Abstract

In the following paper we will examine the effect of technical support and resistence levels on one-day U.S. equity returns. Moving averages and local minima/maxima of varying windows will be studied, as well as round numbers. Ultimately, we find that when an asset price closes arbitrarily near these technical indicators, its range of ensuing one-day returns changes in significant ways. The most notable observation was that moving averages (and to a lesser extent, round numbers) reduce volatility, sometimes substantially. Finally, we conclude by investigating how responsive U.S. equities have been to the studied technical indicators over time, finding that while the overall trend is positive, it has weakened since the Great Recession.

Instructions

This project was created in the writing of an academic thesis, so the organizational structure and documentation may be lacking. Follow these directions to get started:

1. data.py
   • Set filepath to your data csv -- this csv should have the same form as in the first figure of section 2.4.1 in the accompanying 'thesis.pdf'
   • Set savepath to where you want the output (e.g. '/csv') -- the output will be in csv form, and will look like the second figure in section 2.4.1
   • Run the main(bool) function, which takes a boolean argument to determine whether you want to save a csv (I recommend setting it to False while testing)
   • IMPORTANT: change 'RET' in line 33 to 'PRC' and repeat the above procedure to generate a matrix of returns; make sure to rename the output (line 55) to something like 'returns_out.csv'
2. techs.py
   • Set projectpath to your root and filepath to the prices csv generated in step 1
   • Run the main(a, b, c) function, which takes integer window lengths a, b, and c as arguments (these windows will be applied to the Moving Average and Rolling Min/Max indicators)
3. signals.py
   • Set projectpath to your root, csvpath to where you saved the output of step 1 (e.g. '/csv'), and filepath to the actual csv output from step 1 (e.g.'~/csv/data_out.csv')
   • Run the main(a, b, c, j) function, which takes the same three window lengths a, b, and c from step 2, and an additional float parameter j which determines the threshold at which an asset price is considered "close" to a technical indicator (1% was used in my study)
4. stats.py
   • Set projectpath and csvpath to the same as in the previous step; set filepath equal the matrix of returns (not prices) generated in step 1
   • Run the main() function, which generates pairs distributions of returns for each security across each technical indicator, and saves it as a pickle file ('container')
5. output.py
   • Set projectpath to the same as in the previous step; set containerpath to the pickle file that was generated in step 4
   • Run the stats_tests() function to perform the included HOV tests on each security (Brown-Forsythe, Bartlett, Levene, and Fligner-Killeen)
   • Run the adjust_p() function to adjust p-values for multiple sampling
   • The remaining functions (starting on line 164) generate the visualizations seen in the accompanying 'thesis.pdf'
   • Note: there are several superfluous functions in output.py kept for posterity's sake
6. tables.py
   • Set projectpath and containerpath to the same as in the previous step; set returns_out to the matrix of returns generated in step 1
   • All functions from line 22 through line 185 generate the tables seen in the accompanying 'thesis.pdf'