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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

4,510 documents

Amberdata research

The document reviews BTC and ETH conditions after a sharp market squeeze, connecting macro uncertainty and price levels with options pricing. It describes falling implied volatility alongside elevated realized volatility, negative carry, changes in term…

CryptoOptionsVolatilitySentiment
SuperMind

This proposed equity screen ranks stocks by incoming capital, then filters for larger free-float market capitalization and a rising trend in which the 20-day moving average is above the 120-day average. The article describes fund-flow measures such as…

EquitiesChina marketsTrend followingTechnical indicators
SuperMind

The document describes a Chinese stock screen combining daily price range, a turnover measure adjusted by the ratio of current auction volume to prior-day volume, and evidence of a recent limit-up move. It frames these inputs as signs of active trading,…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This selection method screens for stocks with amplitude above 1 and more than one year since listing, then ranks qualifying names by individual-stock heat and takes the top n. The accompanying rationale is that larger price movement may indicate market…

EquitiesTechnical indicatorsMomentumSentiment
SuperMind

The document describes a Chinese stock screen combining moderate turnover, at least one limit-up event in a recent lookback window, and a ranking based on net large-order flow. The stated rationale is that turnover approximates trading activity, a recent…

EquitiesChina marketsSentimentMarket microstructure
FMZ forum

This essay presents Bernard Baruch’s investment principles, emphasizing independent judgment, company quality, and crowd psychology. It favors businesses with real assets, operational advantages, and capable management, while arguing that management quality…

EquitiesSentimentRisk management
BigQuant

The document reviews research on whether investor sentiment helps explain stock return anomalies or appears predictive because of spurious regression. Persistent predictors can seem to forecast returns in finite samples when expected returns also vary over…

SentimentStatisticsFactor investingEquities
SuperMind

This proposed Chinese equity selection rule screens for stocks with turnover between 3% and 12%, then requires the product of the day’s percentage price change and net buying flow attributed to very large orders to be positive. Eligible stocks are ranked by…

EquitiesChina marketsMomentumSentiment
SuperMind

This selection method screens for stocks with turnover within a specified band, a reversal-style price pattern, and a high rank in large-order net buying. The article describes the flow ranking as a way to incorporate market participation and capital…

China marketsEquitiesTechnical indicatorsSentiment
SuperMind

This stock screen selects robot-themed companies using three filters: a daily high-low range condition, positive institutional fund-flow measures, and circulating market value below a stated ceiling. The accompanying examples describe aggregating recent net…

China marketsEquitiesFactor investingSentiment
SuperMind

The document presents a Chinese stock screen for companies associated with the metaverse theme. It selects stocks appearing on the prior day's exchange leaderboard and applies a circulating-share limit; the detailed formula narrows the float to between 3…

EquitiesChina marketsMomentumSentiment
SuperMind

The document describes a stock screen combining recent buying activity, the ratio of actively bought to actively sold volume, and a historical dividend payout measure. Its main text specifies a buying-activity share above 5%, an outside-to-inside volume…

EquitiesMomentumSentimentRisk management
SuperMind

This stock selection approach combines three filters: a 14-period RSI below 65, best bid volume greater than best ask volume, and at least two limit-up sessions in a 500-day lookback. The article frames these conditions as a way to find shares with prior…

EquitiesTechnical indicatorsMomentumSentiment
BigQuant

This study note explains how to build a directed stock network from news coverage: a stock named in a headline is treated as a lead, while stocks mentioned in the article body are followers. Edge weights count their co-occurrences over a rolling window. The…

EquitiesSentimentStatisticsFactor investing
Kraken Learn

This survey reports on U.S. crypto holders’ intentions, confidence, preferences, and portfolio changes. It says 88% planned to continue investing over the next 12 months, compared with 73% in the prior-year survey, and 70% reported greater confidence.…

CryptoSentimentPortfolio constructionDeFi
SuperMind

This stock-screening approach combines three signals: membership in the metaverse theme, positive net buying by major participants during the opening auction, and evidence of institutional accumulation. The article describes these as ways to target a popular…

EquitiesChina marketsSentimentExecution
SuperMind

This Chinese A-share screening recipe combines three filters: RSI below 65, the ratio of external to internal trading volume above 1.3, and prior-day actual turnover between 3% and 28%. The article presents these as measures of technical condition, trading…

EquitiesChina marketsTechnical indicatorsSentiment