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 tutorial introduces FMZ Quant's Blockly visual programming environment through a simple moving-average strategy. It opens a long position when flat and the close is above a 50-period average, opens short when flat and below it, and closes each position…
This note describes a MACD variation that calculates its moving averages with the triple exponential moving average (TEMA) instead of the conventional exponential moving average (EMA). It also replaces the usual single signal line with two discontinued…
This brief BigQuant forum entry asks whether an already running simulated trading instance automatically follows edits made to its original strategy definition, or remains on the earlier version. The response indicates that the running simulation can be…
This BigQuant strategy note describes a stock selection approach based on a fast and slow moving average crossover, identified as MACD-style golden and death crosses. It buys after an upward cross and treats a downward cross as a sell signal. The universe…
This short troubleshooting note explains why a pandas table may appear out of order after a sort. In the reported case, rows were first sorted by date and a field named double_low, then sorted again by index, which disrupted the intended ordering. The…
This Qlib workflow configuration specifies a linear ordinary least squares model using the Alpha158 feature handler for the CSI 300 universe. The data span begins in 2008 and ends in 2020; the training segment runs through 2014, validation covers 2015–2016,…
A forum exchange asks how research conducted with vn.py’s alpha module can be connected to live trading and whether a matching live example is available. The reply says there is no open-source template in the discussion and outlines a minimal workflow: load…
This report studies whether the most unusual intraday price bar contains useful stock-selection information. It defines extremeness as the absolute distance between a bar’s return and the median intraday return, then examines the bar with the greatest…
This study compares six cross-validation approaches for tuning machine-learning models on financial time series. It argues that ordinary shuffled K-fold validation can leak temporal structure and select overly complex models. Expanding-window time-series…
This tutorial explains how VeighNa’s backtesting engine replays historical daily bars to evaluate an equity strategy. It walks through configuring instruments, interval, dates, and initial capital; attaching a strategy and dated signal table; loading data;…
The document describes a Chinese equity screening idea: retain stocks whose codes begin with 60, whose turnover rate falls between 3% and 12%, and whose three technical indicators show a bullish crossover. The accompanying explanation frames these filters as…
The document describes an MT5 script intended to identify times of day when an asset has historically moved more. It proposes calculating average movement over a user-selected number of trading days, then reviewing the result in a column chart to help day…
This stock-screening proposal combines price activity with reported earnings growth. It selects shares whose amplitude exceeds the stated threshold, that had a three-day limit-up streak as of the prior day, and whose year-over-year growth in net profit…
This indicator description combines structural order-block detection with a Fibonacci grid. A bullish block is defined as a bearish candle followed by a configurable run of bullish candles; bearish blocks use the opposite pattern. Optional filters require a…
This forum post asks how to implement minute-level backtesting for a stock strategy that receives signals at the daily level and refines exits using intraday prices. The proposed rules would sell a holding after it rises by a specified amount and then…
The discussion addresses how a strategy can show more losing trades than winning trades while its cumulative account value still rises. The reply explains this through the aggregate size of gains and losses: a smaller number of sufficiently large wins can…
This overview introduces ten supervised learning approaches: linear and logistic regression, linear discriminant analysis, decision trees, naive Bayes, k-nearest neighbors, learning vector quantization, support vector machines, bagging and random forests,…
The document describes a trading system that acts on signals from an Aroon Oscillator alert indicator. A signal is considered formed when a colored indicator icon appears at a bar’s close, providing the system’s stated timing rule. It references an Expert…
This Freqtrade strategy uses three Supertrend direction signals to confirm each trade. It enters a long position when all three buy-side indicators point up and volume is present, and marks an exit when all three sell-side indicators point down with volume…
This trading system uses the ColorJVariation oscillator to generate a signal when the indicator changes color at a bar close. The description identifies that color transition as the point for initiating a trade and notes that the compiled indicator must be…
This document describes an equity factor based on intraday volume moving through a low-to-high-to-low cycle. It smooths volume with a nine-minute neighborhood, identifies a peak and the lowest-volume points before and after it, then measures price-change…
The document surveys the deep reinforcement learning agents available through FinRL, which integrates implementations from ElegantRL, Stable Baselines 3, and RLlib. The listed algorithms include value-based, policy-gradient, actor-critic, and multi-agent…
The article presents a workflow for translating a written trading specification into a QMT strategy. Its example applies ADX and directional indicators to one Chinese-listed equity: buy when ADX exceeds a threshold and positive directional movement is…
This short strategy summary describes a quantitative stock-selection approach built with TabNet, a machine-learning model. It says the approach extracts 98 price and volume factors, trains the model on data from 2010 through 2018, and applies predictions to…