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Knowledge library

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.

Quant Q&A
20,364 documents
SuperMind
12,226 documents
OKX Learn
8,431 documents
Strategy library
7,910 documents
MQL5 code base
7,090 documents
BigQuant
3,481 documents
Bitget Academy
3,298 documents
MQL5 articles
3,012 documents
TradingView scripts
1,976 documents
ProRealCode
1,507 documents
Deribit Insights
1,232 documents
Machine Learning for Trading
1,124 documents
arXiv papers
1,033 documents
Amberdata research
766 documents
FMZ forum
682 documents
FMZ digest
662 documents
vn.py community
560 documents
QuantInsti blog
511 documents
Galaxy Research
340 documents
QuantStart
246 documents
Stratmill research code
219 documents
Robot Wealth
195 documents
NautilusTrader
191 documents
Hummingbot docs
181 documents
Paradigm research
175 documents
Lumibot
164 documents
Kraken Learn
163 documents
Quant course library
157 documents
OctoBot
152 documents
Cryptohopper blog
144 documents
Systematic trading blog (Rob Carver)
132 documents
Qlib
116 documents
TqSdk
86 documents
Quantpedia
86 documents
Hyperliquid docs
79 documents
Freqtrade
68 documents
Hudson & Thames
62 documents
Awesome Systematic Trading
61 documents
backtrader
54 documents
vn.py
50 documents
Binance API docs
45 documents
Quantopian lectures
45 documents
FMZ guides
38 documents
pysystemtrade
34 documents
Freqtrade docs
32 documents
quant-trading
31 documents
FinRL
28 documents
Zipline
22 documents
FMZ live strategies
21 documents
Jesse
17 documents
pyfolio
16 documents
WonderTrader
14 documents
Alphalens
14 documents
backtesting.py
11 documents
Technical Analysis
9 documents
QTPyLib
8 documents
Lumibot strategies
7 documents
QuantRocket
7 documents
Awesome Quant
1 documents

Search the library

3,481 documents

BigQuant

This research summary describes a bull-bear indicator built from changes in equity-market volatility and turnover. It classifies conditions according to whether each measure is rising or falling. Rising volatility with falling turnover is presented as a…

EquitiesChina marketsVolatilityTechnical indicators
BigQuant

The article surveys execution algorithms by their main objective: reducing market impact, controlling total trading costs, or adapting execution to favorable market conditions. It describes TWAP as time-based order slicing, VWAP as matching execution to…

ExecutionMarket microstructureRisk management
BigQuant

This study summary examines an improvement to a one-sided volatility-difference timing model. It reports that bullish signal accuracy rises with trading amount, while bearish signal win rates show no similar relationship. The proposed change uses relative…

China marketsEquitiesTechnical indicatorsBacktesting
BigQuant

This research summary argues that low valuation alone can select weak businesses, so investors should assess company quality before deciding whether its price is reasonable. It evaluates A-share companies using indicators related to profitability, growth,…

China marketsEquitiesFactor investingPortfolio construction
BigQuant

This research summary compares five classifiers for predicting stock returns from historical equity factors: multinomial logistic regression, linear support vector machines, random forests, XGBoost, and deep neural networks. It distinguishes the linear…

EquitiesFactor investingMachine learningStatistics
BigQuant

The document reports a mismatch between a portfolio strategy’s expected rebalance schedule and its simulated trading activity. The author says the backtest rebalances every five days or after a take-profit trigger, while the simulation records trades on many…

BacktestingExecutionPortfolio constructionChina markets
BigQuant

The document summarizes an analysis of Chinese A-share investor research visits, using public company announcements to identify when institutions visit firms. These events are usually targeted visits involving five or fewer institutions, and announcements…

EquitiesEvent-drivenStatisticsChina markets
BigQuant

This note diagnoses a backtest error in BigQuant position-condition functions. Both the opening and closing condition functions attempt to filter a DataFrame using a column named “fantanbili.” The author points out that the prediction output supplied by an…

BacktestingStatistics
BigQuant

The document outlines a systematic stock-selection model built from valuation, profitability, growth, operating-efficiency, and technical factors. It describes a scoring approach: choose candidate factors, test their predictive usefulness, remove redundant…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This summary examines whether frequent constituent changes weaken returns for a portfolio tracking the CSI 300. It reports a Wilcoxon test finding short-term price effects around index adjustments: additions tended to rise and removals to fall, with the…

EquitiesChina marketsPortfolio constructionBacktesting
BigQuant

The article explains “price desensitization”: a cyclical company’s shares stop advancing, or weaken, while the underlying commodity price continues to rise. Its proposed rationale is that equity prices anticipate earnings and may have priced in future gains…

EquitiesCommoditiesMomentumRisk management
BigQuant

This Chinese-language manual explains a portfolio optimizer for constructing benchmark-aware equity portfolios. It describes initializing the optimizer with a dated stock universe and benchmark, refreshing factor data for each trading date, and then…

EquitiesPortfolio constructionFactor investingRisk management
BigQuant

This research digest summarizes two studies on equity signals. The first trains a deep neural network on five years of fundamental data to forecast future fundamentals. The summary reports that, relative to a simple forecast assuming current data persist,…

EquitiesMachine learningFactor investingSentiment
BigQuant

This document summarizes a framework for deciding whether and how to combine single-factor smart beta strategies with multifactor portfolios. It represents each candidate strategy as exposures to a small set of investable elementary smart betas—initially…

Factor investingPortfolio constructionRisk managementEquities
BigQuant

This introductory guide surveys algorithmic trading, from the history and terminology of financial markets to the roles of quants, traders, and market makers. It describes common benefits of automation, such as speed and scalability, alongside constraints…

BacktestingRisk managementMomentumMean reversion
BigQuant

This strategy seeks to outperform the CSI 1000 while controlling industry, style, and individual-stock exposures. It proposes combining value, growth, size, quality, volatility or liquidity, and alternative signals. Factors are cleaned for outliers,…

China marketsEquitiesFactor investingPortfolio construction
BigQuant

The document summarizes a study of a composite fundamental factor based on the Piotroski framework and its stock-selection value in the Chinese A-share market. It reports that stocks in the highest factor decile outperformed those in the lowest by an average…

EquitiesChina marketsFactor investingStatistics
BigQuant

A user asks how to express a specific six-session price pattern in a platform’s daily factor-expression syntax. The condition is that the latest close exceeds each of the previous five closes, while the close from five sessions earlier also exceeds the…

EquitiesTechnical indicatorsFactor investing
BigQuant

This example equity strategy selects stocks using valuation and trading activity. At each 30-trading-day interval, it buys at the open shares with positive price-to-book ratios below 1.5, price-to-earnings ratios below 15, and volume between 1.5 and 2.5…

EquitiesChina marketsTechnical indicatorsBacktesting
BigQuant

The document presents asset allocation as a combination of choosing assets and timing the market, with portfolio risk and expected return as the objectives. It explains that a hidden Markov model infers unobservable market states from observable data, then…

Machine learningStatisticsPortfolio constructionChina markets
BigQuant

This study describes a revised deep Q-network approach for timing individual Chinese stocks. The earlier version used daily OHLCV data and several common factors as its state. The revision instead takes a configurable window of recent closing prices and…

EquitiesMachine learningBacktestingPosition sizing
BigQuant

This forum discussion asks how a visual stock-selection strategy combines multiple factors when no explicit weights are visible, and how the same logic might be represented in SQL. The example lists dividend yield, price-to-book, return on equity, and profit…

EquitiesFactor investingPortfolio construction
BigQuant

The document explains why a stock filter set to the CSI 500 can return fewer securities than the index membership count. In the cited example, the filter is applied to a particular date, and some constituents were suspended, leaving their five-day return…

EquitiesChina marketsStatistics