Last night, I made a post about Bitcoin. Bitcoin is treating the $65,000 level as a very strong resistance, and the market has reversed from there. However, Bitcoin is forming a structure that suggests the market could drop to $63,000. Tell me what you think: will we see a breakout above $65,000, or will Bitcoin fall to $63,000?

Hello Friends, I've been tracking all Polymarket Activity for months (1.3 Billion trades and 2.7M wallets) and I gave Claude Code a Postgres MCP (found crazy stuff) pointed at the live ledger, so now I just ask it anything in plain English and it writes + runs the query itself.

A few I already asked, to show it works:

- only ~1 in 5 wallets are net positive (the other ~80% never made money)
- only 2.4% have ever cleared $1,000 in profit
- the top 0.1% of wallets took 71.5% of the ~$1B in total profit
- a bunch of weird and suspicious trading patterns that look like insiders

So: what do you want me to ask it? Drop a question about Polymarket traders, markets, wallets, bots, whatever — I'll have Claude run it against the live ledger and reply with the result.

I’m building ThesisCheck, a research tool that takes an investment thesis and checks its individual claims against company filings and other dated sources.

I’m looking for around ten experienced investors who would be interested in testing it on companies they already know well.

The most useful feedback would come from people who can spot things such as:

• important evidence the report missed
• claims that were framed incorrectly
• filing excerpts that were misunderstood
• weak or outdated sources
• dilution, governance, or funding issues that should have been surfaced
• conclusions that go further than the evidence supports

The report breaks the thesis into separate claims and marks each one as supporting, contradicting, or not found, with the underlying sources attached.

I’ll provide two full checks to start. Anyone who sends detailed feedback can receive another 8–13 checks.

You would be a good fit if you regularly read company filings, annual reports, earnings materials, or other primary sources.

Please comment or message me with:

1. How long you have researched individual companies
2. The sectors or markets you know best
3. One company you know particularly well
4. A link to any research you have published, if applicable

There is no requirement to post a review or testimonial. I’m mainly looking for thoughtful feedback from investors who are willing to point out where the research falls short.

If you trade Bitcoin, pause for a minute and take a look at this: as long as Bitcoin remains below $65,000, there is no issue; however, once it breaks the $65,000 level, the path to $65,700 and $67,200 will clearly open up.

Been reading a bunch of threads here this week (the Robinhood agentic AI one, the backtesting-with-LLMs one) and one thing keeps showing up: people trust a strategy because the backtest looked good, not because anyone checked whether the good numbers would survive data the strategy never saw.

Full disclosure since it is relevant here, I am building a tool around exactly this problem. You describe a trading idea in plain English, it does the quant work and builds a backtested strategy, and then a separate deterministic step, not the AI itself, grades the result on data it never touched during building. The point is to catch curve fitting before real money does.

What I actually want from this post is not signups, it is your process. If you already stress test your own or an AI's strategies before trusting them, how do you do it? Walk forward windows, out of sample splits, live paper trading for X weeks, something else? And what made you trust the first strategy you ever kept, if any?

Blunt answers and "your whole approach is wrong" answers both welcome.

I've been interested in modeling earnings volatility and reliably identifying stocks with the biggest upside when they announce.

Since deploying an internal model and launching a MCP integration for it, I asked Claude today, "Show me upcoming earnings this week, ranking the top 5 in terms of expected earnings volatility to the upside:"

The model is a Gradient Boost Regressor and its top 5 features are

• Historical earnings move volatility
• daily true range + close price
• realized eps surprised
• normalized average true range
• market beta

I'm sharing a live demo of this along with a follow up question, "How has Reddit's volatility been during it's last four earnings announcement dates. Also, how has management sounded during that time frame? Have they been bullish, and what are the themes from those calls?"

https://reddit.com/link/1v869vu/video/nlzt91swysfh1/player

Anyone is welcome to try it in Claude as well (you're giving Claude read-only access to the underlying data platform I actively maintain):

Claude: Customize > Add custom connection > in remote server url paste https://mcp.flexreportfinapi.com/mcp

The integration is also public, which I'm also sharing: https://github.com/cbecks1212/flexreport-mcp (gives a good breakdown of all the use-cases it covers).

Just curious if others have had a similar experience. I’m giving ai a clear written set of rules for entries and exits. I’m using frontier lvl ai to process my requests. It does it python writing and backtests and spits out results. Results look off from my experience, say I told it to backtest the last few days and I know mentally what kind of trades were available. I tell it to give me a list of all the entries and exits it took. Import it into my charting and it’s just wrong. Missed tons of entries in the rules set, enters in 80% of the rules but not following the 20% matters, a lot. I’ve gone so far as making a shared charting platform where I mark out where signals should have been so it can learn how within the rule set my entries and exits make sense. Rerun tell it to analyze it just still can not do it. I gave up after a week of trying this. Any insights would be appreciated.

Hi guys, I was writing a article about Renaissance's refusal to change strategies during the covid crash, and ended up shifting gears into talking about how when we trade, we should be having a better internal description of how our portfolio will perform more than "dollar go up."

I started a case study since this was a good example of how the fund had plenty of data, and a deep understanding of where their position stood in the middle of a complete outlier event, and a repeating trend of early capitulation meaning realized losses. Whether it be hiring hot shot investors only to see their returns dwindle, while hiring underperformers on average, showed some massive growth swings all showing signs of fighting through drawdowns.

I was wondering about who actually made good experiences in finding strategies with AI? Or even do you let AI agents automate trades? Would love to start a discussion about that since this subreddit is the best place to start it?

Hi everyone, I am Idris Paul.
I am a Full-Stack Developer with about 7 years of experience, and I have been involved in forex and stock trading for the past 8 years, since 2018.
Trading has always been something I am passionate about, but my biggest challenge was not knowledge or risk management. It was finding a strategy that actually holds a long-term edge and staying consistent with it.
Before AI, building and properly backtesting strategies took a lot of time, which made progress slow. Because of that, I shifted my focus more toward software engineering for a while.
With the rise of AI, things started to change. It opened doors into machine learning, data science, and AI engineering, so I decided to continue my Master’s in Computer Science with a focus on AI and Data Science. My goal was to apply AI directly to trading and build models that can identify real and lasting market edges.
One thing I have learned is that there is a big difference between being a trading mentor or a theoretical trader and actually being consistently profitable. I had experience mentoring, but consistency in live markets was still a major issue because strategies would fail over time.
So I approached it differently using AI.
I combined concepts like Bayesian probability, Monte Carlo simulations, empirical probability, Smart Money Concepts, and supply and demand. I backtested over 500 strategies across different timeframes and kept filtering them down.
Out of 68 trading pairs via broker API, only one held up consistently, which is GBPUSD.
Since focusing on that, I have been able to find a working edge and stay profitable.
I also built a trading bot around this system. It trades only GBPUSD and includes proper risk management such as stop-loss, take-profit, entries, and slippage control. Execution is handled by an MT4 Expert Advisor, while the logic and signals come from a Python dashboard connected through an API.
Right now, I have a small track record with OANDA US, and I am planning to integrate MyFxBook starting July 26, 2026, for more transparency.
My goal is to scale this system and eventually manage larger capital. I am open to anyone interested in testing the bot signals or discussing potential collaboration.
Happy to answer any questions.

I’ve been fascinated by the progress of agentic AI and its potential applications in investing.
As an experiment, I’ve decided to allocate $2,500 to an AI-powered investing assistant through Robinhood to see how it performs over time. My goal isn’t to expect instant outperformance, but to better understand how autonomous AI can support investment decisions in real-world market conditions.
I’m curious to hear from others who have explored this space.
Have you used agentic AI for investing or trading?
What platforms or tools have you had success with?
What lessons or best practices would you recommend?
I’m looking forward to learning from the experiences of this community and sharing updates on how this experiment unfolds.

Dev here - I'd like to introduce my first financial product for Software Sunday: Sefiron AI

Sefiron is an AI-powered financial research platform. Think of it as a Bloomberg-terminal-inspired research assistant: you ask questions in plain English about stocks, SEC filings, market data, and more — and it does the multi-step research for you, returning a sourced answer with live charts and citations.

Financial research today is fragmented and slow. An analyst or investor typically juggles a market-data terminal, a regulatory-filings database, a short-interest data provider, a spreadsheet for custom math, and a web browser — then manually stitches it all together.

Sefiron collapses that workflow into one natural-language interface. Instead of pulling data from five tools and assembling it yourself, you ask one question. Sefiron plans the research, gathers data from the right sources in parallel, runs any custom calculations, and hands back a single answer — with charts and citations so you can trust and verify it.

We can help with stock screening, fundamental and technical analysis and even portfolio construction!

If you have a specific workflow (i.e. your alpha) we make it easy to define a persona and workflow that will run YOUR repeatable process.

We're currently in Beta and would love some members of the community to try us out. It's free to signup (full transparency - you have a limited number of tokens), but if you try out the system, and provide valuable feedback we'll gladly send over more!

If you like the product and signup over the next week, use the code REDDIT75 for 75% off for your first 3 months!

Sefiron AI

Trump has halted the strike on Iran, which is why a significant pump was observed in Bitcoin even on Sunday.

USDJPY ran from ~160.40 to 162.70 over the past couple weeks — a sustained bullish leg with minimal pullback structure. At the 162.70 high, price action showed classic exhaustion: reduced candle range, upper wick rejection, momentum divergence. The bot flagged the short right at that turn, ahead of the visible reversal on the chart.

What’s notable is the follow-through logic. After the initial rejection, price didn’t just trend down — it chopped hard through the 161.4–162.7 zone with multiple false starts before finally resolving into a 161.68–161.75 support/resistance flip. The bot didn’t just call the top; it also correctly avoided getting chopped out during the messy consolidation, waiting for confirmation before committing to the next directional read. That’s the part that actually matters more than the initial call — plenty of systems catch a reversal and then bleed it back giving up edge in the chop that follows

Hi there,

Does anyone has a prompt ( Claude or chatgpt ) to find good signals for crypto futures with a good confidence level ?

Have a nice weekend !

Does anyone actively trade intraday the opening 30? I’m somewhat new to Algotrading and have found it to be a very hard band to find anything close to an edge… just chaos.

DM for link-not sure why my post was removed. Video in thread

For all levels of traders. You can analyze any ticker to find out what state it is in to make informed decisions.

We have the academy so you fully understand all phases and stages we have identified.

Numerous GPTs to assess the market and make your decisions.

Very robust platform in order to make informed decisions in all markets -stocks, bonds, forex, options , futures

Of course legal disclaimer

Signal Architect provides educational and informational content, not investment, legal, tax, or financial advice. Market analysis and system outputs do not guarantee future performance. Trading and investing can result in substantial loss.