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 clarifies why log returns and simple returns can appear nearly identical in a price series. A log return is the logarithm of the ratio between consecutive prices, while a simple return is the price change divided by the previous price. Expanding…
The document considers how to estimate partial or full fills for a limit order placed at the best bid or ask in a FIFO order book. It recommends using historical data to examine whether and when simulated orders would have executed, while cautioning that a…
The document explains how to value an American put with a two-step binomial model. Starting from the current underlying price, the tree applies the up and down factors at each step; because their product is the same in either order, the price tree…
The discussion explains why the Sharpe ratio is commonly annualized by multiplying by the square root of the number of periods, under an assumption of independent, identically distributed returns. Over longer horizons, random return components may offset one…
The document asks how to determine whether a factor such as size changes a strategy’s performance, using backtests on large- and small-cap stock groups as an example. Comparing returns or Sharpe ratios alone does not establish that the difference reflects a…
The discussion examines whether a long-only strategy designed for rising markets can be backtested using only historical periods that rose, while excluding declines and joining selected price segments into a synthetic series. Its central distinction is…
The document considers execution for a liquid-futures strategy that generates hourly signals and initially rests a limit order before switching to a market order. The trader reports substantial slippage and asks how to improve waiting times, limit prices,…
The note examines why Hidden Markov Models are used for financial prices despite their first-order Markov assumption: future states depend on the present state, rather than directly on the full path of prior observations. It also asks whether technical…
The note explains why a low R-squared can still be meaningful in return prediction. In regression, R-squared measures improvement in accounting for the target’s variation relative to a baseline that always predicts its mean. A positive value may indicate…
The document describes a reported discrepancy in AAPL returns between two historical calculations. A student found that the return over a specified period differed from class notes prepared earlier, prompting an investigation of Yahoo Finance adjusted…
The discussion weighs whether one person can learn quantitative finance and build predictive trading models, especially with machine learning. It distinguishes learning the subject from earning a living through algorithmic trading: study partners can help…
The discussion examines how market-data latency affects order decisions and claims about market efficiency. When order book updates arrive faster than a non-colocated trader can receive information and send an order, the displayed book may no longer reflect…
The document considers whether a portfolio should rebalance less often when its return forecasts cover a longer horizon. It explains that more frequent rebalancing can incorporate updated views and potentially improve alignment with target exposures, while…
The document discusses how to update a one-step-ahead ARIMA forecast after a new observation arrives. It outlines several choices: refit the model parameters while keeping the selected orders fixed, repeat model selection as well as estimation, keep…
The document discusses how much capital an individual may need to run an automated strategy, with positions held from a day to a year. The responses disagree about the practical budget: one argues that limited capital can help access capacity-constrained…
The document asks how to reduce Monte Carlo estimation error when pricing vanilla options under the Heston stochastic volatility model. The questioner reports that Black–Scholes controls using implied, average, or long-term volatility have not worked well,…
The document explains a forward price adjustment method by scaling each bar’s open, high, low, and close relative to an initial reference price. It shows that, when there are no corporate actions, this produces the same indexed price series as dividing each…
The document collects suggestions for obtaining annual filing data for an analysis of S&P 500 companies. Responses point to financial data packages and providers, including tidyquant, Quandl, and Compustat, as well as tools for downloading filings from the…
The document describes a volatility-modeling workflow using log returns. The author selects an ARIMA specification with the Akaike information criterion, then applies Engle’s test to the ARIMA residuals to check for conditional heteroskedasticity. After…
The document explains a practical way to retrieve stock data for the members of an index from CRSP. It suggests compiling constituent tickers from an external list, such as Wikipedia, and using that list to query CRSP for individual stock prices or returns.…
The document investigates why a Monte Carlo estimate for a European call differs from a binomial-tree value. The accepted explanation identifies the simulation’s use of first-order Euler steps for geometric Brownian motion as a source of bias, especially at…
The document asks how to extend an Excel Monte Carlo pricer to value American options, which allow exercise before expiration. It mentions least-squares methods associated with Longstaff and Schwartz, but the questioner finds that approach difficult to…
The document describes a plan to forecast daily realized volatility from S&P 500 returns and then evaluate a straddle strategy using historical options data. The author has calculated log returns and rolling volatility and asks how to retain fitted values…
The document asks why realized variance of a momentum strategy might help avoid momentum crashes, particularly when markets rebound sharply after steep declines and the strategy’s short positions rise. It refers to a study that uses the sum of squared…