Fintech Data Engineering

This is not financial advice. I am not a professional financial advisor. This is my own opinion and observations.

Financial market forces can appear chaotic and unpredictable. It's as though the eyes of the public are obscured, and with more perfect information on purchases, buyers, sellers, and existing portfolios, we can better predict global markets. There are however some visible parts to this metaphorical blindfold.

Index funds can give an overview of the health of a given industry, Level 2 data gives individual traders access to current market asks/bids, CEOs and government officials must disclose their purchases and sales and their assets are public knowledge, and quarterly earnings reports provide a top level view of the company's finances.

Every bank is reinvesting the money deposited in your account in the form of assets such as securities (stocks/bonds), foreign exchange (Forex), mortgages, loans, leases, and credit, and making significant returns on the money you deposit.

Government monetary policy drives inflation, with interest rate control being so powerful it was able to reverse a potential crash in the stock market in March 2020. Governments can align their monetary policy towards an end goal, such as China's interest in maintaining the renminbi pegged against the US dollar to better compete in the international export market.

Market forces and monetary policy come together to bring impressive market trends such as $AMD multiplying its value 10 fold between 2017 and 2021, with the value of tech stocks such as $NVDA and $MSFT following similarly.

Digital assets such as the cryptocurrency DogeCoin $DOGE have gone from $0.005 to as high as $0.69 over the period of 5 months multiplied in value 138 times at the start of 2021. Tesla $TSLAhad a bull run at the start of May 2019 where the value skyrocketed from a low of$160, to over $1000, then had a 4-1 split and followed a similar trend, with the value hitting as high as $760 in April 2021, multiplying in value 17 times. Option trading has seen a boom over the course of the past few years, and publicly available data on currently traded puts and calls allowed for the famous short squeeze on GameStop $GME in late 2020, which caused the price to skyrocket from $4 all the way to $325, multiplying in value 81 times.

Stock market forces echoes into other markets, with the Nasdaq US Benchmark Real Estate Index showing a spike following the COVID 19' pandemic, and the United States House Price Index showing only 1 direction for prices, up. The value of construction assets is increasing, and the price of building a house reaching an average $120 a square foot in 2021. Land prices in the South Florida area increased suddenly, with a 10,000 square foot lot averaging $40,000 last year doubling in price in 2021, with this price remaining stable to 2024. The increase in online sales of housing has shifted the role of the traditional real estate agent becoming less necessary for some markets, leaving open a margin of 3-10% of the price of the sale to the seller. Real Estate mortgages had seen a drop in interest rates, with 3% rates becoming commonplace, though recently they've stablized to around 5-6% as the federal reserve adjusted their rate to combat inflation.

These echos can also be found in agricultural markets. The average price per hundred pounds per slaughter and feeder cattle has also seen an upward trend. The value of investing in cattle to grow (feeder cattle) in the Okeechobee Cattle Auction has increased over the past year, benefiting farmers with pregnant cattle. Recently, steers of feeder cattle averaged 370 pounds and cost $168 per hundred pounds ($622). Breaker cattle have seen an average price of $60 per hundred pounds and a weight average of 1400 pounds ($840). Milk cows have become much more valuable based on their genetic score in professional dairy operations, with sales reaching as high as $200K in private auctions hosted by the University of Michigan.

These increases in asset value make sense given the federal response to the global pandemic made loaning money incredibly cheap and easy. Small businesses receiving federal loans of hundreds of thousands at low interest rates is a recipe for an asset bull run. Anecdotally there have been rumors of widespread fraud in employee income assistance programs and in requesting investment loans from the US government, which only exacerbated this problem as individuals waste this money on sports cars and more property.

The democratization of data makes it possible for retail investors that have a mind towards software engineering to build tools to solve financial problems and make more educated financial decisions. Publicly available data on floating ask/bids in real time through Level 2 Data provides a real time view of puts and calls for stocks traded in the New York Stock Exchange (NYSE), and can enable a rough prediction on the short term behavior of a given asset. A stock's Price to Earnings ratio (P/E ratio), price history, trading volume, ask/bid history, derivatives behavior (options such as Calls and Puts), and earnings reports can help with determining long and short positions on a stock. and Real estate data such as property sales prices and regional market trends can allow buyers to better predict the price of their asset in the future, estimate the return on investment (ROI) with rental investments in agriculture/residential/commercial markets, and be aware of the overall history of a given region.

There's a scary underside to all this data. Public ownership records from real estate appraisals make it possible to estimate the net worth of an individual or business, information that could be used in cruel ways like offering less to new employees or estimating credit scores more harshly. Cryptocurrency public addresses associated with companies like Tesla make it possible to see exactly how much money they made by switching 10% of their cash to Bitcoin and communities like Twitter make it easier than ever for individuals to make big impacts on markets by simply tweeting about $DOGE and $BTC such as certain tech billionaires:

Let's discuss financial engineering in asset trading in brief detail.

Algorithmic Trading

There are public algorithmic trading tools that allow for you to simulate positions with a given trading bot or outright use those positions in a retail trading API.

Back Testing is using prior asset market trends to test portfolio positions/changes and predict where those positions would be today. You can use a strategy tester or a basic asset portfolio tester to determine what your wealth would have been had you made a financial decision years ago.

Crypto Trading

There's a lot of good things that cryptocurrencies can enable, suddenly transferring thousands of dollars instantly and securely (at least as long as (P \neq NP)) means you can easily transfer money to anyone for any sort of transaction, be it payroll, asset purchases, or simply buying common household goods.

Computational cryptocurrencies such as Etherium $ETH allow for programmable contracts that can allow unique identification for ownership of a given asset non-fungible tokens (NFTs), programmable loans, and more.

That freedom also enables bad behavior, from the wasted electricity that the global power grid now needs to support due to mining, paying ransomware or for illegal items, money laundering (With some NFT art sales serving that purpose), or gambling.

More Resources

Here's a few sites that focus on stock information and metric data:

• Quiver Quantitative exposes public information such as senator stock trades, Robinhood user behavior, r/wallstreetbets trends, etc. in an easy to read interface.

• Portfolio Visualizer let's you backtest a portfolio of stocks, in other words, test if a portfolio would have done well if you bought it X number of years ago.

• TradingView - Asset visualizations, backtesting strategies, and every indicator you can imagine.

• Dividend.Watch - Track portfolios, score the dividend performance of given stocks.

• HawkSight offers an easy way to try back testing. It's a bit like a game, have fun imagining the possibilities.

• Financial Modeling Prep is more of a back testing + machine learning training community.

• Unusual Whales provides a history of puts/calls and open interest.

For algorithmic trading there's a number of exchanges and apis with a wide range of assets to trade/study:

• Polygon - Real time stock data.

• Webull's Quantitative OpenAPI

• Alpaca Markets - An alternative API for algorithmic trading.

• QuantConnect - Open source algorithmic trading.

• Interactive Brokers API - A solid API for algorithmic trading.

• Kraken API

• Coinbase API

In addition, there's a few miscellaneous sites and articles:

• MarketWatch

• What I wish I knew about ESPP and RSUs sooner (company stock benefits. 2024, USA, California)

• Web3 is going just great is a website by Molly White (@molly0xFFF) that covers the failures of cryptocurrencies.

• Investments - A CLI pet project for managing stock portfolios. While not too useful here in the US, it does serve as a good reference for fintech engineering.

• Maybe - A financial tracking agrigator tool to manage portfolios and expenses.

• Wealthfront is an automated portfolio manager for US and foreign stocks.

• Vanguard is a popular investment manager transitioning to automation.

• Boomerang: Travels in the New Third World covers the history behind financial markets and their subsequent crashes throughout the turn of the 2nd millenium.