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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 outlines a way to run multiple parameterized tasks in BigQuant, a low-code platform where a visual workflow is represented as a graph of modules. A parallel mapping function can dispatch different inputs to a function, and the same pattern can…

Machine learningBacktesting
BigQuant

The document describes deploying the small Kronos pretrained model on a quantitative research platform to forecast five-minute candlestick data for stocks. Kronos represents OHLCV sequences as hierarchical discrete tokens using a specialized tokenizer, then…

Machine learningEquitiesBacktestingChina markets
BigQuant

This research summary compares Naive Bayes, linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) as supervised methods for multi-factor stock selection. The described process extracts and preprocesses features and labels, retrains…

EquitiesMachine learningFactor investingBacktesting
BigQuant

The document proposes separating sector allocation from industry selection because broad sector shifts are linked more closely to economic conditions, while industry performance depends on factors such as earnings, valuation, and capital flows. It argues…

China marketsEquitiesFactor investingPortfolio construction
BigQuant

This paper-reading digest summarizes several computer vision methods: InstanceFCN for generating object instance masks from position-sensitive score maps; a two-stream residual network that combines full-resolution detail with pooled features for…

Machine learningStatistics
BigQuant

This article explains Bollinger Bands as a moving average surrounded by upper and lower bands set a chosen number of standard deviations away. It walks through a small closing-price example and gives a second illustrative calculation using a 20-period…

Technical indicatorsVolatilityMean reversionTrend following
BigQuant

The document summarizes research using public Northbound investor holdings and trading-flow data to build 12 factors for Chinese equities. It reports testing those factors from December 2016 through December 2019 across Northbound-held stocks, the CSI 800,…

EquitiesChina marketsFactor investingStatistics
BigQuant

This research summary argues that bond fund rankings based on a single historical performance measure can be distorted by differences in market style, objectives, holdings, manager skill, fund size, and risk controls. Its proposed approach narrows the…

Fixed incomeStatisticsFactor investingPortfolio construction
BigQuant

This proposed Chinese equity screen combines three conditions: price amplitude above a stated threshold, a shortening negative MACD histogram on a 15-minute chart, and evidence interpreted as institutional buying. The rationale is that elevated amplitude…

China marketsEquitiesTechnical indicatorsMomentum
BigQuant

This note argues that quantitative strategy results depend on the combination of input factors and predictive models. It says that different factors can produce substantially different backtests when paired with the same model, and that a given factor can…

Factor investingMachine learningBacktesting
BigQuant

This study examines whether U.S. equity mutual fund managers adjust market exposure when broad stock mispricing changes. It builds a market mispricing measure from a financing-based long-short factor, then orthogonalizes it against macroeconomic variables. A…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This factor study examines whether the share of a stock’s trading volume occurring in the opening call auction contains information useful for equity selection. It builds an opening call-auction volume proportion factor from relative volume and moving…

EquitiesHigh-frequency tradingMarket microstructureFactor investing
BigQuant

This research summary describes a model for estimating whether a company will meet or exceed analyst expectations in its next reporting period. Its premise is that earnings delivery has persistence: stocks that beat expectations in one period are more likely…

EquitiesMachine learningStatisticsFactor investing
BigQuant

The report applies a modified Grinold-style decomposition to attribute equity index returns to dividend yield, nominal earnings growth, and changes in the price-to-earnings multiple. It compares major Chinese broad-market indexes and selected industry…

EquitiesChina marketsUS marketsFactor investing
BigQuant

This overview explains Dow Theory through three trend scales: primary movements that define the broad market direction, secondary moves that correct the primary trend, and shorter daily fluctuations. It also summarizes six principles later writers attributed…

Trend followingTechnical indicatorsMarket microstructure
BigQuant

This brief Chinese-language post describes a stock-selection submission that adds price-to-sales and dividend-yield filters, then increases the weight assigned to dividend yield. The author says the results improved, but provides no performance figures, test…

EquitiesFactor investingPortfolio construction
BigQuant

The document outlines a China equity industry-rotation strategy using the Shenwan Level 1 industry classification. It ranks industries with a composite momentum measure formed from recent 5-day, 20-day, and 60-day returns, weighted 0.4, 0.3, and 0.3. Each…

China marketsEquitiesMomentumFactor investing
BigQuant

The document raises a measurement question about Rank IC, a rank-based measure of association between model predictions and subsequent returns. The author reports that Rank IC computed on a custom training set differs substantially from the value produced by…

StatisticsMachine learningBacktesting
BigQuant

This research report explains how equity risk factors and factor models can help identify portfolio risks, estimate return covariances, and analyze portfolio performance. It distinguishes risk factors from alpha factors and discusses factor returns,…

China marketsEquitiesRisk managementPortfolio construction
BigQuant

This report outlines a framework for researching quantitative commodity signals in iron ore. It groups inputs into fundamental factors covering supply, demand, and inventories, plus a separate sentiment category. Because fundamental data updates less…

CommoditiesFactor investingSentimentMomentum
BigQuant

This research summary examines generalized linear models as ways to combine stock factor exposures into forecasts of future returns. Its workflow includes extracting features and labels, preprocessing features, forming training sets, and retraining on a…

China marketsEquitiesMachine learningFactor investing
BigQuant

This discussion explores representing Chinese stocks that hit the daily price limit as members of a shared hot theme, then counting limit-up names within each theme. The example is state-owned enterprise reform, where stocks from different industry sectors…

EquitiesChina marketsMomentumFactor investing
BigQuant

This summary of a 2018 A-share report reviews quantitative fund products, equity factor styles, and institutional responses to a difficult market structure. It says index-enhanced fund assets rose by 55.31 billion while hedged and actively managed…

EquitiesChina marketsFactor investingMean reversion
BigQuant

This brief indicator note introduces the Consecutive Break (CB) measure and names a close-price input with upward and downward length parameters. Its pseudocode defines an upward count as the number of consecutive bars in which the close exceeds the previous…

EquitiesTechnical indicatorsMomentum