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.
23 documents
The Range Action Verification Index (RAVI) is described as a trend-detection indicator based on the percentage difference between current and past prices. The document gives threshold-crossing rules attributed to its developer: an upward cross of a 3%…
This code implements a collection of cross-sectional and time-series equity alpha factors, mainly using close, open, high, low, volume, returns, and VWAP data. The factors combine operations such as rolling ranks, correlations, moving averages, extrema, and…
This Python module provides utilities for evaluating systematic strategies and constructing several trend signals. It computes annual return and volatility, Sharpe and Sortino ratios, downside risk, maximum drawdown, Calmar ratio, positive-return frequency,…
This document describes a trading rule that measures a spread’s latest value against its recent average and standard deviation. It uses separate lookback windows for the mean and standard deviation, then calculates a z-score to identify unusually high or low…
This document describes helper calculations commonly used to construct quantitative signals from price or other tabular time series. Its functions cover rolling sums, averages, standard deviations, correlations, covariances, ranks, products, extrema,…
The document contains reusable strategy calculations for price returns, volatility scaling, trend following, and MACD signals. Its intermediate trend strategy combines the signs of one-month and one-year returns, weighted by a parameter, and applies that…
This code sample implements parts of the Alpha101 factor set using historical equity fields such as open, high, low, close, volume, returns, and volume-weighted average price. The formulas combine rolling ranks, moving averages, correlations, price changes,…
This Chinese equity screening proposal combines three conditions: daily price amplitude above 1%, a dividend ratio above 25% for 2019, and a 15-minute MACD histogram that is shortening while below zero. The rationale is to find volatile shares with a history…
This note proposes a short-term Chinese equity screen that selects stocks with a price amplitude above one, an appearance on the prior day's trading list with buying greater than selling, and a rising DEA indicator. The rationale is to combine elevated…
This data-preparation workflow builds model inputs for a momentum strategy from asset closing prices. It clips prices using bounds based on an exponentially weighted mean and standard deviation, derives daily returns and volatility, and creates a next-period…
This tutorial demonstrates how to inspect market depth and trade flow in an event-driven backtest. It first reads the nearest visible bid and ask levels, then shows a region-of-interest vector representation that limits depth access to a configured price…
This module describes calendar rules for rolling several futures series: crude oil, NBP natural gas, refined products including RBOB, grains, and ethanol. The rules use contract-specific termination conventions, such as dates near the 25th or 15th of a…
This document presents a Chinese stock screening rule that combines RSI below 65, seven consecutive sessions in which the close is no higher than the open, and a latest price above its five-day moving average. It frames the conditions as a way to identify a…
The document describes three ways to refine spread trading signals. A threshold filter enters or maintains a long or short spread position only when the predicted spread change crosses a chosen boundary; an asymmetric version allows different boundaries for…
This Python class implements a broad collection of formula-based equity signals using daily close, open, high, low, volume, returns, and volume-weighted average price data. Its methods translate rank, correlation, rolling-window, change, volatility, and…
This Chinese stock-selection note combines three filters: MACD above its zero axis, a 2021-to-2018 revenue ratio above 1.1, and a gain below 6% at 9:25. The rationale is to pair positive technical momentum and multi-year revenue growth with a limit on the…
This Python class assembles a subset of Alpha101-style equity signals from price, volume, VWAP, returns, and precomputed factor series. Its methods apply operations such as cross-sectional ranking, rolling correlation, covariance, time-series ranking, decay,…
This data-preparation module builds time-series inputs for a deep-learning momentum model. It reads close prices, clips extreme values using an exponentially weighted mean and standard deviation, then derives daily returns and volatility. The target is a…
This note presents a basic stock-selection filter for shares whose codes begin with 60. It requires the daily high-low range to exceed one percent of the prior close and yesterday’s trading value to exceed a stated threshold. The document interprets the…
The document introduces the Commodity Channel Index (CCI), describing it as a statistical technical indicator that compares price movement with a typical range. It notes that the indicator was first used in futures analysis and later applied to equities. CCI…
This document presents a partial Python implementation of an Alpha101-style factor library. Its functions combine price and volume data using rolling ranks, correlations, covariance, moving averages, standard deviations, price changes, and volume averages.…
This code presents discretized mutual information (MI) and variation of information (VI) measures for comparing two data series. When the user does not supply a bin count, it estimates one from the observation count and, for the bivariate case, the…
This code describes a data formatter for a momentum model. It defines target returns, normalized returns over several horizons, MACD features, and optional change-point, calendar, and ticker identity inputs. It also assigns columns roles such as target,…