This repository is a comprehensive, production-oriented algorithmic trading framework implemented in TypeScript. It is organized as a monorepo with six interdependent packages: `trading-signals` (100+ technical indicator implementations with streaming `add`/`replace` semantics, each verified against external reference data like Tulip Indicators and Skender.Stock.Indicators), `trading-strategies` (strategy base classes, concrete strategies such as SMA crossover, scalp, trailing stop, and a multi-indicator confluence strategy, plus a backtesting engine and report generators), `exchange` (abstract broker interface with real implementations for Alpaca and Trading212, including mock broker for backtesting, candle batching, WebSocket streaming, and a CLI tool), `messaging` (a Telegram bot for managing accounts, strategies, watches, and reports via multi-step wizards), `candles` (labeled historical candle datasets for testing), and `trading-signals-docs` (a Next.js documentation site with interactive indicator demos and a visual backtester). The architecture emphasizes clean separation between signal generation, strategy logic, broker execution, and user interface. The indicator library is the core asset, providing a consistent contract (`IndicatorSeries`, `TechnicalIndicator`, `TrendIndicator`) with rich signal detection and state rollback. The strategy framework supports live trading and backtesting via the same advice-execution pipeline. The project is actively maintained with CI/CD, mutation testing, and a well-defined broker integration template. While the codebase shows high craftsmanship in its indicator implementations and test patterns, the large number of indicators and hand-derived test fixtures introduce maintenance overhead, and the tight coupling to Telegram limits platform portability.
This repository is a comprehensive .NET SDK for the Alpaca Markets API, providing both REST and WebSocket clients for algorithmic trading, market data retrieval (stocks, crypto, options), and account management. The architecture is a layered client library with core (`Alpaca.Markets`) and extensions (`Alpaca.Markets.Extensions`) NuGet packages, targeting multiple .NET frameworks (netstandard2.0/2.1, net462, net8.0, net10.0). Entry points include configuration classes that instantiate typed clients (e.g., `IAlpacaTradingClient`, `IAlpacaDataClient`, `IAlpacaCryptoDataClient`, `IAlpacaOptionsDataClient`) via environment-specific base URLs (live, paper). The library supports advanced order types (bracket, OCO, trailing stop), real-time streaming with automatic reconnection, and efficient pagination using `IAsyncEnumerable`. The codebase exhibits high craftsmanship with clean separation of interfaces, messages, and parameters; fluent builder patterns for orders; and robust testing infrastructure using mocks. The extensions package adds DI integration (`AddAlpaca*Client`), Polly-based retry, and reusable subscription management. While the SDK is actively maintained and well-tested, it relies on Newtonsoft.Json (rather than System.Text.Json) and maintains compatibility with older frameworks, adding complexity. A minor risk is dependency on internal Alpaca API endpoints that could change, but the library's design isolates these concerns.
This repository contains the backend API for a trading platform, specifically QuantDinger. It handles strategies, indicators, and market data, and has various tests and scripts for ensuring the quality and functionality of the code.
This repository provides a comprehensive toolkit for Chinese Llama-3 models, including base and instruction-tuned versions. It offers scripts for pre-training, fine-tuning, model merging, inference (via Hugging Face transformers, llama.cpp, Ollama, and OpenAI API-compatible server), and evaluation on standard benchmarks (CEval, CMMLU, MMLU, LongBench). The repository follows a modular architecture where each functionality is implemented in separate scripts under the scripts/ directory. Key entry points include training scripts (run_clm_pt_with_peft.py, run_clm_sft_with_peft.py), inference scripts (inference_hf.py, llama_cpp/chat.sh, openai_api_server.py), and evaluation scripts (ceval/eval.py, cmmlu/eval.py, etc.). The model supports LoRA adapters for efficient fine-tuning and provides merging utilities to combine base models with LoRA weights. The code demonstrates good practices in handling model quantization, PEFT integration, and multi-platform deployment.