ai trading / algotrading / backtesting
AI Trading Bots: 3 Missing Pieces Before You Risk Real Money
Austin Starks explains three requirements for AI trading: realistic simulation of execution, financial research, and an event trail that explains each decision. The video shows actual NexusTrade research screens, a backtest explorer, and a recorded paper-trading event.
In the October 2 paper event, an exhausted Alpaca snapshot budget prevented an option chain from loading and stopped the rank walk. The screenshots are archival examples; the execution and tool-choice diagrams are illustrative. The event is paper trading, not a live trade or a performance claim.
Transcript
0:01I've been writing about using AI to trade for over three years and nowadays I see a new AI trading tool almost every single day yet all of these tools are missing these three key
0:15things that you need if you're executing real trades using AI so if you want to know how you can use AI to make more money in the stock market then stop and save this
0:26video also follow me because otherwise you might not see my very pretty face again I'm gonna organize this video from things that are the least important to the things that are most important so
0:44number three it's something that I actually see in some tools but not a lot you must have an accurate good simulated trading environment or a paper trading environment this environment should stimulate all the
0:58things as if you're executing real trades this is things like slippage fees costs execution delays all the real things you'll face when you're trading real money your simulated environment must represent that accurately number
1:15two is you need the ability to perform detailed financial analysis.
1:22Most trading platforms expect you to come in with your trading strategy already in your head.
1:27And the reality is the hard part about using AI trading or developing and deploying trading strategies is actually doing the research.
1:38Now, you've made it this far, you obviously like quantitative finance, but the number one, now you've made it this far, click that follow button because you obviously like quantitative finance.
1:46The number one thing all of these tools are missing are the ability to understand each and every single decision the AI or the strategy makes.
1:57If the AI calls tool A instead of tool B, you should know exactly why.
2:03If a tool call takes 30 seconds, you should know exactly which tool.
2:08If you create a trading strategy and you thought the strategy would buy on Tuesday and it didn't buy on Tuesday, you should know exactly why.
2:16And not only that, it should be effortless to understand why.
2:23In other words, you need an event provenance and the ability to replay all of your events for your back tests, for your paper trading and especially your real trading.
2:33Otherwise, when something goes wrong, how are you gonna know?
2:39Follow along to see how I build robust AI trading agents at scale.
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