Backtesting

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

Posts

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 ...
Trading Strategy Scorecard from CloudQuant

52 Traders Interviews Morgan Slade

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The podcast on Massive 30,000 Trades Daily, High-Frequency Quant Trading with Morgan Slade including an interesting breakout trading strategy.
Machine Learning, Quantitative Investing News

Industry News: Machine Learning and Artificial Intelligence News 10/30/2017

AI and ML for CloudQuant, ArcaEx, Corporate earnings reports, Hedge Funds, Microsoft, Alexa, Saturday Night Live, the apocalypse, Elon Musk, and more ...
Crowdsourcing Algorithmic Research

​CloudQuant Is a Trade Strategy Incubator That's Looking to Develop and Fund Algorithm Traders

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A rising population of programmers, data scientists and mathematicians are now looking to write complex codes for automated investment strategies of their own. This is crowdsourced algorithmic trading.
Scorecard

Backtest Visualization on CloudQuant

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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.
Daily ROIC Prior to improvements

Improving A Trading Strategy

TD Sequential is a technical indicator for stock trading developed by Thomas R. DeMark in the 1990s. It uses bar plot of stocks to generate trading signals. ... Several elements could be modified in this strategy. Whether to include the countdown stage, the choice of the number of bars in the setup stage and countdown stage, the parameters that help to decide when to exit and the size of the trade will affect strategy performance. In addition, we could use information other than price to decide whether the signal should be traded.
Algorithmic Trading with other peoples money

Skills to Become a Quantitative Trader

Your Proprietary Trading Algorithm is always your property on CloudQuant. Any trading strategy that you develop is yours. Not ours. You do not transfer ownership of the algo to CloudQuant. You do not transfer any copyrights to CloudQuant. This is fundamental to the operations and success of CloudQuant.
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.