This repository is the Meteora DLMM (Dynamic Liquidity Market Maker) SDK, a comprehensive multi-language toolkit for interacting with the Meteora concentrated liquidity AMM protocol on Solana. The system includes a Rust core library (`commons/`) with swap quoting, fee calculation, PDA derivation, and math utilities; a TypeScript client (`ts-client/`) that provides a high-level `DLMM` class for pool interaction, liquidity management, rebalancing, and limit orders; a Rust CLI for admin and user operations; a Python client that wraps the TypeScript SDK via a REST API; and a market-making bot example. The on-chain program is defined in IDL files with over 50 instructions supporting multiple pool types (permissionless, permissioned, customizable), Token-2022 with transfer hooks, and advanced features like volatility-based dynamic fees and oracle TWAP. Architecturally, the SDK is layered: the Rust `commons` crate contains the core business logic and is reused by the CLI and market-making bot, while the TypeScript client reimplements much of this logic for off-chain use. The CLI serves as a reference implementation for all protocol operations. The market-making bot demonstrates automated position rebalancing using configurable strategies. The Python client is a thin wrapper that delegates to a TypeScript server, making it less independent. The codebase is heavily tested with Rust integration tests and TypeScript unit tests, including property-based tests for bid-ask math and Token-2022 compatibility. The primary data flows are: user requests (swap, add/remove liquidity, claim fees) go through the client SDK, which constructs and signs transactions for the Meteora DLMM on-chain program. The SDK computes quotes off-chain, simulates rebalancing, and derives required accounts. The market-making bot periodically fetches pool state, evaluates active price drift, and executes rebalancing transactions. The codebase demonstrates strong engineering practices with clear separation of concerns, reusable math utilities, and consistent patterns across languages.
This repository implements an agentic crypto trading platform called AI Market Maker (AIMM). Despite the name, the code is not a low-latency market-making engine; it is a multi-agent hedge-fund-style trading stack that combines LLM-driven research agents with deterministic quant logic for backtesting, paper trading, and live trading. The core execution path is a LangGraph workflow: nine Tier-0 specialist agents (monetary, news, TA, on-chain, order flow, etc.) produce structured signals, a weighted arbitrator fuses them into trade intents, a Risk Guard vetoes dangerous proposals, and portfolio management routes execution through an OMS with an in-memory or SQLite ledger. The platform includes a FastAPI backend with REST and streaming endpoints, a Next.js/React dashboard, Docker deployment, Alembic migrations, CI, and a large test suite.
This repository is a high-frequency trading (HFT) research and development framework, primarily written in Java with a significant Python layer for reinforcement learning and data analysis. The actual system provides a modular architecture for backtesting and live execution of algorithmic trading strategies across multiple exchanges (Binance, Interactive Brokers, Kraken, MetaTrader). It supports market making algorithms (Avellaneda-Stoikov, ConstantSpread), factor investing, and a wide range of execution algorithms (TWAP, VWAP, Implementation Shortfall). Reinforcement learning integration is a key feature, with Gymnasium environments communicating via ZeroMQ to Java-based RL agents. The data pipeline ingests tick-level depth and trade data from Parquet/CSV files or live streams, generates OHLCV candles, and feeds into both traditional backtesting and RL training loops. While the framework is functionally rich and demonstrates advanced patterns (Disruptor ring buffers, object pools, time-series utilities), the codebase suffers from maintenance drift: many third-party libraries are vendored or outdated, hardcoded paths are scattered, and several modules contain commented-out or incomplete code. The project is a hybrid of a production-like trading engine and research code, with the dominant character being an application framework for algorithmic trading research.