[PYTHON] Parabolic analysis
Who is the target of this article
--Individual investors --People who want to know what parabolic analysis is doing --People who want to code parabolic analysis by themselves --People who want to use parabolic analysis for trading
1 What is this article?
One of the stock investment chart analysis is parabolic analysis. Please read this article for what parabolic analysis is, but it is currently uptrend or downtrend. Or, it is a technical analysis that shows the turning point of the trend at a glance. Parabolic analysis is often not implemented in smartphone trading apps, and it takes time to display the parabolic analysis results for each issue to see which issues in their portfolio have changed trends. (For example, Monex, Inc. has a function to plot the parabolic analysis results on a chart, but displaying each issue will take a lot of time and work.) Only the issues that have recently undergone a trend change. First of all, it seems that the probability of profitability will increase if the screened stocks are analyzed using Bollinger and the Ichimoku balance table.
In this article, I would like to explain the following contents.
--Explanation of algorithms for parabolic analysis —— A web app that lets you see which stocks in your portfolio have changed trends
★ This is a video of the app created in this article </ b>
2 What do you want to do?
- Explanation of algorithms for parabolic analysis
I will explain the algorithm of parabolic analysis. We also provide Python code created with Jupyter.
- An app that looks for stocks that have changed trends at a glance
I make and operate an application that manages the information of my portfolio. Details Please see this article. This app will be additionally implemented with the function of parabolic analysis and the function of seeing the turning point of the trend at a glance.
3 How do you do it?
3-1 Parabolic analysis algorithm
I read the code described in This article: Draw parabolic SAR with matplotlib and dropped it in the flow chart (some code is added). Masu). First of all, the information dropped in the flow chart was transferred to the Excel sheet. If you enter the OHLC data of the brand in this Excel sheet, you can calculate the parabolic analysis on the Excel sheet. In addition, we have transferred the source code to jupyter, so you can perform the same parabolic analysis on jupyter. The above files are posted on GitHub, so it would be interesting to touch them to understand the operation of parabolic analysis.
Parabolic analysis execution result at paraboric.ipynb
4 What did you do?
Implemented the function of parabolic analysis. In my portfolio, I can now extract stocks that have undergone a trend change within the last three days. It is difficult to display the parabolic analysis result for each stock and find out when the trend conversion occurred, but thanks to this function, it is now possible to immediately extract the stock that has recently undergone the trend conversion.
Below is a chart of the VOO (Vanguard S & P 500 ETF). It is an example that I was able to make a profit by performing Sell because I bought from the downtrend to the uptrend on the 3rd day and the SAR and the candle foot were about to approach each other.
