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


Trevor Trinkino Quantitative Trader

Machine Learning FXCM Webinar with Trevor Trinkino of CloudQuant - Part 2/3

On May 15th Trevor Trinkino presented part two of a three-part Machine Learning webinar with FXCM. Part one is here. Part 2  - Preprocess data for Random Forest. PnL and prediciton improvements... In part two Trevor goes over…
Backtest Homepage showing P&L and Sharpe

CloudQuant rolls out Upgrades to Free Stock Market Backtesting System

CloudQuant, the trading strategy incubator, announces upgrades to our free stock market backtesting system. The web application allows any market enthusiasts to develop a trading strategy using easy to learn Python programming. Anyone who has ever written a spreadsheet macro or a simple program can easily use the system.

Market Turmoil Generates Opportunity for Proprietary Traders

In these times of market turmoil and volatility, the Kershner Trading Group stands ready to provide traders with a firm built on a strong foundation of significant capital investment, innovation-focused trading technology and decades of experience in the active and proprietary trading space. Kershner Trading is actively seeking experienced US Equities Traders
Trevor Trinkino Quantitative Trader

FXCM Machine Learning with Trevor Trinkino

On February 8th Trevor Trinkino presented Machine Learning with FXCM in a webinar. During this presentation, he promised to make available his machine learning Python Notebook and the supporting data file. These are available on our Google drive at:
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.