gammath-spot 4.0

Stock Price-Opining Tools

Gammath™ SPOT

Stock Price-Opining Tool is a DIY stock technical analysis toolset used to analyze stocks and compute gScore that indicates the degree at which a stock is trading at a perceived discount or a perceived premium. The gScore is then used like an indicator in making buy, sell or hold decision on the stock. It also provides tool to generate gScore-history for correlation and tool for backtesting strategy.

How does it do that? It does this in four parts:

1. It provides a gammath_stocks_data_scraper.py app that scrapes the web to obtain stock information necessary to run its gScore computing algorithm
2. It provides a gammath_stocks_analyzer_and_scorer.py app that analyzes the stock data saved on the local storage from step 1 and computes gscores using its algorithm to indicate perceived discount or perceived premium
3. The gScores range between -1 and +1. gScores towards -1 indicates that the tool perceives the stock price to be at a premium while score towards +1 indicates that the tool preceives the stock price to be at a discount
4. It provides a gammath_stocks_gscores_historian.py app that generates gScore and micro-gScores history for correlation. Learnings from this step can be applied in a backtesting strategy
5. It provides a gammath_stocks_backtesting.py app that tests an implemented strategy and generates data to see how respective strategy did
6. All the above apps take a watchlist as an input. A sample watch list is provided in sample_watchlist.csv [https://github.com/salylgw/gammath_spot.git] that can be used and updated for your watchlist

External dependencies

This project uses following free tools that need to be installed (you can use pip install) to be able to use this tool:

1. numpy
2. pandas
3. ta-lib (Install ta-lib using miniconda in case you run into problem: conda install -c conda-forge ta-lib)
4. yfinance
5. pykalman
6. statsmodels
7. sklearn
8. matplotlib
9. backtesting

WHERE to get source code without installing

Get source code from GIT repo git clone https://github.com/salylgw/gammath_spot.git

HOWTO install

pip install gammath-spot

In case you have trouble installing ta-lib then you can install miniconda and use conda install -c conda-forge ta-lib then run pip install gammath-spot

HOWTO use these apps

1. If you installed this software then run: gammath_scraper sample_watchlist.csv > log_scraper.txt
2. If not installed but just obtained the code then go to the directory gammath_spot/gammath_spot where all the source files are and run: python gammath_stocks_data_scraper.py sample_watchlist.csv > log_scraper.txt
3. Above step will save the scraper log in log_scraper.txt, creates a tickers sub-directory where it saves scraped data for stocks in the watch list. Running the data scraper is essential before using the scorer and historian
4. If you installed this software then run: 'gammath_scorer sample_watchlist.csv > log_scorer.txt`
5. If not installed but just obtained the code then go to the directory gammath_spot/gammath_spot where all the source files are and run: python gammath_stocks_analyzer_and_scorer.py sample_watchlist.csv > log_scorer.txt
6. Above step will save the scorer log in log_scorer.txt, analyze the stock data and computes the gScore using Gammath Works' algorithm
7. Go to ticker sub-directory and open overall_gscores.csv in your favorite spreadsheet program or a text editor
8. In overall_gscores.csv, you should see stocks from your watchlist arranged in ascending order of gScores. Lower values (towards -1) indicate that the tool perceives the respective stock to be trading at a premium while higher values (towards +1) indicate that the tool perceives the respective stock to be trading at a doscount. In this file, you'll also see sh_gscore (stock history based gscore) and sci_gscore (current info based gacore) that make up the overall gscore. If you are not interested in backtesting or sub-component score then you can ignore it These are the sub-components There is a lot of useful information stored in tickers/"ticker_symbol" dir that can be checked for details. "ticker_symbol"_signal.txt shows details of the analysis results and "ticker_symbol"_charts.png shows the plotted charts
9. In case you want to collect stock history based historical gscores (for correlation, past performance etc.) then you can do so by using the gScores historian tool. Please note that this tool is slow at the moment so limit the watchlist for this tool to few selected stocks that you have want to zoom into
10. If you installed this software then run: gammath_historian sample_watchlist.csv > log_historian.txt
11. If not installed but just obtained the code then go to the directory gammath_spot/gammath_spot/ where all the source files are and run: python gammath_stocks_gscores_historian.py sample_watchlist.csv > log_historian.txt
12. You can check the tickers/"ticker_symbol"/"ticker_symbol"_micro_gscores.csv (for stock history based micro-gScores and corresponding total gScore) and tickers/"ticker_symbol"/"ticker_symbol"_gscores_charts.png` that shows the plotted charts of price, overall stock history based gScore and micro-gScores
13. You can do backtesting on provided watchlist. If you installed this software then run: gammath_backtester sample_watchlist.csv > log_backtester.txt
14. If not installed but just obtained the code then go to the directory gammath_spot/gammath_spot/ where all the source files are and run: python gammath_stocks_backtesting.py sample_watchlist.csv > log_backtester.txt. You can update the function locally for implementing your own strategy
15. For each stock, it processes (based on a strategy you implement/use) the data collected by scraper app and processes the stock history based gScore/micro-gScores for approximately last 5 years (that were saved from the gscore historian) and saves the backtesting stats in tickers/<ticker_symbol>/<ticker_symbol>_gtrades_stats.csv
16. You can check the backtesting stats to understand if the strategy you use worked historically and then decide whether to use that strategy or not

Investment blog

If you want to see a real example of how the ouput of this tool is used then checkout https://www.gammathworks.com/diy-investment-blog.

Report Issues

If you run into any problem then please contact us using the contact page on https://www.gammathworks.com. You can also purchase technical support at https://www.gammathworks.com/plans-pricing.

Happy SPOTing!

Note: This version of Gammath SPOT is free and open source. If you would like to contribute to this project through your expertise in Python and/or world of finance then please contact gammathworks.com indicating your area of interest and expertise