Candlestick Charting and Trading Strategy Back Testing
The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Technical Analysis refers to the art of predicting future asset price movements based on patterns in historical price and volumes. While somewhat controversial as these methods are not scientific and exist in conflict with the Efficient Market Hypothesis, many professional traders have enjoyed successful careers combining technical trading plans with STRICT risk management. A really disciplined risk manager can theoretically realize a profit from a trading signal which fails as much as 50% of the time.
The embedded application is designed to provide a simple interface for the student or aspiring trader to explore basic technical indicators and begin to define and test trading plans. They can interact with Candlestick pricing charts, configure and overlay simple and exponential Moving Averages (traditional momentum indicators), and back test the performance of simple trading plans against historical data. At present, the application contains ten years of S&P 500 price and volume data. The application is written in R and makes substantial use of the dyGraphs, Shiny, Shinyjs, and xts libraries.
The Application is very much a work in progress. There is quite a lot I intend to do to to extend and enhance the application:
- Price Data - I will extend the functionality to allow the user to select from a standard set of stock and ETF tickers and load up to date data from Google Finance, replacing the current static CSV.
- Back Testing - This functionality is a work in progress. The back end framework is in place however there is quite a bit left to do in terms of exposing parameters to the user and defining trading signals effectively. There are also several choices I need to make about how to present the resulting data clearly and effectively.
- Indicators - Once the Back Testing functionality is robust, I intend to incorporate a broad array of traditional indicators beyond the currently supported Moving Averages, such as VWAP, MACD and volume weighted moving averages.