Backtesting is the process of testing a trading strategy on historical market data to see how it would have performed under those trading conditions. Quantitative Developers and Analysts will use a market simulator (like CloudQuant) to evaluate the trading strategy. Key statistics that show performance are shown on the CloudQuant scorecard. Statistics include Sharpe Ratio, Calmar Ratio, Kelly Edge Percentages, Profit/Loss, Drawdown.

Backtesting Quantitative Algorithms on CloudQuant


$QQQ ETH spreads wider than RTH spreads

US Stock Market pre-market and post-market bid ask spreads are different that regular trading hours

Regular Trading Hours in the US Stock Market is 9:30 a.m. - 4:00 p.m. Trading can happen in the pre-market hours (4:00 a.m. - 9:30 a.m. ET) and in the After Hours market (4:00 p.m. - 8:00 p.m.). The free historical market data in CloudQuant allows you to examine the spread data and the differences between sessions.
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…
Trading Algorithms on CloudQuant

CloudQuant releases new version of free Algorithmic Strategy Backtesting Tools

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The latest version of our Trading Strategy Incubator's free algorithmic development and backtesting tools adds improved Graphical Analysis of Algorithmic Trading Strategies.
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.
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.
85 Percent of Data is Unstructured

Is Crowdsourced Data Reliable?

"Bring us your ideas and we will share the money with you,” agreed Morgan Slade, CEO of the crowdsourced algorithmic trading startup CloudQuant. “For us, engagement means breaking it down into a contractible problem."

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.
data scientist researching trading strategies

Algos and Ethics - a response to a LinkedIn Post

Alessio Farhadi posted “A.I. Trading - A Question of Ethics” on LinkedIn. His main point is that machine learning and algos do not have ethics. ... Fairness to the industry requires that one should review the steps that have been taken by innovators, regulators, broker-dealers, and exchanges to mitigate any potential dangers of using computers and algorithms to trade.
Open, Close, High, Low

Share Ordering Demo using Market, Limit, and Midpoint Peg Orders

The CloudQuantAI github repository holds the share_ordering_demo tutorial/code that demonstrates ways to buy and sell stocks in the CloudQuant backtesting engine using Market, Limit, and Midpoint Peg Order types. There is no single "right way" to do any of these. You will have to think carefully about
Improved Breakout Strategy

Industry News: Machine Learning and Artificial Intelligence News November 13, 2017

AI & ML news covering: the creative process, improving skills, ETFs, Risk, Supervised Learning, RiskGenius, Robo Cops, Fears, NVidia, Quickbooks, SEC Edgar ...