Python Trading

The low learning curve Python programming language has grown in popularity over the past decade. Data Scientists, algorithmic developers, quantitative financial professionals, and market enthusiasts have helped this become a strong tool for algorithmic research, development, and trading. Python for the trading industry comes with tools including:

  • Jupyter notebooks
  • NumPy for High-Speed Numerical Processing
  • Pandas for Efficient Data Analysis and Time Series Analysis Techniques
  • Matplotlib for Data Visualization
  • TA-Lib for Technical Analysis
  • Tensor flow

Posts

Crowdsourcing Algorithmic Research

CloudQuant Continues To Allocate Millions to Crowd Researchers

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CloudQuant allocates $10 million to another crowd researcher by funding and leasing a crowd sourced trading algorithm. The licensor will receive a direct share of the monthly net trading profits.
chicago cityscape and sears tower

Built in Chicago: Wanna try your hand at high-frequency trading? There's an app for that

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Built in Chicago discusses CloudQuant, a Chicago-based algorithmic trading startup, lets anyone try their hand at devising their own strategies.
Algo developer getting paid

Intro to Machine Learning with CloudQuant and Jupyter Notebooks

Trevor Trinkino, a quantitative analysts and trader at Kershner Trading Group recently put together an introduction to Machine Learning utilizing CloudQuant and Jupyter Notebooks. In this video he walks you through a high-level process for implementing machine learning into a trading algorithm, ...
CloudQuant the Trading Strategy Incubator

CloudQuant Launches with Unprecedented $15 Million Allocation to Crowd Researcher

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CloudQuant, the trading strategy incubator, has launched its crowd research platform by licensing and allocating $15 Million (USD) to a trading algorithm. The algorithm licensor will receive a direct share of the strategy’s monthly net trading profits.
New York

New York, midtown - Experienced Quantitative Portfolio Manager or Strategist

CloudQuant’s Global Systematic Trading unit is seeking experienced Quantitative Portfolio Managers and Strategists for the U.S. equity market. Ideal candidates will have an MS or Ph.D. in an Engineering or Pure Science discipline from a top school with formal academic coursework in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Digital Signal Processing,
chicago cityscape and sears tower

Chicago - Experienced Quantitative Portfolio Manager or Strategist

CloudQuant’s Global Systematic Trading unit is seeking experienced Quantitative Portfolio Managers and Strategists for the U.S. equity market. Ideal candidates will have an MS or Ph.D. in an Engineering or Pure Science discipline from a top school with formal academic coursework in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Digital Signal Processing,
San Francisco

San Francisco - Experienced Quantitative Portfolio Manager or Strategist

CloudQuant’s Global Systematic Trading unit is seeking experienced Quantitative Portfolio Managers and Strategists for the U.S. equity market. Ideal candidates will have an MS or Ph.D. in an Engineering or Pure Science discipline from a top school with formal academic coursework in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Digital Signal Processing,
SPY Benchmark Trader

Full Stack UI Developer - Chicago

We are looking for a Web Developer responsible for managing the interchange of data between the server and the users, as well as translating the UI/UX design wireframes to actual code that will produce the visual elements of the application.
Daily ROIC Prior to improvements

Improving A Trading Strategy

TD Sequential is a technical indicator for stock trading developed by Thomas R. DeMark in the 1990s. It uses bar plot of stocks to generate trading signals. ... Several elements could be modified in this strategy. Whether to include the countdown stage, the choice of the number of bars in the setup stage and countdown stage, the parameters that help to decide when to exit and the size of the trade will affect strategy performance. In addition, we could use information other than price to decide whether the signal should be traded.
Algorithmic Trading with other peoples money

Skills to Become a Quantitative Trader

Your Proprietary Trading Algorithm is always your property on CloudQuant. Any trading strategy that you develop is yours. Not ours. You do not transfer ownership of the algo to CloudQuant. You do not transfer any copyrights to CloudQuant. This is fundamental to the operations and success of CloudQuant.