I recently wrote a post on 'How to implement the StockCharts Technical Rank' in which I went over the steps on how to calculate the StockCharts Technical Rank (SCTR) for a group of stocks.

In this post I wanted to put the SCTR indicator to the test and use it in a strategy that selects the stocks with the highest SCTR and holds them for a year.

This story is solely for general information purposes, and should not be relied upon for trading recommendations or financial advice. Source code and information is provided for educational purposes only, and should not be relied upon to make an investment decision. Please review my full cautionary guidance before continuing.

What's the StockCharts Technical Rank?

The StockCharts Technical Rank (SCTR) is a proprietary ranking indicator developed by StockCharts.com, which ranks a stock in relation to it's peers in a group of stocks.

The methodology for this ranking system was developed by John Murphy, a technical analyst who authored several books including 'Technical Analysis of the Futures Market'.

You can find a detailed description of the SCTR on the stockcharts.com website.

In order to calculate the SCTR, Stocks are assigned a score based on six key indicators in three groups: long-term, medium-term and short-term. These scores are then sorted and assigned a SCTR rank.

The SCTR indicator is heavily used on the StockCharts.com website in charting tutorials, sample user charts as well as the analyst blogs.

And advantage of the SCTR is that it only uses price data and is relatively easy to calculate, that it considers multiple time frames and that it uses both trend and momentum indicators.

A disadvantage is that the framework doesn't consider other common ranking criteria such as news & social media sentiment or company growth and value indicators.

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The Game Plan

So here are the steps that I implemented in Python:

  1. Get the list of S&P 500 components
  2. For each symbol: Fetch the last 10 years of daily data from Yahoo Finance
  3. Start with a formation period 10 years ago and calculate the SCTR for each stock
  4. Pick the top 5 stocks with the highest score and buy/hold for a year
  5. Then continue with steps 3. and 4. moving the dates forward until today
  6. Print performance stats for the strategy.

The idea of this strategy is to select the strongest candidates of the SCTR ranking system and hold them for a year in hope that they will continue their strong performance for the holding period.

As the Universe I used the S&P 500 just because the amount of price data needed to run this backtest is still manageable. You may want to backtest this strategy using a larger sample.

Implementation

You can download the complete Python script from my 'B/O Trading Blog' on Substack.

Results

Let's take a look at the results of this strategy.

The backtest for 10 years of data from the S&P Universe resulted in an average yearly return of 40.42%, compared to an average APY of 14.5% for the S&P500. Nice! Based on these numbers I think we can agree that the SCTR system does have some merit.

  • Average return (%) (year): 40.42
  • Average monthly return (%): 3.37
  • Average win rate (year): 68.89
  • Average max drawdown (year): -34.06

Max return

The stock that had the maximum returns during the 1-year holding period was Tesla. The company's price exploded from $24 to $136 between the end of 2019 and 2020. We finished with a 555% return. Sweet!

None

Min return

The company with the worst 1-year return was Etsy, where the price crumbled from about $285 to $116 between the end of 2021 and 2022. The result was a 52% loss.

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Further exploration

The SCTR seems to be a good starting point to serve as a stock screener to select stocks with a strong past performance. However, the score is calculated using only past price data, which makes it relatively easy to calculate but it doesn't consider other important factors. Here are some aspects that one can consider to improve the strategy:

  • Market sentiment
  • Company fundamentals
  • Price ratios
  • News & social media sentiment
  • Optimizing the exit strategy (e.g. stop loss, trailing stop, etc.)

Summary

In this post we discussed a strategy that uses the SCTR ranking system and reviewed some of the backtest results of this strategy. I also provided some suggestions on how this strategy could be improved.

I hope you found this post worth your time. Thanks for reading.

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Have a great day!