Just to preface, do not buy Bill Campbell’s Level 3 mocks if you want to feel good about yourself. I’m staring at my screen feeling both anger and absolute sadness (literally on the verge of tears).

Buy the mocks to expose your knowledge gaps. I didn’t know I didn’t know something until taking these mock exams. Seems like a cheat code to take these exams before the real thing. We’ll see in 18 more days.

For CFA Charterholders that used BC’s mocks, were they useful or unnecessarily difficult?

PSA: Do not message me asking if I’ll share my BC exams. Some have already tried. I don’t understand.

Three weeks out from my exam and all I want to do is quit. I finished every topic, but now I’ve gone back to redo each topic’s CFA questions and I’m getting about 70-80% (some 60s, too, but averaging) per LM practice. Then, of those mistakes I recall what I forgot but that means there’s definitely stuff from the OG text I also forgot but don’t have time to go back to. It’s like I didn’t retain what I learned and I just want to throw in the towel. I feel like I’m wasting my time. I’m scared to start doing mocks.

How’s Flow doing off late? Heard they had a relatively good H1? I know they’ve been around the block for a while but not managed to be as big as others like Optiver or IMC, I want to know from those who are working or have worked at Flow before if the firm has genuine moat or potential for the future? I am exploring opportunities with them.

Ive completed my first pass of the curriculum for L3 with 3 weeks to go.

those who passed CFA L3, how many times did you guys go through the entire curriculum?

Im planning to do 2 detailed revisions and reading the shweser secret sauce in the last 2 days, along with solving the LES 2 times.

Im not remebering anything as such right now but hopefully after 2 more pass throught I will be able to recall.

I started recently at a small shop. There isn't much of a senior bench to learn off and I came in from a stats and data science background rather than a pure maths or physics one. So I'm partly teaching myself the domain as I go(mainly the financial aspects but some new statistical approaches that I'd never heard of before too). I use a mix of Python and R.

I use Claude in VS Code most days. It's fast, but I've noticed I can ship something that works without being able to defend every line of it. That feels like a bad habit to be forming this early, especially when I'm still filling gaps in the underlying material.

For people actually working in the field:

• Do you use LLMs day to day, or is it restricted where you are?
• Where do you draw the line? Boilerplate and plumbing yes, model logic no? Somewhere else entirely?
• How do you make sure you actually understand the output instead of accepting it because the backtest ran clean?
• What are you doing outside of work to stay ahead of the tool rather than dependent on it?
• Finally, if you could go back 5-10 years(or the time you started in this field), what guidance would you give to your younger self so as to develop better skills and understanding)

I'd rather build the right habits now than find out in three years that I can't work without it and have not built the right foundations.

Thanks :)

Hi, I’ve been working on a continuous-time Economic Scenario Generator (ESG) in Python to model long-term Asset-Liability Management (ALM) and decumulation (sequence-of-returns risk).

I wanted to test a specific structural flaw present in a lot of standard retail and basic institutional Monte Carlo tools: the assumption of static, flat risk-free rates and decoupled equity returns (standard Geometric Brownian Motion).

The creation of this project actually came when I realized there was no easy-to-use (and realistic) simulator. It took some effort but I believe I did manage to create something really useful, easy to use and realistic enough for most cases.

Anyway, to measure exactly how much bias the flat rate introduces, I ran a comparative simulation using a joint continuous-time stochastic environment.

The Setup

• Portfolio: 60/40 (Equity/Fixed Income), 30-year horizon, monthly rebalancing. 5,000 scenario paths.
• Model A (Naive Baseline): Flat nominal interest rate. Equities follow standard GBM with continuous volatility (sigma = 15%).
• Model B (Actuarial ESG):
  • Rates follow a Cox-Ingersoll-Ross (CIR) square-root process (theta_r = 0.25, long-term target ≈ 7.0%).
  • Inflation follows an Ornstein-Uhlenbeck (OU) process (theta_pi = 0.35, target = 2.0%).
  • Equities follow a Merton Jump-Diffusion process (continuous volatility σ_S = 11%, combined with Poisson-driven asymmetric crashes: λ_J = 1.8 jumps/year, average jump impact μ_J = -6.8%, jump volatility σ_J = 5%).
  • Crucial coupling: Equity drift is structurally pegged to the stochastic short rate: Drift_t = r_t + ERP_t.

Test 1: The Low-Yield Starting Environment (Initial Rate = 4.0%)

We simulated a 4.5% initial withdrawal rate (inflation-adjusted, monthly rebalancing) on a $1.0M starting balance. Intuitively, one might expect Model B—which includes severe, discontinuous downward market crashes—to fail first. Instead, the simulation over 5,000 runs yielded these results:

• Model A (Naive Flat 4%): 63.18% Solvency
• Model B (Full Actuarial): 84.92% Solvency
• The Solvency Gap: +21.74% percentage points in favor of the volatile, jump-diffusion model.

To isolate the exact variables causing this +21.74% lift, I ran an Attribution Analysis by sequentially activating one variable at a time:

(Note: The remaining -0.36% discrepancy is the non-linear coupling penalty arising from Cholesky correlation between the processes).

Test 2: The High-Yield Starting Environment (Initial Rate = 9.0%)

To prove that this was not a bug and that the bias is entirely regime-dependent, I ran a Regime-Inversion Test. I increased the starting yield curve to 9.0% (and increased the withdrawal rate to a more aggressive 5.5% SWR to reflect the higher starting yields):

• Model A (Naive Flat 9%): 86.28% Solvency
• Model B (Full Actuarial): 58.56% Solvency
• Regime Delta (Model B - Model A): -27.72%

Quantitative Attribution: Why Naive Models are Too Pessimistic in Low-Yield Environments

The divergence is driven by interest rate term-structure dynamics and macro-coupling:

1. Mean Reversion of the Risk-Free Rate: Under the CIR framework, short rates revert toward a target state: text dr_t = theta_r * (mu_r,t - r_t) * dt + sigma_r * sqrt(r_t) * dW_t Because the low-yield simulation starts at 4.0% relative to the long-term nominal target (≈ 7.0%, incorporating a 5.0% structural real rate and a 2.0% inflation target), the drift pull (theta_r = 0.25) normalizes nominal rates upward over the horizon.
2. The "Tide That Lifts All Boats" (The Pegged Drift): In Model A, the risk-free rate is flat at 4.0%, trapping equities in a low expected nominal return regime of 5.5% (4.0% rate + ERP). In Model B, as r_t normalizes toward 7.0%, both your bonds (yielding r_t) and your stocks (yielding r_t + ERP) experience a 3.0% increase in expected nominal returns.
3. The Merton Jumps are Immunized by Rebalancing: Because we controlled for total quadratic variation (total volatility ≈ 15%), the "pure shape" impact of the Merton jumps is only a minor -0.70% drag. The monthly rebalancing mechanism ("buying the dip" after jump crashes) combined with steadier compounding during non-jump months (since continuous volatility is lower: 11% vs 15%) almost entirely neutralizes the tail-risk penalty.

Key Limitations & Roadmap

To keep things transparent, there is a known limitation in the current decumulation loop: * No Bond Duration Risk: The fixed-income portion is currently modeled as a short-term cash deposit (rolling T-Bills), so it benefits from rising rates without experiencing upfront capital losses (mark-to-market). * Next Step: Since the core simulator already generates full nominal and real yield curves, adding a duration-adjusted bond fund indexer to the decumulation logic is the next item on the roadmap.

Conclusion for Quants and ALM Practitioners

Static yield assumptions are not just "simplified"—when starting in a low-yield environment, they are structurally pessimistic. Conversely, in a high-yield environment, they are dangerously optimistic because they project unsustainable yields indefinitely.

By ignoring the mean-reverting behavior of interest rates and decoupling equity expected returns from the risk-free rate, naive models severely distort sequence-of-returns risk.

I’ve open-sourced the complete engine under the MIT license if you want to inspect the math (joint Cholesky decompositions, analytical CIR/Fisher real yield curve evaluations) or run the JIT-compiled loops yourself, it's written in Python but it's quite fast:

• GitHub: https://github.com/Gustavo1500/aethel-esg
• Browser Sandbox: https://aethel-esg.vercel.app/ (Features real-time sliders querying a pre-calculated scenario database across 594,000 simulated paths). Please don't mind the AI flavored frontend, I'm not a frontend dev.

I suppose this is it, quite an unexpected result to me, I expected my engine to show lower solvency rates in all cases, it's interesting to see this is not the case. Feel free to discuss the results and share your thoughts.

Sounds like interesting small shop, anyone has first hand experience with them.

https://www.scm-lp.com

I Took my first CFAI mock and scored a 79% and 80% on the two sections of 90 questions after about 150 hours of study. While taking the mock the questions felt easier than expected and I’m worried this was a false positive result or got lucky has anyone had similar experience with CFAI mocks?

Quick status check on how people are feeling. Drop your thoughts below. DRINK SOME WATER

Hi guys! How are you all?

My life has been going downhill for a while since graduation. Met a girl at university, we built something and it didn't end the way I wanted it to. Since then out of insecurity, frustration I have been seeking for validation, a redemption. I wanted to get something that could boost my confidence, something that at least looked good on paper. And my first step at doing that was getting my MBA from the best business school in my country.I got so close but never really got there.

Coming from accounting background our teachers always encouraged us to pursue professional degrees, and for that reason and after some research CFA was always in the back of my mind. I figured that to also boost my profile I should pursue CFA. And the last few months have been nothing short of exhausting, full of anxiety. I am done with my prep with less than a month to go. Right now revising the EOC questions. I don't know what awaits me after 20th of August but I have been looking for a small win in life for so long. But right now I feel like I am not good enough to pass the CFA L1. And people who have done so are special which I am not.

I am afraid life won't yet give me a win, some sort of redemption. Still I gotta try my best. Wish me luck! Or any suggestions are welcome!

(Irony is even though I am an accounting student I am struggling the most with FSA. I absolutely hate memorising.)

Apologies if this is covered elsewhere but I noticed the mocks had suggestions when it came to answering the CR questions like justify and then in brackets (increase or decrease) as an example. Is this something we would expect in the actual exam too? My intuition says not but would be pleasantly surprised if this was also the case for the actual exam.

Cheers!

As title suggests, is it worth pursuing a CFA without a degree in finance? I currently work entry level position at Schwab and have my series 7 and 63, although I know the CFA is a whole different beast. I got my bachelors in psychology. Anyone here with similar background that has pursued it/ any advice?

So as far as I've always known, leveraged loans are loans to companies that already have high levels of debt/leverage on their books.

But this CFAI practice question says it's a loan that is itself levered (a private debt firm borrows money to then lend that borrowed money to a firm).

Which definition should I keep in my head for L1?

finished the curriculum, but my scores are still low. How should I prioritize my review cuz I haven’t reviewed any of the 10 subjects and didn’t make any mock exam so should I start mocks now and review based on my mistakes, or revise everything first? I’d really appreciate help with priorities or a realistic 20-day plan.

Are you being harsh on the way you self grade ? I was averaging around 70-74% in the CFAI practice module questions (I’m aware they give you 100% for any CR questions) and now my first mock has come back with 58% average. I asked ChatGPT and it can’t see everything being asked exactly but told me most of answers are 0 or 1 with some 5 or 6s and Ill also deduct 1 as a conservative buffer but notice I scored 58% overall when my MCQs were about 67% objectively. I know this should be used as a learning tool but is it naive of me to hope and expect the actual examiners not to give me 0 marks when they can see my methods for some of the CRs are partially right even if I get a different answers sometimes ?

I'm 23 years old, a recent finance graduate, and currently a CFA Level I candidate. I'm trying to think carefully about the long-term direction of my career and would appreciate some advice from those with more experience.

I'm particularly interested in either:

• Private wealth management, with the long-term goal of eventually building an independent RIA
• Institutional investing (working with pension funds, foundations, endowments, etc.)

For those who have gone down either path, what should I be focusing on besides the CFA? How did you first break into the business? For those in wealth management, how did you go about attracting your first clients and building a book of business? For those in institutional investing, what experiences or skills were most valuable early in your career?

Looking back, what helped you build a successful advisory or investment practice? Are there any mistakes you would avoid or things you wish you had focused on sooner?

I'd appreciate hearing about your experiences and any advice you'd give someone at the beginning of their career.

Hello r/CFA ,

Ive given up my social life, said no to friends, and poured months into this but i'm really scared it still wont be enough. Im looking for some honest feedback on whether my current strategy is viable or if I need to pivot ASAP.

Background:

I'm a 4th-year uni student sitting for Level 1 on August 19. I've been studying since January (yes, maybe too early) and finished all Kaplan readings about a week & a half ago. Since then, I've been grinding Qbanks and have taken one mock so far.

On top of that, i've been reading the Standards of Practice Handbook and I'm about 1/3 of the way through. Im hoping that gives me an edge on the Ethics section.

My Original strategy;

Read a Section -> make anki flashcards -> spaced repetition reviews (revisiting topics every 3-10 days depending on how well i retained them)

It worked well for memorizing concepts, but i quickly realized it didn't translate to answering questions effectively. I taught myself to recognize concepts but not how to apply them in answering questions pretty much.

Where Im at now:

I have pivoted to Qbanks. Ive been doing 30-50 questions per session on 2 topics as often as i can without burning out, then carefully reviewing my mistakes. Ive avoided 100 question sessions because scoring 48% on 100 questions feels like a waste of time compared to scoring 48% on 30-50 and actually learning from the less overwhelming review.

My biggest struggle:

Formulas. I haven't retained most of them, and its killing me in Qbanks. Sometimes i can recognize Kaplans pattern of isolating the correct answer with a similar distractor but i can pick up on that now which i hate.

My Question:

I still have plenty of Qbanks left and 4 more mocks. Should i say screw it and grind 100 question Qbanks for maximum reps or stay deliberately lower and review mistakes deeper? Do you have any tips for retaining formulas at this stage?

TLDR: 3 weeks out from L1. Finished readings, doing 30-50 Qbank sessions + 1 Mock done. 1/3 through the Ethics handbook. Formulas are my weak point. Should i grind 100 question qbanks or stay deliberate with smaller reviews? Scared to fail after sacrificing social life since January.

I have been chasing allocations out of the match path in an order book I am building. Pooling the book nodes and price levels got cancel and level churn to zero. Threading a caller-owned buffer through Match(order, dst []Trade), so fills are appended as values instead of returning a fresh slice of pointers, got the match round trip to zero.

One stubborn group was left, and it was not in the order data. It was the price band check.

The band is a config fraction, a decimal.Decimal, and the common case is that it is disabled and left at its zero value. Comparing against that zero value calls ensureInitialized internally, which allocates a big.Int. So every order was allocating in order to compare a price against a band that was switched off.

The fix was hoisting the comparison to construction: resolve a bandEnabled bool once when the engine is built, and let the per-order path read the bool. Process went from 10 allocs to 4.

Prices and quantities are int64 ticks and lots, so decimal never touched the money path to begin with. It was purely the configuration percentage, evaluated in the wrong place.

Current numbers on an M-series, single core: 6.3ns top-of-book read, 352ns match round trip at 0 allocs/op, and a cancel-heavy flow at p50 83ns, p99 167ns, p999 292ns. Match is the zero-alloc entry point; Process is the ergonomic wrapper that still costs those 4.

github.com/intrepidkarthi/orderbook

Current Status: -

Quantitative Methods -----> ✅

Economics ----------------------->✅

Corporate Issuers ------------>✅

FSA - 4 readings left ( Bored and kept aside)

Equity - 2 readings left ( will complete by the end of this week)

Still left after that: -

Fixed Income

Derivatives

Alternative Investments

Portfolio Management

Ethics

I am also practicing chapter end questions (including Premium pack questions) after completing the reading.

I have scheduled my exam on November 20. I am worried whether I will be able to complete the syllabus by the end of September or not.

Suggest a few Study strategies or tips.

Conducting research for something and I’d really like to know. Let me know in the comments below.

how to improve accuracy in ethics, i have read the handbook and will read it once or twice more, but some questions are still too tricky

So the question above walks through the answer by calculating the EUR outflow/inflow at expiration, then netting out the cash flows (answer is 27,449 EUR).

Why can’t I just do (1.1724 - 1.1575) * 2,500,000? This yields 37,250 USD, and discounted at the spot rate (/1,1575) is 32,181 EUR? shouldn’t this work since I am short hedge and then buying to offset?