Hey, so this is a really quick demo of Redprints, which is pretty much a platform where you can compose and refine poly market algorithms, right with a React flowchart.
00:14
So you can see here we have an example blueprint right here, where we have this market data where it's asking, will the Fed decrease interest rates by 50 basis points, you know, et cetera, et cetera.
00:23
You can see a market graph here.
00:25
You can query by different time ranges.
00:27
—before we run out of time, so if I don't do it today, I'll kill myself.
00:29
And then you'll see here, you just saw there, orders just flew in there, right?
00:33
So you can choose to bring in data from price, volume, liquidity, spread, whatever.
00:38
And as every trade comes in, they'll then feed that on to these nodes right here.
00:43
So comparison, right?
00:44
So we're doing a check to see if the price of it is less than 15 cents.
00:49
From there on, we can then do complex logic like rate limiting, as you can see here.
00:54
So only allowing one trade to go through every 60 minutes or maybe 5 every 5 limits.
00:59
You could do logic gates like AND, OR, NAND, XOR.
01:03
You could do manual triggers, and then those end up actually placing an order on Polymarket.
01:10
Yeah, so another awesome thing that we can do is we can use AI chat.
01:14
So you can see that this entire, this entire blueprint— oops, sorry— this entire blueprint was put together using CloudSonic 4.5, and it's a really powerful tool.
01:25
We built in some documentation that they can use to compose these layouts, and we've given it an incredibly powerful tool where we're able to actually run backtesting simulations on, the algorithms.
01:39
So you can see here, the algorithm is really great, right?
01:42
So it's 248 trades, it's done a lot of buys and sells, and it's, you know, well, we've got this on YouTube, right?
01:48
Yeah, yeah.
01:48
But with enough time, with enough conversations, the agent is able to leverage this backtesting data, which we're pulling from ClickHouse, where we have a hundred and ninety million rows of historical order data in order to compose a really amazing algorithm.
02:01
And the end goal is that you could eventually extract this to a Python script or a Rust library or something like that, right?
02:07
Where you can then put this into production systems.
02:10
And this is actually able to place trades on your behalf.