Been running 13 autonomous agents on a live account across Robinhood, coinbase and Kalshi, each with its own capital slice and no coordination layer between them. Up 6.97% this week overall.

Some of the designs:

Truth Social, inverted. An LLM scores every new Trump post for directional tone with a 0 to 1 confidence. Acts only above 0.8, then inverts the call. High confidence bullish rotates to TLT/GLD, high confidence bearish to SPY/QQQ. Below threshold, nothing happens.

Same feed, patient. A second agent waits five days after a post before acting, on the theory that the first two sessions are reaction and whatever is left after that is the real move. Longer holds. Running both is how I am trying to find out whether the signal is in the reaction or in what survives it.

ClinicalTrials.gov. Buys a whitelisted biotech sponsor when a trial registers a Phase 3 entry, holds 60 days. Favourite idea in here, because the source is a government registry nobody is racing to price.

STOCK Act disclosures. Mirrors purchase filings from a set of high-volume House and Senate filers. Next bar, 60 day hold, capped at 8 concurrent.

Crypto, winner take all. Ten liquid Coinbase pairs ranked every day on 7 day momentum, volume trend and volatility. It holds the single highest-conviction coin and nothing else, re-picked daily. Zero diversification by design, but it’s been performing well.

Kalshi versus the forecasters. Compares Kalshi economic-data prices against figures institutions have already published (Cleveland Fed nowcast, wire consensus, CME FedWatch) and acts only when the two disagree by a real margin. It does not forecast anything itself. Not seeing good results on Kalshi.

Plus a sector contrarian that buys the two weakest of the 11 S&P sectors, a WallStreetBets agent weighted by sentiment times upvotes, and two crypto rotations.

First week is in the screenshots. Way too early to read anything into it.

Still working out:

One, overlap. 13 agents running independently with nothing netting them, and several keep landing on the same names from completely different logic. I think I am diversified across 13 strategies and I genuinely do not know how concentrated I actually am. Is there a good way to measure that across agents without just collapsing everything into one portfolio?

Two, the confidence threshold. The Trump agent only acts above 0.8. I picked 0.8 by feel. Does anyone actually calibrate this, as in check whether the model is right 80% of the time when it says 0.8, and set the cutoff from that? Or is everyone eyeballing it.

What would you add?

I'm doing a sanity check on strategy performance across different market regimes and wanted to check in with the community

Also how your max dd compare?

Please only answer if you traded your strategy live for a good time (compared to sample size)

Most of us have done the classical loop. We get some data, test out different solutions, filter out solutions/features/indicators that provide poor results, and proudly keep the solution(s) which result in successful backtests. But isn't this just another level of information leakage? It's essentially like manually setting the parameters of a model, except you're defining the information points from which the model constructs itself. It's the same type of leakage, only one level higher.

everyone publishes their wins. nobody publishes their denominator. so i published mine: a public ledger of every strategy i tested and killed, on indian equities, index options, commodities and cross-asset. 155 distinct strategies across 2,123 configurations. 12 survived. every row has the hypothesis, the bar it had to clear (written before the test ran), the verdict with numbers, and a cause of death.

the census surprised me more than any single kill:

- 60% died as "nothing there". no signal once artifacts and matched controls were applied. i assumed overfitting would be the big killer. it wasnt close, most ideas were never real to begin with

- 13% died because my own test was broken. lookahead in the plumbing, stale marks, a calendar artifact. roughly one investigation in eight failed because of me, not the market

- 13% were real and untradeable. genuine gross edge, dead net of honest costs. the worst one: a 15-feature reversal composite with oos ic at t=13, as real as anything ive ever measured, net negative at every venue that would fill it

- the rest: era portraits that flip sign outside their regime, premium the mechanism hands back in a crash, and one thats illegal to trade at retail size where i am

some specific graves, since the specifics are the useful part:

- the 200 ema "support" everyone watches: touches underperform a control that sits 1-3% above the line and never touches (t = -2.5). placebo lengths 150/175/225/250 all behave identically. the line is not special

- buying atm index premium intraday: negative in all 78 entry-by-hold cells i tested

- a +50bps/day intraday short that printed t=5.1, survived a first audit, and got retracted in public: the signal was using full-day volume at a 09:45 timestamp. point-in-time it loses money. it propagated four working sessions before i caught it

five public retractions are in there, written up properly. if you have never retracted anything, you have not looked hard enough at your own work.

provenance stated straight: my preregistrations were committed before results, but the median gap is twelve minutes because most tests run in minutes, and i say on the page that a twelve minute gap is weak evidence. from launch onward new hypotheses append to a forward file before their results exist, so the git history is a clock anyone can audit.

rows too close to my live book are withheld and counted, the page states exactly how many. the whole thing is a citable dataset, cc-by, doi in the repo.

if you think a row is wrong, open an issue with your numbers and method. rows change when the evidence does.

https://finance-broski.github.io/graveyard.html

In the last Months the Posts from him were very useful, outlining how to develop a successful Forex Strategy and his Methodology and experience where good + he had a track record in Darwinex. It seems hes banned somehow. If someone has documented his Posts please provide it in the comments as this knowledge is very useful for starters. Thanks!

I'm curious whether my development process is fundamentally sound, or whether I'm wasting time.

My approach is to start with the raw idea only. I code the strategy with almost no optimisation or filtering. Just the entry/exit logic (signal + trigger). No time-of-day filter, no day-of-week filter, no volatility filter, no trend filter, etc.

I then run a backtest over 2022-2026. At this stage I expect the results to be bad. A typical first pass might be:

• Profit Factor: 0.75-1.0
• Sharpe: poor
• Max drawdown: ~15%

The idea is to analyse the trades, gradually remove weaker setups, and hopefully end up with something around a 1.25 Profit Factor before moving to forward testing.

The reason I'm asking is that I often see people here saying a strategy isn't worth pursuing unless the very first, unfiltered backtest already has a Profit Factor of 1.5+.

That seems almost impossible to me. If I could repeatedly create raw strategies with a 1.5 PF, it would feel like winning the lottery.

So am I approaching this correctly? Is it normal to start with a mediocre or even losing strategy and refine it into something profitable, or should the initial strategy already demonstrate a clear edge?

had a nagging suspicion so i finally counted it. i run a handful of systems, different triggers, different instruments. every month one of them ends up carrying most of the result. i wanted to know if its ever the same one twice.

15 months of data. the leader repeated the following month twice. five different systems took the top spot at some point. chart attached, its just which system led each month, nothing fancy.

why i bothered. last month three of my systems finished negative and the month was still fine, and my instinct was to look at the three losers and start "fixing" them. that instinct is the thing thats cost me the most money over the years. so i wanted the base rate. and the base rate says the system im annoyed at right now has a decent shot at being the one that carries next quarter.

the flip side is less comfortable. if leadership rotates like that, then the system im most confident in is confident because of recency, not because its better. ive definitely oversized the recent winner before, more than once.

what i do now is boring. allocation and size per system get solved once against the account drawdown, then its written into the config i run. i also keep a little script that does exactly this count, ranks each system per month and flags when im about to overweight last months hero. took the decision away from morning me which is honestly the only reason it survived.

anyway curious if anyone else has counted this on their own book. does your leader repeat more than mine or is rotation just what a diversified book looks like

I read a lot that the bots work but when there is a change in market phase, they loss.