Ning Liu

Code

All my hobby projects live on GitHub repositories. Here are some highlights:

• ISLR

  My code in Python 3 to duplicate the R code labs and exercises in the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani.
  
  For details please visit GitHub: https://github.com/0liu/ISLR
  
  Jupyter notebooks:
  • Chapter 3 - Linear Regression
  • Chapter 4 - Classification
  • Chapter 5 - Resampling Methods
  • Chapter 6 - Linear Model Selection and Regularization
  • Chapter 7 - Moving Beyond Linearity
  • Chapter 8 - Tree-Based Methods
  • Chapter 9 - Support Vector Machines
  • Chapter 10 - Unsupervised Learning

• Swing Trade

  is an example automatic swing trading implementation.
  Features:
  • Grid-based swing trading with automatic trailing stops.
  • Swing trading on pre-defined price zones, which can be determined by historical data analysis.
  • Adaptive order type to reduce timing risk with modes from more passive to more aggressive, used for long/short reversal, stop loss and profit taking.

  For details please visit GitHub: https://github.com/0liu/swing_trading

• ibstract

  is a Python 3 package for asynchronous financial trading data acquiring and management, powered by the async/await syntax provided by the concurrency framework asyncio and aio-libs in Python 3.6. It interacts with both remote broker API server and local MySQL database for efficient and concurrent data download, query, and archiving. The goal is to provide a hassle-free tool to obtain and operate on trading data on demand without worrying about broker API and tedious data management work, so users can focus on algorithm development.

  For details please visit GitHub: https://github.com/0liu/ibstract

  Examples are shown in Jupyter notebooks:
  • Operation on historical data
  • The MarketDataBlock class
  • The Broker class to access IB API

• Stats Dash

  is a dashboard-like Python application for real-time data streaming, statistical analysis and indicator-guided manual trading. Features:
  • Market statistics and technical indicators computing with pandas and numpy.
  • Real-time market message streaming with RabbitMQ.
  • Real-time user control knobs and on-the-fly refreshing statistics and indicators.
  • High-level wrappers of access to MySQL database for historical market data.
  • User interface with Excel spreadsheet, with dynamic column definitions.

  For details please visit GitHub: https://github.com/0liu/stats_dash

• Classic Algos

  provides C++17 templates and Python implementations of classic algorithms and abstract data types, including
  • Sequences: stack, linked list, linked stack, queue, linked queue and double-ended queue (deque).
  • Trees: binary search tree, AVL tree, red-black tree.
  • Sorting: bubble, insert, merge, quick, counting, radix, bucket.
  • Dynamic programming.
  • Graphs: BFS, DFS.

  For details please visit GitHub: https://github.com/0liu/classic_algos