I love building software 💻, exploring new markets 📈, and telling awesome stories 📜.
I’ve raised money from VCs like Contrary Capital, Rough Draft Ventures, and am a RippleX Venture Fellow and Neo Scholar.
I’m currently building Shade with @emersondove, where we're reimagining the way we understand and manage the digital world.
If you’re interested, let's chat.
brandonfan1256 [at] gmail.com.
Mental health and startups. For some, this might be a complete first time seeing these two words together in one sentence. For my year and a half of startups, frankly not a single person in the industry really mentioned it to me at all. Not a single investor call asked how I was doing, well beyond the pleasantries and the beginnings of conversation...
But what about robotics? What about the software ecosystem around robotics? For most people, the major move forward has been the Robot Operating System (ROS). It’s enabled more people to utilize other people’s code by creating drivers and systems that have already been prebuilt.
Let’s take a look at this first backtester, a vectorized one. First, what is a vectorized backtest? The best way is to prescribe an example. Let’s say I wanted to build a trading algorithm that looked at two moving averages and identified when they crossed (hmmm...sounds like the golden cross)...
Trading algorithms are awesome. They’re automated, streamlined, and if made well, can generate amazing profits. Fundamentally, a trading algorithm is a set of rules or logic that takes in pieces of data to ultimately make a decision about when to buy and when to sell specific assets, or universe of assets: a very, very well-crafted function to that takes in input, and determines a set of trading signals (buying and selling).