2026

Started growth.jonasruilong.com as the personal growth hub so I remember what I have done and learned so I dont make the same mistakes again :(

• a timeline of dated snapshots, living threads that deepen over time, plus mission, books, and relationship.

• File-based markdown in git; visibility so some entries stay private or title-only
• Snapshots can open into a thread (or point at an edu note)
• Shared login when viewing protected entries

Achievements:

• Growth site live with the content model in active use
• Snapshots, threads, mission, books, and relationship areas wired together

Started edu.jonasruilong.com as a personal lecture-notes library — Kanti Wettingen → ETH Zürich → Baruch

• Institution → term → course drill-down; file-based markdown in git
• FastAPI + Jinja2; math-first reading (KaTeX, plots, Mermaid)
• OAuth via shared auth when private materials need role gating

Achievements:

• First version of the education website is live
• Notes for Analysis 1&2, ODE/PDE course is uploaded and supports multiple graphs, formulas etc.

Started jonasruilong.com as the personal hub — story and navigation into other apps.

Explored layout ideas and chose a scroll-driven 2.5D “scroll opera”:
Welcome first, themed worlds as portals, Dock later.

• Astro + React for the interactive front end.
• Thin Python backend to serve the build

Achievements:

• First version of the homepage is live
• Good visual design for the homepage
• 3D render of a Dragon, so learnt a bit of Blender as well
• Cut scene between Welcome and EDU
• Heart for Pau

Lecture 1

• Introduction to ODE
• First-order ODE
• Separable ODE
• First-order linear ODE
• Default probabilities and hazard rate
• Exact ODEs

Interview:
Sanity check at the end is useful, you can tell the interviewer that you will do a sanity check

Did more but not going to put it here for now, as I already have it on my edu site

2 Weeks of IMC Prosperity 4 Challenge

Analyse the Problem & Data clearly first

• Bot behaviour

• Price movements etc

Generate clear logic first, then a plan on how to implement it and only then generate the program

Pay attention to small details

• Sometimes how they scale, or had to adjust challenges might reveal information on the data

Should we use ML?

• Well defined problem

• Lack of easy solution, can provide examples but can't code

• High quality data

• Clear and meaningful Evaluation

• Loss Function

• Solution is justified

Decision Trees

• Bagging

• Boosting (Adaptive boosting): combining small decision tress

• Gradient Boosing: fits Trees on error residuals (like gradient descent, figure out error and mimimize it)

Portfolio Management:

• Alpha ($\alpha$): The "edge." How much an investment outperformed its benchmark

• Beta ($\beta$): Market Risk; Volatility relative to the market

• Sigma ($\sigma$): Standard Deviation; The total volatility of an asset. How much its price swings

• Lambda ($\lambda$): Leverage; In options to show % change in option price per 1% change in the stock.

Options:

• Delta ($\Delta$): Price Sensitivity. How much option's price changes for every $1 move in the underlying

• Gamma ($\Gamma$): Acceleration. The rate of change in Delta. It tells you how "stable" your Delta is.

• Theta ($\Theta$): Time Decay. How much value the option loses every day as it approaches expiration.

• Vega ($\nu$): Volatility. How much option's price changes based on a 1% change in implied volatility.

• Rho ($\rho$): Interest Rate. Sensitivity of the option price to changes in risk-free interest rates.

Lec 04

Arbitrage opportunity

Pice bounds for options

Put-call parity

Binomial model

Blackscholes

Lec 05

Pricing by arbitrage

We have multiple linear independent assets (think of polymarket contracts)

Arbitrage portfolio

Lec 06

Economic analysis of asset markets

• Pareto-optimal allocations

• Contract curve

• Market clearing condition: everything sold by one is bought by another

• Comotinicity

• Price responce to risk changes

Lec 07

Uncertain environments:

• compound lottery $λL+(1−λ)L′$

• Preference relation

  • Complete $for any L and L′∈L(Z ) we have L ⪰L′or L′⪰L$

  • Transitive $(L1 ⪰L2 and L2 ⪰L3) ⇒L1 ⪰L3$

• VNM (Von Neumann and Morgenstern) Expected Utility Theory

  • Independence

  • Continuity

Risk Averse

• Utility Index u

• Expected utility function v

• VNM preferences is risk averse if and only if her utility index is concave

Risk increase

• SOSD: Second-order stochastic dominance: if dominant then less risky than other

  • $EL[x] ≥EL′[x]$

  • $\int_{-\infty}^{y} F_L(x)dx \leq \int_{-\infty}^{y} F_{L'}(x)dx$ less weight on smaller returns

• Mean preserving spread -> more risk

• White noise: mean 0, can be seen as risk

Quantifying Risk Aversion

• Certainty Equivalant: uncertain lottery utility compared to a certain lottery

• Risk Premium: Expectation - certainty equivalant $\pi_L = E_L[x] - e_L$

• Risk Aversion coefficient

Exercise notes for Financial Engineering.

Exercise 1

• what financial engineering is
• no-arbitrage principle and Law of One Price
• present values and discount factors
• no-arbitrage forward price
• payoff diagrams for forwards, calls, and puts
• put-call parity
• simple static replicating portfolio

Exercise 2 ipynb

• one-period binomial model
• risk-neutral probability
• replicating portfolio
• multi-period (CRR) binomial tree
• Cox-Ross-Rubinstein parameterization
• converges to Black-Scholes

Continued taking Large Scale AI lectures.

Lecture 3

Supercomputer lecture by CSCS

Lecture 4

Communication overhead

Lecture 5

Model & Data Parallelism

Lecture 6

Mixture of Expert Models

Halbtax: 190Fr +

Zurich - Baden 14.2Fr a day pass

Halbtax plus for 800Fr get 1000Fr (travel for 20% off)

Halbtax plus for 1500Fr get 2000Fr (travel for 25% off)

Monats Abo: 242 * 12 = 2904
Jahres Abo (Baden Zürich): 2226

GA nr 1 3995Fr, nr 2 2860

Generally 30% withholding tax on dividends for foreigners

Swiss-USA tax treaty

• Filling out 8Wben I can reduce it to 15%

• I can fill out DA-1 when filling taxes in Switzerland and get tax credit (must be >100$)

Tax Free / not subject to 15% for Swiss:

• Capital gains, interest on bonds

Estate Tax:

• Need to beware in case of death as US will charge high amount of estate tax. Can prevent by buying ETFs based in foreign countries (UCITS)