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 answer treats a future implied volatility surface as a conditional modeling problem. In the direct approach, simulate outer paths under a calibrated model; at a future state, launch an inner Monte Carlo from that state, price options across strikes and…
The document outlines the Daniel, Grinblatt, Titman, and Wermers approach to evaluating stock strategies or funds against characteristic-matched benchmarks. It describes forming 125 portfolios sorted by size, book-to-market, and momentum, then assigning…
The document considers whether a technical trading rule for cryptocurrency can be assessed with receiver operating characteristic analysis. The proposed labeling marks a buy signal as correct when the underlying asset has a positive return that day and…
The document surveys choices involved in calculating a Sharpe ratio: sampling frequency, simple versus logarithmic returns, and annualization. The answers describe common use of daily or monthly observations, with weekly data appearing less often, and show…
The question asks how to plot a European option’s value across many underlying prices without rebuilding the QuantLib example for each value. The accepted response illustrates setting up a vanilla option with an analytic European pricing engine, attaching…
The discussion considers how to estimate drift from one sampled path when the process is non-stationary, using arithmetic Brownian motion as a simple case. It explains the core limitation of averaging increments: precision depends on the total observation…
The document considers whether adjusted historical closing prices create lookahead bias when used to label stocks by future returns or in backtests. Its central point is that returns calculated from adjusted closes can represent a portfolio that reinvests…
The document asks how to calculate the probability that a geometric Brownian motion starting above a lower barrier touches it by a fixed time. It maps the problem to the minimum of a drifted Brownian motion by taking logarithms, then applies a…
The document explains why trading institutions use backtests despite the inability of historical performance to guarantee future results. One response frames backtesting as part of a scientific process: start with an economic, behavioral, technical, or other…
The document derives the asymptotic variance of an estimated Sharpe ratio using the generalized method of moments and the delta method. It defines the mean return and return variance as moment parameters, constructs their covariance matrix from centered…
The document describes a research problem involving a return series, a conditional mean, and a conditional variance estimated from factor returns. A first trading approach compares each day's realized return with its conditional expectation and trades when…
The document explains why statistical languages such as R or Matlab are commonly useful for model development and backtesting, while lower-level languages are often chosen for stable production systems where performance matters. It presents prototyping as a…
The document surveys ways to stabilize global minimum-variance portfolio weights when the sample covariance estimate produces extreme allocations. It gives the unconstrained solution in terms of the inverse covariance matrix and describes an equivalent…
The document examines an hourly machine-learning signal that predicts the direction of returns measured from an interval’s average price. The key timing issue is that the signal becomes available only after that interval ends, so a backtest that assumes…
The document asks how machine-learning methods such as random forests, support vector regression, gradient boosting, and nearest neighbors compare with GARCH when forecasting volatility beyond a single step. It also asks whether these models use lagged…
The document asks whether Monte Carlo simulation can benchmark tree methods for pricing American options with discrete dividends, and how such a simulation should be built. The response explains a key difficulty: Monte Carlo simulates paths forward, while…
The document discusses how to obtain historical opening auction prices for S&P 500 stocks, distinguishing the auction print from a generic first trade. It describes primary exchange price data as a source of opening and closing auction prices, since the…
The document discusses staircase-shaped prices from an explicit finite-difference solution of the Black–Scholes PDE for a digital option. Its central explanation is grid based: with a discontinuous terminal payoff, the computed value depends on which…
The document concerns constructing a business-day calendar for historical NYSE price analysis. The researcher wants to omit weekends, exchange holidays, and exceptional closures so that observations align with days when trading occurred. A sample calendar…
The discussion asks how to evaluate a stock price forecasting algorithm separately from the trading rules that convert forecasts into trades. One response suggests repeatedly backtesting over shifted date intervals and averaging measures such as net profit…
The document considers whether daily or weekly observations are adequate for testing a strategy over a six-month lookback. It argues that weekly data would yield too few observations for meaningful estimation and suggests intraday data as one way to increase…
The discussion considers ways to search historical stock charts for user-defined or familiar patterns. Suggested approaches include time-series matching, dynamic time warping, classical technical pattern recognition, and unsupervised discovery through…
The document compares storage approaches for large options and time-series tick datasets used primarily in backtesting. When the dominant access pattern is reading sequentially between a start and end time, flat files with binary encoding can avoid the…
The document raises concerns about using unfiltered historical Value at Risk as a desk-level measure for comparing strategies and allocating risk capital. It contrasts this with a preferred use of a common holding period and confidence level, robust…