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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
Lumibot strategies
7 documents
QuantRocket
7 documents
Awesome Quant
1 documents

Search the library

3,481 documents

BigQuant

The article answers practical questions about machine-learning stock strategies. It says the platform separates training and test periods by time, with the test interval serving as the backtest, and describes a setup without a separate validation set. It…

Machine learningBacktestingStatisticsEquities
BigQuant

This research note reviews the CSI Dividend Index and the Bosera CSI Dividend ETF, which tracks it. The index draws 100 Shanghai and Shenzhen A-shares from stocks with high average after-tax cash dividend yields over the prior two years, then weights them by…

EquitiesChina marketsFactor investingPortfolio construction
BigQuant

This monitoring framework tracks valuation measures and fund holdings across major Chinese equity indexes, industry groups, and selected large companies. It covers price-to-earnings, growth-adjusted valuation, price-to-book, dividend yields, and fund…

EquitiesChina marketsStatisticsFactor investing
BigQuant

This research note studies value-based stock selection in Hong Kong equities, using the Hang Seng Composite as its universe. Because Hong Kong main-board companies are not required to publish quarterly reports and may change their fiscal year-end, the…

EquitiesFactor investingStatisticsBacktesting
BigQuant

The discussion asks how to detect whether a value, such as a closing price or trading volume, has risen or fallen continuously over a chosen number of days. It points to a pair of time-series functions: one counts consecutive rises, and a corresponding…

Technical indicatorsStatistics
BigQuant

The discussion explains how to interpret a Chinese stock daily-bar dataset that stores fully back-adjusted prices. It says the dataset includes a back-adjustment factor and describes recovering the underlying market price by dividing the adjusted close by…

EquitiesChina marketsBacktesting
BigQuant

This report summary examines Chinese public mutual funds around the second quarter of 2018, focusing on actively managed equity funds. It describes falling scale and share counts for active equity products, differences in reported returns across fund types,…

EquitiesChina marketsPortfolio constructionStatistics
BigQuant

This report summary examines Chinese public mutual funds around the second quarter of 2018, focusing on actively managed equity funds. It describes falling scale and share counts for active equity products, differences in reported returns across fund types,…

EquitiesChina marketsPortfolio constructionStatistics
BigQuant

The document presents eight discretionary rules for interpreting stock price action. It treats steady small advances as possible accumulation and repeated sharp gains as a potential overextension. It also compares each stock’s behavior with the broad market:…

EquitiesTechnical indicatorsMomentumMarket microstructure
BigQuant

The document groups common Chinese A-share factors into style, technical, and fundamental categories. Style factors include market, size, and value; technical factors include reversal, low turnover, and low volatility; fundamental factors cover profitability…

China marketsEquitiesFactor investingPortfolio construction
BigQuant

The document introduces the Commodity Channel Index (CCI), a technical indicator calculated from high, low, and close prices. It first derives typical price as the average of those three values, then compares typical price with its moving average and scales…

Technical indicatorsStatistics
BigQuant

This project outlines a mean-reversion pairs strategy for Chinese futures. It screens candidate contract pairs for cointegration with an Augmented Dickey-Fuller test, estimates a changing hedge ratio with a Kalman filter, and measures spread behavior with…

FuturesChina marketsPairs tradingMean reversion
BigQuant

This short description presents TEMACandle, an indicator that displays a price series as candlesticks after processing it with the Triple Exponential Moving Average (TEMA) algorithm. The transformation applies TEMA smoothing to the underlying price data and…

Technical indicatorsEquities
BigQuant

This research summary describes a multi-factor investment workflow that combines alpha forecasts, a risk model, transaction-cost estimates, portfolio optimization, and performance attribution. It argues that simple score-based selection or sector and…

EquitiesPortfolio constructionRisk managementStatistics
BigQuant

This brief Chinese-language support note explains a prediction-data problem traced to missing values in a rolling price-range factor. The example factor compares a short moving average of the high-low range with a longer moving average. According to the…

Machine learningStatisticsTechnical indicatorsBacktesting
BigQuant

The report asks whether exposure to macroeconomic policy uncertainty is priced in Chinese A-shares. It describes a news-based economic policy uncertainty index and estimates each stock’s EPU beta through time-series regression. Stocks are sorted into five…

EquitiesChina marketsFactor investingSentiment
BigQuant

This study examines whether price-to-earnings ratios, cyclically adjusted P/E (CAPE), and the bond-stock earnings yield measure (BSEYD) can signal major downturns in the Shanghai and Shenzhen composite indexes. It also describes return features in the two…

EquitiesChina marketsStatisticsBacktesting
BigQuant

This troubleshooting note describes a SQL binder error that occurs when a normalization function is used in a query joining two stock-factor tables. Both tables contain a date column, so passing an unqualified date reference to the function leaves the…

EquitiesFactor investingTechnical indicators
BigQuant

This discussion examines why published quantitative findings and attractive backtests may fail to reproduce or translate into tradable returns. It explains how publication incentives, selective reporting, repeated testing, flexible data choices, and…

StatisticsBacktestingRisk managementFactor investing
BigQuant

The document explains how to construct factors grouped by Chinese stock market concepts when a built-in grouping expression does not recognize the concept field. For standard industry groupings, it gives a group-mean example using a level-two industry…

EquitiesFactor investingStatisticsChina markets
BigQuant

The document describes a problem encountered when using an initialization module to set a day counter and prevent duplicate daily trades. In backtests, the counter reportedly advanced across several days, but in simulated trading the same first-day trade ran…

ExecutionBacktestingRisk management
BigQuant

This short forum post asks how to add market-timing conditions to a strategy backtest. The author has seen strategies reduce or alter exposure after several consecutive market declines and is interested in alternatives based on whether the Shanghai Composite…

EquitiesTechnical indicatorsBacktestingChina markets
BigQuant

This brief introductory response defines quantitative investing as finding patterns in historical data, translating them into a model and strategy, and using software to execute trades according to the resulting rules. It highlights discipline as a benefit:…

StatisticsMachine learningBacktestingRisk management