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 article collects learning materials for applying machine learning to algorithmic trading, grouped into books, blogs, research papers, videos, and podcasts. The topics span neural networks, structured data, regression, clustering, nearest-neighbor…
This study considers whether a company’s decision to capitalize research and development spending conveys information about future project profitability. Because accounting rules allow judgment in deciding whether development costs should be capitalized, the…
This study turns unusual intraday stock behavior into a measurable event signal. It describes days when a stock repeatedly moves against the direction of the broader index, then uses correlation to screen for these cases. The resulting event samples are…
This release overview describes VeighNa 4.0 and its new vnpy.alpha module for developing machine-learning, multi-factor strategies. The module is organized around feature datasets, model training, strategy research, workflow management, and example…
The article describes a framework that links macroeconomic and style factors with traditional asset-class allocation. It proceeds from selecting factors and estimating asset exposures to building factor-mimicking portfolios, forecasting their returns,…
This Expert Advisor runs separate long and short systems using signals from the ColorMaRsi-Trigger indicator. Each side has its own magic number, position sizing settings, and switches for enabling entries and exits, allowing its parameters to be configured…
This MetaTrader 5 Expert Advisor trades reversals in the direction of the FatlMacd indicator. It forms a signal at bar close when the indicator changes direction. Running the advisor requires the compiled FatlMacd indicator file in the platform's Indicators…
This Expert Advisor variant adds a configurable weekly trading window to an EA based on a Spearman rank correlation histogram. Inputs set whether interval-based trading is enabled and define the start and end by weekday, hour, minute, and second. The example…
The article compares long and short training windows for an AI stock selection strategy and recommends evaluating each window against a fixed validation period. In its rolling experiments, extending the sample from 2005 to 2021 changed labels, factor…
This Chinese-language post sketches a stock selection approach that ranks shares by trading activity, using volume and turnover as proxies for capital strength, and also considers share price. It proposes selecting highly ranked stocks on these measures,…
This notebook demonstrates a Monte Carlo approach to forecasting stock prices with geometric Brownian motion. It estimates drift and volatility from historical log returns, generates many simulated price paths, and splits the price series sequentially into…
This career guide outlines a self-study path for aspiring quantitative developers. It emphasizes strong programming and numerical implementation skills, with language choices shaped by likely workplaces: C++ and Python for broad applicability, while Java or…
This example compares a grid market-making strategy in HftBacktest using CME market-by-order Level-3 data and Level-2 depth reconstructed from that same feed. Quotes are arranged around a reservation price that shifts with inventory, with position limits,…
This BigQuant framework describes a workflow for computing and evaluating minute-frequency stock factors. Researchers define factors in SQL against a specialized derived minute-bar table, assign an output table name, and run the program to calculate and…
The document introduces fund-of-funds structures and distinguishes four types according to whether the parent and underlying funds are actively or passively managed. It focuses on an actively managed parent investing in passive sector ETFs, and describes an…
This stock-selection proposal combines a turnover-rate range of 3% to 12%, a 2021 listing year, and a weekly bullish-bar condition. The article explains the weekly condition using K and D readings above 50, a rising K value, and a positive MACD histogram…
This Expert Advisor uses a color transition in the ColorXCCXCandle indicator to generate a signal when a bar closes. The document describes the trigger but does not explain how candle colors are calculated, whether a color change prompts a buy or sell, or…
This indicator description explains a Fractal ZigZag port that uses ZigZag logic to identify swing highs and lows, then plots or estimates a version intended to avoid redrawing. It says that values are backdrawn by two bars from the current bar, a detail…
The document outlines a Chinese-equity screening idea that selects stocks classified in the metaverse theme, requires a positive recent price return, and applies a threshold to a 2019 dividend-related measure. It presents the screen in both a platform…
This document describes a stock-selection rule based on three conditions: RSI below 65, seven consecutive bearish daily candles, and a listing age greater than a chosen number of years. It presents the screen as a way to identify established companies after…
This document outlines a daily screening rule for Chinese equities in the metaverse industry. It selects stocks when the KDJ indicator has just formed a bullish crossover and the volume ratio is between 1.5 and 6, with selection performed before 10 a.m. The…
The document describes a trading-system risk analytics tool that processes closed-trade history and reports risk and performance measures. Its stated functions include Monte Carlo simulation, Value at Risk, Kelly Criterion position sizing, and more than a…
This expert advisor trades from the Omni_Trend indicator. It creates a signal when a large colored dot appears and acts on that signal after the bar closes. The document describes the signal rule but does not explain how the indicator calculates its dots or…
This brief strategy description proposes checking stocks one by one to identify which has produced the highest return under an event strategy, then buying that stock. It specifies daily Chinese stock-bar data, a backtest beginning in 2020 and running through…