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

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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“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.”
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.
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.
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.
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Short Term Stock Trading Strategies

... A few weeks ago I demonstrated a strategy using the RSI indicator; I received several emails from readers asking me to explain the difference between the RSI Indicator and the Stochastic Indicator. ...
Algorithmic Trading with other peoples money

My First Algo on CloudQuant

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Last week I mentioned my efforts to produce an algorithm on CloudQuant.com. ... I used a well-documented strategy as defined in Jason Perl’s book “DeMark Indicators”. The strategy is a Bearish TD Sequential Flip. This strategy is a basic technical strategy that utilizes bar data. ...
quantitative algo trading conceptual dashboard

An Incubator for Trading Strategies

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GLG Partners has hired a head of machine learning. Financial News reports that this is a new role entirely. The article points out that the volume of data that GLG is utilizing is growing. A similar article reports that BlackRock is putting a greater emphasis on computer models.