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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
Alphalens
14 documents
WonderTrader
14 documents
backtesting.py
11 documents
Technical Analysis
9 documents
QTPyLib
8 documents
QuantRocket
7 documents
Lumibot strategies
7 documents
Awesome Quant
1 documents

Search the library

3,481 documents

BigQuant

The document presents a market-timing experiment that applies a Gaussian hidden Markov model to daily CSI 300 data. Its feature set combines the one-day log return, five-day log return, and log high-low range. The model is configured with six latent states…

China marketsEquitiesMachine learningStatistics
BigQuant

The document describes turning intraday order-book and trade data into monthly equity-selection factors. It introduces volume order imbalance (VOI), order imbalance ratio (OIR), and market price bias (MPB). MPB classifies trades by whether buyers or sellers…

EquitiesFactor investingHigh-frequency tradingMarket microstructure
BigQuant

This introductory note defines quantitative investing as using numerical methods, models, and algorithms to make investment decisions, including automated execution. It lists potential advantages: rules can reduce emotion-driven choices, computers can…

StatisticsMachine learningBacktesting
BigQuant

The document demonstrates a simple cross-sectional factor evaluation for Chinese stocks. It queries daily records for 20-day average turnover and a five-day forward close-to-close return, then groups observations by date and calculates the Spearman rank…

EquitiesChina marketsFactor investingStatistics
BigQuant

The document proposes an equity factor combining turnover with changes in shareholder counts. It first scales shareholder change by the total shareholder count, aiming to reduce the influence of differences in company size. It then applies a signed…

China marketsEquitiesFactor investingMomentum
BigQuant

This review examines target-risk funds, which aim to maintain a relatively stable portfolio risk level through fixed style categories such as aggressive, moderate, and conservative. It describes their role in US retirement accounts, their fund-of-funds fee…

Multi-assetPortfolio constructionRisk managementStatistics
BigQuant

This platform troubleshooting note explains that a shared code sample may run without producing backtest data because it is incomplete. The reported sample lacks a data source and a backtest module, and contains only some backtest component methods. It also…

BacktestingEquitiesChina markets
BigQuant

The report introduces unsupervised learning, focusing on dimensionality reduction and clustering for financial analysis. It describes factor analysis and principal component analysis as ways to compress many stock or bond characteristics into a smaller set…

Machine learningStatisticsFactor investingPortfolio construction
BigQuant

This Chinese-language research digest introduces two overseas studies in quantitative stock selection and timing. The first examines factor timing empirically, asking whether investors can adjust factor exposure over time, and discusses the challenges and…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This document summarizes a machine learning timing strategy that uses a long short-term memory network to model stock market time series. It presents the model as a way to learn dependencies across observations and adapt to sharp changes in a trend. The…

Machine learningEquitiesChina marketsBacktesting
BigQuant

This research summary outlines a progression from single-factor IC and IR analysis toward attribution in machine-learning models and causal analysis. It says IC and IR are foundational for traditional multifactor research but do not capture interactions…

Machine learningStatisticsFactor investing
BigQuant

The document describes monthly stock-selection factors built from news sentiment. Summing a stock’s monthly article sentiment scores combines the direction of coverage with its volume, while averaging scores captures sentiment without the same direct…

EquitiesSentimentFactor investingBacktesting
BigQuant

This report examines how corporate tax figures relate to business performance and how tax accounting differs from tax cash flows. It distinguishes tax payable, income tax expense, taxes and surcharges, deferred tax assets or liabilities, and cash taxes paid.…

EquitiesFactor investingChina marketsBacktesting
BigQuant

This report applies dynamic drawdown control to an enhanced equity strategy based on GARP, combining valuation and growth ideas. It describes using relative price strength (RPS) to adapt the drawdown-control signal to the market’s relative strength, with the…

EquitiesChina marketsRisk managementFactor investing
BigQuant

This report outlines a strategic and tactical framework for multi-asset allocation. Strategic allocation favors assets with relative strength over a medium-term horizon, while tactical timing adjusts asset weights according to changing conditions. The…

Multi-assetFactor investingPortfolio constructionRisk management
BigQuant

The document explains Elliott Wave theory as a framework for interpreting market trends through a repeating five-wave impulse followed by a three-wave correction. It describes nested wave degrees, three structural rules for impulse waves, and common forms…

Technical indicatorsTrend followingEquitiesChina markets
BigQuant

This forum post describes a daily stock rotation setup: invest the available cash in one stock, sell it the next day, and repeat. The author reports that orders filled close to fully invested earlier in the backtest but did not use all available capital near…

EquitiesBacktestingPosition sizing
BigQuant

The article explains how orders travel from a broker through trading infrastructure into an exchange order book, where matching determines whether and at what price they execute. It describes price priority and time priority, then presents a price-selection…

Market microstructureExecutionEquities
BigQuant

The document links prospect theory and mental accounting to the disposition effect: investors may treat gains and losses differently, which can slow how prices incorporate new information. It proposes that post announcement price drift lasts longer when the…

EquitiesEvent-drivenMomentumStatistics
BigQuant

The document summarizes research on whether hedge funds that perform well continue to do so, depending on overall market conditions. It describes two performance measures based on returns across hedge fund industries: RET_DOWN for weaker markets and RET_UP…

Multi-assetStatisticsRisk management
BigQuant

This Chinese-language forum post asks how BigQuant applies transaction costs to convertible bond strategies. It shows a per-order commission configuration with equal buy and sell rates and a minimum charge, then reports that a sell order still appears to…

BacktestingRisk managementChina markets
BigQuant

This Chinese-language Q&A covers practical questions about equity factors, model templates, backtest assessment, and trading tools. It names market capitalization, turnover, momentum, quality, valuation, growth, and profitability as commonly used factors,…

EquitiesFactor investingBacktestingRisk management
BigQuant

The document describes an A-share stock screen combining three conditions: a 14-period RSI below 65, free-float market capitalization between 5 and 10 billion yuan, and a MACD value from two sessions earlier below zero. It presents RSI and MACD as technical…

EquitiesTechnical indicatorsChina markets
BigQuant

This Chinese research summary explores using data-mining models to time factor exposure and rotate among equity factors. It describes random forests conditioned on external market variables to assess when factors may be effective, alongside approaches that…

EquitiesFactor investingMachine learningStatistics