Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
179 documents
This tutorial explains how to use Python notebooks for quantitative research, from collecting and saving exchange candlesticks to plotting price and trading-activity measures and testing strategies across symbols. It demonstrates paginated retrieval of…
This document presents a hand-coded version of a KDJ-style indicator intended to match the implementation described by TradingView, after the author observed that TradingView and FMZ produced different values. The calculation first finds the highest high and…
This note surveys seven pitfalls in quantitative investing: survivorship bias, look-ahead bias, storytelling, data mining, signal decay and trading costs, outliers, and asymmetric long-short payoffs. It explains how current index constituents can distort…
This article explains why a strong historical backtest may fail in live markets, particularly when a strategy has been tuned to a small or unrepresentative sample. It recommends splitting time-ordered data into a training period for parameter selection and a…
The article explains how the Kelly criterion can set leverage and capital allocation to maximize long-run compounded growth. Under its simplifying assumptions of normally distributed strategy returns, stable estimated means and standard deviations,…
This career guide outlines a self-study path for aspiring quantitative developers. It emphasizes strong programming and numerical implementation skills, with language choices shaped by likely workplaces: C++ and Python for broad applicability, while Java or…
This historical essay introduces John Maynard Keynes’s views on probability and uncertainty, drawing on his work in probability theory and economics. It contrasts objective probabilities, which may exist independently of human beliefs, with the estimates…
The document introduces Occam’s razor as a preference for explanations or solutions that require fewer assumptions when they account for the same observations. It stresses that simplicity is a guiding heuristic, not a scientific law, and that evidence must…
The document explains Value at Risk (VaR) as a loss threshold for a portfolio over a specified period at a chosen confidence level. It outlines common uses, including setting risk limits for individual strategies and portfolios, comparing risk across…
This essay applies ideas from philosophy of science to judging trading strategies. It contrasts testable claims, which make predictions that evidence could disprove, with claims that cannot be meaningfully tested. For strategies whose edge is inferred from…
This report overview describes the longstanding use of machine learning and artificial intelligence in quantitative investing. It notes that applications were already present during an early-1990s wave of interest, and that use continued in areas such as…
This article introduces Monte Carlo methods through random sampling examples, contrasting an approach that can return a promising answer without guaranteeing the optimum with randomized search that keeps trying until it finds a valid solution. It illustrates…
This discussion examines how starting portfolio composition can distort a simple account-value profit calculation for a cryptocurrency strategy. It compares two accounts following the same price move: one begins with a bitcoin and no cash, while the other…
This Chinese-language article surveys quantitative finance work through six role types: desk quant, model validation, research, quant development, statistical arbitrage, and capital modeling. It describes how these roles differ in their proximity to trading,…
The essay cautions against treating a single factor as a reliable cause of an investment outcome. It uses stock reactions to restructuring announcements to show that the same news can be welcomed in a bull market and ignored or sold in a weak market. It also…
This tutorial outlines a Fisher Transform indicator calculated from the midpoint of each price bar and the highest high and lowest low over a rolling period. A ratio between zero and one controls how much the newly normalized price affects the recursively…
This essay uses hypothetical investment examples to explain how returns compound asymmetrically: a loss requires a larger percentage gain to recover, and alternating gains and losses can produce a modest long-run result despite large individual moves. It…
This FAQ explains practical design and troubleshooting points for FMZ Quant Workflow strategies. It covers host-version requirements, JavaScript-only code nodes, sequential execution, trigger behavior, reading data from connected parent nodes, and sharing…
The document presents Benford’s law as a quantitative screening method for assessing whether company financial figures may have been manipulated. It explains that in many naturally occurring datasets, the first nonzero digit appears with a nonuniform…
A trader asks why MACD golden-cross detection sometimes disagrees with a charting exchange during five-minute backtests. The comparison covers both simulated and live tickers over a two-day period. The trader reports that some cross signals were incorrectly…
The article describes three ways strategy research can produce misleading backtests: look-ahead bias, excessive parameter optimization, and curve fitting. Its examples show how using a bar’s eventual close to trigger an earlier trade, or assuming a breakout…
This article argues that trading volume cannot be interpreted through a fixed rule that rising prices must come with rising volume. It recommends judging volume relative to the prior price and volume trend, market setting, and position within a move, with…
The article argues that traders can be misled by intuitive, familiar interpretations of price action and market narratives. Examples include buying a presumed leader after a technical pullback, expecting small caps to rise when large caps lead, or chasing a…
The document compares six programming-language options for building quantitative trading strategies: visual programming, EasyLanguage, Python, MATLAB/R, C++, and Java/C#. It evaluates them by capability, speed, extensibility, and learning difficulty, then…