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.
16,761 documents
The document collects questions about calibrating one- and two-factor Hull–White models to cap prices, calculating instantaneous forward rates for the drift function, and obtaining short-rate paths after calibration. The replies distinguish the model’s short…
The document addresses how to reconstruct historical trading dates for the S&P E-mini futures contract from its first appearance onward, especially when a holiday calendar does not cover the earliest years. The suggested approach is to request a futures…
The document addresses how to select lookback lengths for trading indicators, warning against searching for the single period with the best historical result. Repeatedly optimizing a parameter on the same data creates data-snooping bias and can make a…
The document compares bootstrapping realized strategy returns with resampling the underlying price series, and explains why neither approach directly provides a convincing test of a strategy’s added value. Resampling strategy returns can obscure whether…
The document considers using principal component analysis on industry peers’ fundamental metrics to inform equity analysis. It provides variance shares and loadings for thirteen components, then asks what the first two components represent. The response…
The document asks how to measure and simulate gradual rises followed by sharp market declines. It presents threshold GARCH as a way to model return asymmetry, with APARCH as a related model family, and points to jump diffusions, often paired with stochastic…
The document asks how a Monte Carlo approximation of conditional value at risk can be expressed as a linear optimization problem. The approximation uses a threshold variable and the positive part of each scenario's loss beyond that threshold. Introducing one…
The document discusses how to calculate a stressed performance scenario under the PRIIPs framework when the product has a recommended holding period (RHP). The response distinguishes the historical data used to estimate stressed volatility from the future…
The document explains how dollar bars can be used in a machine-learning workflow despite their irregular time spacing. A bar retains the familiar structure of a time bar, such as open, high, low, and close, so these values can be supplied as model features.…
The document examines the assumption that a small order has negligible influence on market observations and price dynamics. That assumption can make historical backtests easier because the recorded order book or other market data are left unchanged by…
The document proposes a staged way to learn Monte Carlo methods, while emphasizing that the right material depends on the problem. It starts with simple simulations such as dice, coin tosses, estimating areas, and pricing a vanilla option. It recommends…
The document concerns Monte Carlo valuation of a barrier option when the underlying follows a jump-diffusion process. The question reports a simulated value that differs from the cited paper and jump probabilities that sometimes fall outside the valid…
The document explains why a monthly strategy that ranks stocks by price-to-earnings ratios and takes opposite positions in the top and bottom groups can show misleading backtest performance. Historical prices and earnings are not enough to reconstruct the…
The response discusses how implied volatility differences between expiration dates can affect calendar spread pricing. It suggests that when front-month implied volatility is below back-month volatility, calendars may have broader profit ranges and higher…
The document surveys ways to turn trading signals into mathematical strategy rules. One suggestion is to combine implemented signals in a factor model and estimate coefficients, which can produce a fitted formula and measures of statistical fit. The response…
The document asks how a futures backtester should represent the funds required when opening long or short positions. It considers using margin requirements calculated through CME's SPAN system and asks where historical margin data can be obtained, noting…
The document asks how to evaluate intraday strategies whose long-only trades may benefit from an asset’s longer-term upward drift. Its examples enter an S&P 500 or gold position at the market open and exit around midday. The concern is that apparent strategy…
The document discusses how to assess a market-making strategy that starts and ends each day flat, using a series of daily dollar profit and loss figures. One response says the annualized Sharpe formula based on mean and standard deviation is appropriate if…
The document considers an out-of-sample portfolio process that updates monthly. It uses a rolling 36-month covariance estimate to construct a minimum-variance portfolio, reverse-optimizes those weights into implied returns, combines the estimates with…
The document explains why Broadie–Kaya exact simulation is not automatically the practical choice for the Heston stochastic volatility model. Its main advantage is that it can simulate across arbitrary time steps without the time-discretization bias…
This discussion explains the basic direction of a pair trade when two stocks diverge from a relationship assumed to persist. Under that assumption, the trader shorts the stock judged relatively overpriced and goes long the one judged relatively underpriced.…
The document considers how to compare hedging strategies for two models that price options on WTI crude oil futures. It questions whether a traditional delta hedge is meaningful when the underlying is a futures contract, since buying or selling futures does…
The document asks where to find algorithmic trading strategies that were once profitable but later stopped working. Its answer points to public compilations of strategies, while cautioning that detailed disclosure is unlikely. It gives several reasons:…
The document gives a standard-error approach to estimating how many observations are needed to assess a strategy’s returns. It relates sample size to measured return variability, a chosen confidence level, and the desired width of the confidence interval.…