IBD Style Relative Strength Percentile Ranking of Stocks (i.e. 0-100 Score).

Related tags

Third-party APIs Wrappers stock-market stocks relative-strength-index relative-strength
Overview

relative-strength

IBD Style Relative Strength Percentile Ranking of Stocks (i.e. 0-100 Score).
I also made a TradingView indicator, but it cannot give you the percentile ranking, it just shows you the Relative Strength: https://www.tradingview.com/script/SHE1xOMC-Relative-Strength-IBD-Style/

Daily Generated Outputs

Stocks: https://github.com/skyte/rs-log/blob/main/output/rs_stocks.csv
Industries: https://github.com/skyte/rs-log/blob/main/output/rs_industries.csv

Known Issues

Unfortunately the close prices loaded from the price history API are not always split adjusted. So if a stock had a split recently there is a chance the relative strength value will be wrong...

Calculation

Yearly performance of stock (most recent quarter is weighted double) divided by yearly performance of reference index (SPY by default).

Considered Stocks

Tickers from ftp://ftp.nasdaqtrader.com/symboldirectory/nasdaqtraded.txt disregarding ETFs and all stocks where the industry and sector information couldn't be retrieved from yahoo finance.

How To Run

Run EXE

  1. Open the latest successful run here: https://github.com/skyte/relative-strength/actions/workflows/exe.yml
  2. Download exe-package at the bottom (need to be logged in into github)
  3. Exctract the relative-strength folder and enter it
    • If needed open config.yaml and put in your preferences
  4. Run relative-strength.exe

Run Python Script

  1. Open config.yaml and put in your preferences
  2. Install requirements: python -m pip install -r requirements.txt
  3. Run relative-strength.py

Separate Steps

Instead of running relative-strength.py you can also:

  1. Run rs_data.py to aggregate the price data
  2. Run rs_ranking.py to calculate the relative strength rankings

*** Output ***

  • in the output folder you will find:
    • the list of ranked stocks: rs_stocks.csv
    • the list of ranked industries: rs_industries.csv

Config

Private File

You can create a config_private.yaml next to config.yaml and overwrite some parameters like API_KEY. That way you don't get conflicts when pulling a new version.

Data Sources

Can be switched with the field DATA_SOURCE

Yahoo Finance

(Benchmark: Loads 1500 Stocks in 20m)

  • Is default, no config necessary.
TD Ameritrade

(Benchmark: Loads 1500 Stocks in 18m)

  1. Create TDAmeritrade Developer Account and App
  2. Put in your API_KEY in config.yaml and change DATA_SOURCE.
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Comments
  • Output folder Not being Created

    Output folder Not being Created

    Hi, I was hoping you could help me, I've tried running your application but I just get a black command window then a few lines get created and then it closes. Unfortunately I can't see the lines because it closes to quickly. There is no output folder created so I suspect it's and access issue but is there any way of seeing the text that gets created in the command window before it closes.

    Thanks

    opened by Chilly100 2
  • exe-package expired

    exe-package expired

    Hi, in the "How To Run", "Run EXE" says: "Download exe-package at the bottom (need to be logged in into github)", looks like this exe-package is expired, can you check? thank you

    opened by jn11 1
  • Incorrect RS formula with negative index returns

    Incorrect RS formula with negative index returns

    Hi,

    today the output rank for NFLX on rs_stocks.csv is 92, on IBD it is 6.

    The reason for such a high value is the formula being used for the relative strength doesn't take into account the index could have a negative return. This gives stocks with huge negative returns a fantastic relative strength and therefore fantastic rating.

    By changing the formula to a relative value centered around 1, we get the correct rank ordering as per IDB.

    rs = (1 + stock_return) / (1 + index_return)

    for full example:

    rs_list = []
for i in [1, 2, 3, 4]:
    l = min(len(stock_closes), 63 * i)
    stock_return = (stock_closes[-1] - stock_closes[-l]) / stock_closes[-l]
    index_return = (index_closes[-1] - index_closes[-l]) / index_closes[-l]
    rs_period = (1 + stock_return) / (1 + index_return)
    rs_list.append(rs_period )

relative_strength = (rs_list[0] * 0.4) + (rs_list[1] * 0.2) + (rs_list[2] * 0.2) + (rs_list[3] * 0.2)

    and then after adding the data to a dataframe, rank is

    df['rank'] = df['rs'].rank(pct=True) * 100

    Comparing the results to IBD gives a very high level of similarity (using 12000 tickers) and gradually gets looser using less tickers. However the order of the rank remains consistent.

    Additionally, the formula currently sums the quarterly strengths of the stock, and divides by the sum of the quarterly strengths of the index.

    (stock_rs1 + stock_rs2 + stock_rs3 + stock_rs4) / (index_rs1 + index_rs2 + index_rs3 + index_rs4)

    The normal formula quoted has the relative strength of the stock and index per quarter multiplied by that periods factor, and then summed.

    (stock_rs1/index_rs1) + (stock_rs2/index_rs2) + (stock_rs3/index_rs3) + (stock_rs4/index_rs4)

    Hope this helps.

    opened by carrickpaul 1
  • Would you like to be added to OPENBB ?(Open Source finance platform)

    Would you like to be added to OPENBB ?(Open Source finance platform)

    @skyte

    Hi, I recently made a suggestion that your ratings would be a valuable addition to the Open Source finance platform, OPENBB (formely Gamestonk terminal).

    Are you interested?

    If yes, I'm sure they would like to connect with you to discuss further. You can reach OPEN BB via https://twitter.com/openbb_finance or https://discord.com/invite/xPHTuHCmuV .

    opened by hairygodfather 1
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