ML
MoneyLion Inc.
stock NYSE

Meta is full of incompetencey. Nothing they produce picks up steam. The entire open source ML space focuses on safe tensors and vllm compatibility amd meta thought it would be a good idea to not do that and develop llama.cpp. And all their open source models seem to just suck especially when you compare them to any qwen model of even half the size. Then there is the metaverse which completely failed and the company even renamed themselves to meta because they thought it was the next big thing. Amd now there is the meta glasses which are being pushed hard with marketing yet noone will wear them. This company is trash and is being held together by instagram and facebook which are infinite ad money generating machines.

Also have this orderbook simulator coded up with market noise and market maker modules done up too. I'd host it somewhere to let you guys / other RDDT trading subs send in orders and have a scoreboard kinda thing (but really mostly so I can collect data to train ML models on the resulting orderbook snapshots 🥶) but I don't know how. And retail dominated orderbook snapshots would probably be different from institution dominated cause of the spoofing, iceberg orders, iceberg frontrunning, execution algos and all the other bs tricks that institutions pull

**French engineering student hesitating between a straightforward way towards quant, and a more risky, but potentially more rewarding way.**
Hi everyone,
I am a Télécom Paris (Top 3-5 Grande Ecole) engineering student choosing my final-year master. I want to work in systematic quant research, alpha research, or quant trading.
I have two options at Institut Polytechnique de Paris:
* [M2 Data Science (École Polytechnique)](https://www.ip-paris.fr/education/masters/mention-mathematiques-appliquees-statistique/master-year-2-data-science)**: #1 Grande Ecole.** ML, optimisation, deep learning, RL, data engineering and statistics.
* [M2 SFA (ENSAE)](https://www.master-statistique-finance.com/IP_Paris/course_list.php)**: Renowned Grande Ecole specialized in stats/finance, less prestige.** Financial time series, econometrics, derivatives, stochastic calculus, portfolio construction and quant finance, with some ML. It is essentially a quant finance master, though newer (9 yo) and less known than El Karoui/M2MO (35 yo). Though some classes are held in common.
My GPA is around **3.8/4.0**. Relevant grades: 20/20 Financial Markets, 19/20 Econometrics, 16.8/20 Statistical Learning, 16/20 Markov Chains/Time Series, but I also had some setbacks due to health issues.
I see two paths:
1. **SFA → quant internship/job in Europe → maybe US MFE/MSCF later.** Safer if US admissions fail, but possibly redundant with a later CMU/Columbia-type degree.
2. **Data Science → ML/quant internship → US MFE/MSCF.** More complementary for ML-driven quant research, but riskier: if I do not get into a strong US program, will DS make me less competitive for European quant roles than SFA?
A friend from Télécom with a 3.67/4.0 GPA and an ML internship was admitted to CMU MSCF and Columbia w/ 15k scholarship, while having never studied probability. So applying to US quant programs after my final year seems realistic.
My questions:
* Is a US quant degree worth this risk (and the 100k$), even though I can acquire the same, if not better skills, for sure and for 167€ in France ?
* Does the recruiting process of a fund rely more on skills or on the clout of the school ?
* The situation in the US right now is very complicated, but the compensations there are still very high. What are your thoughts for a european like me ?

Can you do a ML research (not coursework) masters, which is in CS dept? Or something in optimization theory?
Or apply now to trading firms, even smaller firms?
Another bachelor's doesn't feel great here.

Hey StationImmediate530, I appreciate the detailed feedback and the book recommendation! Marcos López de Prado’s work is fantastic for institutional machine learning.
I think we are operating on two different definitions of "scalping" here, likely due to the environments we are talking about.
**1. HFT vs. Retail M1 Scalping** You are describing true High-Frequency Trading (HFT) and market-making. You are 100% correct that institutional HFT requires Level 2/3 order book data, nanosecond execution, and platforms like JAXMARL. However, developing algorithmic strategies natively in MQL5 for the MT5 retail environment has a very different definition of "scalping." Here, it simply means capturing short-term momentum bursts on the M1 chart. That is absolutely achievable using smoothed price action, tick data, and structural range boundaries without needing full depth-of-market feeds.
**2. The Over-Fitting Assumption & Live Results** Curve-fitting is the biggest trap in algorithmic development, and I would normally agree with you that a 100% backtest is an immediate red flag. The reason I am confident in this, however, is that **I have already been running this exact EA and setfile on a live account for the last few weeks**, and it is performing identically to the backtest.
Since I can't attach a screenshot to a reply, here is the exact live execution data from my terminal just today (July 2nd) on XAUUSD:
* The engine executed two distinct momentum cycles (one long, one short).
* **The Long Cycle:** Triggered a cluster of 0.02 lot entries at exactly 14:11:00. The momentum stalled a few minutes later, and the EA's active management closed them all out in profit between 14:13:00 and 14:15:00.
* **The Short Cycle:** Triggered at 15:48:00. The EA aggressively trailed and closed the cycle out entirely by 15:56:27.
* **The Result:** 12 total micro-transactions executed, every single one closed in profit (netting $42.10 after commissions) with absolutely zero floating drawdown. The live dashboard win rate holds at exactly 100%.
**3. The Economic Edge** The economic explanation for why this works isn't about predicting macro trends or utilizing machine learning. It is structural risk management. The EA exploits short-term volatility by aggressively choking out trades at break-even (Entry + 1 point) the second momentum stalls. The edge is purely mathematical: neutralizing risk before a micro-trend has the chance to reverse.
I completely agree that backtests shouldn't be blindly trusted, which is exactly why I pushed this to a live environment to validate the logic. I’ll definitely add the de Prado book to my reading list for broader ML concepts, but for this specific MT5 precision engine, the live market is already validating the thesis.

1. Quant is an academic field and you do need some academic brilliance to break in and be successful as a quant. By the looks of it, you don’t seem to have any at all?
2. Internship and work experience would. A strong research focused masters would outweigh your grades as well.
3. Really hard to say. You seem to be practically starting from zero.
4. Data Science, ML/AI research, dev (fringe).

Hi everyone,
I'm 23 years old from India and currently at a lowest point of my life. I'm looking for honest advice from people who have been in similar situations or work in quantitative finance.
Here's my background:
\- B.E. in Computer Engineering failed one year 
\- Strong interest in mathematics, programming, statistics, AI/ML, and financial markets.
\- My dream is to become a quantitative researcher or quantitative trader and eventually pursue a master's degree in the USA or Singapore.
The problem is that my academic journey has been far from ideal.
\- I scored 78% in Class 10 50 in class 12 th. 
\* I also faced failures during engineering, which delayed my graduation by about two years.
\* Seeing my friends graduate, get jobs, or move abroad has been mentally difficult, but I've decided to rebuild instead of giving up.
I'm ready to work as hard as necessary. I'm willing to spend the next 2–4 years strengthening my mathematics (calculus, linear algebra, probability, statistics, optimization), programming (Python/C++), machine learning, and finance.
My questions are:
1. Is breaking into quant still realistic with this academic history?
2. Would relevant projects, internships, research, and a strong master's application outweigh my earlier grades?
3. If you were starting from my position today, what roadmap would you follow?
4. Are there any careers closely related to quant that could serve as a stepping stone while I continue preparing?
I'm not looking for sympathy just practical advice and if no how could i better my life decisions from now on or which finance field should i pursue my career in
Thank you for taking the time to read this.

ML jargoon, should i rephrase?

10% per trade at 5-10x leverage is your actual problem, not the strategy. Even a real edge blows up at those levels. Most people running profitable systems risk 0.5-2%.
Win rate is a distraction btw. What matters is expectancy after costs. 40% WR can print money, 80% WR can blow up.
On the LLM idea, don't. It won't fix look-ahead bias or execution costs. My own system is boring hardcoded rules with fixed SL, one position at a time, no ML. Been running live for months and it holds up because the logic is simple enough to actually debug. Pick one, run it small for 3 months, and diagnose what breaks. That's where the real learning is imo

In ML there can be false positives which is likely why a system banned the account but following human review it was deemed incorrect

 Context:
  \- MS CS (ML focus), graduating Dec 2027, currently mid-internship
  \- Citizenship: European
  \- Target: quant researcher / quant trader summer 2027 internships in EU or UK (or Asia but i dont have a visa :/ )
  Resume anonymized (names/schools/companies redacted).
  Looking for general feedback — content, ordering, bullet phrasing, anything that stands out or reads as a red flag. Happy to expand on any point. Thanks!

It seems that 2-3 years is where people have their real breakthroughs. So the good news is your almost halfway there. I guess understanding market mechanics is probably my weakest point, as my background is a senior software engineer + undergrad in mechanical engineering + masters in machine learning. As DePrado would put it, for a sucessful quant fund you need 4 types of experts: Developer, Mathematician/Statistican, Market Expert, And Machine Learning (assuming you're using ML). Its really hard for one person to cover all 4 to the highest level.

Aus der guten Gastro kann ich dir sagen dass wir in den Drink genau so viel ML Schnaps und Mischgetränk reinpacken wie in einen mit Eis und dann halt dein Getränk das Glas halt nicht ganz füllt.

Your data set is too wide and if you got that much data, it’s probably free data which is generally terrible for ML applications.

This is one of the most honest writeups I've seen here — point-in-time universe, purged CV, deflated Sharpe, and you still shipped a "no alpha" verdict. Most people never reach that stage; getting an honest *no* is most of the game.
One reframe before you burn more cycles: you're asking a classifier to predict the *direction* of a 10-day triple barrier from price-derived features across the whole universe. On liquid US equities that's about the lowest signal-to-noise target there is — 50.5% is roughly what the efficient-market null predicts, so your pipeline may be telling you the truth, not failing.
Two things that tend to move the needle more than another model:
1. Meta-labeling (which you listed) only helps *on top of a primary signal that already has an economic prior*. It won't rescue a from-scratch price classifier — it decides bet/size on trades a real edge already flagged. The missing piece is usually the primary hypothesis, not the ML.
2. Equity alpha is mostly *relative*, not absolute. A cross-sectional model (rank names within the universe each day) often survives where per-ticker barrier classification dies — you're trading the spread, not the level. And the informative features are usually *not* price transforms (flows, positioning, events, cross-asset), which lines up with your own AFML/event-driven read.
Curious — were your 60 features all price/volume derived, or did any encode a genuine economic prior (something you'd expect to predict returns *before* touching the data)?

I think you need to reverse your process with better models to trade the markets. Your technical indicator one won't hold up. You are also trading the most difficult and hardest market.
You have to ask yourself the question: what is motivating this market? If you can't find the factors then you won't make money.
Your ML overlay comes later.

The problem is that you built an „everything model“. I believe that one model can be good at one particular thing on HFT basis. Like, it might be good at finding a structure that predicts particular situations (for example after a big drop).
The mentality that you can just throw in all the data you have and predict all assets with the same model at all times is just not getting you anywhere because markets are dynamic and chaotic systems and far more complex then any ML model could learn in finite amounts of time.

**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?

I think you'd probably get interviews for top firms, esp. if you're applying in QR/algodev type roles. Not sure how much trading recruiting has pivoted to ML lately. I'd just make sure to apply early, if you have a hook (research publication?) feature that, and maybe tighten up some of the language -- "agentic systems" reads a bit vague, BS-y, and imprecise. You want to sound like a precise person.

[https://github.com/Mosaic-agent/Mosaic-fund-agent](https://github.com/Mosaic-agent/Mosaic-fund-agent)
following command currently works without llm
### 1. Data Ingestion & Configuration (Pure ETL/System)
• import — Import historical market data (stocks, ETFs, MF NAV, commodities, indices, or AMC disclosures) into ClickHouse.
• config — Display current non-sensitive configuration settings.
• telemetry — Display live system telemetry dashboard.
### 2. Market Scanners, Alerts & Reports (Non-LLM/Deterministic)
• signals — Combine macro, news, valuation, flow, ML, and anomaly signals into a unified per-ETF composite score.
• macro — Scan live macro & geopolitical events and map them to ETF impact.
• macro-themes — Run the long/short macro themes engine (uses Google News RSS + Yahoo Finance prices, no LLM required). Defined in
macro_theme_agent.py.
• comex — Run COMEX commodity pre-market signal analysis (Gold/Silver/Copper).
• premium-alerts — Scarcity Premium Alerts for international ETFs (MAFANG, HNGSNGBEES, etc.).
• scan-setups — Scan all 18 tracked ETFs for volume-volatility setups.
• scan-trends — Scan all 18 tracked ETFs for short, medium, and long term trend status.
• etf-news — Fetch free news that can impact Indian ETFs.
### 3. Quantitative Sizing, Models & Backtesting (Mathematical)
• risk — Adaptive Kelly position sizing — blends LightGBM forecast with GARCH Risk Governor.
• drift-monitor — Monitor GOLDBEES ML prediction model drift (evaluates hit ratios, directional accuracy, and AUC over time).
• crossover — Run a Moving Average Crossover backtest (SMA/EMA) for a stock or ETF.
• correlate — Map stock anomalies to company filings and global macro trigger events (uses purely mathematical filters and event mappings).
Defined in correlation_tools.py.
### 4. Dashboards & Services (Port Listeners)
• ui — Launch the Mosaic Data Hub web UI (Streamlit server at :8501 ).
• studio — Launch the Mosaic Studio workspace React UI (runs on :8502 ).
──────
All commands are declared in the CLI entry point file main.py.
──────
### Summary of Work Done
1. Inspected main.py command imports and verified that commands like news , ask , chat , analyze , deepdive , research , and
portfolio-wf utilize LLM agents or LangGraph synthesis steps.
2. Verified that scripts like macro_theme_agent.py and correlation_tools.py contain no LLM components.
3. Formulated and presented the filtered list of non-LLM CLI commands.