MagicEthan/EliteQuant_Python

This system is a quantitative trading platform that supports both backtesting and live trading simulations. It provides an event-driven architecture to execute configurable strategies against historical or live market data from various sources. The system manages portfolio state, including account balances, positions, and trades, while offering a graphical interface to monitor performance and order status in real-time.

The application now includes dedicated GUI windows to monitor and interact with live trading data. Users can view real-time order status, trade fills, portfolio positions, account balances, and strategy performance through new UI components (ui\_order\_window, ui\_fill\_window, ui\_position\_window, ui\_account\_window, and ui\_strategy\_window). These windows are integrated into the main application window and update dynamically via an event-driven architecture that processes tick, order status, fill, position, and account events from the backend.

source/gui · high confidence

Introduced new modules for managing trading accounts and portfolio positions. The \source/account\ directory now includes \AccountEvent\ and \AccountManager\ to handle account state, balance, and commission tracking. Similarly, \source/position\ introduces \Position\, \PositionEvent\, \ContractEvent\, and \PortfolioManager\ to track open positions, calculate unrealized and realized PnL, and manage contract information. These components enable the system to process and store account and position data from events.

source/account, source/position · high confidence

A new README.md file has been added to the resource directory, providing step-by-step instructions for building and installing the nanomsg C++ library and its Python bindings, including specific paths for Windows/Anaconda environments.

resource · high confidence

The \hist\ directory now includes local CSV files (\AAPL.csv\ and \AMZN.csv\) containing daily stock price data (Date, Open, High, Low, Close, Adj Close, Volume) for Apple and Amazon, respectively, starting from January 2010.

hist · high confidence

The \out\ directory now contains three new CSV files that provide the foundational data for the simulation: \equity.csv\ tracks the daily portfolio value starting from January 2010, \positions.csv\ records daily holdings for AMZN, AAPL, and cash, and \trades.csv\ logs the initial sequence of buy and sell orders for AMZN. These files establish the baseline state and historical transaction history required for the system to function.

out · high confidence

Users can now run backtests and live trading simulations using a new event-driven architecture. The \Backtest\ class in \backtest\_engine.py\ orchestrates data feeds, brokerage simulation, portfolio management, and strategy execution, supporting multiple data sources (local, Quandl, Tushare). The \LiveEngine\ in \live\_engine.py\ launches a PyQt5 GUI that connects to a background server process, supporting multi-language text and dark/light themes. Configuration is split into \config\_backtest.yaml\ for backtest parameters and \config\_client.yaml\ for client-side strategy and UI settings, allowing users to define and test strategies like \MovingAverageCrossStrategy\ or \BuyAndHoldStrategy\ with specific tickers and windows.

source · high confidence

Added a new data layer in source/data that provides a unified interface for retrieving market data. This includes a base class (DataFeedBase) and specific implementations for backtesting against local CSV files, online sources (Yahoo Finance, Quandl, and Tushare), and live market streams. The update also introduces core data structures for market events, including BarEvent, TickEvent, and HistoricalEvent, alongside a DataBoard to track the latest tick and bar data for each symbol.

source/data · high confidence

Added new modules to support core trading infrastructure: a logging system that records trade data to CSV files, a risk management framework with a default pass-through policy, and utility functions for symbol handling. These components provide the foundational structure for tracking transactions, enforcing order compliance, and supporting other system components.

source/log, source/risk, source/util · high confidence

Added a new \source/performance\ module containing a \PerformanceManager\ class that records equity, positions, and trades in a format compatible with the \pyfolio\ library. This manager tracks performance metrics over time and provides methods to generate tear sheets and save results to CSV files, alongside a basic \ReportManager\ class.

source/performance · high confidence

A change to existing behaviour in source/server — 7 commits, 13 files.

log/ctp, source/server · medium confidence · unverified

Written by watchdog.canine.dev from the codebase's own history, inside the signed delivery this page is composed from.

• CAI 40 → 39 (-0.8)
• Rubric changed (rubric-2026.08.19 → rubric-2026.09.15) — scores are not directly comparable.

• Code Health 97 → 97 (+0.1)
• Architecture 100 → 89 (-11.4)
• Maturity 60 → 60 (-0.0)
• Readiness 0 → 0 (+0.0)
• Security 85 → 85 (+0.0)

• Dependency hygiene not measured — no supported dependency manifest was read
• Duplicated block (14 lines × 3) (source/data/backtest_data_feed_local.py)
• Duplicated block (5 lines × 2) (source/performance/performance_manager.py)
• Duplicated block (7 lines × 2) (source/live_engine.py)
• Duplicated block (7 lines × 2) (source/position/position.py)
• No exposed public API
• early-stage repository — too little history to judge knowledge freshness
• git history depth insufficient
• git history depth insufficient
• single-maintainer — knowledge-concentration (bus factor) risk

• Documentation: no installation or build instructions (README.md)
• Documentation: no usage examples (README.md)
• Duplicated block (10 lines × 2) (source/event/backtest_event_engine.py)
• Duplicated block (11 lines × 2) (source/position/position.py)
• Duplicated block (14 lines × 3) (source/data/backtest_data_feed_local.py)
• Duplicated block (5 lines × 2) (source/performance/performance_manager.py)
• Duplicated block (7 lines × 2) (source/data/backtest_data_feed_quandl.py)
• Duplicated block (7 lines × 2) (source/live_engine.py)
• Duplicated block (8 lines × 4) (source/gui/ui_account_window.py)
• Hotspot: source/event/client_mq.py (source/event/client_mq.py)
• Secret: generic-api-key (source/data/backtest_data_feed.py)
• TodoComment (source/brokerage/backtest_brokerage.py)
• TodoComment (source/brokerage/backtest_brokerage.py)
• TodoComment (source/data/live_data_feed.py)
• TodoComment (source/gui/ui_main_window.py)
• TodoComment (source/position/position.py)

Written by watchdog.canine.dev from the codebase's own history, inside the signed delivery this page is composed from.

MagicEthan/EliteQuant_Python was measured the same way every project in this corpus was: the same rubric, at a pinned commit, with the result published in full. Point a surveyor at a repository you know and see whether you agree with it.