AI trading needs closed-book exams, too.
If you keep tuning a strategy against the same stretch of historical market data, its backtest curve may look better and better. But once you start trading for real, the market gives you new questions.
So when I hear that an “Agent can evolve by itself,” I have to ask one more thing: how does it prove that it has actually learned?
There’s a detail in @NeoSoulAI’s official documentation worth calling out. After adjusting the model, strategy, or runtime mechanisms, the system has to be evaluated again. That includes holding out test data by time period and checking whether it still works on a different batch of samples.
It’s a simple requirement. You may have mastered the old questions, but you still have to take a test with new ones.
Its evoevo is responsible for organizing decision data, including information sources and timestamps, the reasoning behind decisions, actual outcomes, and post-trade reviews. Here, the word “timestamp” matters to me.
There’s a big difference between a message that appeared before an order was placed and one that became available only after the market had moved. Mix later messages into the reasoning behind an earlier decision, and you can make the analysis look airtight—but it may not help with the next trade.
The validation path NeoSoul lays out also includes running in Paper mode. The Agent gets real-time information and makes decisions without using real money, before gradually moving into a restricted live-trading environment.
Live trading raises another set of questions. How much of the edge gets eaten up by fees? Is there enough liquidity? How are order anomalies handled? Each of these needs to be tested separately; simulated results can’t simply be carried over as-is.
There’s another easy-to-overlook detail: the documentation mentions regression testing.
Put simply, after a change, have things the system used to do well gotten worse? Fixing one problem while breaking another kind of judgment is also a “upgrade” that needs to be caught.
That’s why I’m willing to keep following NeoSoul. It lays out specific checks for self-improvement, while leaving room to assess whether those checks are actually effective.
Of course, spelling out a process doesn’t mean it has already delivered great results.
I’d like to see more comparisons across versions going forward: how the test periods are chosen, how costs are calculated, which errors have become less frequent, and which ones remain unresolved. Even if an update makes no progress, explaining why would be more useful than simply saying, “The Agent has gotten smarter again.”
#NeoSoul #AI交易
If you keep tuning a strategy against the same stretch of historical market data, its backtest curve may look better and better. But once you start trading for real, the market gives you new questions.
So when I hear that an “Agent can evolve by itself,” I have to ask one more thing: how does it prove that it has actually learned?
There’s a detail in @NeoSoulAI’s official documentation worth calling out. After adjusting the model, strategy, or runtime mechanisms, the system has to be evaluated again. That includes holding out test data by time period and checking whether it still works on a different batch of samples.
It’s a simple requirement. You may have mastered the old questions, but you still have to take a test with new ones.
Its evoevo is responsible for organizing decision data, including information sources and timestamps, the reasoning behind decisions, actual outcomes, and post-trade reviews. Here, the word “timestamp” matters to me.
There’s a big difference between a message that appeared before an order was placed and one that became available only after the market had moved. Mix later messages into the reasoning behind an earlier decision, and you can make the analysis look airtight—but it may not help with the next trade.
The validation path NeoSoul lays out also includes running in Paper mode. The Agent gets real-time information and makes decisions without using real money, before gradually moving into a restricted live-trading environment.
Live trading raises another set of questions. How much of the edge gets eaten up by fees? Is there enough liquidity? How are order anomalies handled? Each of these needs to be tested separately; simulated results can’t simply be carried over as-is.
There’s another easy-to-overlook detail: the documentation mentions regression testing.
Put simply, after a change, have things the system used to do well gotten worse? Fixing one problem while breaking another kind of judgment is also a “upgrade” that needs to be caught.
That’s why I’m willing to keep following NeoSoul. It lays out specific checks for self-improvement, while leaving room to assess whether those checks are actually effective.
Of course, spelling out a process doesn’t mean it has already delivered great results.
I’d like to see more comparisons across versions going forward: how the test periods are chosen, how costs are calculated, which errors have become less frequent, and which ones remain unresolved. Even if an update makes no progress, explaining why would be more useful than simply saying, “The Agent has gotten smarter again.”
#NeoSoul #AI交易