volume profile, VWAP, market profile studies Rationale: within a narrow range, ln(P) is approximately linear, the two scales converge and linear gives you cleaner, more auditable measurement. 🔰 THE PART ALMOST NOBODY TALKS ABOUT 1️⃣ Fibonacci levels change: A 0.618 retracement on linear is not the same price as a 0.618 on log. Log Fibs compute the retracement in percentage space. On a 5x impulse the difference between the two can be double-digit percentages. If you swing trade off Fibs across large moves, you must decide which one is your system and never switch mid-analysis. 2️⃣ Moving averages are linear objects: An SMA/EMA is computed on price, not on log-price. So on a log chart the MA is still a linear-space calculation being rendered in log space. It is not "wrong," but do not treat an MA slope on log as a growth-rate line the way you would a semi-log trendline. 3️⃣ Pattern geometry warps: Wedges, triangles, and channels are geometric shapes. Change the scale, change the geometry. A "rising wedge" on linear can render as a clean parallel channel on log. Both readings cannot be right. Choose your reference frame first, then read the pattern. 4️⃣ Volatility is proportional, not absolute: Crypto returns are far closer to log-normal than normal. Your risk model should think in percentage terms (ATR%, standard deviation of log returns), not raw dollar swings. Log charting is the visual expression of the same idea. 5️⃣ Backtests inherit the scale: If you draw your levels on log and backtest on linear, you are testing a different strategy than the one you traded. Reproducibility dies here. 🔰 THE OPERATING FRAMEWORK ➡️ LOG for BIAS: cycle position, HTF trend, long-term structure, ratio analysis, valuation models ➡️ LINEAR for EXECUTION: LTF structure, entries, stops, targets, sizing Zoom out in log space. Zoom in in linear space. Never let the two contaminate each other in a single thesis. 🔰 THE DISCIPLINE RULE: A trendline drawn on log and a trendline drawn on linear are two different objects. So are the Fibs, the channels, and the patterns built on them. Therefore: → Declare your scale before you draw → Keep it fixed across the entire analysis → Label the scale on every chart you publish → If your level only holds on one scale, that is information, say so out loud Analysts who don't state their scale are not publishing a level. They are publishing a picture. 🔰 CryptoPatel Note: Log is the true coordinate system of an exponential asset. Linear is the coordinate system of a trade. Professionals use both and they never confuse which one they are standing in. Save this one.

The crypto market is no longer driven only by retail investors. Over the past few years, institutional money has started playing a much bigger role, bringing new levels of capital, credibility, and long-term interest to the industry. This shift is changing how the market behaves and where opportunities are emerging. Large investment firms, hedge funds, asset managers, and public companies are no longer watching crypto from the sidelines. Many are adding digital assets to their portfolios through regulated products, helping crypto become part of the traditional financial system. Bitcoin and Ethereum have received most of the institutional attention so far. The launch of spot ETFs and other regulated investment products has made it much easier for professional investors to gain exposure. As more capital enters the market, liquidity improves and confidence continues to grow Retail investors still play an important role, but the market is evolving. Understanding where institutional money is flowing can help identify long-term trends before they become widely recognized. Following innovation, adoption, and real utility may become more important than simply chasing the latest hype. Institutional adoption does not guarantee that every project will succeed, but it is helping crypto mature into a global financial asset class. As more professional capital enters the space, the next phase of growth may be driven not only by excitement but also by long-term investment and real-world use cases. $BTC $ETH

🚀 $ETH | BULLISH LONG TRADE SETUP 🚀 📈 Position: BUY / LONG 💎 Market Outlook $ETH continues to hold above key intraday support, and buyers remain in control of the short-term trend. If momentum stays strong, price could extend toward higher resistance zones. 🎯 Entry Zone 🟢 $1,775 – $1,782 💰 Take Profit Targets 🥇 TP1: $1,790 🥈 TP2: $1,800 🥉 TP3: $1,825 🏆 TP4: $1,850 🛑 Stop Loss 🔴 $1,748 📊 Trade Plan • Enter only within the planned entry zone. • Take partial profits at each target. • Consider moving your stop loss to breakeven after TP1 if price moves in your favor. • Watch for sustained buying volume to confirm bullish continuation. ⚠️ Risk Management • Use disciplined position sizing. • Respect your stop loss at all times. • Never risk more than you can afford to lose. 🔥 Ride the Trend • Stay Patient • Protect Your Capital 🚀 $ETH