Emma S.
Quantitative Analyst · ex-Citadel · 12 years in global markets
12 years running quantitative strategies across equities, FX, and commodities. At Citadel I built systematic trading models covering A-shares, Hong Kong, and US markets. Left to build financial intelligence tools that give independent investors access to the same analytical frameworks used by institutional desks. Every agent I publish has a model I've actually run in production.
Experience
Senior Quantitative Analyst · Citadel Securities
2018 – 2024Systematic equity strategies for A/H/US markets. Managed $2B+ AUM across discretionary and fully systematic books.
Quantitative Researcher · Goldman Sachs
2012 – 2018Rates and FX quantitative research. Built macro factor models used by the fixed income desk.
Education
- 2012
University of Chicago
MS, Financial Mathematics
- 2010
Peking University
BS, Mathematics
Awards & Recognition
- 2015
CFA Charterholder
CFA Institute
Agents by Emma S.
Investment Banking AI Analyst
Your deal team in a box: 10 specialist analysts for investment banking, equity research, private equity, and fund operations. Hand it a deal, a company, or a portfolio, and get back pitch books, models, research notes, and fund reports as analyst-grade drafts, ready for your review. ## What it does - **Pitch books:** comps, precedents, and an LBO assembled into a branded 15 to 20 slide pitch deck (.pptx), client-ready - **Market & sector research:** sector overview, competitive landscape, peer comps, and a shortlist of ideas in a structured HTML or Word brief - **Earnings analysis:** an earnings call plus the latest filings turned into a model update and a desk-ready research-note draft - **Financial modeling:** DCF, LBO, 3-statement, and comps models as working Excel files with live formulas, not hardcoded numbers - **Meeting prep:** a one-page briefing pack on the company, the people, and the open questions before every client meeting - **Fund valuation review:** GP packages reconciled into a valuation template and an LP-ready reporting pack - **GL reconciliation:** accounting breaks found, root cause traced, each item routed for sign-off - **Month-end close:** accruals, roll-forwards, and variance commentary, close-ready - **KYC screening:** onboarding docs parsed, the rules engine run, and compliance gaps flagged with an audit trail ## Where it fits - **A banker prepping a pitch:** "Build comps and an LBO for [target], then drop them into our pitch template." A branded deck in hours, not days. - **An equity research analyst on earnings day:** "Read [company]'s Q3 call and 10-Q, update the model, and draft the note." Model and note ready to edit before the desk opens. - **A PE associate at quarter-end:** "Reconcile these GP statements into our valuation template and prep the LP report." A valuation review plus an LP-ready package. - **A fund accountant at month-end:** "Run the GL reconciliation and write up the variances." Breaks flagged with root cause and commentary. ## How it works 1. **Point it at your inputs:** filings, earnings calls, GP packages, onboarding docs, or just a company name. 2. **It routes to the right agent:** each task maps to a specialist that knows the workflow, the templates, and the checks. 3. **You get a draft back:** Excel models, branded decks, research notes, or reconciliations, structured for a human to review and sign off. Slash commands: `/comps` `/dcf` `/earnings` `/ic-memo` Built for investment banks, equity research desks, PE/VC funds, and wealth-management teams. Every output is an analyst draft for human review. No transactions executed, no ledger posts.
No-Code Data Analyst
Upload a CSV or Excel file, describe what you want to learn, and get back an insights report with charts, narrative, and business recommendations. No code required. ## What it does - **Insight reports:** your data turned into findings, not just tables - **Charts:** the right visualizations chosen for what you asked - **Plain-language narrative:** what the numbers mean, in words - **Recommendations:** concrete business next steps ## Where it fits - **A founder reading sales data:** "What's driving the dip in Q3?" Charts plus a plain-language read. - **A marketer on campaign numbers:** "Which channels actually converted?" A ranked answer with visuals. - **An ops lead on a spreadsheet:** "Find the outliers in this data." Flagged anomalies and what to do. ## How it works 1. **Upload your file:** a CSV or Excel sheet. 2. **Describe the question:** what you want to learn. 3. **You get a report back:** charts, narrative, and recommendations, no code written. Built for anyone who needs analysis without writing code. Every report is a draft for you to sanity-check against your numbers.
Open-Source Finance Analyst
Bring a finance question or dataset and get back analysis reports, quant strategy backtests, and financial models, from a free, open-source skill collection. ## What it does - **Analysis reports:** a finance question worked into a structured write-up - **Quant backtests:** a strategy tested against historical data - **Financial models:** valuation and projection models built from your inputs - **Open source:** a free, inspectable skill collection ## Where it fits - **An analyst sizing an idea:** "Backtest this momentum strategy on these tickers." Performance stats plus a write-up. - **A quant prototyping:** "Build a DCF from these assumptions." A working model to stress-test. - **A researcher exploring data:** "Analyze this dataset for signal." A structured findings report. ## How it works 1. **Bring a question or data:** a strategy, a dataset, or assumptions. 2. **It runs the analysis:** backtest, model, or report as needed. 3. **You get output back:** results and a write-up to review. Built for analysts and quant researchers. Outputs are drafts for human review, not investment advice.
Quant Strategy Backtester
Bring a finance question and get back quant strategy backtests, financial models, and market research reports. ## What it does - **Quant backtests:** a strategy tested against historical data with performance stats - **Financial models:** valuation and projection models from your inputs - **Market research:** a sector or question worked into a research report - **Analyst-grade:** outputs structured for review, not black boxes ## Where it fits - **A quant prototyping:** "Backtest this mean-reversion idea." Stats plus a write-up to judge it. - **An analyst modeling:** "Build a three-statement model from these assumptions." A working model to stress. - **A researcher on a sector:** "Summarize the landscape and key players." A structured brief. ## How it works 1. **Bring a question:** a strategy, assumptions, or a sector. 2. **It runs the work:** backtest, model, or research. 3. **You get output back:** results and a write-up for review. Built for quant researchers and financial analysts. Outputs are drafts for human review, not investment advice.