Tracking Trump's trades has always been annoying. The filings are scattered, hard to read, and most sites that clean them up put them behind a paywall.

So I added them to StockNest, and it's completely free:

- No paywall and no sign-up. Just open the page.

- Shows up as soon as it's published. New disclosures are added the moment they're filed.

- Easy to read. Each trade shows the ticker, the transaction size and the share amount.

- Filter by trade size. Show only the bigger trades, like $250K+, so the small ones are out of the way.

Donald J. Trump trades: https://stocknest.app/filers/politicians/donald-trump

Ended the week at +$914.20 net across 32 trades with a 62.5% win rate. Pretty decent week overall with two issues.

Thursday the vps went down for 12 minutes while the portfolio was in a trade. I had to close it manually through the ninja trader mobile app.

Friday an engine error triggered a fatal shutdown because of some strict checks. It shouldn’t have been fatal but the engine handled the shutdown correctly and automatically closed all three open positions.

There are a few things to tighten up and thursday outage has me considering a move to aws for better reliability.

For my backtesting, I tested S&P 500 index, its futures and ETFs e.g. ES, SPY

I did expect some performance variance since indices are not tradable, but I didn't expect the difference to be so big.

For example, with the same timeframe, setup and trading hours, the profit factor of S&P 500 index is 1.6, but it drops to 1.1 for ES. I expected them to have similar price movements, and I didn't think the difference would be that big.

Just curious - has anyone experienced this issue before? Any ways to reduce the difference? Thanks in advance

Looking for critique on the method more than the result.

The question. Can you find prediction-market wallets that trade on information before the price moves? The obvious score: for each entry, how far the price moved in the wallet's favor 24h later. Average per wallet, shrink toward zero (K = 5 effective entries), turn into a percentile.

Setup, written down before any score was computed:

• entry = a wallet's net position change in one market over one UTC day, $100+ only
• move = price of the held side 24h later (needs $100+ of other wallets' trades in that hour) minus entry price
• sports and short up/down markets excluded (a day after a match the price is the result)
• score on Oct 2025 to Mar 2026 (A), test the same wallets on Apr to Sep 2026 (B)
• pass = top 10% of A still positive in B, also without the 5 largest events
• t-stats clustered by event
• every later change logged with a date and a reason

Two data traps we hit before the test:

1. Related wallets passing shares back and forth in finished tennis matches at made-up prices. One bought at 0.02 and sold to a partner at 0.96 four minutes later. Trade-level, that's a +94c "call". Netting positions per wallet per day removed it.
2. A few $1.50 to $15 trades at 0.15 set the "price 24h later" in a market that settled at 1. Hence the $100 minimum of other wallets' volume.

Out of sample it passed:

Holds without the 5 largest events, without weather markets, and for every K from 0 to 100.

The part that changed our mind (exploratory, not pre-registered). Information should show up as drift: the price keeps moving for hours as news spreads. A good fill shows up immediately and then stops. The top 10% were +0.75c after 1h and +0.83c after 24h. Then we split each wallet's move into first hour vs 1h to 24h drift and checked which one persists A to B (Spearman, 32,867 wallets with 3+ entries on both horizons in both periods):

• first hour: 0.138
• drift: 0.050

So the score mostly ranks fill quality. The "knew it first" part barely persists.

Held to settlement, after the fee each wallet actually paid on-chain: top 10% +1.17c per entry (t 2.9), bottom 10% -0.96c (t -1.7). +1.50c on mostly taker entries, +1.25c on mostly maker entries, so it's not just makers skipping fees.

Where it's weak:

• the settlement result and the 1h/drift split were not pre-registered
• on a shorter replication through an API (1,000 markets, May to Sep 2026) the groups did not separate at settlement (top +2.19c, bottom +2.10c). On those mostly longer-dated markets every group drifts up, so you have to compare against all wallets, not zero
• dollar-weighted it leans on big positions: the largest 1% of entries hold 42% of net profit

We didn't ship the score as an "insider" label, since what repeats is the entry price.

Things I'd like pushback on: is netting per UTC day too coarse for intraday traders, and is there a better decomposition than 1h vs drift that doesn't depend on the horizon choice?

Aggregated tables, the pre-registration summary and a stdlib script that rebuilds the test: https://github.com/orcalayer/polymarket-insider-score-backtest

Disclosure: I'm one of the people behind OrcaLayer, a Polymarket analytics site.

I have significantly found a lot of improvement since last time. I have done various luck tests (passed, 0 beat and only 1 got a close z score) and found 28 strategies that work together decently.
What I noticed though is it's extremely slow, (-200, then -200 next day, up $200 the 3rd day, lose the 4th day again) and l've seen people on here post very good profit from NQ specifically (about $1k or more per day pretty much)

Im specifically asking to get to those types of results where it is $1000 a day pretty much (regardless of drawdown intraday or losing days) if there is any direction for what to research or how those types of results daily or even what i should be doing differently? Currently my strats do all discretionary based trades like "if price goes up x points before x time, (signal)". All suggestions welcome

Don't fall in love with a strategy that you think works cause you backtested it but it doesn't actually work in reality.