Technical Analysis

Technical analysis is a trading analysis technique used to evaluate stocks (or other financial instruments) in order to predict future price changes. This is accomplished by analyzing statistics gathered from trading activity (last trades, bid/ask prices, volume, etc.) Technical analysts focus on market data charts to forecast future price changes.

Many crowd researchers at CloudQuant use TA-Lib to assist in their use of Technical Indicators.

Posts

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.
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

Medium.com

The Patient Chart Pattern Trader

“Although HFT and algo trading dominate market activity nowadays to the tune of about 80% of transaction volume, there are still a number of old school chart pattern traders around. This is evident from social media messages where these traders post charts with patterns, such as head and shoulders, triangles, trendlines, double tops and bottoms, just to name a few. Although some of those chart traders aim to only teach their “art” to new traders, some are obviously patient enough to trade with it.” 
“Chart pattern trading is a style that is more suitable for recreational trading rather than professional. This is one reason it was never considered seriously by the majority of hedge funds. In addition to requiring patience, slow chart pattern formations offer enough time for detection and competition is high at diminishing returns.”
“The conclusion is that chart trading was a style for patient traders during times when everything was slow, from data collection, to chart drawing, to analysis and to executive trades. Nowadays the word is faster by several orders of magnitude. Good chart traders could obviously survive the new dynamics but the expectation should be low given dominance of algos. At the same time, learning that old style of trading is more interesting in the context of studying the reasons it is no longer applicable to the markets.”
See what Michael Harris has to say about it on Medium.com
Note: CloudQuant has seen many chart pattern traders bring their discipline of trading and analysis to their python based algorithms.
Algorithmic Trading with other peoples money

My First Algo on CloudQuant

, ,
Last week I mentioned my efforts to produce an algorithm on CloudQuant.com. As a reminder, they are a strategy incubator that provides historical data going back to 2011. If you can produce a trading or investment strategy that produces decent results they will license the algo from you and pay you a portion of the profits from that strategy. You don’t have to provide any capital; they provide it. This works for any python algo developer who are long on skills and short on capital. I used a well-documented strategy as defined in Jason Perl’s book “DeMark Indicators” (ISBN 978-1-57660-314-7 Bloomberg Press, New York 2008). The strategy is a Bearish TD Sequential Flip. This strategy is a basic technical strategy that utilizes bar data. CloudQuant lite, the default version, provides one minute bars. CloudQuant provides access to much more granular data on other versions of the data simply by requesting to be upgraded. For my purposes, and the purposes of this strategy the 1 minute bar data is perfect and fits easily for any python algo developer. The idea behind the strategy is to predict when a series of down bars indicate a time to buy. One hopes to buy low! The first component of the strategy is the “TD Bearish Price Flip.” The bear flip occurs when the close of a bar is greater than the close 4 bars earlier immediately followed by a close less than 4 bars earlier.
DeMark Indicator Sequential Bear Flip

TD Sequential Bear Flip in UNP

The second component is a countdown. The countdown gives a buy indication when an uninterrupted series of 9 closes occurs with each close being less than the close 4 bars earlier. Once we have the bear flip followed by a countdown of an uninterrupted series of 9 then I submit a buy at the market for that stock. This gets me into the position. To get out of the position I want to take profits or losses quickly. The strategy takes a profit at $0.15 per share and stops any losses at $0.30. If all this sounds complicated, it really isn’t. All told there are only about 60 lines of code written in Python. Python is easier to write than excel macros. There are more comment lines explaining what I was doing than lines of algo code. After back testing the strategy the results were mixed. Some losses, some winners. Nothing spectacular. The algo won’t likely get funded as written. The team at CloudQuant helped me out and placed the algo into their public scripts so that anyone can clone it and run the same tests. Better yet, you can use it as a foundation to start your own algo. Here are some ways that another python algo developer might want to consider to improve the strategy performance:
  1. Do a data study to optimize the target price for loss and profit taking.
  2. Do a data study to see if there is a max period of time that you should hold a position.
  3. Utilize other data other than just the close price in each bar
    1. Does the BarView open, high, low affect the success rate?
    2. Does the bidvol, or askvol affect the success rate?
    3. Does the vwap affect success rate?
    4. Does the spread between bid price and ask price change with success rates?
  4. Read the DeMark indicator book and apply more of the techniques past page 3 🙂
  5. Change the bar length to see if you can do this as a different time length
  6. Use a data driven approach to pick which stocks to run this algo upon
  7. Are there times of day approaches that would work better? Do the study.
  8. Does this strategy work for a market that is trending up or down better?
  9. Does this strategy work better for market capitalization size?
  10. This is implemented as a buy (long only) strategy. Implementing the sell short strategy could improve the returns.
 

Source Code to this strategy

The code is in the public scripts of the CloudQuant trading strategy incubator application. Access to this code does require registration. https://app.cloudquant.com/#/strategies?script=Base%20Working%20Strategies.CQ_TD_Sequential_Base