Ankur Trapasiya

Finance and machine learning, worked through in code.

Notes from a backend engineer learning both. Each series reads in order, and every number comes from a script you can run.

Latest

  1. RL for trading: the market as an environment, and where that breaksNOV 15 2026
  2. Frictions and capacity: a strategy that works small can fail bigNOV 13 2026
  3. States and rewards: what you tell the learner decides what it learnsNOV 11 2026

Series · read in order

No. 01 · 14 parts

Quantitative Finance 1

return, risk and what a mix buys you

  1. 01Quantitative finance in ten ideas
  2. 02What is quantitative finance?
  3. 03Probability: how often something happens
  4. 04Expected value: what a risky choice is worth on average
  5. 05Returns compound: multiply, never add
  6. 06Mean and standard deviation: return and risk in two numbers
  7. 07Sharpe, Sortino and information ratio: return per unit of risk
  8. 08Covariance and correlation: why a mix is safer than its parts
  9. 09Beta and alpha: how much of the market you carry, and what is left
  10. 10Portfolio optimisation: choosing the weights
  11. 11Value is an estimate: discounting, growth and the margin of safety
  12. 12Why prices are hard to beat: efficiency, random walks and a roulette wheel
  13. 13What a trade really costs
  14. 14Data that lies: the bugs that look like alpha

No. 02 · 14 parts

Machine Learning 1

from a straight line to a forest

  1. 01Machine learning for quantitative finance in ten ideas
  2. 02What machine learning is: rules learned from examples
  3. 03The data is part of the model: splits, gaps and outliers
  4. 04Regression vs classification: predicting a number or a label
  5. 05Linear regression: the first model, a straight line
  6. 06Every feature has a timestamp: leakage, scaling and twin features
  7. 07Decision trees: a model made of yes/no questions
  8. 08Nearest neighbours: predict what similar past cases did
  9. 09Bias and variance: where a model's errors come from
  10. 10Scoring a model: a number means nothing without a split, a metric and a baseline
  11. 11Bagging: many copies of a model, averaged
  12. 12Random forests: bagging with one extra rule
  13. 13Boosting: models that learn from the last one's mistakes
  14. 14Choosing a model is a systems decision

No. 03 · 8 parts

Quantitative Finance 2

from raw prices to a portfolio you could run

  1. 01Quantitative finance, part two, in ten ideas
  2. 02Time series: returns are the data, prices are not
  3. 03Indicators: a price pattern turned into a number
  4. 04Alphas: a formula, a universe and a test of whether it worked
  5. 05The fundamental law: skill times the root of independent bets
  6. 06Portfolio construction: the frontier meets the real constraints
  7. 07Evaluating a strategy: where the return came from
  8. 08Frictions and capacity: a strategy that works small can fail big

No. 04 · 8 parts

Machine Learning 2

learning what to do, not just what to predict

  1. 01Machine learning for trading, part two, in ten ideas
  2. 02Reinforcement learning: learning from reward, not from labels
  3. 03MDPs: a state, an action, a reward and the value of what follows
  4. 04Q-learning: one update rule, repeated
  5. 05Exploration: when to try something worse on purpose
  6. 06Dyna: learn a model of the world, then practise in it
  7. 07States and rewards: what you tell the learner decides what it learns
  8. 08RL for trading: the market as an environment, and where that breaks