Victor Sunday@Sunvic567

AI Stock Trading Agent is an AI-powered financial analysis system designed to assist users in making data-driven stock trading decisions. The agent collects and analyzes historical and real-time market data such as stock prices, trading volume, technical indicators, and market trends. Using Machine Learning models and AI-based analysis, it identifies patterns and predicts potential price movements. The system can generate Buy, Sell, or Hold signals based on the analyzed market conditions. It can also evaluate factors such as moving averages, RSI, MACD, volatility, and market momentum to improve its decision-making process. An AI agent can continuously monitor market conditions, analyze multiple stocks, and provide insights through an interactive dashboard. The project can be further enhanced with portfolio management, risk assessment, news/sentiment analysis, automated backtesting, and paper trading features. Rather than guaranteeing profits, the system is intended as a decision-support and educational tool that demonstrates how AI and ML can be applied to financial markets.

The Problem Small shops keep credit ledgers in notebooks or spreadsheets and decide from memory who to trust. This breaks down when outstanding balances quietly cross risk thresholds, once-punctual buyers start paying late, or owners must personally recall hundreds of payment histories to answer "should I give them credit again?" Revenue leaks silently, and bad debts accumulate without visibility. The Solution Owed replaces that fragile memory with a consistent, auditable, always-up-to-date credit score per customer. It answers three questions merchants actually ask: How risky is this customer right now? — Low / Medium / High / Critical with a confidence score. Why? — A plain-English explanation referencing actual transactions. What should I do? — A recommended next action, the best time to follow up, and a ready-to-send customer message. Key Features Customer ledger management — Add customers and track every credit and payment transaction per customer with live balance computation. AI credit assessment — One-click analysis per customer returns risk level, confidence score, behavioral summary, recovery probability, recommended next action, best time to follow up, and a customer-ready message. Risk-prioritised dashboard — See total outstanding credit, count of High/Critical customers, and a customer list sorted by risk priority so urgent accounts surface first. Bulk CSV import — Import hundreds of transactions in one go with automatic customer de-duplication and a clear summary of imported / skipped / duplicates / errors. Activity log — Every meaningful action is appended to a per-business audit trail with human-readable descriptions and structured metadata. Auth & multi-tenancy — Email/password sign-up, automatic business provisioning on sign-up, and Row-Level Security that prevents any cross-tenant leakage.

Remem is a Memory-as-a-Service API that solves the most overlooked problem in AI agent development, every agent forgets everything the moment a session ends. Developers plug Remem into any AI-powered product using two API calls and their agent instantly gains persistent, searchable memory across sessions, users, and agent frameworks. Unlike solutions that do basic cosine similarity retrieval, Remem uses a hybrid scoring engine combining 70% semantic relevance, 20% recency decay, and 10% importance weighting, returning the right memories, not just the most similar ones. Memory manages itself through TTL-based expiry via pg_cron and automatic duplicate detection. Enterprise teams get full data sovereignty through BYOD Supabase support, meaning user data never leaves their own infrastructure. Embedding generation runs on AMD Instinct MI300X GPUs via Fireworks AI, making memory retrieval fast enough for real-time agent loops. Remem ships with a published Python SDK on PyPI, full Mintlify documentation, a live production API, and a demo support agent showing the stark before-and-after contrast of agents with and without persistent memory. This is not a hackathon prototype, it is production infrastructure that any developer can use today with a single pip install.

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🌎 Online Hackathon | 💻 Dates: 28 August–4 September 2026 | 🏆 $6,000 Prize Pool Build AI trading agents on Alpaca — autonomous agents and trading apps using Alpaca's Trading API, MCP server and CLI. 🌍 Fully online hackathon. Join and build from anywhere in the world.

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.

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