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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

4,510 documents

BigQuant

The article argues that traders should avoid entering sectors after a short burst of broad attention and instead look for less crowded themes. It proposes treating three consecutive strong sessions and a sharp cumulative rise in leading stocks as a possible…

EquitiesMomentumBreakoutSentiment
SuperMind

This stock-selection rule focuses on shares with codes beginning with 60, turnover between 3% and 12%, and a relatively high ranking for net large-order volume. The article presents turnover as an activity filter and large-order net flow as an indicator of…

China marketsEquitiesMarket microstructureSentiment
SuperMind

This Chinese stock screen selects shares with turnover between 3% and 12%, a rising KDJ K value, and prior-day opening-auction turnover above 0.26. The article presents the auction turnover condition as a rough indicator of improving market sentiment, and…

China marketsEquitiesTechnical indicatorsMomentum
Amberdata research

The document explains how changes in cryptocurrency ETF holdings and assets under custody (AUC) can be used to observe institutional participation in Bitcoin and Ethereum. Rising balances may reflect increased exposure and confidence, while declines may…

CryptoSentimentMarket microstructureRisk management
Amberdata research

This market recap reviews Bitcoin and Ether options during a period of sharply higher realized volatility amid geopolitical risk. It contrasts strong short-dated volatility with softer one-month implied volatility, discusses inversion in both term…

CryptoOptionsVolatilityDerivatives pricing
Kraken Learn

The document explains how decentralized prediction markets let traders buy and sell shares tied to event outcomes. A share price reflects market participants’ estimates of an outcome’s likelihood and can change as information arrives. The election example…

DeFiCryptoSentimentRisk management
Amberdata research

This newsletter links crypto market expectations to macroeconomic developments, including an inflation surprise, the upcoming FOMC decision, tariff uncertainty, and movements in equities and volatility measures. It describes Bitcoin as closely tied to…

CryptoOptionsVolatilitySentiment
Lumibot

The strategy uses a fixed equity watchlist and a daily agent workflow to review SEC Form 4 filings available as of each decision time. Its research step filters recent filings, opens the source documents, and focuses on non-derivative open-market purchases…

EquitiesEvent-drivenSentimentPortfolio construction
Cryptohopper blog

This introductory article explains crypto market sentiment as the collective mood of investors and describes how optimism or fear can affect supply, demand, and short- to medium-term price moves. It recommends treating sentiment as one input alongside…

CryptoSentimentTechnical indicatorsRisk management
SuperMind

This post proposes a Chinese equity screen that selects stocks associated with the metaverse theme, positive institutional flow, and turnover between 2% and 9%. It explains the intended roles of the filters: sector exposure targets a growth theme,…

EquitiesChina marketsSentimentTechnical indicators
SuperMind

This daily stock-selection recipe combines a positive MACD reading with a share price below 12 yuan and an outside-to-inside trading-volume ratio above 1.3. It treats MACD as a technical trend signal and the volume ratio as a short-term indicator of trading…

EquitiesChina marketsTechnical indicatorsSentiment
BigQuant

This market note evaluates several indicators for judging whether Chinese equities may be approaching a short-term bottom. It reviews northbound investor flows, fund equity exposure, and a turnover-to-volatility measure. The note reports that foreign…

EquitiesChina marketsSentimentStatistics
SuperMind

The post outlines a Chinese equity screen for stocks in the metaverse concept group whose price is above a long-term moving average, then ranks candidates by individual-stock popularity. It describes the moving-average condition as a way to identify prices…

EquitiesChina marketsTechnical indicatorsSentiment
BigQuant

The study examines whether news and social-media sentiment relate to intraday stock liquidity and returns. It combines stock-linked news sentiment with minute-level Twitter and StockTwits measures, then uses regressions and event studies to analyze liquidity…

EquitiesMean reversionSentimentBacktesting
Amberdata research

This newsletter links macroeconomic developments to crypto and options-market positioning. It discusses how stronger employment data and rising Treasury yields changed market-implied expectations for the next Federal Reserve decision, and frames inflation…

CryptoOptionsVolatilitySentiment
BigQuant

This document describes a market timing model for the CSI 300 based on the Guosen Investor Sentiment Index. It adapts a risk appetite approach by comparing 28 Shenwan first-level industries. The proposed index uses the Spearman rank correlation between…

EquitiesSentimentChina marketsStatistics
MQL5 code base

The document describes an indicator that identifies common candlestick formations and marks bullish and bearish patterns with different colors. Its detection logic uses the average candle body size as a reference, and an optional state machine suppresses…

Technical indicatorsForexSentimentStatistics
SuperMind

This Chinese A-share screening example combines three conditions: turnover between 3% and 12%, a newly formed KDJ bullish crossover, and more than two limit-up days in the prior ten days. The article interprets turnover as a liquidity filter, the crossover…

EquitiesChina marketsTechnical indicatorsMomentum
ProRealCode

This indicator compares the current period’s first support and resistance levels with the prior period’s levels, which are calculated from highs, lows, and closes. When both levels rise, it labels sentiment bullish; when both fall, bearish. A higher…

Technical indicatorsSentimentVolatility
SuperMind

This stock-screening idea selects Chinese A-share companies classified in the metaverse sector, with circulating share capital at or below the stated threshold and a ratio of reported external to internal trading volume above the specified cutoff. The…

China marketsEquitiesSentimentMarket microstructure
Lumibot

This document presents a QuantStats tear sheet for a strategy labeled news-sentiment-generic, compared with SPY over January 4–15, 2026. It reports a 1% total return for both, while the strategy has higher annualized return and volatility, a lower Sharpe…

SentimentBacktestingRisk managementEquities