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


Backtest Research Life Cycle for Trading Strategies

Backtesting Trading Strategies

If you knew your trading strategy would work 50% of the time, would you commit your scarce savings to trade it? What if it worked 75% of the time? Backtesting gives one the confidence to know that your trading strategy will work.
Harami Sell Signal on Google Dec 20, 2017

Harami Sell Signal with Three Inside Down Demonstrated on $GOOG

December 19, 2017 Monday Google pressed new highs, but Tuesday closed out the day with a Harami Sell signal. Watch out for today. A negative close today boosts the negative outlook with the emergence of a Three Inside Down pattern. In this event, it will most likely mean that there will be a “little coal in Google stockholders stockings for Christmas”.

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.

$GE – Short Term Buy Signal – Piercing the Line

Technical analysis shows a piercing the line trading signal. This post includes links to source code show how to capture this signal with TA-LIB
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.

Backtest Visualization on CloudQuant

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.
Crowdsourcing Algorithmic Research

CloudQuant Continues To Allocate Risk Capital to Crowd Researchers

CloudQuant allocates risk capital to another crowd researcher by funding and leasing a crowdsourced 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

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, …