r/datavisualization

Been working on this for a while and finally feel good enough about it to share.

It's a stock research dashboard that pulls together macro signals (FRED, EIA, CBOE), insider trading filings (SEC EDGAR Form 4 XML), FINRA short interest data, and 13F institutional positioning — and uses those to produce a per-ticker "confluence score" showing how many independent signals are aligned bullish or bearish for a given stock.

Stack: Python, Streamlit, PostgreSQL/SQLAlchemy, Plotly, pandas, yfinance, Render for hosting.

A few things I'm reasonably proud of:

- The lag scan (cross-correlates each macro signal against forward price returns, scans 1–12 week lags, applies Bonferroni correction for multiple comparisons, and validates out-of-sample)

- Congressional Trade Tracker — parses STOCK Act disclosures and flags cluster activity (3+ members buying the same ticker within 45 days)

- Short Squeeze Radar — combines FINRA short interest % with insider cluster detection and macro confluence score

- Signal Reliability Score — a meta-score on top of the lag scan that tracks whether each signal's historical lead time is holding up

Live at: unstructuredalpha.com — free to browse, no account required for most pages.

Would love any feedback on the approach or the UI. Happy to answer questions about the architecture.

Czołem erPolacy,

Pewnie nie raz zauważyliście, że sklepy sieci Żabka w polskich miastach potrafią stać niemal naprzeciwko siebie.Przez ostatnie tygodnie po godzinach budowałem otwartoźródłowy projekt Żabkozbiór, który wyciąga na światło dzienne twarde dane o zagęszczeniu Żabsonów. Jeśli kiedyś zastanawialiście się, jaka jest mediana odległości między Żabkami, jak bardzo da się oddalić od jakiejkolwiek Żabki w Polsce, albo czy któryś sklep znajduje się pod poziomem morza, LOOK NO FURTHER!

Przykładowe dane/wykresy, które znajdziecie na stronce:

-Historia ekspansji Żabki - prawie połowa sklepów powstała po 2022 roku, a otwarcie pierwszych 1000 sklepów zajęło 13 lat!

-Analiza porównawcza z Paczkomatami - wszyscy wiemy, że Żabki i Paczkomaty zawładnęły Polską. Zobaczmy, których jest więcej, jak bardzo i gdzie

-Analiza odległości pomiędzy żabkami - jaka jest mediana, średnia, jaka jest minimalna i maksymalna odległość pomiędzy dwiema Żabkami

-Analiza wartości brzegowych - Która Żabka jest wysunięta najbardziej na każdą ze stron świata? Która jest położona najwyżej? Najniżej?

-Analiza korelacji z ilością obserwacji płazów - Czy więcej Żabek oznacza więcej żabek? W okolicy której Żabki nie ma żadnych żabek?

-Analiza zagęszczenia Żabek per jednostka administracyjna per liczba, zagęszczenie ludności oraz zagęszczenie powierzchni

-Analiza korelacji między zagęszczeniem Żabek a wskaźnikami ekonomicznymi - bezrobocie i średnia płaca

-Przy której ulicy w Polsce znajduje się najwięcej Żabek?

Źródła danych:

- Wszystko zaczęło się od strony 'Znajdź Żabkę blisko Ciebie' na oficjalnej stronie Żabka. Okazało się, że serwuje ona publicznego JSONa z listą wszystkich aktywnych sklepów wraz ze współrzędnymi i innymi przydatnymi analitycznie danymi - chociażby datą otwarcia.
-Dane postanowiłam wzbogacić danymi z GUS BDL nt. powierzhni jednostek administracyjnych, na których Żabka się znajduje, a także ich średnią płacą i bezrobociem
-Z GUGIK wzięłam pliki z kształtami granic województw, powiatów i gmin
-Inpost ShipX API - stąd biorę listę aktywnych Paczkomatów do analizy porównawczej Żabka vs inPost
-GUGIK NMT - Dane dot. wysokości n.p.m każdej z Żabek
-GDOŚ - Dane dot. tego, które Żabki znajdują się na terenie parku narodowego lub otuliny
-GBIF - Dane dot. obserwacji płazów w Polsce do analizy żabkowatości Żabek.

Kod źródłowy: https://github.com/barankiewicz/zabka-stats

Jestem ciekawa, co sądzicie!

Hi guys, recently I designed a Visualization Layer for companies to track their progress, KPIs, events in real time.

It is a dashboard which builds on-the-go using the data from context layer of the company.

This is just an early version and im yet to integrate the backend.

Please share ur thoughts and feedback, it would be really helpful for building further.

I trade Nifty/BankNifty options actively, and for years my setup was a mess of Excel sheets, broker windows, and random OI websites that update once every 3 minutes.

So over the last few months I built MarketGrok (marketgrok.com) — a Bloomberg-style terminal for NSE F&O, as a solo developer. No team, no funding. Just me, a laptop, and way too much chai.

What's inside:

War Room — one screen with Composite Score, PCR, Max Pain, OI Buildup, sentiment meter, option chain, live news feed, top movers, and 34 global indices. This part is completely free.

Live NSE data for 200+ F&O stocks — no dummy/delayed data anywhere. That was rule #1 while building.

Greeks computed server-side using Black-Scholes, because broker APIs don't actually give you Greeks. Had to build that math myself.

OI Pulse, IV Studio, PCR Radar, Greeks Desk — the deeper Pro tools.

Charts built from scratch — CPR (matches Zerodha's calculation exactly), Ichimoku with forward projection, 13 drawing tools, works on mobile touch.

Breakout scanner that beeps when a stock hits a 500-day high. Simple, but it's caught moves I would've missed.

Some honest lessons from building this:

Payment gateways in India hate anything trading-related. Razorpay rejected me outright for the category. Took three attempts before getting approved.

A race condition in my signup flow silently created duplicate users for ~25% of registrations. Found it only after going live. Always use atomic upserts, folks.

Timezone bugs will humble you. IST + JavaScript's toLocaleString = pain.

The War Room is free to use, and Pro tools have a 7-day free trial (no card needed) if you want to poke around.

Not here to hard-sell — genuinely want feedback from people who actually trade F&O. What's missing? What would make this a daily-use tool for you?

Disclaimer: This is an analytics tool, not investment advice. I'm not a SEBI-registered advisor. Trade at your own risk.

Hey Guys, My name is Ross. I am pretty new to the KCBS world and I hated their website. It was so hard to see the information about me and my cooks. My day job is writing software, so I built this app in my free time. I compiled all the public information about contests, teams, reps, seasons, rankings, regions, records, etc. Also a cool little planner that can be used to figure out what contests you might be able to attend, etc. I hope you take the time to check it out. Have a great day!

It's been kind of annoying not having a clean, easy place to just quickly check the yield curve, so I made one for myself and just keep it pinned in chrome. The thing I think I'll use most is the quick views above the chart for quarterly reporting to get a sense of where my company's insurance blocks might move (I'm at a life/annuity shop).

Was curious what people who actually watch curves (or other metrics I have on here) think. Are those the right lookback windows? Is layering 4 curves at once useful or does it just get noisy? Keeping ads off so I feel like it runs pretty smooth. Open to suggestions if something's missing or misleading.

Hi, I’ve never posted here before and I’m new to game development; please excuse my lack of knowledge on this subject. (No AI solution still please)

I’m trying to design a side-scrolling horror RPG. I actually JUST learned the word “flowchart” today (Id been calling them ‘reverse brackets’ up until now, lol).

I have no clue how to sort my ideas for decision based gameplay. I took inspiration from Detroit: Become Human, and the flowcharts they use seem to be my best bet. Problem is, I have no idea where to start.

Anybody have a good resource for development like this? If you need more details lmk, I tried to explain the best I could.

Everything else (character info, art, and game mechanics) is on OneNote for me right now, but I’m trying to branch out.

(PS, I chose the feedback flair because discussion didn’t feel right and there was no ‘question’ one).

I wanted a library like vis.js but needed it canvas-based for server-side rendering without a headless browser in node, so I built one. I'd love to get some feedback.

Been experimenting with the Microsoft Fabric Apps preview — built one end-to-end using VS Code with the Rayfin SDK instead of going through the browser UI. Wanted to share the actual experience and get a reality check from others doing similar.

What I did:

- Set up the Fabric Apps extension in VS Code and scaffolded a project using the Rayfin SDK
- Used prompt-driven iteration to tweak the UI layout — letting the agent reshape components rather than hand-coding every change
- Deployed directly from VS Code and validated it against an actual semantic model

What worked:

- The scaffold-to-deploy loop is genuinely faster than the browser once you're set up
- Prompt-based UI adjustments handle layout-level changes well — saves a lot of back and forth
- VS Code gives you actual version control over your app definition, which the browser workflow doesn't

What's still painful:

- The preview is early — some SDK methods are undocumented or behave inconsistently
- Agent instructions don't flow through to the DAX engine. All business logic still has to live in the semantic model. Don't expect the agent layer to compensate for a weak model
- Deployment errors are cryptic if your workspace isn't configured exactly right

Where I'd actually like input:

1. Has anyone got this running against a live Fabric Warehouse or Lakehouse, or only tested on imported models?
2. Are you trusting the agent to scaffold the full app structure, or just using it for UI-layer edits?
3. Is this in your actual delivery pipeline yet, or still a "great for demos, wouldn't ship it" situation?

Recorded a full walkthrough of the whole setup-to-deploy process on my channel - BI Artillery. Happy to drop it in the comments if the sub allows it — don't want to step on self-promo rules.