"System builder who automates success."
You don't want to trade. You want to build a machine that trades for you.
These are the stats that matter for your trading type. Know them. Respect them.
Uptime required. If your internet goes down for 1 minute during a crash, you could lose everything.
Value destroyed by Knight Capital in 45 minutes due to a deployment error. Code is a loaded weapon.
Of your time is maintenance and debugging. only 20% is "building".
The amount of sympathy the market has for your "API Error".
You are an engineer at heart. You despise manual repetitive tasks. If you can't systematize it, you don't want to do it. Your goal is to wake up, check the server, and see profit.
You've probably said one of these. Here's why it's costing you money.
"Automated trading is passive income."
False. It's active engineering. You aren't trading the market; you are trading the maintenance of a complex machine. When it breaks (and it will), you are the mechanic on call.
"I can set it and forget it."
Entropy exists. Markets evolve. A system that printed money in 2020 might lose money in 2024. You must constantly monitor for "drift".
"The coding is the hard part."
Coding is easy. Logic is hard. Writing a bad strategy in Python just means you lose money faster. A profitable manual strategy is the prerequisite for a profitable bot.
Once you solve a problem, it stays solved. Your systems can run 24/7 without you getting tired.
You build complex Rube Goldberg machines for simple problems. You spend months building and zero days trading.
You are an engineer monitoring a power plant.
Checking CPU usage, latency, and API connection status. Is the machine alive?
Scanning error logs for exceptions. "Why did that order get rejected?"
Writing the code for Strategy V2.0. You are always building the next version.
Stressing the new code against historical crashes. "What happens if Bitcoin drops 50% in an hour?"
Pushing updates (carefully) to the staging environment.
Your bots trade the Asian session. You sleep (mostly), but keep your phone loud for alerts.
Learn from those who came before you. The wins AND the wipeouts.
Co-founder of Two Sigma. Views trading as a technology problem, not a finance problem. built systems to process petabytes of data.
Firms like Virtu Financial that trade millions of times a day with near-zero losing days. They are the ultimate Architects.
Lost $440 million in 45 minutes because of one bad software deployment. They went bankrupt the next day.
Be honest. How many of these sound familiar?
Launch "Version 1.0" before you think it's ready.
Don't automate until you have manually profitable trades.
Build kill-switches, not just entry signals.
Discover how different personalities and styles connect
Data-driven decision makers
Cover your blind spots by studying these