 Today, more than $200 billion are traded across more than 6 billion trades a day on the New York Stock Exchange. And that's just one of the 60 major stock exchanges around the world. Across them all, some $5.3 trillion shuffles around today, mostly by high-frequency trading algorithms. And with that kind of money on the line, investors have literally trillions of reasons to try and gain a competitive advantage. And to do this, over the last decade, they've turned to big data. And in a modern surveillance state, there's near infinite amounts of data. So what information is even valuable? And how do you make use of it? Don't forget to subscribe so you never miss an upload, and let's jump in. These days, trading firms use a wide range of big data to gain an edge over their competitors. Founded four years ago, Orbital Insight started with analyzing massive satellite images of oil tanks with floating lids around the world. As a tank is filled, its lid rises, while it's emptied, the lid sinks. And the sun casts crescent moon-like shadows on the lid of the tank. By detecting and calculating the changes of those shadows, the company can estimate how much oil is stored in 20,000 tanks and monitors around the world. Orbital Insight sells this data to hedge funds and energy companies amid the increasing uncertainty in the world's oil markets. This allows funds to keep day-to-day updates in the energy markets, as opposed to just following the company's quarterly filings, and gain valuable insights into emerging markets where oil storage data is either less available or less reliable. Last year, the company disclosed that China had been secretly stockpiling oil reserves, sending markets tumbling amidst the higher supply. Orbital Insight's moved past its oil foundations, and today monitors more than a quarter-million retail locations, 3,300 shopping malls, 25,000 oil storage tanks, and several other fields like real estate, commodity markets, and commercial airports. During the two outbreaks of E. Coli at Chipotle in 2015, Orbital observed a sharp decline in car traffic and reported it to their clients before a rapid 40% plunge in the stock price. And a year later, they found that the cars parked outside JCPenney Stores fell 5% in the fourth quarter of 2016, and used that information to estimate the decline in sales before it was reported. And Orbital Insight isn't the only company in the satellite trading space. RS Metrics has been using satellite surveillance since 2008. Their big break came in 2010, when their data about a surge in cars parked outside Walmart predicted the stock was undervalued. And that prediction came true, as the stock would rise 10% in the following months. And when traders wanted to monitor what Old Muskie was up to, RS Metrics flew plain over Tesla's Fremont plant, informed clients of Tesla's new general assembly structure, car manufacturing activity, and output production rates three days prior to becoming public knowledge. Another interesting example came last November, when a train carrying 268 wagons of iron ore valued at more than $600 million derailed in the desert of Western Australia. Iron ore prices soared on the news that the supply of a material used across thousands of products was interrupted. But traders using RS Metrics data carefully analyzed satellite images of the accident and saw the ore piled into a flat area where it could easily be reloaded. They bet that prices would soon fall back down. And a few weeks later, the panic had subsided and the traders had made a fortune. All this satellite imagery isn't cheap. Real-time satellite data cost tens of thousands of dollars a year. And despite how it may seem, the practice is perfectly legal. These traders have actually been taken to court over insider trading violations. But the courts have appaled that this information is out in the open and no one involved is a true insider to any of that information. And during the recent COVID outbreak, analysts closely watched China's emissions, not because they suddenly grew green thumb, but because the emissions closely monitored both China's factory production and how many cars were on the road. Since China was the origin of the outbreak, investors watched how quickly China got back to work to better understand how long the virus would affect the rest of the world. And this data is especially useful in China, where you can't necessarily trust the infection numbers published by the government. Satellite images showed a dramatic reduction in nitrogen dioxide emissions, which are released by cars, power plants and factories in major Chinese cities between January and February. Nitrogen dioxide only stays in the atmosphere for less than a day before reacting with other gases. So this data gives a real-time view of the country's economy. A similarly temporary downturn in emissions even happened after the 2008 recession and emissions picked back up as the world climbed out of that recession. And these traders don't just use satellite imaging in space to predict market movements. High-frequency trading algorithms designed by investment firms combed through social media sites to analyze millions of treats from news and industry leaders to gauge market sentiment. But obviously Twitter isn't the most reliable of sources, so they use machine learning to try and weed out these unreliable sources. But these systems aren't perfect and can still fall victim to the same misinformation as humans. In 2013, after the Associated Press's Twitter account was hacked and a fake story about an explosion that injured the then-President Barack Obama was posted, $130 billion in stock value was wiped out in a matter of minutes. Although stock prices recovered shortly, it showed how high-frequency traders prey on social media and how it can be manipulated to impact the algorithms that run the market. Reddit groups have even talked about purposely posting fake stories about an airline crash on Twitter with the hopes of the algorithms picking up the story and crashing the stock price, all so they can profit. As time moves forward and data becomes more ubiquitous and hedge funds continue to try to get any edge they can over their competitors, data will continue to pour into these firms. And if the past is any prologue, the data will continue to provide key insights into global markets and the traders who have access to it will continue to profit tremendously. But what do you think of these trading firms using big data? Is it an unfair advantage over the common working man or is it just part of the system? Be sure to let us know down in the comment section below. Have a great day and remember, there's always more to learn.