> For the complete documentation index, see [llms.txt](https://traider-agent.gitbook.io/traider-agent/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://traider-agent.gitbook.io/traider-agent/roadmap.md). # Roadmap #### Building the Future of TrAIder Agent Our mission to revolutionize memecoin trading is far from over. As we continue to innovate, we’re excited to share the next steps for TrAIder Agent, including groundbreaking functionalities and features designed to elevate your trading experience. *** #### 🌟 Upcoming Features **1. Limit Orders** Take control of your trades like never before: * Set conditions for the AI to **buy** a token when its market cap reaches a specified threshold. * Create rules to **sell** a token once its market cap hits a target value. * Define **multiple limit orders** at different price points for the same token, giving you more strategic flexibility. **2. Top Trader Learning AI** Empowering users with insights from the best: * The trading agent will **analyze behaviors of top traders** and provide actionable tips (optional for users to enable or disable). * Examples of warnings and tips include: * "Top traders do not typically buy tokens under an 8k market cap." * "This token has a high top 10 holders’ distribution (>30%). Consider avoiding this trade." **3. DCA (Dollar Cost Averaging)** * Introduce a **DCA function** where the AI progressively buys a token as its price decreases, helping users mitigate market volatility. **4. Anti-MEV and Jito Tip Integration** * Implement **anti-MEV protection** to safeguard trades from frontrunning and other manipulative practices. * Add support for **Jito tips**, enabling faster and more secure trade execution. **5. EVM-Chain Compatibility** Expand trading opportunities across chains: * Introduce compatibility with EVM-based chains, allowing users to trade on **Ethereum**, **Binance Smart Chain**, and more. **6. Custom LLM Development** Redefining AI capabilities while optimizing costs: * Build our **own large language model (LLM)** to reduce reliance on Anthropic Claude’s API. * Unlock thousands of new possibilities for custom features and AI advancements tailored to our users’ needs. *** #### 🤖 Future AI Agents We’re introducing a new suite of specialized agents designed to enhance your trading experience. \ \ Here’s what’s coming (in chronological order): **1. Chart Integration Agent** Bringing data-driven insights directly to your charts: * Display **entry prices** and trading history on live charts. * AI-powered recommendations such as: “You’re up 200% on this token. Now might be a good time to take some profits.” **2. Twitter Sniper Agent** Gain a first-mover advantage: * Monitor specific Twitter accounts for new posts. * Extract contract addresses (CA) from text or images and execute buys instantly. * Filter purchases based on criteria like **market cap thresholds** or **red flags** (e.g., high developer token holdings). **3. AI Copytrader Agent** Elevating traditional copytrading: * Copy trades based on specific wallets’ actions while analyzing their relevance to **top trader patterns**. * Detect and avoid "copytrader farming" wallets exhibiting suspicious behavior like rapid buy-sell cycles. * Ensure trades align with AI insights and broader market trends. **4. AI Vision Agent** Stay ahead of the curve: * Scan for newly deployed tokens that meet your predefined parameters (e.g., minimum market cap, volume, sell activity). * Compare tokens against those favored by top traders and deliver **relevance scores**. * Provide users with tailored recommendations based on the latest market trends. *** #### 🚀 The Path Forward With these exciting updates and features on the horizon, the next step is to explore how you can make the most of TrAIder Agent. Dive into the next section to learn how to use the platform and unlock its full potential! --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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