InfoFi discourse heating up again on the timeline 👀
Usually a decent signal we're entering bullish territory. When niche narratives start recirculating, liquidity's moving back into alts.
Keep tabs on $INFO plays and related tokens. This narrative died hard in the bear—if it's resurfacing now, early movers could catch decent upside before the herd arrives.
Finally going live with my first algo trading bot after 6-9 months of deep research.
Started down the rabbit hole through @therobotjames, @ScottPh77711570, @dima_quant, @ZenomTrader, @algojan, @MrMilkTrading and others. Spent months grinding through Rob's ATFS book, research papers, Dave Whitcomb's Peak Trading Research, Kevin Davey, Rene Balke, and NeuroTrader888 on YT.
Where I'm at: • Recreated Rob's continuous trend-following strategy (adapted EWMAs for crypto) • 3-month forward test complete • Bot running live on testnet • Taking it live end of this week or next
Not a 1:1 recreation—tweaked the EWMAs using Rob's tips and hand-picked a few coins. Running multiple speed configs.
Still skeptical. This could crash and burn or actually work. High risk, high learning curve.
Gemini crushes on visuals—charts, graphs, interactive models—but still fumbles basic logic errors even with clear instructions.
Asked it to backtest a simple MA crossover strat across time slots. Results looked fire... too fire. Ran an audit with Claude—turns out Gemini had signal-bar contamination. Classic rookie mistake.
Claude? Catches bugs like a hawk but feels overly cautious. Even after multiple prompts, it defaults to pessimistic takes.
Current workflow: → Ideate + initial code with Gemini → Debug + audit with Claude → Back to Gemini for testing + interpretation → Send final numbers to Claude for second opinion (outputs vary a lot)
No single AI is perfect. Stack them. Verify everything. Trust, but audit.
This is massive for real-world asset tokenization and emerging market liquidity. Traditional finance is finally bridging to crypto infrastructure at scale.
Watch this space - if execution delivers, we're looking at a blueprint for how developing economies can leapfrog legacy systems entirely.
After 8 years trading perps, one truth remains: most valuations in crypto are pure marketing.
TVL? Levered loops. Volume? Wash trades. High yields? Token emissions.
So we built something different at Inflection Labs: Holders Revenue Multiple (HRM). Market cap divided by annualized programmatic buyback revenue. No treasury games. No LP bribes. Just hard-coded buy pressure.
We ran an equal-weight portfolio rebalanced weekly for 13 weeks. Results:
All four moved to AVOID. When fees drop 40% in a week, buyback floors bend.
Meanwhile two divergences:
1. Uniswap Catalyst $UNI revenue surged +73.4% WoW. Sept 4 saw historic single-day burn: $1.15M worth of UNI (184k tokens) in 24 hours. Driver: Robinhood Chain where Uniswap captured 98% of flow.
But our model keeps $UNI at NEUTRAL (29.0x). Why? We need revenue durability, not day-one retail meme spikes. If burn rates stay elevated, multiple will compress fast.
2. $LIT vs $HYPE Relative Value Trade June 6: $LIT traded at 36% discount to $HYPE (12.3x vs 19.2x) 13 weeks later: $LIT +220.2%, $HYPE +41.4% Today: $LIT trades at 28% premium (34.9x vs 27.2x)
With both seeing fee contraction, that trade is exhausted. Both in AVOID. Step aside until fee capture stabilizes.
Every Monday 7am ET we publish The Standings: refreshed board, rebalanced basket, verified performance.
No price targets. No sponsor bags. Just data and structure.
$BTC sitting at VAH — this is the line. Clean grind above = we're heading to $72-73K. Reject here? Expect a sweep back to higher lows around $61.4K. Watch this level closely.
$LIT looking solid at $2.56 base. Structure is there, but it's riding $BTC's wave. No BTC pump = no $LIT breakout.
$XAU bearish until proven otherwise. Not flipping bullish without clear confirmation.