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

Algo Trading powered by Alternative Data Sets

Conversations: Effects of Alternative Data Sets on Trading Algorithms

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What effect can alternative data sets have on trading algorithms? We asked a few of our teammates and systematic traders what the effect of alternative data sets is on trading algos. We thought we could spread some insight as to why our alternative…
Trading strategies require thought

Conversations: Recommendations to Someone Starting out at CloudQuant

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The CloudQuant team discusses their helpful thoughts for beginners on CloudQuant. We want to boost everyone starting out on our platform in their algo development and backtesting. Everyone in our company uses the CloudQuant website and coding platform in one way or another. We all use our own application, just like the crowd researchers. When we say that our free backtesting tools are "institutional grade" we really mean it. Every algo we run in our trading and investment strategies is proven in the same backtesting engine as the crowd uses. We rely on the scorecards, the reports, and the simulated trades to ensure that our trading is successful.
Quantamental alternative data

Conversations: Learning Python within CloudQuant

What was your experience like learning Python within CloudQuant? We asked our portfolio managers and product management teammates who code in Python to explain their starting experiences in programming with Python with CloudQuant. We wanted to share with everyone what encouraged them to keep learning throughout the years. Everyone here codes as part of their job. This includes the CEO all the way down to the interns. We rely on our Backtesting Engine to ensure that trading algorithms work well before committing money to the automated trading strategies. But we also use JupyterLab in our daily work. We generate our reports, monitor our systems, and do all sorts of tasks in Python. Python has overtaken the spreadsheet in CloudQuant.
backtest chart

Conversations: What we wish we knew when we started AlgoTrading

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CloudQuant's portfolio managers and quantitative algo traders look back on their starts in Algorithmic Trading. This candid overview allows everyone to see the "Things We Wish We Knew When We Started AlgoTrading".  This is a short collection of the interviews with some of our amazing coders here in the office
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

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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

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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

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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”.