# Сопутствующие статьи по теме Prediction

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Prediction", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

The 4 Truths Behind Polymarket's LP Market-Making Incentives and the Fee Trap

Polymarket, a prediction market platform, has recently shifted its incentive structure towards rewarding Liquidity Providers (LPs) to solve its core problem of low market depth. While most markets remain free, it now charges a taker fee on specific markets (all Crypto markets, NCAAB basketball, and Serie A football) to fund new LP reward programs. The fee is calculated on a symmetric curve, highest near 50% probability. The platform has introduced two main incentive systems: one rewards LPs whose limit orders are executed (Maker Incentives), and another rewards LPs simply for providing resting liquidity, even if orders aren't filled (Liquidity Incentives). A third system allows anyone to sponsor additional rewards for specific markets. A key argument is that the fees paid and rewards earned could be a strong anti-sybil metric for a potential POLY token airdrop, valuing genuine liquidity provision over mere trading volume. However, a counter viewpoint argues the LP program is a potential trap. Critics claim that the displayed ROI for LPs is misleading as it doesn't account for "LP wear and tear"—losses from filled orders that can't be easily exited. They state professional market makers avoid it due to insider trading risks and that the model of subsidizing liquidity with massive daily rewards is unsustainable. The concern is that widespread fee implementation could erase Polymarket's competitive edge over traditional betting platforms. Proposed solutions include a fixed fee on profits only, using a POLY token for native liquidity, and charging for premium products like parlays instead of core markets.

Odaily星球日报03/22 04:08

The 4 Truths Behind Polymarket's LP Market-Making Incentives and the Fee Trap

Odaily星球日报03/22 04:08

Crypto Morning Brief: Prediction Market Kalshi Raises Over $1 Billion, Block Recalls Some Laid-Off Employees

Crypto & AI Daily Digest **Key Market Events:** - The Bank of Japan kept its benchmark interest rate unchanged at 0.75%, as expected. - US initial jobless claims for the week of March 14 came in at 205,000, lower than the forecast of 215,000. **Major Funding & M&A:** - Prediction market platform Kalshi raised over $1 billion in a new funding round, doubling its valuation to $22 billion. - OpenAI is acquiring startup Astral to expand its presence in the programming sector. - Animoca Brands announced a strategic investment in AVAX and a partnership with Ava Labs to develop the Avalanche ecosystem, focusing on Asia and the Middle East. **Corporate News:** - **Meta** experienced a significant AI Agent malfunction, leading to a two-hour leak of sensitive company and user data. - **Block** (formerly Square) has quietly recalled some of the employees it laid off in February, with CEO Jack Dorsey admitting the decision may have been a mistake. - **Crypto.com** is cutting approximately 12% of its workforce as part of a company-wide push to integrate enterprise-level AI tools. - **Gemini** has reduced its headcount by about 30% this year and reported an annual loss of approximately $585 million. **Token & Ecosystem Updates:** - The Perle Foundation unveiled the tokenomics for its PRL token, with 37.5% allocated to the community. - Perpetual DEX edgeX has launched a page for its EDGE token airdrop, with claims open until April 1st.

marsbit03/20 01:13

Crypto Morning Brief: Prediction Market Kalshi Raises Over $1 Billion, Block Recalls Some Laid-Off Employees

marsbit03/20 01:13

Using AI for Weather Prediction: Earn $200 a Day While Doing Nothing?

Using AI for Weather Prediction: Can You Really Earn $200 a Day? This article explores how to leverage AI and data analysis to profit from weather prediction markets like Polymarket, focusing on Shanghai’s temperature forecasts. The system relies on Shanghai Pudong Airport (ZSPD) weather station data, sourced via Wunderground, rather than general city forecasts. Key insights include: - Temperature data is reported in whole Fahrenheit values in METAR format, not Celsius, affecting precision. - Historical data shows daily high temperatures most frequently occur between 11:00-13:00, peaking at 12:00 in summer (27.6% of days). Three effective prediction methods were implemented: 1. **Integrated Forecasting**: Combines Weather Company (WC) and ECMWF model data, weighted by weather conditions (e.g., sunny days favor WC). 2. **Real-Time Correction**: Uses morning temperature rise data and historical patterns to extrapolate the daily high, adjusted for cloud cover and wind. A Kalman filter dynamically weights real-time data vs. forecasts. 3. **Temperature Trend Model**: Predicts whether the day will be warmer/cooler than the previous day using pre-dawn data (pressure changes, wind, cloud cover, recent trends). It performs best in winter (clear signals) but poorly in autumn (63.7% accuracy). Two failed methods—Fourier analysis (systematic underestimation) and ERA5 peak-time prediction (insufficient precision)—were discarded. Case studies demonstrate the system identifying mispriced market opportunities, such as recognizing nighttime warming from moist air during rainfall, when public sentiment lagged. Limitations include autumn inaccuracy, lack of real-time pressure data, and unresolved coastal wind effects. Ultimately, the goal isn’t perfect accuracy but leveraging informational edges when odds are favorable.

marsbit03/18 12:18

Using AI for Weather Prediction: Earn $200 a Day While Doing Nothing?

marsbit03/18 12:18

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