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AlphaVantage recently made fundamental data available through theirAPI and thanks to some new contributors to the AlphaVantage.jl Juliapackage you can now easily import this data into your Julia project.
This fundamental data describes the underlying business informationabout a company and is more fluid and open to interpretation than thestock price. I’ll run through each of the new functions and try andexplain what data it returns.
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- Jan 1, 2021 AlphaVantage recently made fundamental data available through their API and thanks to some new contributors to the AlphaVantage.jl Julia package you can now easily import this data into your Julia project.
The new data comes in through four different categories and functions:
plus a listing status to see what stocks are active.
The real value add though (if I do say so myself) comes from theability to pull out the annual or quarterly time series of a metric ofa stock easily and in a programatic manner. Using the metaprogrammingcapabilities of Julia I was able to generate hundreds of functionswith just a for loop. Using these new functions you can now easily pull the quarterly revenue of Apple, cash flow from financing of Tesla or a timeseries of the current liabilities for Ford.
Firstly, we can get a list of stocks that are actively trading.
5 rows × 7 columns (omitted printing of 1 columns)
| symbol | name | exchange | assetType | ipoDate | delistingDate | |
|---|---|---|---|---|---|---|
| Any | Any | Any | Any | Any | Any | |
| 1 | A | Agilent Technologies Inc | NYSE | Stock | 1999-11-18 | null |
| 2 | AA | Alcoa Corp | NYSE | Stock | 2016-11-01 | null |
| 3 | AAA | AAF First Priority CLO Bond ETF | NYSE ARCA | ETF | 2020-09-09 | null |
| 4 | AAAU | Goldman Sachs Physical Gold ETF | NYSE ARCA | ETF | 2018-08-15 | null |
| 5 | AACG | ATA Inc | NASDAQ | Stock | 2008-01-29 | null |
Over 9000 stocks and ETF’s are listed. Which you can then do some simple sorting to look at the oldest listed stocks.
5 rows × 7 columns (omitted printing of 1 columns)
| symbol | name | exchange | assetType | ipoDate | delistingDate | |
|---|---|---|---|---|---|---|
| Any | Any | Any | Any | Any | Any | |
| 1 | BA | Boeing Company | NYSE | Stock | 1962-01-02 | null |
| 2 | CAT | Caterpillar Inc | NYSE | Stock | 1962-01-02 | null |
| 3 | DD | DuPont de Nemours Inc | NYSE | Stock | 1962-01-02 | null |
| 4 | DIS | Walt Disney Co (The) | NYSE | Stock | 1962-01-02 | null |
| 5 | GE | General Electric Company | NYSE | Stock | 1962-01-02 | null |
When googling some of these stocks though, the IPO date doesn’t appear to be 100% correct. General Electric became a public company in 1896!
1 rows × 7 columns
| symbol | name | exchange | assetType | ipoDate | delistingDate | status | |
|---|---|---|---|---|---|---|---|
| Any | Any | Any | Any | Any | Any | Any | |
| 1 | AAPL | Apple Inc | NASDAQ | Stock | 1980-12-12 | null | Active |
They have correctly recorded Apple’s IPO date though, so it might justbe something about older stocks, or something else I am missing.
The first new function is company_overview which does what it sayson the tin.
Jl Results 2020
Here we get a dictionary with 59 different metrics about the company. There are lots of different quantitate and qualitative values about the company in question and provides a useful overview.
The income statement summarises a companies revenues and expenses. In short it shows where the money was coming in (revenue) and where it was going out (expenses).
Both the annual and quarterly results come back. For the annual reports there are the last 5 years. For the quarterly reports, the last 21 quarters.
Then what I have done is written the functions that allow you to extract any of the fields on a quarterly or annual basis. Which means you can easily plot some graphs and summarise the results.
Here we have Apple quarterly total revenue, with a predictable pattern peaking in the first quarter.
Jl Results 2021 Winners
A balance sheet summarises a companies assets, what it owns and its liabilities, what it owns to other people.
Again, like the income statement, any of these keys can be extracted quarterly or annually.
As per the intro I’ve plotted Fords cash and short term investment balance against something the owe, the total liabilities.
The cash flow statement shows the changes in the balance sheet. It helps judge a companies ability to meet its cash needs, i.e. pay their employers or service their debt.
Each company reports their earnings each quarter and summarise their performance of the previous quarter. There are more dates available for earnings, but also slightly different fields for the quarterly and annual results.
There you go, lots more functions for the package and somethingdifferent than just looking at stock prices. This fundamental dataadds another dimension to any quantitate analysis of different stocksso go grab your free API key fromAlphaVantage and get exploring!
If you are new to AlphaVantage you can also check out my previous poston getting market data into Julia here.
Prior results: 2019
Personal genomics company 23andme has a new preprint out describing their current approach and results:
- Durand, E. Y., Do, C. B., Wilton, P. R., Mountain, J. L., Auton, A., Poznik, G. D., & Macpherson, J. M. (2021). A scalable pipeline for local ancestry inference using tens of thousands of reference haplotypes. BioRxiv, 2021.01.19.427308. https://doi.org/10.1101/2021.01.19.427308
Ancestry deconvolution is the task of identifying the ancestral origins of chromosomal segments of admixed individuals. It has important applications, from mapping disease genes to identifying loci potentially under natural selection. However, most existing methods are limited to a small number of ancestral populations and are unsuitable for large-scale applications.
In this article, we describe Ancestry Composition, a modular pipeline for accurate and efficient ancestry deconvolution. In the first stage, a string-kernel support-vector-machines classifier assigns provisional ancestry labels to short statistically phased genomic segments. In the second stage, an autoregressive pair hidden Markov model corrects phasing errors, smooths local ancestry estimates, and computes confidence scores.
Using publicly available datasets and more than 12,000 individuals from the customer database of the personal genetics company, 23andMe, Inc., we have constructed a reference panel containing more than 14,000 unrelated individuals of unadmixed ancestry. We used principal components analysis (PCA) and uniform manifold approximation and projection (UMAP) to identify genetic clusters and define 45 distinct reference populations upon which to train our method. In cross-validation experiments, Ancestry Composition achieves high precision and recall.
It’s pretty good, but technical. For most readers, an illustration may suffice. Results from my profile for my close family:
Quantitatively, we see these have some discrepancies. Putting the values into a spreadsheet, we get this result:
Jl Results 2021 2022
| Ancestry | Emil | Brother | Sibling cohort mean | Difference | Parental mean | Father | Mother | Mother’s mother |
| Scandinavian | 70.80% | 79.50% | 75.15% | -4.10% | 79.25% | 77.30% | 81.20% | 91.20% |
| French and German | 16.60% | 6.80% | 11.70% | 1.85% | 9.85% | 13.00% | 6.70% | 0% |
| British and Irish | 0.00% | 2.00% | 1.00% | 0.15% | 0.85% | 0.00% | 1.70% | 2.50% |
| Broadly Northwestern European | 8.10% | 9.90% | 9.00% | 2.90% | 6.10% | 2.80% | 9.40% | 3% |
| Ashkenazi Jewish | 2.90% | 0.80% | 1.85% | -0.75% | 2.60% | 5.20% | 0.00% | 0% |
| Eastern European | 1.40% | 0.60% | 1.00% | 0.05% | 0.95% | 1.00% | 0.90% | 1.90% |
| Southern European | 0.00% | 0.30% | 0.15% | 0.15% | 0.00% | 0.00% | 0.00% | 1.40% |
| Unassigned/trace | 0.20% | 0.10% | 0.15% | -0.25% | 0.40% | 0.70% | 0.10% | 0.00% |
Jl Results 2021 Passers
So, if a parent was pure ancestry, the children must be exactly half of the parents’ mean. However, if parent’s homologous (the two copies of the same numbered chromosome) differ in ancestry, and they give a chromosome that’s a mix of these chromosomes (recombination), then children can differ from their parents’ mean ancestry. In my family’s case, there are 3 dead grandparents which I don’t have data from. For my parents, we can compute the expected value for the sibling cohort (i.e., me and my brother), and we can compute the actual average. These are fairly closely in line. We see that Scandinavian ancestry appears to be decreasing over time or generations: 91.2% in grandparent, 79.3% in parents, 75.2% in children. If this is not just a coincidence, I am guessing it is a modeling bias because they train their models on older people who has verified pure ancestry of that local region (as far as family records go), and it declines somewhat in accuracy for the younger generations due to haplotypes (blocks of DNA being inherited together) being mixed up.