Klingo

Sent instruction to 30 users With credit bonus so you guys can test around as well Enjoy!

Want early access to the EVPoly Web App before it goes public on @EVplusAI ? Reply + RT this post. The app comes with all 7 strategies on @Polymarket -fully automated. Plus LP Rewards farming (MM 2.0). No more complex setup - straightforward onboarding - ready to trade in minutes.

Well, nobody say No to $4M funding. I just applied to the $4M @BagsHackathon with my project EVPLUS on @BagsApp Check it out here 👇 bags.fm/apps/4c4d52d1-…

The math for the Premarket strategy is simple: - We assume the 5-minute market is fully coin-flip 50/50. - Others assume they can predict the outcome. - We become their counterparty. - We only need to be right 4 out of 10 times. - We also earn maker rebate.

In EVPoly, there is a strategy called "Premarket". The idea comes from the quote post: There is 0 edge in predicting the 5-minute market direction before it happens. Bot places a ladder of limit orders BEFORE market open and starts cancelling them from t-30s to t+30s. Priority high price then to low price. So if you get filled at 0.4, your counter-party tried to predict the outcome, and want to front-run others, which give you a good entry. (t-30s) If you get filled at 0.2, it is in t+30s window. Low win rate, high reward.

EVCurve works based on a simple theory: - @EVplusAI server scans market history and creates a "map" - Every minute, the bot sends a request to the Alpha service on the EVPlus server asking, "Hey, is this a good time to buy Up/Down?" - The algorithm on the server uses the map to respond to the bot with the fair price. - The bot starts chasing the limit to get filled but not over the fair price, and if the price suddenly dumps, the bot cancels the operation mid-way (so only partially filled). From the testing phase, this strategy is performing around break-even. The plus point is that we also get maker rebates and generate good volume. So I keep it. You can freely edit the bot settings to improve the strategy. Just ask AI.

@DegenApe99 @Polymarket

How to trade @Polymarket Up or Down 5-minute markets? After many tests while developing EVPoly, I figured out a reality check. It is all randomness. No technical analysis, price action, or orderbook structure can help you predict the outcome of the next 5 minutes. It's a coin flip. Whether you do deep calculations or not, after a large number of trades, it all reverts to 50/50. So there is no edge to predict the future. On top of that, the new fee structure increases your inventory cost by ~1%. If you do 50/50 coin flips, you are losing money in the long term. So how to deal with that? Two solutions: - Take advantage of other traders who think they can predict the future - Take the trade when the outcome is 99.5% on your side (and you pay 99c) These two strategies will come with EVPoly, the new @EVplusAI product this week.

What is "Bonding" in @Polymarket? Bonding means you buy the "sure bet." Will BTC hit 70k in March? BTC hit 70k on Binance, and you still see 0.1c NO on the orderbook. You buy it, that's a sure-win bet. Now another kind of bonding is the "pretty sure bet." Like there is 1s left and BTC price is up 0.05% compared to 5m ago. You see 1¢ of Down on the orderbook, you buy that. There is risk: For a "sure bet," it's settlement risk. For a "pretty sure bet," if in the last 1s somebody dumps 50 BTC and drags it down >0.05%, then you lose. The questions you need to ask yourself: How many times did Binance hit a price then erase the candle? How many times have we had that flip situation in the last 1s? And how fast are you compared to others? EVPoly will be the solution, but you, as the user, will still need to deal with all of this uncertainty. Coming soon.

If the edge is publicized, then it's actually round.

EVClaw trades based on 5 in-house signals - Dead Cap: your margin is low, your capital is "dead". We trade against you. - Whale: copy-trading profitable whales only, not all whales. - OFM : Order Flow Momentum - FADE : fade the bad traders, follow the good ones. - CVD: smart money vs dumb money flow, not a normal CVD. Right now, Dead Cap + Whale carry the most weight. The other three are lighter. You can adjust the weights yourself or ask @OpenClawAI to run a Monte Carlo simulation on your real trade history to find the optimal weights. Prompt example in the video below 👇

EVClaw = OpenClaw x @EVplusAI x HyperliquidX EVPoly = @PolymarketBuild

The current market environment is a meat grinder for anyone relying on intuition rather than raw data. Most retail participants remain exit liquidity because they cannot interpret the hidden flows that move the tape. The $SOL analysis on the alpha dashboard reveals how a professional desk identifies a high probability entry using more than just price action. The system generates a strong long signal with a dpm of 12 based on real time wallet behavior and volume clusters. A deep dive into the cohort breakdown shows that the skilled and gold levels are heavily stacked in long positions while the wrecked and bleeding accounts are failing to catch the trend. This divergence is a classic indicator of smart money accumulation. The cursor highlights the orderbook heatmap which shows a significant 26.8 percent liquidity imbalance at 84.58. This level serves as the primary magnet for the move. The transition to the trading terminal shows the seamless execution of the strategy. A long order for $SOL is filled at 84.60 without the typical slippage or emotional hesitation found in manual trading. The terminal handles the position management by automatically setting the tp and sl levels based on the volatility profile of the asset. The charts confirm the breakout from the local range as the ai signal aligns with the physical reality of the order flow. Success in this game is about removing the human element and letting the machine hunt the edge.

A normie flow of how EVClaw works. 1. ENTRY A. Everything starts from the @EVplusAI Alpha Dashboard (sourced from Hyperliquid node). B. After hard filtering, a prompt with all candidate signals + rich context is sent to @openclaw AI Agent 1. C. Agent 1 makes the decision, then passes it to Executor bot that handles Chase Limit + SL/TP. 2. EXIT A. Open positions are monitored by two Python bots. B. These bots trigger and propose an early close to AI Agent 2. C. Agent 2 decides to close or hold. If Close, the Executor bot will handle that 3. TRACKING & LEARNING All early closes and SL/TP exits are recorded, fed into Agent 3, and output as a per-symbol lesson database. And the cycle keeps going.