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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
Quantpedia
86 documents
TqSdk
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
Quantopian lectures
45 documents
Binance API docs
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 article discusses recruiting and interviewing in China’s quantitative trading industry. It argues that candidates need not come from finance: people trained in fields such as chemistry, mathematics, statistics, and computer science may bring useful…

Risk managementStatistics
BigQuant

The post outlines a proposed equity workflow that links market breadth, sector selection, stock screening, and portfolio exposure. It would compare broad-market indices with industry or concept indices using beta, select sectors with higher beta, then choose…

China marketsEquitiesFactor investingPosition sizing
BigQuant

The note answers how to prevent a backtest from buying stocks that cannot realistically be traded because they are suspended or locked at a one-price limit. It says BigQuant's backtesting module handles these cases by automatically canceling an order when…

BacktestingEquitiesExecutionChina markets
BigQuant

This daily report summarizes an RSRS-based market timing model for major Chinese equity indices on April 7, 2020. The model retained bullish views on the Shanghai Composite, CSI 500, and ChiNext, while taking a more cautious stance on the SSE 50 and CSI 300.…

EquitiesChina marketsTechnical indicatorsMomentum
BigQuant

This brokerage report reviews Chinese equity style behavior during the week of April 13–17, 2020, then outlines a style outlook using macroeconomic conditions, historical calendar patterns, cross-sectional dispersion, fund flows, valuations, and macro event…

EquitiesFactor investingStatisticsChina markets
BigQuant

The article describes inferring a fund’s constituents and weights from its total value time series. It compares Extended Linear Cloning (ELC), which uses regression coefficients as asset importance scores and retains the highest scoring candidates, with…

Machine learningPortfolio constructionStatisticsEquities
BigQuant

This brief BigQuant support note explains a common cause of a “no data left after dropna” error when backtesting a strategy on the SSE 50 universe. The error can occur because the strategy’s filters leave no eligible stocks, rather than because the backtest…

EquitiesChina marketsBacktesting
BigQuant

This study constructs equity selection factors from trade by trade data to measure aggressive buying. It aggregates transactions into minute level buying and selling amounts, then derives buying share and buying intensity measures. The proposed factors can…

EquitiesFactor investingMarket microstructureBacktesting
BigQuant

This overview explains how AI quantitative investment platforms combine machine learning, deep learning, and natural language processing with financial data analysis. It describes a general workflow in which platforms collect and process market and financial…

Machine learningRisk managementPortfolio constructionExecution
BigQuant

The document briefly explains a variation of the Schaff Trend Cycle indicator. It says the indicator is calculated from the difference between two RSI oscillators that use different lookback periods, and identifies the item as a colored version of that…

Technical indicatorsMomentum
BigQuant

This study proposes a passive index that selects between the A-share and H-share of the same company according to which listing is cheaper after currency adjustment. It examines price gaps across dual-listed Chinese companies, using panel regression to…

China marketsEquitiesMean reversionBacktesting
BigQuant

This report examines shifts in China’s A-share market and argues that both industry fundamentals and market style help explain relative sector returns. It describes a stepwise regression that attributes sector performance to beta, company size, cyclicality,…

China marketsEquitiesFactor investingStatistics
BigQuant

This research note evaluates 31 quality factors for stock selection in Hong Kong equities and compares their effectiveness with results in mainland A-shares. Twelve factors are reported as notably effective, particularly measures tied to operating cash flow…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This Chinese-language weekly report presents a cautious near-term market view after large-weight industries weakened relative to smaller industries. It describes an industry allocation signal favoring market-cap-weighted exposure, which would reduce exposure…

EquitiesMomentumVolatilityFactor investing
BigQuant

This report is presented as the opening installment in a series on fundamental quantitative stock selection, focused on the lithium battery industry chain. Its stated aim is to combine fundamental information with traditional multi-factor selection,…

EquitiesChina marketsFactor investing
BigQuant

This overview introduces statistical learning methods for prediction and data analysis. It covers linear and logistic regression, linear and quadratic discriminant analysis, resampling, predictor selection, regularization, dimension reduction, nonlinear…

StatisticsMachine learningBacktesting
BigQuant

This forum post describes a long-short stock routine that first attempts to close existing positions, then buys the three highest-ranked stocks and sells short the three lowest-ranked. It checks whether each security can be traded before sending an order,…

EquitiesExecutionMarket microstructurePosition sizing
BigQuant

The document introduces Altman's Z-score as a composite financial-risk measure built from five ratios: working capital, retained earnings, earnings before interest and taxes, market equity relative to liabilities, and sales, each scaled against assets except…

EquitiesChina marketsFactor investingBacktesting
BigQuant

This research review examines momentum in China’s A-share market as institutional ownership and analyst attention have grown. It compares traditional lookback returns with measures that account for price paths, remove limit-up days, use earnings announcement…

EquitiesChina marketsMomentumFactor investing
BigQuant

This research summary examines how earnings forecasts can improve equity valuation and stock selection in China’s A-share market. It argues that historical return on equity alone has limited selection value there, while substituting forecast earnings for…

EquitiesChina marketsFactor investingStatistics
BigQuant

This research note describes extending mean-variance portfolio allocation with higher-moment information, especially co-skewness and co-kurtosis among assets. Mean and covariance alone can miss differences in joint tail behavior: portfolios with similar…

Portfolio constructionStatisticsRisk managementBacktesting
BigQuant

This article presents six proposed bullish chart setups: a pullback that holds above a limit-up level and a moving average, rising volume after a contraction, a two-stage expansion from a price low, a large down day on reduced volume, a sequence of pullbacks…

EquitiesTechnical indicatorsMomentumBreakout
BigQuant

This report summary lays out a framework for analyzing government interest-rate bonds through supply and demand, return decomposition, duration timing, and portfolio allocation. It attributes supply partly to fiscal conditions and economic pressure, and…

Fixed incomeFuturesTrend followingMean reversion
BigQuant

This submission describes a composite stock factor combining three ranked measures: three-year net profit growth, the inverse of summed turnover over five periods, and the inverse of five-period trading-amount variability. The intended profile is high growth…

EquitiesFactor investingStatisticsRisk management