PolyRanger

PolyRanger

सदस्य
2.3M
24h दृश्य
2.2K
अधिकतम दृश्य
8.5K
प्रति दिन पोस्ट
0.3
सहभागिता दर
1.5%

संबंधित अपडेट

दिखा रहे हैं 5/5 आइटम
8.5K 5
Polymarket had it right all month. $75.5M said BTC hits $75K in March → 100%. It did. Now March 26: → Dipped below $70K → confirmed 100% → Recovery above $71K by midnight → 50/50 Market's telling you: support is being tested. 50/50 bounce = no conviction either way. Perfect range to trade. $70K floor or break. → polyranger.com
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6K 3
On US election night 2024, a French trader made $85 million on Polymarket. One night. One bet. More than most hedge funds return in a year. The platform had $3.7 billion riding on Trump vs Harris. When the results came in, 70% of traders lost money. The top 0.04% of wallets took home 70% of all profits. The three biggest earners on the platform are Theo4 ($22M, 89% win rate), Fredi9999 ($16.6M, 73% win rate), Len9311238 ($8.7M, 100% win rate) didn't get lucky. They found mispricing the market
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6.2K 5
The bot problem in prediction markets is real. Fast actors don’t just trade - they dominate: - Front-running - Arbitrage extraction - Liquidity manipulation Retail users? Always late. So we asked a simple question: What if access - not speed - defined the edge? Here’s how PolyRanger approaches it: 1) Aggregation > Isolation Bots thrive in fragmented markets. We unify markets across platforms. More transparency, less local inefficiency to exploit. 2) One interface, faster decisions Switchi
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Most people think prediction markets reward those who are right. The data says otherwise: • 14/20 top traders are bots • ~$40M extracted via arbitrage • <0.04% of users capture >70% of profits So who actually wins? 1. Speed Bots and automated traders dominate execution. 2. Math Arbitrage + market making = consistent edge. 3. Narrative A small group of informed traders capture asymmetric bets. Prediction markets don’t reward being right. They reward speed, math, and narrative. We’re buildin
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2.2K 2
AI model race on Polymarket In December, AI model markets peaked at $36M monthly volume. Then: - $29M in January - $22M in February So what happened? At first, attention was driven by hype: new models, benchmarks, headlines. But prediction markets don’t sustain hype - they price expectations vs reality. As narratives stabilize: • Uncertainty drops • Price discovery improves • Volume declines The edge disappears. Prediction markets don’t reward noise. They reward timing, structure, and infor
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