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 suggests using T3 in place of an exponential moving average when smoother or faster indicator levels are desired. It presents T3 as similar to EMA in its calculation, making it a plausible extension for systems that already use EMA-based levels.…
This MetaTrader 5 Expert Advisor bases its signals on the ColorBearsGap indicator. A signal is generated when a bar closes and the indicator histogram changes color. The document notes that a supporting TradeAlgorithms library can help the advisor work with…
This document describes an Expert Advisor that trades changes in the direction of the CoppockHist oscillator. A signal is generated when a bar closes and the indicator direction has reversed. The method therefore depends on the indicator’s direction change…
This document describes a cross-sectional momentum strategy for equity mutual funds. It first limits the universe to no-load funds, then ranks eligible funds by their trailing six-month returns. The portfolio holds the top decile, equally weighted, and…
The described expert advisor begins by opening equal, minimal-sized long and short positions, each with predefined take-profit and stop-loss levels. When one position reaches its profit target, it closes; the losing side is enlarged at price intervals, with…
This project description outlines a LightGBM system for classifying the direction of futures prices over a configurable forward horizon. It processes one-minute bars across multiple contracts, builds technical and statistical features, and labels future…
This research summary proposes an information-distribution uniformity factor, UID, based on the idea that uneven arrivals of information create changes in share-price volatility. It uses intraday stock data to calculate daily high-frequency volatility, then…
This note outlines a screen for Chinese stocks associated with the metaverse. It combines a reported rounded-bottom price pattern with positive large-order net flow above 0.05 for at least three consecutive days. The final stated selection logic also adds an…
This essay contrasts two stylized accounts of a quantitative trader’s working day. The less automated workflow involves checking strategies and orders manually, adjusting allocations and contracts, monitoring live positions, recording profit, slippage, and…
This forum exchange addresses how to skip a planned stock purchase when the stock opens more than 9% above its previous close. The suggested workflow is to add the opening price and prior close to the prediction dataset, merge those values with the…
This stock-selection rule targets companies classified in the metaverse industry. It requires the previous day's actual turnover rate to fall between 3% and 28%, and at least two daily limit-up moves during the preceding 500 days. The article presents…
This article examines how the length of a training window affects an AI stock selection model. It compares long historical samples with shorter rolling samples and recommends checking each choice against a held-out backtest period. In the author's…
This forum exchange addresses how to choose a target metric when optimizing strategy parameters in VeighNa’s Jupyter-based workflow. A user shows a Sharpe ratio target and asks which field represents total profit, after an attempted profit-field name did not…
This stock-screening idea targets shares in the metaverse industry, ranks them by the day’s opening auction amount, and selects the top five that have closed lower for seven consecutive sessions. The document expresses the down-day condition as a…
This document describes a versioned template for trading multiple currency pairs, with moving averages offered as a sample entry logic. It says the entry logic can be changed, so the moving-average example is a starting point rather than a fixed strategy.…
This Chinese stock selection post proposes combining a positive MACD reading with three consecutive down days and a 2021 time filter. Its explanation frames the setup as pairing a longer-term upward indication with recent weakness, but the supplied code does…
This article introduces paper trading as a way to run strategies in live market conditions against a virtual portfolio. It describes using simulated funds to explore strategies before committing capital and to continue experimenting without risking real…
This outline maps a beginner’s path through quantitative trading. It starts with the purpose and requirements of quant trading and the components of a complete strategy, then introduces a trading platform, its configuration, common APIs, and strategy…
The document introduces the DMH, an updated directional movement indicator described in John Ehlers’s December 2021 TASC article. Its central change is the use of Hann windowing to modernize the traditional directional movement approach, which was originally…
This document outlines an expert advisor that combines the Relative Strength Index (RSI) with RFTL, searching for RSI troughs and peaks as part of its trading logic. It states that the advisor operates only on hedge accounts, which allow positions to be held…
This proposed equity screen combines three filters: prior-period price amplitude above 1%, circulating shares no greater than 5.5 billion, and 2021 revenue more than 1.1 times 2018 revenue. The document gives example formula and Python-style implementations…
This trading system looks for price breaks of support or resistance levels. It uses a simple moving average of lows to confirm a downside break and a moving average of highs to confirm an upside break. A minimum standard deviation condition is also required,…
This community post presents a strategy backtesting framework that uses an iWencai selection function to choose stocks and run minute-level simulations. That outlines a basic workflow for testing intraday rules on a filtered equity universe: define a stock…
This overview presents QLib as an open-source quantitative research platform that supports data preparation, model training, portfolio construction, risk analysis, backtesting, and execution research. It notes support for supervised learning, market-dynamics…