Skip to content

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

This report describes a Chinese equity-selection process built from daily price and volume data. Genetic programming generates candidate factors: Rank IC is used to find factors with linear predictive relationships, while mutual information targets nonlinear…

EquitiesChina marketsMachine learningFactor investing
BigQuant

This practitioner report reviews DeepAlpha, a neural model used to rank stocks from factor inputs, and compares it with other approaches. The author says a simple fully connected network appeared competitive with decision-tree and linear models, and…

Machine learningEquitiesFactor investingBacktesting
BigQuant

This overview catalogs machine learning approaches that can be applied to quantitative investing, grouping them into tree models, linear models, unsupervised methods, deep learning, ensembles, and online learning. It links model families to tasks such as…

Machine learningFactor investingStatisticsBacktesting
BigQuant

The document summarizes research on converting stock factors with strong negative-return associations into a long-only selection process. Because short selling is limited in the A-share market, it describes reversing the use of such signals: remove stocks…

China marketsEquitiesFactor investingPortfolio construction
BigQuant

This report describes a monthly stock selection model that uses a random forest, an ensemble of decision trees trained with bagging, to estimate each stock’s probability of rising in the next period. Its workflow covers feature and label preparation,…

China marketsEquitiesMachine learningFactor investing
BigQuant

This research review explains how earnings beta can measure a company’s exposure to systematic risk through the co-movement of its earnings with aggregate market earnings. It compares eleven earnings series built from realized or analyst-forecast earnings,…

EquitiesFactor investingStatisticsRisk management
BigQuant

This literature summary covers two separate topics. The first describes a framework for studying how mutual fund managers’ personal characteristics relate to performance, decomposing outcomes into overall results, return and risk, and timing and security…

Multi-assetOptionsPortfolio constructionVolatility
BigQuant

This research summary describes an adaptation of Robert Whitelaw’s time-varying Sharpe ratio, or Tsharp, for timing the Shanghai Composite. Unlike a conventional Sharpe ratio, the measure estimates returns and variance through regression, making it…

China marketsEquitiesStatisticsBacktesting
BigQuant

This opinion article argues that algorithmic trading can harm investors who rely on widely known indicator signals or chase sharp price moves, while creating opportunities through the liquidity and volatility it adds. It describes three potential…

EquitiesExecutionRisk managementTechnical indicators
BigQuant

This study organizes fifty Wind order-flow measures into eight groups, including inflows, outflows, net active buying, and large-order net flows. It assesses their stock-selection value with industry-neutral portfolio sorts, cross-sectional regressions, and…

China marketsEquitiesFactor investingMarket microstructure
BigQuant

This document summarizes an ETF tracking workbook and describes the market landscape it was designed to monitor. It reports that broad-market and sector or theme ETFs formed the largest non-money-market categories, with examples of major index exposures and…

China marketsEquitiesPortfolio construction
BigQuant

This market-monitoring report reviews Chinese equities during the early 2020 pandemic shock. It combines trading activity, valuation, turnover, sentiment, sector relative performance, fund concentration, and style-factor signals. Its readings describe…

China marketsEquitiesFactor investingStatistics
BigQuant

The document reports a platform-specific issue in which an XGBoost classifier’s probability prediction function returns only endpoint values of zero and one. The user expected continuous class probabilities that could be used to rank securities, based on…

Machine learningEquities
BigQuant

This discussion distinguishes machine learning for execution optimization from machine learning for directional trading. Execution begins with a known trade and seeks to reduce its cost; alpha generation must identify price direction and capture enough of…

Machine learningHigh-frequency tradingMarket microstructureExecution
BigQuant

This article describes combining two stock selection models designed for different Chinese equity market conditions: a rebound after an index decline, and a mild market pullback after stocks hit their daily price limit. Each model includes market environment…

China marketsEquitiesBacktestingMomentum
BigQuant

This Chinese-language support post describes a feature-engineering question: how to count, over a recent window, the days when a five-day moving average exceeds a ten-day average. The proposed first feature assigns a positive or negative value according to…

EquitiesTechnical indicatorsMachine learning
BigQuant

This forum post raises a data-timing problem in a trading workflow. In the described backtest, a signal generated on one day is placed the next day, and pre-market processing can apparently retrieve that day’s opening and closing values through historical…

BacktestingExecutionStatistics
BigQuant

This article describes 25 reasons retail investors may lose money in equities. It groups them around information and research gaps, overreliance on one strategy, emotional and herd-driven decisions, limited planning and experience, concentrated portfolios,…

EquitiesRisk managementPortfolio constructionSentiment
BigQuant

This review explains why diversification may help more during declines than during gains, and why average correlations can hide that difference. It recommends estimating an asset pair’s downside relationship by conditioning on the portfolio’s main growth…

Multi-assetPortfolio constructionRisk managementStatistics
BigQuant

This review describes a method for identifying crowded markets and distinguishing potential bubble build-up from unwinding. It measures sector or factor clustering from return covariances and principal components, then pairs that measure with relative…

EquitiesFactor investingMomentumPortfolio construction
BigQuant

The document raises a question about possible look-ahead bias in a stock-selection workflow. It points to a five-day return calculated from prices shifted forward in time and asks whether using that value as a feature in a test set leaks future information.…

EquitiesMachine learningBacktestingStatistics
BigQuant

The document presents a modified MACD indicator built by replacing standard exponential moving averages with a three-stage smoothing expression. It applies that transformation separately to closing prices over 12- and 26-period spans to form DIF, then…

Technical indicatorsMachine learningEquities
BigQuant

This report summary examines a 2018 forecast of CSI 300 constituent changes and the potential trading flows associated with index funds adjusting their holdings. The forecast uses the index's periodic review rules, including measures of past-year trading…

EquitiesEvent-drivenExecutionChina markets