🚆Roadmap

Overview

This Maiga roadmap outlines the development timeline for the AI-driven trading signal platform, bonding curve-based AI agent launchpad, Proof of Trading (PoT) token, and gamification features. The focus is on iterative development, token launch execution, and long-term scalability to ensure a sustainable ecosystem.

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Phase 1: Foundation, MVP Development & PoT Token Launch Prep (Q1-Q2 2025)

Phase 1.1: AI Signal MVP & Initial Community Testing

✅ Goal: Develop and launch the AI signal system with Telegram bot integration.

• Set up backend infrastructure, APIs for market data aggregation (Binance, TradingView, DEX Screener, etc.).

• Implement basic AI signal generation (5-min, 1-hour signals), confidence scoring.

• Build and deploy the AI signal bot on Telegram for closed beta users.

• Integrate data annotation (user voting on signal accuracy).

• Collect feedback and refine AI signal accuracy.

Phase 1.2: PoT Token Launch Preparation

✅ Goal: Finalize tokenomics, audits, and partnerships for $MAIGA PoT token launch.

• Tokenomics finalization (supply distribution, incentives, trading fee mechanics).

• Smart contract audits and security testing.

• Strategic partnerships for liquidity and exchange listings.

• Marketing and awareness campaign for PoT token.

Phase 1.3: PoT Token Public Launch

✅ Goal: Successfully launch $MAIGA token and establish initial liquidity.

• Listing on major CEX/DEX platforms.

• Users must own $MAIGA to launch or trade AI agents.

• Liquidity injection into bonding curve mechanics.

• Reward early adopters via airdrops and trading incentives.

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Phase 2: AI Agent Trading Expansion & Gamification (Q3-Q4 2025)

Phase 2.1: Bonding Curve Smart Contract & AI Agent Marketplace MVP

✅ Goal: Implement bonding curve mechanics for AI agent launchpad.

• Develop smart contracts for AI agent launch using a bonding curve model.

• Internal testing of bonding curve pricing mechanism.

• AI agent token marketplace prototype (users can list, buy, and sell AI agents).

Phase 2.2: AI Agent Trading Platform Deployment

✅ Goal: Enable decentralized AI agent trading and improve user experience.

• Deploy bonding curve launchpad for AI agents.

• Launch AI agent token trading on-chain (based on bonding curve mechanics).

• Implement leaderboard for most-traded AI agents.

• Introduce dynamic agent fee models (AI agent owners can set trading fees).

Phase 2.3: Gamification & Community Scaling

✅ Goal: Increase engagement with viral mechanics.

• Point and rewards system integration (users earn XP for engaging with AI signals and launching AI agents).

• Referral incentives for AI agent trading.

• Enable social sharing for AI signal performance.

• AI Agent trading competitions with token rewards.

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Phase 3: Ecosystem Growth & Institutional Partnerships (Q1-Q2 2026)

Phase 3.1: Full AI Agent Marketplace & Trading Volume Growth

✅ Goal: Scale AI agent marketplace activity.

• Enable cross-platform AI agent listing (multi-chain compatibility).

• Optimize bonding curve mechanics based on market feedback.

• Implement buyback & fee-burning mechanisms for long-term sustainability.

• Onboard market makers to support liquidity growth.

Phase 3.2: Institutional & Enterprise Expansion

✅ Goal: Expand beyond retail traders and secure larger liquidity providers.

• Explore AI signal integration for hedge funds & trading firms.

• Institutional onboarding for AI agent-backed trading.

• Form global partnerships with AI and crypto ecosystems.

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Phase 4: AI Evolution & Mass Adoption (Q3-Q4 2026)

Phase 4.1: AI Evolution & Model Enhancement

✅ Goal: Improve AI model capabilities with live market trading.

• AI agents adapt to user data & trading behavior.

• AI-backed hedge fund concept for advanced trading strategies.

• AI agent liquidity pool for managed investments.

Phase 4.2: Global Expansion & Regulatory Compliance

✅ Goal: Establish a strong regulatory and business framework for AI agent trading.

• Legal framework for AI trading compliance.

• Expansion into multiple jurisdictions.

• Regulatory-friendly AI trading models for institutional adoption.