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

RSI

Technical Analysis Library (TA-LIB) for Python Backtesting

Anyone who has ever worked on developing a trading strategy from scratch knows the huge amount of difficulty that is required to get your logic right. … TA-LIB Turbo-Charges Your Research Loop: TA-Lib is widely used by quantitative researchers and software engineers developing automated trading systems and charts. This freely available tool allows you to gather information on over 200 stock market indicators.
Candlestick market data chart

Understanding Candlestick Bars & Market Data for Beginning Algo Programmers

In this video, we introduce you to Candlestick Bars, a store of Historic Market Data, how to access that data via Pythons Lists and how pointers work in lists.
Trend Analysis in a Candlestick Market Data Chart

ZigZag Strategy Suggestion from Quora

A suggested a Zig-Zag trading strategy that bounces back and forth on the stock market to make small profits. Testing shows the strategy wouldn’t work.
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
Sample python code from the CloudQuant trading strategy backtesting and trade simulation platform

Python Algorithm Trading – The 4 Basic Elements

Creating a python algorithm for trading means that one must cover four basic building elements. Market data, order processing, tracking/analysis, and backtesting. These four elements are all required to build a successful trading strategy.