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

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.
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
Battle of the Quants June 2017

Battle of The Quants – Discusses Crowd Researching in NY

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Crowdsourcing in fund management and trading is the move to utilize anyone with an internet connection to participate in the research with the goal of finding new and better ways of trading. During the discussion the differing approaches being taken with the business models, and the technology, and the challenges each are facing.
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Let The Market Take You Out Of Your Trade

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LearnToTradeTheMarkets.com published a very interesting article advocating Why You Should Almost Never Manually Close Trades. This post goes into detail examining that most traders “self-sabotage.” In other words, traders are their own worst enemy. They get emotional when trading. This is one of the main reasons that CloudQuant uses algorithms. The use of stop loss orders, and programmatic reasons to enter or exit the market are essential to our trading strategies. “If you manually close a trade when it’s against you, you are voluntarily taking a loss. Read that last sentence again, maybe even a third time. Trading is about maximizing your winners so that they offset your losing trades, that’s how you make money. You’re going to have losing trades, but you don’t need to voluntarily take them, most of the time.” An example of using programmatic stop loss and programmatic profit taking are included in several of the CloudQuant public trading strategy scripts written in python. Read the full posting on LearnToTradeTheMarkets.com    
Sample python code from the CloudQuant trading strategy backtesting and trade simulation platform

Code-Dependent: Pros and Cons of the Algorithm Age

Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment.

Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.

Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences. Recent news items tie to these concerns:

Read the full article on Pew Research Center
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Social Sentiment in Trading Algorithms

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Bloomberg recently wrote that “It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market.” (1) One of the most interesting new sources of data is social sentiment. We have found that the incorporation of social sentiment data is definitely improving the quality of algorithms as shown in our backtesting on CloudQuant. Over the next couple of weeks an intern from the University of Chicago who is mastering in Financial Mathematics is working on incorporating social signals into a DeMark Indicators script that is available for all registered users to see in the CloudQuant base working scripts. I look forward to seeing how this improves. And I look forward to seeing her quantitative reasons for why social sentiment and other changes to the TD Sequential script improves. (1) Finding Novel Ways to Trade on Sentiment Data | Tech At Bloomberg