thehardwarestore.ai

This repository is a production-grade, AI-powered stock analysis platform for CN, HK, US, JP, KR, and TW markets. In production it runs a scheduled analysis pipeline that fetches market data from multiple providers (akshare, yfinance, tushare, efinance, futu, etc.) with priority-based fallback, feeds the data to LLM backends via LiteLLM, generates structured decision reports, and pushes them through 15+ notification channels (WeChat, Feishu, DingTalk, Telegram, email, Discord, Slack, etc.). It also exposes a FastAPI REST API, a React/TypeScript web dashboard, an Electron desktop client, and bot integrations for Feishu/DingTalk/Discord. The system includes agent-based chat with tool use, screening, backtesting, alerts, portfolio management, decision-signal tracking, and market review capabilities.

Fincept Terminal is a native C++20/Qt6 desktop financial intelligence platform, open-sourced under AGPL-3.0, with a proprietary Enterprise edition. It functions as a comprehensive trading, research, and analytics terminal supporting equities, options, futures, crypto, forex, and prediction markets. The architecture is a modular monolith: a single Qt Widgets process with embedded Python 3.11 for scripting, SQLite for persistence, and a DataHub pub/sub system for real-time data flows. Key entry points are the main window (WindowFrame) and screen router (DockScreenRouter), with over 100 data connectors, 20+ broker integrations, backtesting engines (QuantConnect/LEAN compatibility), AI agents (LangGraph/Agno-based), and extensive analytics (corporate finance, portfolio optimization, options pricing, economic indicators). The repository is actively maintained with a monthly release cycle, but contains a very large codebase with some boilerplate and incomplete features, making it powerful but complex to reuse partially.

Qlib is an open-source, AI-oriented quantitative investment platform developed by Microsoft. It covers the full pipeline for quantitative research and trading: data ingestion and feature engineering (Alpha158, Alpha360, high-frequency), model training (traditional ML like LightGBM/XGBoost, deep learning in PyTorch including LSTM, GRU, Transformer, GATs, and reinforcement learning), backtesting with realistic costs and slippage, portfolio optimization, and online serving for production-like deployment. The platform is highly modular and config-driven, with a core library (qlib) and a separate contrib layer for community extensions. It includes a CLI (qrun) for running full workflows from YAML configs, extensive experiment tracking via MLflow, and a rich set of evaluation metrics. While primarily a library/application, it also functions as a research benchmark suite with many example models and data collectors for global markets. The codebase is active and well-maintained, though some components (e.g., TFT model, older RL code) show signs of technical drift due to dependency version locks and deprecated patterns.

This repository is a curated list (awesome-list) of open-source quantitative finance libraries, packages, and resources. It provides a structured README.md organized by category, with inline language tags, and a pipeline that parses the list, enriches it with GitHub metadata (stars, last commit dates), and generates a static website deployed to GitHub Pages. The repo also includes automated tooling for pull-request review: a validation script that checks entry format, section placement, URL security, duplicate detection, and a full CI-driven PR review workflow. The core logic resides in parse.py (multi-threaded GitHub API enrichment), site/generate.py (static site generator), scripts/validate_readme.py (entry validation), and scripts/review_pr.py (automated PR review with offline URL reachability checks and cross-PR duplicate detection). The project is primarily a reference list, not an application or data pipeline, but it includes high-quality supporting tooling for maintainers and contributors.

This is a Python library for quantitative finance, specifically portfolio optimization and risk management. It implements Conditional Value-at-Risk (CVaR) optimization using Benders decomposition, mean-variance optimization via quadratic programming, and entropy pooling for adjusting scenario probabilities under user-defined views and stress tests. The library also includes fully flexible resampling (FFR) for simulation, Black's model for option pricing, and a set of helper functions for financial statistics. It is designed as a monolithic Python package with a clean API, extensive tests (100% coverage), and 14 Jupyter notebooks that serve as reproducible examples. While the primary character is an application library, it has a strong research and educational orientation, referencing academic papers and providing walkthroughs. The code is well-structured, but domain-specific and depends on cvxopt with GLPK for linear programming, which may limit portability.

foureng is a Python library for quantitative finance: Fourier and characteristic-function based option pricing. It exposes unified dispatchers (`price_strip` and `price`) over a registry of characteristic-function models (BSM, Heston, VG, Kou, Bates, CGMY, NIG, 3/2, rough Heston, etc.) and numerical engines (COS, Carr-Madan FFT, FRFT, Lewis, Hilbert, PROJ, CTMC, SINC, SWIFT, contour, plus Monte Carlo baselines). The product layer covers European vanillas, Bermudan/American, barriers, Asians, lookbacks, cliquets, faders, variance/credit instruments, and multi-asset payoffs. It is a library, not a service or CLI: usage is through the Python API and Jupyter notebooks.

This repository, ai-hedge-fund, is a production-grade simulation engine for running AI-managed hedge funds in educational and research contexts. It provides a dual-mode system supporting both historical backtesting and live paper trading, all adhering to point-in-time data integrity to prevent look-ahead bias. The architecture is modular and protocol-based, with swappable components for data acquisition (Financial Datasets API), LLM providers (Anthropic, OpenAI, DeepSeek, Google, xAI, Kimi, TypeSafe/Jev), broker execution (simulated/paper), signal generation (quant models and LLM agents emulating investors like Buffett, Munger, Graham), and risk management. The engine includes a hash-chained ledger for tamper-evident record keeping, prompt caching to eliminate redundant LLM costs during backtests, and a textual TUI for interactive management. It is explicitly not intended for real trading and is released under MIT license. The codebase is well-structured with clean abstractions, though it carries some technical debt from being a ground-up v2 rebuild that is not yet fully integrated with its predecessor, and contains hardcoded constants and magic numbers that limit configurability.

Binance Grid Trader is a cryptocurrency trading bot that implements grid trading strategies for Binance Spot and Futures markets. The application features a modular architecture with separate layers for exchange gateways, event processing, strategy management, and user interface. It provides both a graphical interface built with PyQt5 and script-based execution modes for running trading strategies without UI. The system connects to Binance via REST and WebSocket APIs to receive market data and execute trades based on configurable grid strategies.

OpenBB is an open-source financial data platform that provides a unified, multi-interface access layer to over 20 data providers (FMP, FRED, CBOE, Yahoo Finance, Intrinio, etc.) through a modular provider-extension architecture. The core library (`openbb_core`) defines a standardized Fetcher pattern (transform_query -> extract_data -> transform_data), Pydantic-based standard models for equities, derivatives, fixed income, economics, and more, and a command routing system exposed via a Python SDK (OBBject), a REST API (FastAPI), a CLI (argparse translator), a desktop app (Tauri + React), and an MCP (Model Context Protocol) server for AI agents. The platform includes domain-specific extensions (equity, crypto, economy, quantitative, technical analysis, charting) and a cookiecutter template for scaffolding new providers or OBBject extensions. While the codebase is large and actively maintained, there is notable technical drift in the form of hardcoded API URLs, mixed sync/async extraction, deprecated endpoints, and code duplication across many provider implementations. The charting extension's plugin-based technical indicator system, the country/exchange validation utilities, and the LRU cache with TTL are high-craftsmanship, reusable components that demonstrate non-obvious software engineering skill.

Jesse is a mature, full-stack algorithmic trading framework designed for cryptocurrency backtesting, live/paper trading, hyperparameter optimization, Monte Carlo simulation, and rule significance testing. It combines a Python library with a FastAPI web server and a Nuxt.js dashboard, offering a comprehensive environment for developing and validating trading strategies. The repository also includes an MCP (Model Context Protocol) server for AI-assistant integration, over 180 technical indicators (many with Rust-accelerated backends), and a research API for programmatic strategy analysis. Architecture is modular with distinct layers: CLI entrypoint → web controllers / MCP tools → service layer → simulation engine (backtest/optimize/live) → exchange adapters. State management uses global singletons for in-memory simulation state, while persistent data is stored via Peewee ORM (PostgreSQL, optional TimescaleDB). The indicator library showcases a clean pattern with sequential/non-sequential modes and Rust fallback. However, the codebase shows signs of organic growth with some tightly coupled modules (global config, store singletons) and a large static frontend bundle that is opaque to analysis. Key strengths include the breadth of supported exchanges (10+), robust backtesting with realistic fee/margin/leverage modeling, and a well-designed MCP tool layer for AI-assisted development. Weaknesses include exchange driver duplication, a complex Rust subprocess for coordination, and dependency on many external APIs that require ongoing maintenance.

Pinkfish is a Python library for backtesting equity trading strategies, primarily used via Jupyter notebooks. It provides a modular framework with components for data fetching (Yahoo Finance via yfinance), trade logging, portfolio management, performance statistics, and signal generation. The core architecture revolves around a Strategy class pattern, a Portfolio class for multi-symbol backtesting, and a TradeLog class for simulating trade entries/exits with realistic open/close fills. The library includes a wide range of technical indicators (both custom and TA-Lib wrappers), comprehensive performance metrics (Sharpe, Sortino, drawdown, etc.), and support for signal notifications via Pushover/email. The codebase is well-structured with clear separation of concerns, but uses some global state in TradeLog and relies on a static stock market calendar, introducing minor maintenance risks. The examples cover diverse strategies such as momentum, moving average crossovers, and sector rotation, serving as both documentation and reusable templates.

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 implements a comprehensive backtesting and live-trading framework for prediction markets (specifically Polymarket and Kalshi), built on a heavily customized NautilusTrader engine. It provides historical replay of L2 order book data from multiple vendors (Telonex, PMXT, Polymarket native), realistic execution modeling (fill models, fee models with maker rebate inference), and a library of trading strategies (e.g., EMA crossover, microprice imbalance, late favorite hold). The framework supports parameter search optimization (random grid, TPE via optuna), joint-portfolio multi-market backtests, and generates detailed HTML/CSV/JSON reports with equity curves, drawdown, and Brier advantage analysis. A Rust native extension accelerates data conversion (book diffing, payload sorting), and a CLI TUI (main.py) discovers and runs backtest entrypoints. While primarily an application framework, it also includes numerous one-off research scripts and Jupyter notebooks (e.g., BTC snapshot model walkforward), giving it a hybrid character: a production-quality backtesting infrastructure with a heavy research emphasis. The codebase is well-tested (50+ test files), documented, and follows clean engineering practices (config validation, structured event logging, concurrency patterns). However, it is tightly coupled to NautilusTrader internals, relies on several environment variables, and contains large monolithic loader classes (e.g., Telonex loader at ~3600 lines) that limit reusability outside its specific domain. Maintenance appears active (last commit 2026-05-16) with low technical drift, though the heavy dependency on NautilusTrader and optional Rust build are potential friction points.

HyperView is a CLI-driven toolkit for downloading TradingView market data, backtesting Python trading strategies with TradingView-parity fill assumptions, and optimizing parameters via Bayesian search (Optuna). The system is organized as a monolith with subcommands: download-data, backtest, hyperopt, pine-optimize, pine-batch-optimize, list-data, list-strategies. It features a pluggable strategy framework with auto-discovery, 20+ TA-Lib indicator wrappers, a backtester that simulates intrabar SL/TP paths, and a two-stage Pine input optimizer that exports optimized Pine scripts with injected best parameters. The codebase is well-structured with dataclasses, type hints, and a rich CLI output using the `rich` library. It includes unit tests for the Pine optimization pipeline and CLI parser. The repository is primarily an application, though it contains data engineering patterns in its downloader and caching layers.

This repository is a self-paced learning roadmap for algorithmic trading, structured as a hierarchical collection of markdown files. It curates resources, certifications, and project ideas across artificial intelligence, computer science, mathematics, finance, cloud engineering, and software engineering. The repository is entirely documentation-driven; there are no executable applications, data pipelines, or machine learning models. Its primary value lies in its organized curriculum, reusable topic templates, and role-based learning paths (e.g., quant developer, algorithmic trader). While not a software project, it demonstrates high organizational craftsmanship through consistent documentation structure, templates, and CI/CD for link checking and diagram rendering.

This is an investment analysis framework implementing the Turtle Investment Strategy. It provides a modular, multi-agent system for stock analysis that combines quantitative financial analysis, qualitative business assessment, and valuation modeling. The framework collects data from Tushare API (with yfinance fallback), processes financial statements, analyzes annual report PDFs, and generates investment reports. Key features include a two-tier stock screening system, deterministic pre-computation of metrics to prevent LLM calculation errors, and a structured workflow with validation checkpoints. The architecture separates concerns into data collection, processing, analysis, and reporting layers with clear interfaces between components.