1. Alphas: a formula, a universe and a test of whether it worked

    In trading slang an alpha is a formula that turns today's data into a forecast of the next few days' return.

    Quantitative Finance Series 2tradingstatistics

  2. RSI, stochastic %K and PPO: three oscillators on one crash

    An oscillator is an indicator that swings inside a fixed range, so a reading means the same thing at any price.

    tradingstatistics

  3. MDPs: a state, an action, a reward and the value of what follows

    A Markov decision process is the formal shape of learning by reward.

    Machine Learning Series 2machine-learningreinforcement-learning

  4. The SMA cross: two averages, and the day they swap places

    A moving-average cross is the day a short average of the price overtakes a long one, and it is the oldest way to say 'the trend has turned'.

    tradingstatistics

  5. Bollinger Bands: an average with a margin that breathes

    Bollinger Bands are a 20-day average of the price with one line two standard deviations above it and one two below.

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  6. Indicators: a price pattern turned into a number

    A technical indicator is a summary of recent prices: an average, a distance from the average, a return over a window.

    Quantitative Finance Series 2tradingstatistics

  7. Reinforcement learning: learning from reward, not from labels

    A supervised learner is handed the right answer for every example and learns to predict it.

    Machine Learning Series 2machine-learningreinforcement-learning

  8. Machine learning for trading, part two, in ten ideas

    Ten ideas take machine learning from predicting a number to deciding what to do: a learner can be taught by reward instead of by labels, a decision is judged by…

    Machine Learning Series 2machine-learningreinforcement-learning

  9. Time series: returns are the data, prices are not

    A price history is ordered in time, and three habits follow: work with returns, because a price is almost perfectly correlated with yesterday's price and a…

    Quantitative Finance Series 2statisticsreturns

  10. Quantitative finance, part two, in ten ideas

    Ten ideas take a trading idea from raw prices to a portfolio you could actually run: returns are the data, an indicator is a summary of the past, an alpha is a…

    Quantitative Finance Series 2portfoliorisk

  11. Backtesting in Python

    A backtest runs a trading rule over past prices and reports what would have happened: the return, the risk, the number of trades.

    Engineeringpythonbacktesting

  12. Building models with scikit-learn

    scikit-learn is one interface, fit and predict, wrapped around every classical machine-learning method, plus the scaffolding that turns a model into a result…

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  13. Choosing a model is a systems decision

    Accuracy is one column in the table. Training time, time per prediction and its worst case, whether the model keeps the data, whether it can be explained, how…

    Machine Learning Series 1machine-learningvolatility

  14. The Python stack for machine learning

    Almost all machine learning work is done in Python, through a few dozen libraries that each exist for one kind of job.

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  15. Boosting: models that learn from the last one's mistakes

    Boosting builds a strong model from many weak ones trained in sequence, each focusing on what the previous ones got wrong.

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  16. Random forests: bagging with one extra rule

    A random forest is bagging with one extra rule: at every split, each tree may only choose from a random handful of the features.

    Machine Learning Series 1machine-learningensembles

  17. Portfolio optimisation: choosing the weights

    Portfolio optimisation chooses how much to put in each asset so that, for the risk you accept, the expected return is as high as possible; it works because…

    Quantitative Finance Series 1statisticsportfolio

  18. Bagging: many copies of a model, averaged

    Bagging trains many copies of the same model, each on a slightly different random resample of the data, and averages their answers; the copies make different…

    Machine Learning Series 1machine-learningensembles

  19. Beta and alpha: how much of the market you carry, and what is left

    Plot a stock's daily return against the market's and fit a straight line.

    Quantitative Finance Series 1statisticsrisk

  20. Sharpe, Sortino and information ratio: return per unit of risk

    A return on its own means little until you know the risk taken to get it; these three ratios divide extra return by risk, and they differ only in what they…

    Quantitative Finance Series 1statisticsportfolio

  21. What a trade really costs

    A trading idea becomes profit only after paying the bid-ask spread, walking the order book, moving the price and, for a short, borrowing the shares.

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  22. Scoring a model: a number means nothing without a split, a metric and a baseline

    A model's score is meaningless on its own.

    Machine Learning Series 1machine-learningevaluation

  23. Bias and variance: where a model's errors come from

    A model's error on new data comes from three places: bias (it is too simple to capture the real pattern), variance (it changes too much depending on which data…

    Machine Learning Series 1machine-learningoverfitting

  24. Data that lies: the bugs that look like alpha

    Most of the alpha in a backtest is a data bug with a name: a feature that used tomorrow's number, a universe built from today's survivors, a thousand ideas…

    Quantitative Finance Series 1backtestingstatistics

  25. Covariance and correlation: why a mix is safer than its parts

    Covariance and correlation measure whether two things tend to move together; in a portfolio, that one number decides how much of the risk cancels out, which is…

    Quantitative Finance Series 1statisticsportfolio

  26. Nearest neighbours: predict what similar past cases did

    k-nearest neighbours keeps every training row and, asked about a new case, finds the k most similar old ones and averages their answers.

    Machine Learning Series 1machine-learningvolatility

  27. Why prices are hard to beat: efficiency, random walks and a roulette wheel

    An efficient market is one where no reliable edge survives after costs, because any pattern worth money is traded away by the people who find it.

    Quantitative Finance Series 1statisticstrading

  28. Decision trees: a model made of yes/no questions

    A decision tree makes a prediction by asking a short series of yes/no questions about the input, like a flowchart an expert would draw; it learns which…

    Machine Learning Series 1machine-learningdecision-trees

  29. Value is an estimate: discounting, growth and the margin of safety

    A price is observed; a value is estimated.

    Quantitative Finance Series 1valuationstatistics

  30. Mean and standard deviation: return and risk in two numbers

    The mean tells you where the middle of your data is, and the standard deviation tells you how far a typical value strays from that middle; in finance the first…

    Quantitative Finance Series 1statisticsreturns

  31. Returns compound: multiply, never add

    Returns chain by multiplying growth factors, not by adding percentages, and that one fact explains why +50% then −50% loses money, why a five-year gain is not…

    Quantitative Finance Series 1statisticsreturns

  32. Every feature has a timestamp: leakage, scaling and twin features

    For every feature a model uses, ask when its value became knowable and whether you could compute it live.

    Machine Learning Series 1machine-learningvolatility

  33. Linear regression: the first model, a straight line

    Linear regression draws the straight line that best fits your data, where "best" means the total of the squared misses is as small as possible; each number it…

    Machine Learning Series 1machine-learningregression

  34. Expected value: what a risky choice is worth on average

    Expected value is the average result you would get if you could repeat a risky situation many times: multiply each possible outcome by its probability and add…

    Quantitative Finance Series 1statisticsprobability

  35. Regression vs classification: predicting a number or a label

    Most machine learning answers one of two questions: "how much?" (regression, the answer is a number) or "which one?" (classification, the answer is a category).

    Machine Learning Series 1machine-learningvolatility

  36. Probability: how often something happens

    A probability is a number between 0 and 1 that says how often something happens in the long run.

    Quantitative Finance Series 1statisticsprobability

  37. The data is part of the model: splits, gaps and outliers

    Which rows a model trains on, how the test rows are chosen, what is done about missing values, and how far one wild value can pull a fit all change the result…

    Machine Learning Series 1machine-learningvolatility

  38. What machine learning is: rules learned from examples

    Machine learning is teaching a computer by example instead of by instruction: you show it many past cases with the right answers, it works out the pattern on…

    Machine Learning Series 1machine-learningfraud

  39. Machine learning for quantitative finance in ten ideas

    Ten ideas carry machine learning for finance: the target decides the problem, the data is part of the model, a linear model is a weighted sum, every feature has…

    Machine Learning Series 1machine-learningensembles

  40. What is quantitative finance?

    Quantitative finance is the practice of making investment and risk decisions from measured numbers and tested rules rather than judgement alone.

    Quantitative Finance Series 1quantitative-financecareers

  41. Quantitative finance in ten ideas

    Ten ideas carry most of quantitative finance: returns multiply, a return means little without the risk taken to earn it, correlation decides how much risk…

    Quantitative Finance Series 1statisticsportfolio

  42. Letting my homelab do things on its own

    A chat window only answers when I ask. I wanted my homelab to also do small jobs for me on its own, so I tried two AI agents, OpenClaw and Hermes Agent.

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  43. Switching to a smaller model

    I expected a new, smaller model to be slower on my homelab, and at first it was. Then I found one setting that more than doubled its speed.

    homelabllmself-hosting

  44. Setting up my homelab

    I wanted my own private AI, running at home, where nothing I ask leaves the house.

    homelabllmself-hosting