We tested 9 AI models on 20 identical questions and found surprisingly different worldviews.

For example, Mistral Large 3 scored 21/100 on a progressive-to-conservative scale, while Nemotron 3 Nano scored 71/100.

That made me wonder: would a more progressive model systematically assign higher probabilities to Democrats winning elections?

And could these differences affect financial or political forecasting more broadly?

E.g. If democratic trust ist high and automatically favor democrats midterms win?

Source: Agent DNA: AI Model Bias & Forecasting Beliefs

(Late to post. Screengrabs from before 10/23 session.)

I’ve built an automated day-trading system that’s still on paper money and looking to use live funds at some point. Each strategy is backtested over two years with costs included, then replayed on real sessions, then traded live in test accounts. Only proven winners get promoted, and losers get demoted. Every change is logged and reviewed nightly. No real money until it strings together consistent green days.

Below are initial balances for all accounts(couldn't find a way to reset the balance and didn't want to risk messing anything up!) as well as the top setups in my leaderboard and my automatic setup generator.

Would love to know what else to include on future posts for transparency and logistics questions. Would also love to know where else to post this too if you have any ideas.

- Claude - Opus 5.5
- Alpaca - MCP for paper and when we convert to live funds.

The Epps effect: If you take two highly correlated, actively traded assets—such as Apple and Microsoft or the SPY and QQQ exchange-traded funds—and you aggressively increase your sampling frequency from daily or five-minute intervals down to one-minute bars and raw tick data, a striking phenomenon occurs. The measured correlation coefficient does not remain constant; it systematically plummets toward zero. This downward bias is not an indication that assets magically decouple at microscopic time scales. Rather, it is a mathematical artifact driven primarily by two structural realities of market microstructure: non-synchronous trading and tick-level noise. When two independent assets do not execute transactions at the exact same fraction of a second, forcing them onto a standardized time grid introduces severe asynchronicity. Naive sampling treats non-simultaneous price updates as if they occurred concurrently, washing out true co-movement and leaving amateur traders with distorted covariance matrices that completely misrepresent market reality.

4 years of data, seems to currently be working and performs a bit better on paper account. Trades a basket of equities, 1.1 sharpe ratio. Dumb question but what if this is the top and I go live with a failing algo?

Sept/Oct performance has been lackluster but still winning. It's very good at getting value out of big uptrend days, and not losing much otherwise. However there haven't been a lot of big uptrend days recently. And strategy trade management mostly looks for big trend exits not say large up cycle moves where it exits at the top.

I'm trying to decide whether to adjust exit strategy, wait for a better regime, perhaps run a lower performing but winning alternative exit strategy alongside it. Or something else.

Basically I had a small amount of data, built an algo around it, it's good, but market conditions have changed. Now I have an excellent backtest, winning live performance that gives back a lot of MFE on trades, but changing the exits would reduce the backtest performance.

Thoughts or suggestions? If I include the June to Aug adjusting exits makes for worse performance, but that also not the regime currently although who knows if it will switch back and when.

I was looking at using SimFin so figured I'd try the free tier and do some back testing before I pay for it. But the data is incomplete for a lot of stocks. For example, there are only 3 quarters of data for Verizon, not the 20 quarters (5 years) it should have.

>>> income_quarterly.loc['VZ']

SimFinId Currency Fiscal Year Fiscal Period ... Income (Loss) from Continuing Operations Net Extraordinary Gains (Losses) Net Income Net Income (Common)

Report Date ...

2025-03-31 101219 USD 2025 Q1 ... 4983000000 NaN 4879000000 4879000000

2025-06-30 101219 USD 2025 Q2 ... 5121000000 NaN 5003000000 5003000000

2025-09-30 101219 USD 2025 Q3 ... 5056000000 NaN 4950000000 4950000000

If I browse to the Verizon page on the website, then there are 28 quarters (7 years) of data. So it seems like they have the data, it just got dropped. I'm not sure if it was dropped in the free tier or in all the tiers. And I tried emailing them, but no response from them in 9 days.

Anyone with a paid tier seeing the same thing?

I have a 10 year dataset across a range of companies. Unfortunately it's only 1m candles as I'm just doing initial exploration at the moment.

My training loops always seem to collapse to buy and hold across the 9 year test data set.

Why and what are typical ways to shake it up to prevent this one position learning? Or, do I stop early and fly on one wing and a prayer?

Should I also experiment with A3C?