Trading Strategy

A trading strategy is a plan (often implemented in an algorithm) that defines when a trader will place orders to enter and exit an investment. Trading strategies range from simple sets of rules that an individual follows all the way to highly complicated applied artificial intelligence computer systems.

Many successful trading strategies go beyond orders to buy and sell. They often include risk management, hedging, and money management.


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.
SPY Benchmark Trader

Machines Poised to Take Over 30% of Work at Banks, McKinsey Says

New technologies are poised to sweep through investment banks, relieving many rank-and-file employees of roughly a third of their current workload, according to McKinsey & Co. The shift, already stoking angst on Wall Street, may take only a few years. CloudQuant sees opportunities.
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.
Trend Analysis in a Candlestick Market Data Chart

Algorithms for Trading

The hardest part of starting any project, including building a quantitative trading strategy, is figuring out where to start. To that end, this post covers a basic overview of a few algorithms for trading. We hope to help you get your creative energy to level up.
Quantitative Strategy, Trading, and Algo Development Industry News

Why You Should Always Question The Status Quo -- Tradeciety

“If you are so worried about algos, why don’t you look at Python? Read up on it. Cloudquant, Quantopian. The tools are there, the information is there, for free.”
Innovation in Trading

The next wave of broker innovation will be Crowdsourced algos

The next wave of broker innovation likely will be geared toward democratizing quantitative trading, according to Kershner Trading Group Founder and CEO Andy Kershner. That would vastly expand the universe of high-level quant traders globally, which Kershner roughly estimated stands at perhaps 5,000 today.
Battle of the Quants June 2017

Battle of The Quants - Discusses Crowd Researching in NY

Crowdsourcing in fund management and trading is the move to utilize anyone with an internet connection to participate in the research with the goal of finding new and better ways of trading. During the discussion the differing approaches being taken with the business models, and the technology, and the challenges each are facing.
Python based Trading Strategies by Machine Learning

Trading Strategy development—Powered by Machine Learning

Join us at the NY MarketsWiki Education to hear Morgan Slade’s thoughts on the The Algorithmic Trading Tesseract brings cloud computing, alternative data, machine learning, and crowd researchers together forming a revolutionary crowd in the financial industry.
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Let The Market Take You Out Of Your Trade

, published a very interesting article advocating Why You Should Almost Never Manually Close Trades. This post goes into detail examining that most traders “self-sabotage.” In other words, traders are their own worst enemy. They get emotional when trading.
Sharpe Ratio distribution

Four Problems with the Sharpe Ratio

If you are an algorithmic trader, developer, or data scientists they you have already heard of the Sharpe Ratio. Many of you use this measurement as your score card for how well your algo performs.