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
This brief forum exchange discusses how a Chinese futures platform constructs its dominant-contract continuous series and whether it offers a smoothed, adjusted series comparable to another vendor’s data. A respondent says the platform currently lacks that…
This forum post questions the target-return calculation in a one-day equity strategy’s labeling code. The strategy is described as buying near the open on one day and selling at the close of the next, yet the label reportedly divides a later session’s high…
This forum post raises operational problems encountered after submitting previously successful stock strategies for simulated trading. The author reports that some buy instructions appear a day early and are later marked invalid, that incomplete purchases…
The article discusses a leveraged Martingale approach to trading LTC/USDT perpetual contracts. It proposes changing the traditional doubling rule to a smaller add-on multiplier so the strategy can withstand more adverse price movement, then withdrawing…
This report summary describes an index-timing model that applies wavelet analysis to filter price series before using a support vector machine to predict direction. It compares daily and weekly data and different model settings for the Shanghai Composite and…
This document describes a Chinese equity screen for metaverse-related stocks. It combines a volume-ratio range with a morning-star candlestick signal, and also discusses adding market capitalization and other filters. The accompanying explanation frames the…
This guide explains a strategy-level indicator accessor for calculating technical indicators using only market data available at the strategy’s current time. It describes built-in single- and multi-column indicators, Fibonacci retracement levels, and custom…
This tutorial demonstrates a workflow for evaluating WorldQuant Alpha#100 on Chinese equities. It builds the factor from price and volume signals, including the close’s position within the daily range, the relationship between closing price and ranked…
The document describes a MetaTrader 4 testing workflow for expert advisors that trade multiple symbols and evaluate results by total pips rather than account currency. It says this helps compare symbols with different pip values and avoids relying on swap…
The script describes a two-sided futures strategy on hourly bars. It identifies confirmed swing low and swing high fractals, then enters long when price breaks above a bullish fractal’s high during a short-over-long moving-average uptrend. It enters short…
This stock-selection rule combines three filters: a high-to-low daily range above 1%, a daily decline between 4% and 5%, and a positive price-to-earnings ratio. The article offers formula and Python examples, although the displayed formula does not appear to…
This stock-selection idea screens shares whose codes begin with 60, whose high-to-low daily range exceeds 1%, and that meet a reversal or “wraparound” candlestick condition. The article gives formula and Python examples, but the definitions are not fully…
This stock screen combines three conditions: daily amplitude above 1, at least one limit-up event within the past month, and a 20-day moving average above the 120-day moving average. The moving-average relationship is intended to identify an upward trend,…
The report compares machine learning approaches with traditional linear methods for predicting stock-level excess returns from alpha factors. Rather than predict excess return directly, it forecasts return dispersion with an AR(1) model and predicts…
This configuration defines an equity prediction workflow for China’s CSI 300 universe. It applies robust feature normalization and missing-value filling, drops labels with missing values, and ranks labels cross-sectionally. An ordinary least squares linear…
This stock-selection rule combines a 14-period RSI below 65 with three consecutive declining sessions and a condition intended to identify upward divergence between short- and longer-term moving averages. The post presents the rule as a way to screen for…
The document describes an Expert Advisor organized as separate long and short systems, each with its own entry and exit switches, settings, and trade identifier. It uses signals from the ColorSchaffJJRSXTrendCycle indicator and allows long and short…
This summary of a Huatai Securities research report discusses using alternative target labels and ensemble learning to improve machine-learning stock selection. It reports comparing models built with different labels across multiple training-window lengths.…
This Chinese equity research report builds a stock-selection factor from how each stock’s trading value co-moves with the rest of the market under two conditions. When a stock is relatively high in its recent intraday range, stronger co-movement is…
This stock-selection screen combines trading activity, company size, market value, and profitability. Its final criteria require turnover of up to 12%, circulating market value between 10 and 100 billion yuan, circulating share count no greater than 5.5…
This document outlines an equity screen beginning with stocks whose daily price range exceeds 1%, market capitalization is at most 10 billion yuan, and average net profit is positive, while excluding stocks that hit the daily limit on the prior session. Its…
This document presents an equity selection rule using a turnover range of 3% to 12%, seven consecutive down days, and a pattern described as the start of a strong advance. It gives indicator-formula and Python examples intended to identify the setup,…
This document describes an equities screening rule combining a turnover range, beverage and alcohol industry classification, and a comparison between displayed best bid and ask quantities. It presents the intended screen as selecting stocks with turnover…
This reference catalog groups Python libraries used across quantitative finance. It covers strategy research and backtesting, technical indicators, risk and portfolio analysis, scientific computing, derivatives pricing, factor and time-series analysis,…