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Bitcoin came to dominate the financial headlines in late 2017, as it skyrocketed to nearly $20,000, and a serious sense of FOMO set in. The Coinbase app became the most downloaded app in the Apple App Store; people used money they might not have had — their credit cards — to get in on the action. Naturally, it all came crashing down by the end of 2018, with Bitcoin losing 80 percent of its value from its high in 2017. People were financially ruined and it seemed like the Bitcoin skeptics had the last laugh.

However, after three years, Bitcoin is having a Michael Jordan moment, finding itself back in the spotlight, blowing past $20k and registering a 269 percent year-to-date performance in the process. In some ways it seems like 2017 all over again, with Bitcoin poised to go on “another of those runs.” Although this article will NOT definitively state that Bitcoin will continue to surge, it may be likely. The main hypothesis is that as Bitcoin reaches new heights, it gains more publicity, which in turn may create more interest in the asset, possibly pushing the price higher, which then generates more publicity, therefore creating a loop, in which each variable continues to drive each other. This article will use Google Search data as a proxy for interest in Bitcoin and see whether it is positively correlated with the price of Bitcoin. …


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Photo by lo lo on Unsplash

Disclaimer: This is for informational purposes only and is not to be construed as financial or investment advice. It is not an endorsement of any company or a recommendation to buy, sell or hold any security. Investors should determine for themselves whether a particular security or product is suitable for their investment needs or should seek such professional advice for their particular situation.

Introduction

Simple moving averages are well-known investing strategies, often being mentioned in popular websites such as marketwatch.com, and they supposedly could be indicative of where a stock is headed in the near term. This article will examine whether they are viable trading strategies i.e. whether they generally outperform a simple buy and hold strategy. …


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Photo by Ricardo Viana on Unsplash

Code breaks down, and there is usually some poor soul that spends hours and hours sifting through lines of code, trying to find the bug(s). I’ve been there myself, and it’s like being trapped in a maze, unsure of where to go next. The problem is that code is often an unorganized, jumbled mess, with poor documentation of what it actually does. Functions can be a a great remedy for this, allowing for better organization and readability, which can ultimately cut down on time spent troubleshooting.

If you want to skim the article or jump to see a real world application of functions in action, feel free to look at the Jupyter Notebook used later in the article. …


The Improbable just got a lot more Probable

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Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

Introduction

After Pfizer announced that it’s COVID-19 vaccine had a 90 percent success rate last week, equity markets instantly stopped and changed course. Stocks like American Airlines and AMC, which had been battered by the pandemic, were all of the sudden given new life by a sudden influx of money, while the so-called “stay at home” stocks suddenly had the wind taken out of their sails (Zoom had a brutal week). This rapid change of events left “quant” investors picking up the pieces of what had just happened. …


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Photo by Markus Winkler on Unsplash

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

There are many articles and how-to’s on making machine-learning models to predict stock prices. I’m not here to declare that they’re wrong or misguided. In fact, I think it’s wonderful that so many people have taken an interest in trying to solve an age-old problem, and that the contributions made to programming languages such as Python, where one can now spin-up a relatively sophisticated machine learning model in minutes, have helped “democratize” investing to a certain degree. Instead, what this article intends to show is the evidence supporting the idea that stock prices likely cannot be predicted. Specifically, the article will use Python to highlight the idea that stock prices follow a random-walk, more or less. The main thrust of the article is that random walk does not preclude the possibility of beating the market, nor advocates tossing investment models aside, but it should signal that these models should be built with extreme care and diligence, and be constantly re-evaluated. …


A One-Stop-Shop for all your Financial Data

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Photo by Chris Liverani on Unsplash

Introduction

When it comes to investing, there are many important data points to consider, many of which come from disparate sources. Pulling data from each source can be pain-staking and time consuming. For example, many financial APIs mainly offer pricing data at a point in time, requiring you to go elsewhere for other important information related to stocks, making data wrangling complex and drawn out. Enter Financial Modeling Prep. It’s a one stop-shop for all your financial data, boasting EOD and intraday stock prices, quotes in realtime, a long history of financial statements and ratios, earnings dates and transcripts, an IPO calendar, and analyst estimates and recommendations. Oh, and this barely scratches the surface in terms of what else it offers. …


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Photo by Karan Bhatia on Unsplash

One of the things that helped propel Trump to the White House was his promise to revitalize American manufacturing by re-writing “one sided” trade deals and rolling back onerous regulations. During the first three years of his presidency Trump imposed tariffs on a variety of imported goods, including steel and aluminum, with the hopes of reviving American manufacturing, and he frequently boasts that this helped produce what he calls the “greatest economy ever.” Unfortunately, Trump’s strategy did little to restore American manufacturing. …


Putting Some Pizzaz into your Data

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Photo by Annie Spratt on Unsplash

Introduction

You just finished putting together a report and now want to highlight key figures for your boss. For those of you familiar with Excel, conditional formatting is a great way to highlight data that meet certain criteria. However, say you did your analysis with pandas and want to do the same thing. Rest assured, when it comes to conditional formatting, pandas can do everything Excel can and then some.

For this walk-through, we’ll be looking at the weekly performance of Nasdaq-100 stocks this past week to highlight some winners and losers. …


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Photo by Adam Nowakowski on Unsplash

This past week was choppy for financial markets, with the Nasdaq ending the week down about 1 percent. With the absence of new, positive catalysts, financial markets may be gripped by uncertainty stemming from the upcoming election and the fact there has been no agreement from Washington on more stimulus to help power the economy, which may be flashing warning signs as retail sales in August showed signs of losing momentum. Additionally, the lack of excitement in the markets may also be due to retail investors and day traders being called back to their jobs as the economy continues to open back up. …


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Photo by William Iven on Unsplash

As an investor, not only do you want to watch your investment grow, you want it to grow as much as possible. However, being far from a sure thing, an investment carries a certain degree of risk, and while there is a possibility of achieving a great return, there is also the possibility of suffering a substantial loss. There are some investors that are willing to take on a high degree of risk, but I think it’s reasonable that many investors are typically risk-averse. The idea of incurring a large loss is probably an investor’s biggest fear. At the same time, an investor does want to be in a position to earn the highest return he or she can. It’s this dynamic of risk-and-reward that an investor has to weigh, and I think the central questions are what the highest return an investor can earn while not taking an excessive amount of risk, or what is the least amount of risk an investor can take while maximizing his return? …

About

Curt Beck

Stumbled into a data-centric role several years ago and have not looked back! Passionate about leveraging technology to uncover answers and improve the world.

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