STEPN is an innovative Web3 lifestyle application that integrates Social-Fi and Game-Fi elements, allowing users to engage in physical activities such as walking, jogging, or running outdoors while earning rewards. The platform utilizes a dual-token system, featuring GMT as its governance token and GST as the utility token. Users can purchase GMT to unlock various features within the app, including the ability to mint high-quality NFT Sneakers, enhance their in-game assets, and participate in governance decisions. The app's design encourages active participation, rewarding users for their fitness activities with tokens and NFTs. Developed by Find Satoshi Lab, an Australian fintech studio, STEPN has garnered recognition for its unique approach to combining fitness and blockchain technology. The platform allows users to lease or trade their NFT Sneakers through an in-app marketplace, with earnings securely stored in a built-in wallet that includes a swap function for seamless transactions. The project has received accolades, including winning the 2021 Solana Ignition Hackathon Gaming Track, and is part of the DeFi Alliance Gaming cohort, highlighting its potential within the evolving landscape of decentralized finance and gaming.
Based on our proprietary technical analysis algorithm. This analysis uses real-time market data from multiple timeframes.
| TF | Price | RSI | TSI | ADX | ADX Cross | Stoch | EMA 9 | EMA 50 | Momentum | Vol Spike | Signal |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 Min | 0.0147 | 47.0 | 53.6 | 29.0 | short | short | 0.0148 | 0.0148 | short | neutral | short |
| 1 Hour | 0.0147 | 41.4 | 38.9 | 36.1 | short | short | 0.0148 | 0.0151 | short | neutral | long |
| 4 Hours | 0.0147 | 33.3 | 31.1 | 30.6 | short | short | 0.0150 | 0.0160 | short | neutral | long |
| 1 Day | 0.0147 | 29.7 | 34.2 | 37.2 | short | short | 0.0160 | 0.0195 | short | neutral | long |
Comprehensive analysis generated by our AI symbol-analysis system.
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Raw kline charts with multi-timeframe analysis from our proprietary data.
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