Python

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

Machine Learning, Quantitative Investing News

Industry News: Machine Learning and Artificial Intelligence News for the week ending October 9, 2017

AI & ML FINTECH perspective: Chicago startups with chatbots, JP Morgan, Hedge Funds, OCBC, UBS, Morgan Stanley, Google, Zillow, GoPro, Snap, …
Scorecard

Backtest Visualization on CloudQuant

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The Quantitative Strategy Backtest ScoreCard is saving time for crowd researchers who are able to visualize the results of multi-day backtests quickly, even as the backtest is running.
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 Launches with Unprecedented Risk Capital Allocation to Crowd Researcher

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CloudQuant, the trading strategy incubator, has launched its crowd research platform by licensing and allocating risk capital to a trading algorithm. The algorithm licensor will receive a direct share of the strategy’s monthly net trading profits.
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

How do you get from being a Data Scientist, Software Engineer, or Markets Enthusiast to being a Quantitative Algo Developer? Algorithmic Trading requires both technical, and functional skills.

Overview of Core Technical Skills

Programming

Programming is the ability to express your trading ideas so that a computer can repeat the process. You need this skill to be able to code your algo. Structured backtesting is another use of your programming skills. CloudQuant uses Python, a high-level language that is easy for anyone to learn who has ever worked with any programming or macro language, like MS Excel VBA.

Simulation (BackTesting)

To test your algo you will need to test it against historical data. This is called “Backtesting.” Backtesting is more than checking to see if you made a profit or loss. It includes understanding how and why you made a profit and loss and systematically improving that algo.

Statistics

CloudQuant’s backtesting provides several reports that are full of statistics. Understanding what each of these statistics means is essential to improving your algo. Having a base understanding of statistics is also important.

Management of Risk

Order processing and trading involves risk. CloudQuant meets our regulatory required risk and our own functional requirements for pre-trade and post-trade risk management within our production trading system and our backtesting simulation tools. Understanding how risk works will help you improve your algorithm skills.

Learn more

Read the Full Post and See the training available at Experfy.com
Trend Analysis in a Candlestick Market Data Chart

Algorithms for Trading

The hardest part of starting any project, including building a quantitative trading strategy, is figuring out where to start. To that end, this post covers a basic overview of a few algorithms for trading. We hope to help you get your creative energy to level up.
Quantitative Strategy, Trading, and Algo Development Industry News

Why You Should Always Question The Status Quo — Tradeciety

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CloudQuant was mentioned in this article by Tradeciety about trading psychology. We particularly like the comment saying “If you are so worried about algos, why don’t you look at Python? Read up on it. Cloudquant, Quantopian. The tools are there, the information is there, for free.” Change is progress, stagnation is death. That is the truest truth, especially in capitalism and thus also in trading. Grow or die. But then the real question is, why do so many people want to go backward? Everything was better before, we will make this and that great again, bla bla bla, yada yada yada. And why are so many people afraid of change when change is the only thing that ever brought humanity forward?
In Germany, we have this saying:”The farmer doesn’t eat what he doesn’t know”…that exactly describes the herd mentality. People are afraid of change because it involves an unknown factor. What will happen if we do that? What will happen if do this? I want to go back to my mommy. Where is my mommy? That is exactly what is happening in politics and in the world right now. Read the full article on Tradeciety
Python Scripts in CloudQuant's Algorithmic Trading and Quantitative Strategy Backtesting Application

Algo Developers – Entry Level

CloudQuant is THE trading strategy incubator. We’re building a free python data research tool for ordinary people with extraordinary trading ideas. We license and fund the best trading strategies and pay our users a share of the profits. Our group is a FINTECH startup housed under the umbrella of a trading firm with existing infrastructure and financial resources.