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Elaborate nights by @msgsahil5 | Stardance
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Title: Elaborate nights by @msgsahil5 | Stardance
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Cosmic Notes by @Kaiser-Fahim | Stardance
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Title: Cosmic Notes by @Kaiser-Fahim | Stardance
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My awesome macropad! by @kkashew | Stardance
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Title: My awesome macropad! by @kkashew | Stardanc Title: My awesome macropad! by @kkashew | Stardance...
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Untitled by @Ahmad_Bulbul | Stardance
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Title: Untitled by @Ahmad_Bulbul | Stardance
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Wills Bot! by @Gooderswills | Stardance
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[{"ship_num": 1, "ts": "2026-0 [{"ship_num": 1, "ts": "2026-06-18T12:54:56Z", "hours": 0.0, "multiplier": 1.3, "payout": 0, "blessing": "neutral", "text": "I followed the slack bot tutorial, added some new commands and used javascript and ssh’d into a server for the first time. This is my first project", "repo": "https://github.com/Gooderswills/Wbot", "demo": "https://app.slack.com/client/E09V59WQY1E/C0266FRGV"}]...
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[{"post_id": 3708, "ts": "2026 [{"post_id": 3708, "ts": "2026-06-06T19:18:17Z", "hours": 0.3494, "text": "I followed the tutorial completely, got the bot on a nest server and now it is fully functional on slack, I also added another command to it and then updated the server with the new code reflected"}]...
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Title: Wills Bot! by @Gooderswills | Stardance
De Title: Wills Bot! by @Gooderswills | Stardance
Devlogs:
I followed the tutorial completely, got the bot on a nest server and now it is fully functional on slack, I also added another command to it and then updated the server with the new code reflected
Ships:
I followed the slack bot tutorial, added some new commands and used javascript and ssh’d into a server for the first time. This is my first project...
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My awesome macropad! by @sagarGouda | Stardance
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Title: My awesome macropad! by @sagarGouda | Stard Title: My awesome macropad! by @sagarGouda | Stardance...
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My first slack bot! by @rus1n | Stardance
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Title: My first slack bot! by @rus1n | Stardance
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HJMJ by @norcarde | Stardance
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Title: HJMJ by @norcarde | Stardance
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Unity Game by @jsalasb1 | Stardance
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Title: Unity Game by @jsalasb1 | Stardance
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Notefy by @Red- | Stardance
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Title: Notefy by @Red- | Stardance
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Sigma by @toby | Stardance
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Title: Sigma by @toby | Stardance
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Untitled project by @anthonym | Stardance
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Title: Untitled project by @anthonym | Stardance
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isnt this a glorified github repo by @yujie | Star isnt this a glorified github repo by @yujie | Stardance...
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Title: isnt this a glorified github repo by @yujie Title: isnt this a glorified github repo by @yujie | Stardance...
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First project - Macropad by @DJshelfmushroom | Sta First project - Macropad by @DJshelfmushroom | Stardance...
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Title: First project - Macropad by @DJshelfmushroo Title: First project - Macropad by @DJshelfmushroom | Stardance...
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1793206270.7974784
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test by @ghemyn | Stardance
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ghemyn
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Title: test by @ghemyn | Stardance
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1793408100.1820347
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My awesome macropad! by @X1aoma | Stardance
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Title: My awesome macropad! by @X1aoma | Stardance
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rpiMinecraftServer by @Gonza | Stardance
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Title: rpiMinecraftServer by @Gonza | Stardance
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1793350379.4728525
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CraftNet by @hackclub | Stardance
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hackclub
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Title: CraftNet by @hackclub | Stardance
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1793260041.5448046
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hwsteno by @nimit | Stardance
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nimit
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[{"post_id": 56101, "ts": "202 [{"post_id": 56101, "ts": "2026-08-27T20:04:57Z", "hours": 0.5, "text": "Time Spent: 30 minutes I did some light research on layouts and steno keyboards. A lot of sources point towards Plover, which is also what QMK docs suggest, which means less work for me to do! I then made a layout based on what I learnt, and printed a plate for that. While the plate was printing, I modelled a case for it too."}, {"post_id": 56103, "ts": "2024-07-03T00:00:00Z", "hours": 1.0, "text": "KEYCAPS i need keycaps so I printed some and it took a few iterations but it finally fit the stem properly yippeee I also made a render while my printer was doing printing stuff. An issue I had was that the plate only sat on the case and was loose? so I also printed a small accent border, and that also acted as a nice anchor for the plate to stick to the case! And here’s the final result! I do think the ironing on the plate could’ve been better, but I’m proud of it regardless!"}, {"post_id": 56102, "ts": "2024-07-01T00:00:00Z", "hours": 2.0, "text": "Time Spent: 2 hours Soldering time I guess :p Honestly, it’s not that hard with how few switches there are. I will say that these Gateron KS-33 switches have a really annoying pin placement, so I had to be extra careful with my soldering this time. I decided to go for a COL2ROW layout, only because that is the orientation I soldered the first diode in. After that, I connected everything to a pico and boxed it all in! I just needed keycaps now and I should be good to go :) I didn’t really take pictures during the process but here is the end result!"}]...
|
Title: hwsteno by @nimit | Stardance
Devlogs:
Tim Title: hwsteno by @nimit | Stardance
Devlogs:
Time Spent: 2 hours Soldering time I guess :p Honestly, it’s not that hard with how few switches there are. I will say that these Gateron KS-33 switches have a really annoying pin placement, so I had to be extra careful with my soldering this time. I decided to go for a COL2ROW layout, only because that is the orientation I soldered the first diode in. After that, I connected everything to a pico and boxed it all in! I just needed keycaps now and I should be good to go :) I didn’t really take pictures during the process but here is the end result!
KEYCAPS i need keycaps so I printed some and it took a few iterations but it finally fit the stem properly yippeee I also made a render while my printer was doing printing stuff. An issue I had was that the plate only sat on the case and was loose? so I also printed a small accent border, and that also acted as a nice anchor for the plate to stick to the case! And here’s the final result! I do think the ironing on the plate could’ve been better, but I’m proud of it regardless!
Time Spent: 30 minutes I did some light research on layouts and steno keyboards. A lot of sources point towards Plover, which is also what QMK docs suggest, which means less work for me to do! I then made a layout based on what I learnt, and printed a plate for that. While the plate was printing, I modelled a case for it too....
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1793267684.5080197
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1793872484.5080197
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MultiCrono by @AfonsoPCP | Stardance
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AfonsoPCP
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0
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[]
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Title: MultiCrono by @AfonsoPCP | Stardance
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1793278393.6719222
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1793883193.6719222
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Cosmic by @notsahilprasad | Stardance
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notsahilprasad
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Title: Cosmic by @notsahilprasad | Stardance
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1793238662.448466
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1793843462.448466
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8472
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My awesome macropad! by @raxxapaolo03 | Stardance
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raxxapaolo03
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Title: My awesome macropad! by @raxxapaolo03 | Sta Title: My awesome macropad! by @raxxapaolo03 | Stardance...
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1792788471.1081543
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A personal knowledge system by @Krishna | Stardanc A personal knowledge system by @Krishna | Stardance...
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Title: A personal knowledge system by @Krishna | S Title: A personal knowledge system by @Krishna | Stardance...
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1792788696.3836327
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JIVSETU by @shrimanjadha07 | Stardance
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Title: JIVSETU by @shrimanjadha07 | Stardance
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1793159742.6990147
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Untitled project by @ahepworth | Stardance
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ahepworth
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Title: Untitled project by @ahepworth | Stardance
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1793213695.2580051
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My first slack bot! by @Haroon_Fares | Stardance
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Title: My first slack bot! by @Haroon_Fares | Star Title: My first slack bot! by @Haroon_Fares | Stardance...
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My awesome macropad! by @tomek08_konto | Stardance
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tomek08_konto
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Title: My awesome macropad! by @tomek08_konto | St Title: My awesome macropad! by @tomek08_konto | Stardance...
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A Website with Cards by @sofiayzy | Stardance
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Title: A Website with Cards by @sofiayzy | Stardan Title: A Website with Cards by @sofiayzy | Stardance...
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Untitled project by @yuito | Stardance
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Title: Untitled project by @yuito | Stardance
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jivsetu by @shrimanjadhav07 | Stardance
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Title: jivsetu by @shrimanjadhav07 | Stardance
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Stash by @CarlosGaburel | Stardance
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Title: Stash by @CarlosGaburel | Stardance
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1793249422.6709034
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My first slack bot! by @Alek | Stardance
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Alek
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Title: My first slack bot! by @Alek | Stardance
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1793288858.241693
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.. by @g | Stardance
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Title: .. by @g | Stardance
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1793314204.653522
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My awesome macropad! by @krishnasreenivas01 | Star My awesome macropad! by @krishnasreenivas01 | Stardance...
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Title: My awesome macropad! by @krishnasreenivas01 Title: My awesome macropad! by @krishnasreenivas01 | Stardance...
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demox by @devansh_maheshwari_cs24 | Stardance
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Title: demox by @devansh_maheshwari_cs24 | Stardan Title: demox by @devansh_maheshwari_cs24 | Stardance...
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My awesome macropad! by @kevin2505 | Stardance
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Title: My awesome macropad! by @kevin2505 | Starda Title: My awesome macropad! by @kevin2505 | Stardance...
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chronovisor by @subhansh | Stardance
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[{"post_id": 33951, "ts": "202 [{"post_id": 33951, "ts": "2026-07-24T09:14:45Z", "hours": 7.6253, "text": "Today was about giving the satellite engine a skeleton key. The satellite engine could see, but it couldn’t understand . Day 2 was all about layering context on top of context until patterns start to surface. Environmental Layer First ISRIC SoilGrids v2.0: Pulled clay percentage, sand, silt, pH, and organic carbon. Every number comes with uncertainty bounds because soil data is never clean. USGS Earthquake Catalogs: If a location has had 20+ quakes, that’s active fault territory and anything buried is scrambled. WorldPop Population Density: Tells you whether you’re looking at farmland or wilderness. Water Table Depth: Estimated from elevation. Shallow groundwater rots organic material; deep water means preservation. Archaeological Databases Wikidata & Pleiades: Hit Wikidata SPARQL directly for Pleiades entries—that’s every known archaeological site in the ancient world. GBIF Species Occurrence: Cross-referenced with GBIF data (civilizations cluster near water and arable land, and species distribution proves it). NOAA World Magnetic Model: Plugged in NOAA’s WMM for IGRF magnetic field calculations—buried structures distort local magnetic fields. NASA VIIRS Nighttime Lights (VNP46A2): Added night light data to map modern human activity patterns. Geospatial Helpers: Integrated OpenTopography DEM availability checks, geocoding via Nominatim, and full site suitability scoring based on known site proximity, climate, and magnetic anomalies. Web Archive Evidence Wayback Machine CDX API: Search by place name and coordinates, running archaeological keyword matching across archived pages. OSM Overpass: Querying historic and heritage feature tags. Building Density Analysis: Sparse buildings in a fertile area = something’s off. Cross-Reference Engine Pleiades, Wikidata, GBIF, and VIIRS nightlights all get matched against each other. If an ancient settlement shows up in three sources but the area is now empty, that’s a red flag. Temporal Trends: Pulled temperature trends from Open-Meteo (2020–2023) to analyze climate shifts. Batch Scanning: Built batch capabilities so you can process 20 sets of coordinates simultaneously. Fusion Engine — The Actual Brain Took every data source and wired them into a 7-component weighted scoring system: Satellite Anomaly Score ( 35% ) Signal strength × data quality × inverse correlation bonus when NDVI/NDWI/thermal agree. Soil Preservation ( 20% ) Clay > 350g/kg = +35%, Sand > 600g/kg = -25%, pH 6.5–8.0 sweet spot. Seismic Filter ( 10% ) 20+ quakes = -25%, Moderate = -10%, Quiet = neutral. Water Table Depth ( 10% ) <5m = -20%, >30m = +20%, No data = +30%. OSM Historic Context ( 10% ) Historic features nearby = +30%, Dense = neutral, Sparse = survey opportunity. Temporal Consistency ( 10% ) Significant NDVI trend + $R^2 > 0.3$ = +30%, Change events = +5%/event. Web Archive Evidence ( 5% ) Archaeological content in history = +55%, Multiple hits = +25%."}, {"post_id": 30711, "ts": "2026-07-18T14:53:52Z", "hours": 10.4211, "text": "Chronovisor — Devlog 1 🍓😺 So I had an idea that probably sounds a bit out there at first glance: What if you could point at any spot on Earth and get a read on whether something is buried underneath? And what if you could do it without ground-penetrating radar, drones, or anything physical—just satellite data and a bunch of free APIs? I decided to see if I could actually build it. I’m calling it Chronovisor . What I actually did The core of the project is a satellite data engine that pulls imagery directly from Google Earth Engine. Right now, I’m tapping into Sentinel-2 data (10m resolution, available from 2017 onward) and calculating spectral indices directly from the raw band data. I’m tracking: NDVI (Vegetation health) NDWI (Moisture levels) NDBI (Built-up/urban areas) Plus: BSI, SAVI, and NDMI. The underlying theory is straightforward: buried structures mess with the surface. A stone foundation stops roots from growing properly. Ancient walls alter water drainage. Different materials retain heat differently. If you can read those subtle patterns from orbit over time, you can potentially spot an archaeological site or a forgotten structure without ever setting foot on the ground. For the heavy lifting, I built an anomaly detection pipeline that flags anything sitting two standard deviations away from the mean across the time series. The math isn’t overly complex, but it’s highly effective. When a specific coordinate consistently shows up as “weird” across multiple independent satellite passes, you know you’ve got a real signal. The piece I’m most proud of is the structural probability scoring . I detrend the NDVI and thermal data to strip out seasonal spikes, then check if they’re inversely correlated. If you have uncharacteristically low vegetation right where the thermal signature is unusually high (or vice versa), that’s the classic signature of subsurface interference. The code evaluates this and outputs a score from 0 to 100. It doesn’t declare a definitive “find”—it just screams, “Hey, look closer at this exact spot.” On the infrastructure side, I set up: A FastAPI backend utilizing a two-tier cache (in-memory + disk) with SHA-256 hashing to keep things snappy. A clean CLI entry point for quick scripting. A Leaflet-based frontend styled in a dark theme (obviously). Managing Expectations: What it actually does I want to be completely transparent about what this tool is—and what it isn’t. This system does not tell you what is underground. It analyzes surface patterns and scores how likely it is that something is disrupting them. When the system flags a “possible buried structure,” it is strictly saying that the vegetation and thermal patterns match the footprint of subsurface interference. Nothing more. The signal processor uses real analytical tools—Fast Fourier Transforms (FFT), harmonic detection, and autocorrelation. However, it requires legitimate sensor data to function. It doesn’t conjure findings out of thin air, and the EM field mapping elements are tools for processing input data, not magic discovery engines. What’s next Right now, the satellite engine is great at spotting anomalies, but it has zero context about the ground it’s looking at. Next up, I’m building connectors for: Local soil composition data Seismic activity logs Water table estimates An archaeological database queried via Wikidata SPARQL. The goal is to evolve the output from a basic “this spot has weird NDVI” into something incredibly actionable: “This spot has a strange NDVI anomaly, sits on a clay soil type known to preserve structures, is located within 2km of a known ancient settlement, and suffers from low seismic noise.” That’s the kind of signal that gets interesting. Stay tuned."}]...
|
Title: chronovisor by @subhansh | Stardance
Devlo Title: chronovisor by @subhansh | Stardance
Devlogs:
Chronovisor — Devlog 1 🍓😺 So I had an idea that probably sounds a bit out there at first glance: What if you could point at any spot on Earth and get a read on whether something is buried underneath? And what if you could do it without ground-penetrating radar, drones, or anything physical—just satellite data and a bunch of free APIs? I decided to see if I could actually build it. I’m calling it Chronovisor . What I actually did The core of the project is a satellite data engine that pulls imagery directly from Google Earth Engine. Right now, I’m tapping into Sentinel-2 data (10m resolution, available from 2017 onward) and calculating spectral indices directly from the raw band data. I’m tracking: NDVI (Vegetation health) NDWI (Moisture levels) NDBI (Built-up/urban areas) Plus: BSI, SAVI, and NDMI. The underlying theory is straightforward: buried structures mess with the surface. A stone foundation stops roots from growing properly. Ancient walls alter water drainage. Different materials retain heat differently. If you can read those subtle patterns from orbit over time, you can potentially spot an archaeological site or a forgotten structure without ever setting foot on the ground. For the heavy lifting, I built an anomaly detection pipeline that flags anything sitting two standard deviations away from the mean across the time series. The math isn’t overly complex, but it’s highly effective. When a specific coordinate consistently shows up as “weird” across multiple independent satellite passes, you know you’ve got a real signal. The piece I’m most proud of is the structural probability scoring . I detrend the NDVI and thermal data to strip out seasonal spikes, then check if they’re inversely correlated. If you have uncharacteristically low vegetation right where the thermal signature is unusually high (or vice versa), that’s the classic signature of subsurface interference. The code evaluates this and outputs a score from 0 to 100. It doesn’t declare a definitive “find”—it just screams, “Hey, look closer at this exact spot.” On the infrastructure side, I set up: A FastAPI backend utilizing a two-tier cache (in-memory + disk) with SHA-256 hashing to keep things snappy. A clean CLI entry point for quick scripting. A Leaflet-based frontend styled in a dark theme (obviously). Managing Expectations: What it actually does I want to be completely transparent about what this tool is—and what it isn’t. This system does not tell you what is underground. It analyzes surface patterns and scores how likely it is that something is disrupting them. When the system flags a “possible buried structure,” it is strictly saying that the vegetation and thermal patterns match the footprint of subsurface interference. Nothing more. The signal processor uses real analytical tools—Fast Fourier Transforms (FFT), harmonic detection, and autocorrelation. However, it requires legitimate sensor data to function. It doesn’t conjure findings out of thin air, and the EM field mapping elements are tools for processing input data, not magic discovery engines. What’s next Right now, the satellite engine is great at spotting anomalies, but it has zero context about the ground it’s looking at. Next up, I’m building connectors for: Local soil composition data Seismic activity logs Water table estimates An archaeological database queried via Wikidata SPARQL. The goal is to evolve the output from a basic “this spot has weird NDVI” into something incredibly actionable: “This spot has a strange NDVI anomaly, sits on a clay soil type known to preserve structures, is located within 2km of a known ancient settlement, and suffers from low seismic noise.” That’s the kind of signal that gets interesting. Stay tuned.
Today was about giving the satellite engine a skeleton key. The satellite engine could see, but it couldn’t understand . Day 2 was all about layering context on top of context until patterns start to surface. Environmental Layer First ISRIC SoilGrids v2.0: Pulled clay percentage, sand, silt, pH, and organic carbon. Every number comes with uncertainty bounds because soil data is never clean. USGS Earthquake Catalogs: If a location has had 20+ quakes, that’s active fault territory and anything buried is scrambled. WorldPop Population Density: Tells you whether you’re looking at farmland or wilderness. Water Table Depth: Estimated from elevation. Shallow groundwater rots organic material; deep water means preservation. Archaeological Databases Wikidata & Pleiades: Hit Wikidata SPARQL directly for Pleiades entries—that’s every known archaeological site in the ancient world. GBIF Species Occurrence: Cross-referenced with GBIF data (civilizations cluster near water and arable land, and species distribution proves it). NOAA World Magnetic Model: Plugged in NOAA’s WMM for IGRF magnetic field calculations—buried structures distort local magnetic fields. NASA VIIRS Nighttime Lights (VNP46A2): Added night light data to map modern human activity patterns. Geospatial Helpers: Integrated OpenTopography DEM availability checks, geocoding via Nominatim, and full site suitability scoring based on known site proximity, climate, and magnetic anomalies. Web Archive Evidence Wayback Machine CDX API: Search by place name and coordinates, running archaeological keyword matching across archived pages. OSM Overpass: Querying historic and heritage feature tags. Building Density Analysis: Sparse buildings in a fertile area = something’s off. Cross-Reference Engine Pleiades, Wikidata, GBIF, and VIIRS nightlights all get matched against each other. If an ancient settlement shows up in three sources but the area is now empty, that’s a red flag. Temporal Trends: Pulled temperature trends from Open-Meteo (2020–2023) to analyze climate shifts. Batch Scanning: Built batch capabilities so you can process 20 sets of coordinates simultaneously. Fusion Engine — The Actual Brain Took every data source and wired them into a 7-component weighted scoring system: Satellite Anomaly Score ( 35% ) Signal strength × data quality × inverse correlation bonus when NDVI/NDWI/thermal agree. Soil Preservation ( 20% ) Clay > 350g/kg = +35%, Sand > 600g/kg = -25%, pH 6.5–8.0 sweet spot. Seismic Filter ( 10% ) 20+ quakes = -25%, Moderate = -10%, Quiet = neutral. Water Table Depth ( 10% ) <5m = -20%, >30m = +20%, No data = +30%. OSM Historic Context ( 10% ) Historic features nearby = +30%, Dense = neutral, Sparse = survey opportunity. Temporal Consistency ( 10% ) Significant NDVI trend + $R^2 > 0.3$ = +30%, Change events = +5%/event. Web Archive Evidence ( 5% ) Archaeological content in history = +55%, Multiple hits = +25%....
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My awesome macropad! by @Maia | Stardance
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Title: My awesome macropad! by @Maia | Stardance
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Shrimp classifier by @patipond_tiy | Stardance
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Title: Shrimp classifier by @patipond_tiy | Starda Title: Shrimp classifier by @patipond_tiy | Stardance...
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Title: My first slack bot! by @koky | Stardance
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My first slack bot! by @nishad_raghuvanshi | Stard My first slack bot! by @nishad_raghuvanshi | Stardance...
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Title: My first slack bot! by @nishad_raghuvanshi Title: My first slack bot! by @nishad_raghuvanshi | Stardance...
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Sky Observation AI Agent by @guilherme_bianconi_sa Sky Observation AI Agent by @guilherme_bianconi_santana | Stardance...
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Title: Sky Observation AI Agent by @guilherme_bian Title: Sky Observation AI Agent by @guilherme_bianconi_santana | Stardance...
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My first slack bot! by @Tigerente10 | Stardance
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Stock ticker by @Royalblueshadow | Stardance
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[{"post_id": 18619, "ts": "202 [{"post_id": 18619, "ts": "2026-06-29T20:23:14Z", "hours": 8.3983, "text": "Final Changes Over the last week I have implemented plenty new features and fixed as many occuring bugs as possible. Now I am at a point where I am happy to release a ship for my project for everybody to try. So lets talk about whats new: calculation of datasets Like popular stocktrackers I wanted to implement a way to track the best and worst performing stocks of the day to give the users an overview of which outliers there are in the market. Using a github S&P500.csv I scrape the symbols of the stocks and batch it to get specific Ticker objects that give me the price information of any given stock. Using this information I can calculate the current daily percentage change and compare it to the other stocks. Using a Panda Dataframe I order all my results and use the head and tail of the Dataframe to show a clickable button for the user to give more details on the performing stock. Bug fixes for this feature: Problems occured for the scraping of symbols of S&P500 since there can be symbols that yahooqueery doesn’t recognise. To fix this issue a dictionary and type check is implemented to only add actual data to the Dataframe. I have also added a detailed view of the amount of symbols skipped and a dataframe that shows which errors occured for them. Another Problem occured in the implementation of the buttons for the best and worst performing stocks. Cklicking the button didn’t update the shown stock. To fix this I dove into state_sessions in streamlit and used callback functions and keys to hopefully give you a bugfree experience Stylisation Lastly I have implemented the most important step for a good Software. I have customised the design of the website. Now the website renders in a fullscreen view. The primary Colors and button colors are matched according to the positive or negative price changes and the font is also adjusted for a cleaner view And now thank you for reading this long devlog, hopefully you can try my project and have fun with it ;D"}, {"post_id": 13327, "ts": "2026-06-21T20:35:48Z", "hours": 4.6939, "text": "What has changed since the last devlog Introduced a candlestick chart for every stock to gain more financial insight Added a toggle to change between the line and candlestick chart Introduced different time intervals to understand the pricing of the stock, instead of just the daily view Toggling options for the time intervals Added a more accurate price difference calculator based on the given time interval Improved the design of the website, with colors and alignments Shipped the webstie to this domain for you to try out: https://stock-tracker-jxprylb6sjqnwkmav9cwq8.streamlit.app/"}, {"post_id": 11198, "ts": "2026-06-18T10:12:06Z", "hours": 2.6894, "text": "Im building a stocktracker, that gives you the option to input any stock using the yahoo finance api and show the daily, weekly, monthly and yearly price information. In the future I would also like it to create an overview of the best and worst performing stocks and ones that are under their yearly average. Currently I have implemented the system using the streamlit and plotly libraries. By now I have implemented the input for the required stock, as well as the visual price graph of the day. This is done using the history of yfinance and adjusting the database to scrape for the closing price every minute. Furthermore the daily change (in Percent and real value) is calculated in comparision to the closing price of the last day"}]...
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Title: Stock ticker by @Royalblueshadow | Stardanc Title: Stock ticker by @Royalblueshadow | Stardance
Devlogs:
Im building a stocktracker, that gives you the option to input any stock using the yahoo finance api and show the daily, weekly, monthly and yearly price information. In the future I would also like it to create an overview of the best and worst performing stocks and ones that are under their yearly average. Currently I have implemented the system using the streamlit and plotly libraries. By now I have implemented the input for the required stock, as well as the visual price graph of the day. This is done using the history of yfinance and adjusting the database to scrape for the closing price every minute. Furthermore the daily change (in Percent and real value) is calculated in comparision to the closing price of the last day
What has changed since the last devlog Introduced a candlestick chart for every stock to gain more financial insight Added a toggle to change between the line and candlestick chart Introduced different time intervals to understand the pricing of the stock, instead of just the daily view Toggling options for the time intervals Added a more accurate price difference calculator based on the given time interval Improved the design of the website, with colors and alignments Shipped the webstie to this domain for you to try out: https://stock-tracker-jxprylb6sjqnwkmav9cwq8.streamlit.app/
Final Changes Over the last week I have implemented plenty new features and fixed as many occuring bugs as possible. Now I am at a point where I am happy to release a ship for my project for everybody to try. So lets talk about whats new: calculation of datasets Like popular stocktrackers I wanted to implement a way to track the best and worst performing stocks of the day to give the users an overview of which outliers there are in the market. Using a github S&P500.csv I scrape the symbols of the stocks and batch it to get specific Ticker objects that give me the price information of any given stock. Using this information I can calculate the current daily percentage change and compare it to the other stocks. Using a Panda Dataframe I order all my results and use the head and tail of the Dataframe to show a clickable button for the user to give more details on the performing stock. Bug fixes for this feature: Problems occured for the scraping of symbols of S&P500 since there can be symbols that yahooqueery doesn’t recognise. To fix this issue a dictionary and type check is implemented to only add actual data to the Dataframe. I have also added a detailed view of the amount of symbols skipped and a dataframe that shows which errors occured for them. Another Problem occured in the implementation of the buttons for the best and worst performing stocks. Cklicking the button didn’t update the shown stock. To fix this I dove into state_sessions in streamlit and used callback functions and keys to hopefully give you a bugfree experience Stylisation Lastly I have implemented the most important step for a good Software. I have customised the design of the website. Now the website renders in a fullscreen view. The primary Colors and button colors are matched according to the positive or negative price changes and the font is also adjusted for a cleaner view And now thank you for reading this long devlog, hopefully you can try my project and have fun with it ;D
Ships:
I made a stocktracker that can show the price changes over a various timeline of vastly different stocks using the yahooquery api. It also shows the 5 best and worst performing stocks of the day with a button to show the details. I am very proud of what I implemented as I hand-coded everything. Currently it still faces one problem of an api limiter as scraping the yahooquery database limits drastically if a large dataset is called. That is why the dataset size can be set manually to adjust to these rate limitations. Feel free to try my project and write a stock symbol to get started ;D...
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BDRailway by @sadnan141414 | Stardance
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Title: BDRailway by @sadnan141414 | Stardance
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My first slack bot! by @FrancisX | Stardance
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Zeus AI by @Cheeselover90 | Stardance
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Title: Zeus AI by @Cheeselover90 | Stardance
Devl Title: Zeus AI by @Cheeselover90 | Stardance
Devlogs:
After spending nearly 30 minutes, I am making progress on the UI of Zeus AI. Your local LLM that works with your desktop and catches business errors while improving sales and business performance! Who would use this? Trying to get a good feel for the market ❤️...
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My awesome macropad! by @mywaysrj | Stardance
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