QR Analyst Training Program

A structured path through quantitative research methodology

AlgoGators Investment Fund — Internal Resource

Investment Proposal Framework

Every analyst builds toward the same structure: a complete Investment Proposal (IP) that documents hypothesis, data, methodology, and backtest results.

Section 1

Hypothesis

Economic mechanism. Why does this inefficiency exist? What will the signal predict?

Section 2

Data

2a: Data sourcing & cleaning. 2b: QD approval gate. Data must exist before backtest.

Section 3

Methodology

3a: Signal rules. 3b: Model assumptions. Every assumption tested & documented.

Section 4

Results

Sharpe ratio, annual return, win rate, profit factor. Equity curve & drawdown analysis.

Four-Phase Curriculum

11 weeks organized into four phases, building from foundations to research readiness.

Phase 1

Foundations

Weeks 1–2

  • What defines a research edge
  • Fund markets and asset classes
Phase 2

Tooling & Hypothesis

Weeks 3–4

  • Statistics for quant research
  • AlgoGators toolstack & data pipeline
Phase 3

Research Execution

Weeks 5–9

  • Writing the hypothesis (Section 1)
  • Data sourcing & cleaning (Section 2)
  • Signal construction (Section 3a)
  • Model assumptions (Section 3b)
  • Backtest results & performance (Section 4)
Phase 4

Readiness

Weeks 10–11

  • Alternative data sources
  • Live research workshop & pitch

11-Week Training Grid

Select a week to view its content. Each page is a self-contained reference for the topic.

Week 1

What Is a Research Edge?

 Week 2

Fund Markets & Asset Classes

 Week 3

Statistics for Quant Research

 Week 4

Fund Toolstack & Data Pipeline

 Week 5

Writing the Hypothesis (IP §1)

 Week 6

Data Sourcing & Cleaning (IP §2)

 Week 7

Signal Construction (IP §3a)

 Week 8

Model Assumptions (IP §3b)

 Week 9

Backtest Results & Performance (IP §4)

 Week 10

Alternative Data Sources

 Week 11

Live Research Workshop

Analyst Outcomes

By the end of Week 11, you should be able to do all of this:

Write a clear, falsifiable hypothesis grounded in economic mechanism, not data mining
Identify and source the data your hypothesis requires, understanding lookback bias and survivorship bias
Construct signals with explicit entry, exit, and position sizing rules
Test and document every model assumption you make — never submit an IP without Section 3b
Build a backtest with Sharpe ratio, drawdown analysis, and walk-forward validation
Distinguish between a real edge and overfitting, and know how to survive transaction costs
Communicate your research in the Investment Proposal format — the lingua franca of this fund
Pass the live workshop pitch and be cleared to submit your first IP