Channel
@quant_feed
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
154subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002238568874 |
|---|---|
| Type | Channel |
| Username | @quant_feed |
| Created | Between 1 June 2024 and 23 September 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/quant_feed |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 19:00 | 154 | no change |
| 7 Aug 2026, 09:32 | 154 | first reading |
20 posts held, back to 23 September 2024 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 21 October 2024. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 8 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
19 reactions across 6 posts, in 5 distinct kinds. The most used accounts for 31.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 6 | 31.6% | |
| 😁 | 6 | 31.6% | |
| 🔥 | 3 | 15.8% | |
| 😭 | 3 | 15.8% | |
| 😢 | 1 | 5.26% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 9 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 19reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 23 September 2024 to 21 October 2024, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Human Incentives Over Sophisticated Models: Insights from Recent Findings on Risk Management __paleologo @ twitter orig The critique centers on the flawed analysis of financial incentives rather than misaligned risk models. The author argues that human behavior driven by poor incentives led to significant errors in judgment. The referenced report is characterized as akin to a Russian absurdist novel, lacking depth…
😢1
Reflecting on My First Day in the FX Market: A Journey Beginning with Harlow Meyer Savage salr_nyc @ twitter orig December 12, 1988 marked my entry into the FX market, kicking off with a successful navigation of the essentials, including finding the toilets and attending the company Christmas party. Reflecting on that time, I remember the HFT operators actively engaging at the Harlow Meyer Savage USDJPY desk in 2…
Understanding the Fed Dot Plot: A Critical Look at Economic Projections and Their Accuracy gametheorizing @ twitter orig The Fed's Economic Projections often miss the mark, illustrating that their members struggle to accurately predict their future decisions. This reflects a broader societal issue, where honesty and transparency are compromised for the sake of managing expectations. The Summary of Economic Project…
Exploring the Predictive Power of Private Equity Data on Public Equity Returns quantseeker @ twitter orig Recent findings indicate that private equity deal data from FactSet can be a valuable predictor of public equity returns, encompassing both general market performance and specific sector outcomes. This underscores the interconnectedness of private and public market dynamics, implying that savvy investors can l…
Understanding the Different Types of PCA: A Simplified Guide PtrPomorski @ twitter orig PCA can often create confusion, but it’s crucial to distinguish between its variants. First, standard PCA is exclusively linear. KernelPCA introduces non-linearity by utilizing a selected kernel, expanding analytical capabilities. For massive datasets, IncrementalPCA serves as a linear PCA alternative that processes data in chu…
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Enhancing Feature Diversity: Insights from Early Quantitative Experimentation BeatzXBT @ twitter orig Diving into advanced feature engineering, I've leveraged new datasets to diversify my models, though the performance hasn’t yet yielded significant alpha. Shared code for reproducibility; though it's pretty basic, it's user-friendly. My initial features from 6 months ago laid the groundwork for subsequent iteratio…
Understanding Assumptions and Best Practices for Candlestick Close Prices crypto_hades @ twitter orig 1. When calculating candlestick close prices, relying solely on the last trade price can lead to inaccuracies, especially in low liquidity environments like trading shitcoins. 2. Consider alternatives such as midprice, microprice, VWAP, or TWAP for a more accurate representation of close prices, as outdated last t…
Exploring the Viability of Low Win Rate Strategies in High Frequency Trading larpcapitalwc @ twitter orig There’s substantial potential for decent risk premia in low win rate strategies, provided you execute a high volume of trades. Picture holding a strategy with a mere 10% win rate and only one trade per day; it’s a psychological challenge. After enduring 20 consecutive losses, maintaining confidence to keep tra…
Exploring the Top Craft Beers for Quantitative Analysis in Flavor Profiles therobotjames @ twitter orig Garage beers and shed beers dominate the craft beer conversation right now—both are praised for their quality and distinct character. The community is unanimously on board, with shed beers often considered their close cousins. There's a clear consensus that trying these beers can be a transformative experience, …
😁6
Enhancing Mean Reversion Strategies: Adjusting Lookback Periods and Volatility Thresholds GoshawkTrades @ twitter orig Mean reversion strategies can falter during sudden volatility spikes. A straightforward adjustment, such as shortening the lookback period or setting a minimum volatility threshold, can enhance performance significantly. This tweak has been beneficial for improving various strategies. We've assist…
Leveraging Rust in Delta 1 MM System: A Timely Production Rollout Amidst Market Challenges 0xLoris @ twitter orig We're officially integrating Rust into our Delta 1 mm system after a year of pushing through the bear market. We deployed at 3:50 PM on a Friday before a long weekend. Big thanks to the team for their hard work to make this happen. Zig looks promising, while Redis isn’t hindering our performance. We pr…
🔥3
Embracing the Basics: The Value of Starting from the Fundamentals in Quant Finance quantymacro @ twitter orig I find value in embracing the basics; they’re the foundation for deeper understanding. Critics labeling my insights as "too basic" miss the point—it's a signal of growth potential. Sticking to fundamentals at this stage sets me up to tackle more complex topics down the line. If I'm already advanced and sti…
Showing the 12 most recent of 20 posts we hold for @quant_feed. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank — 616,995 of 1,169,250entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 8 August 2026 — this entry's latest reading, not the date you are reading this.
“quant feed” (@quant_feed), 154 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/quant_feed.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.