MIT
Mason Industrial Technology, Inc.
stock NYSE

Full disclosure: this is my own project and it's free + open source (MIT), no signup, no ads, nothing uploaded — everything runs locally. Mods, happy to move this to a weekly thread if that's the norm here.
Like a lot of you, I kept wondering "what if I'd been buying SCHD/DGRO/VYM this whole time instead of what I actually bought?" Every backtester I found made me pretend I'd invested a clean "$X/month," which isn't how anyone's contributions actually look — real life is lumpy dates and amounts.
So I built **Divvy**. You feed it your actual contribution calendar (a Fidelity ledger export, a plain `date,amount` CSV, or a 1099 dividend import) and it replays those exact dollars, on those exact dates, into whatever basket of tickers you want — with DRIP. Then it shows you, side by side:
* **Dividend income** — lifetime and trailing-12-month run-rate (first-class metric, not an afterthought)
* **Total return**
* **Money-weighted XIRR** (so lumpy contributions are handled honestly)
There's a CLI and an interactive local "Portfolio Experiment Lab" where you can add/remove tickers and drag weight sliders and watch the dividend + return numbers update live. There's also a projection tool: "how much do I need to invest per month to hit $X/month in dividends?"
You can try it with zero data first (synthetic mode) to kick the tires.
Repo: [https://github.com/DanMat/Divvy](https://github.com/DanMat/Divvy)
Obligatory and sincere: **this is a backtester, not investment advice.** Past performance tells you nothing about the future — it's a "what would have happened" tool, not a "what will happen" one.
Would genuinely love feedback from this community: what dividend metrics do you most want to see compared, and which brokers should I add import adapters for next?

There are a few more options I didn't mention as well. MIT might be the best in this scenario. I often use LIT orders and have never had an issue with executing. But then again, I use a different platform, broker, data feed, etc.

Hopefully understood that participation trophies aren't handed out at Morgan Stanley, Goldman, or other high ranking firms. So, the edge naysayers weighing in on my Blackjack strat, I'd like to hear informed comments.
Hypothetical, on real traded data to date. A board review called on losing a major client, because money goes where it's treated best, might play out as the following.
"Jones here, called BS on the guy at Goldman that has returned a double, plus, with 90% WR last quarter. Jones' statistical analysis showed there was no edge, so our client left when GS wooed him away."
Would not like to be Jones, called on the carpet, to justify his selection of those 2 title choices.
https://preview.redd.it/k37azw30w8bh1.png?width=1525&format=png&auto=webp&s=fc12485519b307eb510e8b8d3383312e26832cf0
There are procedures on the modified Martingale depicted, that I'm willing to detail after the 90-day run, but even an early close appears to beat MIT single pooled bank & strat best effort with high probability. The insert normalizes the MIT Blackjack Team's 9-year best run against their OG strat best case (26%/month), shows WR of 56% knocks down realized gain to only 4%/month. Comp'd against mine (where the dealer's "shoe" is the SPY, face cards are VWAP, VIX, Price Action..., pretty sure you've seen my stuff last day or two), a WR of only 31% still beats the MIT strat (their's acknowledged "edgy" because of biometric investments at casinos to ban skilled "players").
Presented for entertainment and to provoke thoughts (hopefully not snark). It's the Socratic Process, everyone gains. I'm simply letting the class of those interested do the remaining heavy lifting.
"Not financial advice"

My pick for 3 is based on my belief there is an edge. If true, run it till it fails. Failure of an edge should appear quickly if there truly is none. Allows discovery with limited bank, done so quickly to not waste time. Upside, if edge exists, it is banked. If none, done. I'd argue that is smart use of capital.
Note that MIT target of a double in 90 days suffered significant setbacks over the 9 year run where they only cleared 50% annually. Yet, they are accredited with having an edge.
That's what I'm trying to reconcile. Appreciate the help.

"So, intelligent analysts care to refute that an edge exists? "
You're trying to comparing a completely known and solvable game to a market that never has even 2 minutes that are completely alike. MIT blackjack team could trust their data so long as the casino's rules didn't change.

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----
If you are new to investing, you can find curated resources in the r/investing wiki for [Getting Started here](https://www.reddit.com/r/investing/wiki/index/gettingstarted/).
The reading list in the wiki and FAQ has a list of books ranging from light reading to advanced topics depending on your knowledge level. Link here - [Reading List](https://www.reddit.com/r/investing/wiki/readinglist)
Podcasts and videos can be found in the wiki here - [Podcasts and videos](https://www.reddit.com/r/investing/wiki/medialist)
If you know nothing about the capital markets - the Getting Started section at the SEC educational site can be a good place to start - [investor.gov](https://investor.gov) \- there are also short 30 second videos on basics. The SEC (Securities and Exchange Commission) is a US regulator with a focus to protect US investors through regulatory oversight of the securities markets.
The FINRA education site at [FINRA Education](https://www.finra.org/investors/learn-to-invest) also contains numerous free courses and educational materials. FINRA is a not-for-profit SRO (self regulatory organization) which is self-funded by it's members which are broker-dealers. It works under the supervision of the SEC with a mandate to protect the investing public against fraud and bad practice.
For formal educational materials, several colleges and universities make their course work available for free.
If want to learn about the financial markets - an older but reasonably relevant course is [Financial Markets (2011) - Yale University](https://www.youtube.com/playlist?list=PL8FB14A2200B87185) This is the introduction to financial markets course taught by Prof. Shiller from Yale. Prof Shiller won the Nobel prize in economics in 2013.
Another relavant course from MIT is a lecture series on Finance Theory taught by Prof Andrew Lo - [Financial Theory (2008) - MIT](https://www.youtube.com/playlist?list=PLUl4u3cNGP63B2lDhyKOsImI7FjCf6eDW).
A more current course can be found at NYU Stern School of Business by Prof Aswath Damodaran - [Corporate Finance Spring 2019](https://pages.stern.nyu.edu/~adamodar/New_Home_Page/webcastcfspr19.htm). Prof Damodaran offers the latest materials and webcast lectures to this class here - https://pages.stern.nyu.edu/~adamodar/New_Home_Page/corpfin.html

**Profile check: MS Quant Finance / Financial Engineering (ETH Zurich, Imperial, HEC-tier programs)**
Background: Dual-degree undergrad (CS (Hons.) + Economics) at a strong tier-1 CS-focused university, followed by a 1-year CS/AI master's degree. Graduating with a 4+1-year structure.
GRE (est.): 319 (165Q + 154V)
CGPA: 8.4/10 cumulative, weak first year (\~6.1/10), strong upward trend since, and now finishing around 8.4.
**Course grades (out of 10):**
* Linear Algebra: 6
* Probability & Statistics: 4 initially, retaken → 10
* Multivariable Calculus: 8
* Partial Differential Equations: 7
* Convex Optimization: 8
* Econometrics: 8
* Game Theory: 10
* Money & Banking: 10
* Macroeconomics: 9
* Microeconomics: 8
* Causal Inference: 7
* Behavioral Economics: 9
* Foundations of Finance: 10
* Data Structures & Algorithms: 4 initially, retaken → 10
* Network Science: 9
* Data Science: 9
* Statistical Machine Learning: 8
* Analysis & Design of Algorithms: 8
Final year (in progress/planned): Stochastic Processes & Applications, Real Analysis, Bayesian ML, Reinforcement Learning, plus self-study (Akuna Options 101, MIT OCW stochastic processes, and math for finance).
**Other signal:**
* Codeforces Expert (competitive programming)
* TA experience: algorithms, convex optimization, game theory, applied CS/crypto — including large-class sections (900+ students across subjects)
* Research (no publications yet): ML on sequential/time-series data (possible thesis direction), deep learning-based cryptanalysis, applied ML on structured scientific datasets (classification, dimensionality reduction, interpretability)
* Work experience: quant research-style alpha signal work; DS/ML internship in fintech/banking; ML research internship (multimodal models, uncertainty/statistical inference, data pipelines); some general SWE experience
* Projects: ML-based market regime detection on ETF data (coreset methods); demand forecasting/network optimization for e-commerce logistics
* Competitions: top \~5% globally in a large international trading/quant competition; cleared first stage of a major quant research competition, top \~10-12%
**Question:** Given the trajectory (weak first year, strong recovery, two courses retaken and mastered), the math/CS/econ coursework mix, the competitive programming background, and the applied quant/ML experience but no publications (hopefully soon)—is this a realistically competitive profile for top MFE/quant finance master's programs, or am I overreaching on my target list?

MIT Blackjack Team comparison. Edgy or not?

Hi guys my lifelong goal is to become a quant researcher at a top firm like citadel securities. Currently, I am an incoming high school freshman and I have been grinding AMC 10/AIME and F=ma/USAPhO, math and physics olympiad, and my target school is MIT. Ik what skills are needed for the interview, but I’m also going to be majoring in physics just in case quant doesn’t work out for me. I also have very good network with people from other quant firms. What should I do now to get a job at my dream firm?

Reformpaket: ihr seid in die Falle getappt (MIT Quellen!)