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 asks how to value a discretely sampled arithmetic Asian option at an intermediate time using Monte Carlo simulation. It gives a simplified setup with three observation dates, zero interest rate, zero strike, and a single simulated path. At the…
The document examines how to calculate daily performance for a long-short portfolio with equal capital allocated to each side. It defines each asset’s simple daily return from consecutive closing prices, then combines the long return and short return by…
The document concerns an error encountered when running a rebalancing optimization in R’s PortfolioAnalytics package. The example uses monthly return data, long-only, full-investment, and box constraints, with risk and return objectives. A non-rebalancing…
The discussion considers how to obtain timestamped news for research linking Forex price series to news releases, especially when professional machine-readable feeds are costly. Suggestions include collecting headlines from websites, parsing RSS or Atom…
The document collects practical ways for a small trading operation to reduce exposure of proprietary data, code, indicators, and strategy parameters when hiring a quant. Suggested controls include limiting access to only the resources needed, separating…
The document asks whether financial institutions use GARCH models in risk systems that calculate expected shortfall. It cites a practical primer by an author whose listed affiliations include a bank, as an example of practitioner-oriented discussion. The…
The document considers how to estimate a portfolio’s return distribution for an expected shortfall constraint when only a limited history of asset returns is available. It questions whether a normal approximation based on the central limit theorem is…
The document addresses how to find long historical series of nonfinancial measurements for testing correlations with a financial analysis or trading algorithm. It suggests looking at unusual public datasets and gives examples of possible sources, including…
The document describes a volatility pattern in monthly electricity futures that changes as the delivery month progresses. Because electricity is delivered throughout the month, the contract price converges toward the monthly average as delivery unfolds. The…
The discussion distinguishes geometric-average and arithmetic-average Asian options under the Heston stochastic volatility model. It reports that a closed-form pricing formula is available for the geometric Asian option, while an analogous closed form is not…
The document describes an attempt to estimate hedge fund alpha using the Fung-Hsieh seven-factor model. The proposed analyses include a pooled estimate across funds and annual estimates, using monthly after-fee excess returns as the dependent variable and…
The document discusses how to simulate a stock’s future value over annual periods. Its clearest practical point appears in the second answer: if the modeled random variable is a log return, exponentiating that draw and multiplying by the starting price…
The document compares Interactive Brokers demo and personal paper-trading accounts for testing Trader Workstation API applications. Demo accounts provide simulated data and can help check connectivity or exercise account types, but the answers describe…
The document discusses ways to improve finite difference methods for solving the Black-Scholes equation, with attention to accuracy, speed, and stability. Its main recommendation is to transform the equation into the heat equation, which has simpler…
The document reports a numerical comparison between an adjusted Kirk approximation and the Bjerksund–Stensland spread option model. The author implements both formulas, adds a weight for the second spread leg, and includes call and put calculations. Across…
The document separates machine learning used to approximate an already computed option pricing function from methods that learn both a claim’s value and its optimal hedge. For fast pricing, a model is trained on examples generated by slower numerical pricing…
The document considers whether historical futures data from one crypto exchange can support backtests for a strategy traded on another. The motivating issue is a change in listed contracts that leaves little history for the replacement market, prompting the…
The document presents an attempt to forecast VIX using a constant-mean GARCH(1,1) model fitted to daily log changes in the VIX. It shows a historical sample, a training cutoff, a multi-step forecast, and forecasted conditional variances across dates and…
The document raises a measurement question about calculating Sharpe ratios from a vectorized backtest. Its example constructs a strategy-return series by retaining bar-to-bar log returns only between an entry and an exit, while recording zero returns outside…
The document frames portfolio construction as choosing weights across return series to maximize the Sharpe ratio, defined in the question as average portfolio return divided by its standard deviation. It provides a small sample return matrix and a CVXPY…
The document reviews an R workflow for fitting an ARMA mean and a GARCH(1,1) volatility model with a skewed Student distribution, then calculating value at risk for two return series. Its response clarifies that the quantile of fitted model results…
The document asks how to apply a Cholesky factor to simulated normal draws when generating two correlated geometric Brownian motion processes. It compares a Matlab pattern that stores time steps in rows and processes in columns with a Python pattern that…
The document discusses using Empirical Mode Decomposition (EMD) of EUR/USD prices as input to a machine learning model. It addresses a discrepancy between promising test-period results and poor predictions on the latest observation. The proposed explanation…
The document explains why a geometric Brownian motion simulation can appear to rise consistently when its drift is strongly positive. Applying Itô’s lemma to the GBM model shows that the log return over a fixed horizon is normally distributed, so the price…