Why This First Run Matters (Regardless of Outcome) Let's talk about why V1 matters. Not "did we make money?" (though that's nice). But "did we learn what we needed to learn?" Spoiler: We're learning A LOT. And that's the whole point of calling it V1. 🦍📊

Here's what we did that might seem counterintuitive: We RELAXED the strategy parameters. Why? To generate as many trades as possible across ALL 22 strategies. Not to maximize profits. To maximize DATA. We needed the Gorillas to debate. To decide. To execute. To monitor. To succeed. To fail. All of it. Logged. Tracked. Analyzed.

V1 is stress-testing two critical flows: 1️⃣ DECISION FLOW • Are the 8 Gorillas analyzing correctly? • Is Silverback's final decision sound? • Are we executing at the right price? 2️⃣ MONITORING FLOW • Is Kong making smart adjustments? • Are trailing stops being used correctly? • Can we adapt to changing conditions? These are SYSTEM questions, not strategy questions.

Every single decision is logged in Supabase: 📊 Signal detected 🗣️ Agent debates (all 8 gorillas) ✅ Final decision (YES/NO/confidence) ⚡ Execution data (entry/SL/TP) 📈 Monitoring decisions (HOLD/CLOSE/TIGHTEN/REDUCE) 💰 Performance outcome (win/loss/%) Decision → Execution → Performance Full lineage. Every trade. Every decision.

Example: Kong just used a trailing stop on a DOGE position. Original thesis: Parabolic SAR bearish flip + 21 EMA stop Entry: $0.1630 Kong's decision a few hours later: "Position remains aligned with bearish thesis (multi-timeframe bearish alignment, RSI oversold on 15m but stronger timeframes intact). Small profit (3.4%) with ATR-based trailing stop recommendation (1.48% trail distance) to lock in gains while respecting volatility. Technicals suggest continuation potential but need protection from reversal." That's not a static SL trigger. That's THINKING. We're logging hundreds of these decisions to see: Is Kong smart or lucky?

SIGNAL INSIGHTS SO FAR: ✅ 22 strategies generating diverse opportunities ✅ Regime detection correctly filtering bad setups ✅ Qwen 3 32B choosing reasonable signals from candidates ❌ TOO MANY signals getting through (need tighter filters) ❌ Some strategies performing better than others (data shows which ones) V2 will be MORE selective. We now know which strategies work in which regimes.

DECISION FLOW INSIGHTS: ✅ Gorillas are providing diverse perspectives (Degen vs Grizzly debates are 🔥) ✅ Silverback is synthesizing well (confidence scores correlating with outcomes) ✅ Risk parameters are being respected (Chad is doing his job) ❌ Some debate rounds could be shorter (speed vs depth tradeoff) ❌ News context sometimes overweighted (minor headlines affecting decisions) Adjustments identified. Ready for V2.

✅ Kong has full context (knows original thesis, sees current conditions) ✅ Trailing stops being used intelligently (not just static triggers) ✅ Partial profit-taking working well (REDUCE action preventing full reversals) ❌ Some positions held too long (thesis invalidated but Kong held) ❌ Monitoring frequency could be dynamic (15 min is too slow sometimes) These are SOLVABLE problems. We have the data.

What V2 will look like based on V1 data: 🎯 TIGHTER signal filters (only high-conviction setups) 📊 DYNAMIC monitoring (check more frequently on volatile positions) 🗣️ STREAMLINED debates (faster decisions without sacrificing quality) 📰 BETTER news weighting (major catalysts only, ignore noise) 🔄 ADAPTIVE strategies (activate/deactivate based on regime performance) Data-driven improvements. Not guesses.

Here's why this approach matters: Most algo traders: 1. Backtest 2. Deploy 3. Lose money 4. Guess what went wrong 5. Repeat Us: 1. Deploy with logging 2. Generate data 3. Analyze systematically 4. Identify specific issues 5. Fix with precision V1 isn't meant to be perfect. It's meant to be INFORMATIVE.

And because everything's public on Discord, YOU can see: • Which strategies work • How Gorillas debate • When Kong nails a decision • When we take losses • What we're learning in real-time This isn't a black box getting tuned in secret. It's an experiment running in public. Learn with us. Question us. Challenge us.

V1 might not be profitable. And that's... fine? Because we're gathering: ✅ 100+ fully documented trades ✅ 800+ monitoring decisions ✅ Thousands of data points on decision quality ✅ Real-world performance across market conditions You can't buy that data. You have to generate it. V2 will be MUCH sharper because V1 was INTENTIONALLY broad.

This is what responsible AI deployment looks like: Not "we built the perfect system, trust us." But "we built V1, we're testing it transparently, here's what we're learning, here's how we'll improve." Iterate in public. Learn systematically. Improve with evidence. If more AI projects did this, we'd have fewer rug pulls and more innovation.

Want to see the data yourself? 📊 Live tracker: 💬 Discord (all decisions): 🗂️ Every trade logged with full context V1 is running. Data is flowing. V2 is being designed. Come watch the experiment. Welcome to transparent AI development. 🦍⚡ AI stands for Ape Intelligence.

We will continue running V1 for a full two weeks at least, then we will stop the trading for a little while and start testing new things before we start V2. This will all still be visible in discord so it may look a bit weird there during those times but hey, you will be able to know and see we are working hard to make it better 🦍