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.
21,023 documents
The document contains hourly open, high, low, close, and volume observations for the ADA-USDT market. The visible records begin in early May 2018 and continue through the end of December 2018, with gaps in the displayed sequence. The fields support basic…
This document explains the structure of a historical trading strategy backtester. It loads bar or tick records over a selected date range, initializes a strategy with a warm-up period, then replays the remaining data. The engine tracks simulated orders and…
The document explains utilities for turning incoming trades into one-minute OHLCV bars and combining minute bars into larger time windows. It tracks price extremes, closing price, volume changes, and open interest, then sends completed bars through…
The document describes a graphical workflow for downloading historical bars, configuring a CTA strategy backtest, reviewing performance statistics, and inspecting trades on a candlestick chart. Data can come from a domestic market data service, an…
This guide explains a workflow for researching CTA strategies with historical market data. It covers obtaining and storing data, configuring a backtest with a strategy, date range, slippage, fees, contract multiplier, tick size, and starting capital, then…
This document provides four-hour candlestick observations for BSV/USDT. Each entry records a timestamp, open, high, low, close, and volume. The series shown runs from late November through the end of December 2018 and offers a coarser view of price movement…
This document presents 30-minute candlestick observations for BSV/USDT, with timestamps and open, high, low, close, and volume fields. The visible sample starts at the end of November 2018, includes records from early December, then skips ahead to late…
This document contains 30-minute candlestick records for BIX/USDT. Each row reports a timestamp, open, high, low, close, and trading volume. The visible records begin in July 2018 and resume near the end of December after an omitted portion, so they provide…
The document presents a workflow for evaluating individual trades from a Turtle-style strategy backtest on an hourly Bitcoin instrument. It configures a backtest with a historical date range, fees, slippage, contract size, tick size, and starting capital,…
This document describes a workflow for importing historical bar data from a CSV file into a trading database. The operator configures the input file, instrument symbol, exchange, bar interval, and the column names corresponding to timestamps and OHLCV…
This introductory lesson explains how to install and use Python through Anaconda. It describes Anaconda’s package and environment management features, including creating separate environments so projects can use different Python versions and dependencies. It…
This analysis workflow loads historical bars into a time-indexed table and plots closing prices to inspect gaps. It applies a Ljung–Box test for randomness, an Augmented Dickey–Fuller test for stationarity, and autocorrelation plots to examine serial…
This introductory document explains why Python comments help developers understand and maintain programs. It describes comments as notes attached to statements, code sections, functions, or variables, and says they can clarify a variable’s purpose for both…
This analysis workflow loads historical bars into a tabular dataset and plots closing prices to inspect gaps. It applies a Ljung–Box test for serial dependence, an augmented Dickey–Fuller test for stationarity, and autocorrelation and partial autocorrelation…
This module describes a real-time radar for calculating derived values from multiple contracts. Users define named rules with a Python mathematical expression and assign contract symbols to formula variables. Incoming tick data for any assigned contract…
The document derives an approximate single implied volatility for a portfolio of options whose components have different implied volatilities. It begins with the condition that the portfolio’s modeled value at the common volatility should equal the sum of…
This document demonstrates how to turn a backtest’s sequence of fills into completed trade records. It accumulates signed position, traded value, and price-based profit and loss until exposure returns to zero, then derives each trade’s duration, volume,…
The document compares two martingale derivations of the Black–Scholes partial differential equation. With the bank account as numeraire, requiring the discounted option price to have zero drift yields the familiar PDE. The attempted stock-numeraire…
The document offers historical volatility and correlation estimates as starting points for a foreign currency option model with domestic equities, foreign equities, and an exchange rate. Using weekly observations over five years for the DAX, S&P, and EUR…
The document discusses how to calculate p-values for estimated GARCH coefficients and whether the degrees of freedom should account for the model’s parameters. One response recommends using the sample size minus the total number of estimated parameters,…
The document examines how Actual/Actual ISMA determines coupon amounts for a fixed-rate bond with a short or long stub period. Its example has a first coupon running from the issue date to a February payment date, followed by monthly coupons. The initial…
The document derives an expression for the expected value of a process described by a stochastic differential equation with drift and diffusion terms. Rewriting the equation in integral form separates accumulated drift from the stochastic integral. Under the…
The document explains how deterministic constants and trends can be specified in Johansen cointegration models. It lists five model forms, ranging from unrestricted constants and trends to no constant or trend, and says nested specifications can be compared…
The discussion distinguishes uncertainty in portfolio allocations from uncertainty in the inputs used to construct them. Mean-variance optimization can produce a precise allocation from estimated returns and covariances even when those parameters are poorly…