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 document describes an Expert Advisor that trades signals from the XPVT oscillator. A signal occurs when the oscillator’s main and signal lines cross as a bar closes. The EA therefore uses a bar-close confirmation rule rather than acting on an intrabar…
This article explains how exchange trading charges, withdrawals, blockchain network costs, and margin interest can reduce cryptocurrency trading returns. Trading costs lower profit per transaction and raise the price movement required to break even. The…
This configuration defines a Qlib equity forecasting workflow using the CSI 300 universe and the Shanghai CSI 300 index as benchmark. It sets a data window from 2008 through 2020, with training through 2014, validation over 2015–2016, and testing over…
This short Python example sketches a grid approach for ETC/USD using 15-minute data. It builds price levels by repeatedly increasing a starting level by five percent, locates the current price within those levels, and places an initial buy. The intended loop…
The primer explains how crypto order books organize bids and asks by price and size, and how market orders, limit orders, price levels, and depth relate to trading conditions. It distinguishes periodic full-book snapshots from event data that records…
The document proposes screening Chinese stocks for convergence among the 5-, 10-, 20-, 50-, and 200-day moving averages. It treats alignment across short, medium, and long horizons as a possible sign that their trends agree, and suggests buying when the…
The document describes an Expert Advisor built from two parallel trading systems using signals from the UltraFatl indicator. Inputs prefixed for long trades and short trades are configured separately, including distinct magic numbers, position sizing…
This review examines the low-risk effect: securities with lower measured risk have historically delivered stronger risk-adjusted returns than the standard CAPM relationship would predict. It explains betting against beta (BAB), which leverages a low-beta…
This guide compares the Explorer, Adventurer, and Hero tiers of a cryptocurrency market-data MCP service. It frames selection around weekly call allowances, exchange coverage, historical lookback, and the number of API keys. Explorer is presented for lighter…
The post describes a Chinese equity screen seeking companies with no losses, scale above a stated minimum, market capitalization below a stated ceiling, and a recent position increase greater than 5%. It frames the approach as a short-term filter based on…
This report tracks equity factors using information coefficient (IC) summaries and the relative returns of factor-ranked long portfolios. It describes ranking stocks by factor values, selecting the top decile for each factor, and weighting holdings by the…
This brief page introduces a day-trading plan that uses Fibonacci retracement levels to identify potential entries. It points readers to a video about building a plan, common trading mistakes, and applying the process to find entry points. The page does not…
This note describes a Chinese equity screening rule that admits stocks with daily amplitude above 1%, at least one limit-up session during the prior 25 days, and a 20-day moving average above the 120-day average. It presents the combination as a way to find…
This discussion addresses whether a trader should use hyperparameter search to find an optimal combination of existing factors. The response says that “optimal” needs a clear definition: it could mean the best backtest result or the best factor-analysis…
This document explains how fill models shape simulated execution in NautilusTrader. With L2 or L3 data, fills use recorded price levels and sizes; with L1 data, configurable probabilities can govern fills when limit prices are touched and adverse one-tick…
This Chinese stock selection rule filters for turnover between 3% and 12%, seven consecutive non-rising closes, and overlap with at least five moving averages. The stated intent is to find stocks that have fallen in the short term while showing a potentially…
This Chinese equity screening rule combines three price conditions: daily amplitude above 1%, a session low down between 4% and 5% from the prior close, and an opening auction return between -2% and 5%. The accompanying rationale interprets these filters as…
This Chinese A-share backtest example uses a five-day versus twenty-day moving-average crossover to generate buy and sell signals. Its trading handler divides available cash equally among the stocks signaled for purchase, so if only one stock qualifies on a…
This stock selection idea combines three filters: daily price amplitude above a threshold, an indication of major-player control on the previous day, and a rounded price pattern. The accompanying explanation interprets amplitude as a sign of higher activity…
The document describes an Expert Advisor that trades signals from the i-KlPrice_Vol indicator. It checks for a breakout of an overbought or oversold level when a bar closes, with separate order identifiers and position sizes for normal and strong signals.…
This document presents an Expert Advisor associated with the Sidus indicator and reports two historical GBP/USD tests on the one-hour timeframe. Both use a fixed lot size and a one-bar shift. The forward-period report covers 2008 to early 2009 and shows…
This research applies feature selection to multi-factor stock selection in the full Chinese A-share universe, with the CSI 300 and CSI 500 as benchmarks. It constructs industry- and market-cap-neutral strategies and tests feature ranking methods with…
This brief BigQuant project note describes a stock strategy built from three factors that had undergone stability testing. It says the factor weights were selected through a grid search over three parameters, with a reported weighting of 1:8:1. The note also…
The document outlines an equity screening approach combining price range, turnover, opening-auction activity, and afternoon large-order net inflow. Its stated selection rule looks for stocks with amplitude above one and a product of the previous day’s…