CFA® Level I Journey · Feb 2026 Exam · Aaryaman Singh

Turning engineering intuition into capital markets signal.

I prepared for CFA Level I as an Engineering student at UofT, working full-time at CIBC, not with a finance degree, but with a systems-thinking, quantitative lens. This is exactly how I did it.

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When I committed to CFA Level I for February 2026, I was in the middle of my Mechanical Engineering degree (Mechatronics) at UofT and working full-time as a Process Engineering Analyst at CIBC. That meant long days and late-night problem sets, not exactly a traditional “finance major” pipeline.

So I didn’t treat the curriculum like a collection of facts to memorize. I treated it like an engineered system: inputs, constraints, failure modes, and a target output (passing on the first attempt while not burning out). Ethics, FSA, Fixed Income, Equity weren’t just topics, they were subsystems with different weights and failure probabilities.

My primary resource was Kaplan Schweser: the Core package from day one, and the CFA institure Practice Pack added roughly six weeks out when mocks became the main driver. I built an Excel tracker that treated each reading like a node in a network, with time estimates, difficulty, and dependency structure. It told me where to push and where to accept “good enough”.

The hardest parts of the journey were FSA and Fixed Income. FSA forced me to slow down and build flowcharts for how statements connected instead of brute forcing ratios. Fixed Income only clicked when I reframed it as a control system and Taylor expansion problem, duration and convexity as approximations, not magic.

“I didn't approach this as a finance student would. I approached it as an engineer, decompose the system, identify the critical path, optimize for signal not noise.”

Timeline: October 2025 → Feb 2026

Five distinct phases — each with a different objective, tempo, and failure mode.

01

Oct 2025

Foundation: Ethics, Quant, and the tracker

I started with Ethics and Quantitative Methods, treating them as the language of the exam. I built my Excel tracker in this phase, each reading as a row, with estimated hours, difficulty, and links to questions. Most study blocks were 1–1.5 hours on weekdays, usually carved out around commuting and evenings.

Ethics first

Quick first pass to understand Standards language and how vignettes are structured.

Quant as toolbox

TVM, statistics, and distributions framed as tools I’d reuse across the rest of the curriculum.

Excel tracker

Columns for topic, reading, hours, status, score, and notes updated after every session.

Lessons learned

Consistency beats intensity; an imperfect but daily 60–90 minutes moved the needle most.

  • 📌 Building a tracker early gave me a control panel for the rest of the journey.
  • 🧠 Quant concepts became far easier when tied to real examples, not just formulas.
  • ⏱ 1–1.5 hours/day was sustainable alongside engineering and CIBC.
02

Nov – Dec 2025

Deep Content: FSA, Fixed Income, Equity

This was the heavy lift: FSA and Fixed Income, with Equity as the bridge between textbook and Bloomberg. I built flowcharts for FSA and treated Fixed Income as a control systems problem, which helped me keep the moving parts straight.

FSA flowcharts

Mapping how income statement, balance sheet, and cash flow linked — not memorizing ratios blindly.

Fixed Income reframed

Duration/convexity as approximations, similar to Taylor expansions and system responses.

Equity & Bloomberg

Connecting DDM/FCFF models to actual tickers I saw at CIBC gave the formulas context.

Lessons learned

Deep work here paid off later: mocks exposed gaps, but nothing felt completely foreign.

  • 📊 Visualization (flowcharts, diagrams) beat linear notes for FSA retention.
  • 📉 Fixed Income became manageable when I stopped treating formulas as black boxes.
  • 🏦 Seeing concepts on Bloomberg screens anchored the theory.
03

January 2026

Mocks Begin: error log and Practice Pack

The first full mock was humbling. Instead of just staring at the score, I built an Excel error log: topic/subtopic, error type, root cause, and takeaway. Around this time I bought the Schweser Practice Pack — the closest thing to exam feel.

Error log design

Each mistake tagged as concept, process, or carelessness — patterns drove my review blocks.

Practice Pack

Higher-quality mocks with better item-set construction; treated as dress rehearsals.

3-day protocol

Day 1 exam, Day 2 deep debrief, Day 3 targeted repair and light re-testing.

Lessons learned

Mistakes are only useful if they become data — not just frustration.

  • 📈 Score trends mattered more than any single mock.
  • 🧾 The error log turned “I’m bad at FSA” into specific, fixable weaknesses.
  • 🧪 Practice Pack felt closest to exam difficulty and structure.
04

Late Jan – Feb 2026

Final Sprint: high-frequency only

In the final four weeks I shifted to a high-frequency-only mindset: Ethics, FSA, Fixed Income, and Equity were treated as ~55% of the exam. Everything else got time, but not at their expense. I tapered like an athlete instead of cramming like a student.

Topic weighting

Study hours roughly matched exam weight — especially for Ethics, FSA, FI, and Equity.

Formula sheets

One handwritten sheet per major topic; rewriting forced consolidation, not memorization.

Anxiety journaling

Short nightly entries capturing worries and reframing them into concrete actions.

Lessons learned

Quality of rest in the last week mattered as much as marginal study hours.

  • 🎯 High-weight topics received disproportionate attention by design.
  • 🧾 Formula sheets helped during mocks, not just the final week.
  • 🧘‍♂️ Managing anxiety was a daily practice, not a night-before fix.
05

Exam Day · Feb 2026

Exam Day: execution only

By exam day, my only job was to execute. I had a clear hour-by-hour plan, in-exam tactics, and anxiety protocols I’d rehearsed during mocks.

6:00–7:30 AM

Wake up, light breakfast, no new content. Quick scan of formula sheet + Ethics checklist.

7:30–8:30 AM

Arrive early, short walk outside, 3 deep breaths. No discussion of questions with anyone.

In-exam tactics

90 seconds/question, Ethics first, flag-and-move, no answer changes unless you find a concrete error in your reasoning.

Lessons learned

The calmer I stayed, the more I could hear the patterns I’d practiced for months.

  • ⌚ Time discipline came from mocks, not from willpower on exam day.
  • 🧠 Pre-written “confidence lists” the night before helped anchor belief.
  • 🚶‍♂️ Short walks and breathing were as important as any last-minute review.

Topic Breakdown

10 topics, different weights — not all of them deserve equal time.

Daily small bites. I treated Ethics like a language, not a chapter — constant exposure, vignettes from Schweser and CFAI, and a dedicated checklist of Standards to review in the final week.

I leaned on my engineering math background: connecting TVM, distributions, and regression to real data. Lots of short question blocks, very little passive reading.

Focused on diagrams and intuition: IS-LM, supply/demand shifts, and policy lags. I skipped deep derivations and prioritized question-driven learning.

Most of my deep work went here: multi-pass reading, custom flowcharts, and question sets tagged by statement type. Every FSA miss went into the error log with full explanation.

Treated this as an extension of engineering economics: WACC, capital budgeting, and leverage framed as design decisions with trade-offs.

I tied every valuation model (DDM, FCFE, multiples) back to examples from CIBC and public company filings, turning formulas into stories.

I reframed bonds as control systems: yield curve shifts as inputs, duration/convexity as response approximations. Heavy focus on question banks and error log entries.

Focused on payoff diagrams and intuition: long/short calls, puts, and basic strategies. Minimal formula memorization beyond what was necessary.

CAPM, efficient frontier, and risk/return trade-offs mapped nicely to engineering optimization logic, making this one of the smoother topics.

Treated as targeted memorization of structures and risk/return profiles. Kept it tight, leaning on summary tables instead of long notes.

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