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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
WonderTrader
14 documents
Alphalens
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

22,592 documents

SuperMind

This post proposes selecting stocks classified in the metaverse sector that appeared on the previous day's trading leaderboard and have a nonempty name for an outstanding convertible bond. It frames recent leaderboard activity as a short-term attention…

EquitiesChina marketsEvent-drivenRisk management
SuperMind

This post describes a simple stock selection screen combining a 14-period Relative Strength Index below 65, membership in a beverage and alcohol import-export industry category, and a positive price-to-earnings ratio. The RSI condition is intended to capture…

EquitiesTechnical indicatorsChina marketsRisk management
SuperMind

The article proposes a short-term Chinese equity screen requiring a 14-period RSI below 65, displayed best-bid volume greater than best-ask volume, and at least one limit-up event during roughly the prior month. It frames the combination as a way to find…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The article describes a Chinese equity screening idea that combines three conditions: at least five moving averages converging, a circulating share count no greater than 5.5 billion shares, and market capitalization below 10 billion yuan for companies…

EquitiesChina marketsTechnical indicatorsRisk management
SuperMind

This document describes an equity screen requiring turnover between 3% and 12%, circulating market capitalization between 1 and 55 hundred million yuan, and a three-limit-up sequence on the previous day. The stated intent is to identify recently strong…

EquitiesBreakoutMomentumTechnical indicators
ProRealCode

The document outlines an equity screen combining a daily price-range condition, positive large-order net volume for at least three consecutive days, and a weekly price crossover above a 30-week moving average. The stated aim is to find stocks with buying…

EquitiesTechnical indicatorsSentimentTrend following
BigQuant

The document describes a partial-replication method that selects a small set of stocks to track an index by capturing the systematic factors driving its returns. It estimates the number of factors, uses principal components to estimate factor loadings, then…

EquitiesFactor investingPortfolio constructionBacktesting
FMZ forum

The document argues that long-term investing depends heavily on temperament and a repeatable personal system. It identifies habits that can undermine decisions, including constant price checking, reacting strongly to losses, chasing quick gains, comparing…

EquitiesRisk management
SuperMind

This stock-selection approach combines a minimum intraday range with afternoon large-order net inflows, positive auction-period buying signals, and net buying by very large orders above a stated threshold. The article frames range as a measure of activity,…

China marketsEquitiesMarket microstructureTechnical indicators
BigQuant

This Chinese-language forum post reports a NameError in a deep neural network stock strategy tutorial: the variable holding stacked factor data is undefined when downstream code tries to read its date range. The shown workflow expects that data frame to…

Machine learningEquities
SuperMind

This document presents a Chinese stock selection idea combining three signals: today’s increase in holdings above 5%, the prior day’s price above its 250-day moving average, and a 2019 dividend payout ratio above 25%. It frames these as indicators of capital…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This document describes a Chinese stock watchlist screen using three technical conditions: daily amplitude above 1%, at least one limit-up event in the prior 25 days, and the current average price above its five-day moving average. It interprets amplitude as…

China marketsEquitiesVolatilityMomentum
SuperMind

This post proposes a Chinese A-share stock screen combining three conditions: MACD above zero, positive price-to-earnings ratio, and ranking by capital strength from high to low. It interprets positive MACD as a sign of an upward trend, positive P/E as a…

EquitiesTechnical indicatorsMomentumChina markets
BigQuant

This article adapts a machine-learning value strategy to Chinese A-shares. It trains an XGBoost regression model on accounting and company characteristics to forecast future price-to-book ratios. The gap between forecast and current price-to-book, scaled by…

EquitiesMachine learningFactor investingBacktesting
SuperMind

The proposed stock screen selects shares with turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and circulating share capital no greater than 5.5 billion shares. The post frames turnover as a liquidity measure, the volume…

EquitiesMarket microstructureTechnical indicatorsChina markets
SuperMind

This stock selection approach combines three filters: daily amplitude above 1, a weekly five-period moving average crossing above the ten-period average, and an increase in institutional holdings relative to float shares. The document presents the crossover…

EquitiesTechnical indicatorsMomentumRisk management
SuperMind

This note outlines a simple screen for Chinese metaverse-related stocks. It requires the 30-day moving average to rise relative to its previous reading and the price-to-earnings ratio to be positive. Formula and Python examples are included to show how the…

China marketsEquitiesTrend followingTechnical indicators
SuperMind

This note presents a stock-selection filter using three conditions: daily price amplitude above one percent, observations from 2021, and a measure formed by multiplying the prior day’s turnover rate by the ratio of today’s auction volume to yesterday’s…

China marketsEquitiesVolatilityMarket microstructure
SuperMind

This note describes a Chinese equity screen for metaverse stocks. It selects the five highest-ranked stocks by the day’s auction amount, then requires the DEA line to be rising compared with its previous value. The note gives an equivalent screening…

China marketsEquitiesTechnical indicatorsMomentum
BigQuant

This Chinese-language post reviews several strategy ideas in the context of a reported shift from leading stocks toward reversal patterns. It describes a multi-factor approach that looks for rebound or reversal behavior after attention-grabbing stocks pull…

China marketsEquitiesMean reversionMachine learning
SuperMind

This stock-selection rule screens for shares with daily amplitude above 1, a price move above a short moving average, and a low K value. The article’s final rule describes price crossing above the three-day moving average with K below 20. It also provides…

EquitiesChina marketsTechnical indicatorsMean reversion
BigQuant

This study asks whether cash flow measures predict stock returns better than conventional profitability ratios. It constructs a direct cash flow template that groups operating receipts and payments and separates them from financing, taxes, investment, and…

EquitiesFactor investingStatisticsPortfolio construction
BigQuant

The report introduces generative adversarial networks (GANs) for quantitative finance. A generator creates synthetic market data while a discriminator tries to distinguish it from real data; alternating training aims to make the generated series resemble the…

Machine learningStatisticsBacktestingEquities
SuperMind

This stock screen targets the metaverse theme and combines elevated prior-day turnover with a rising intermediate trend: the 20-day moving average must exceed the 120-day average. Its final proposed rule adds annual net-profit growth above 20% and a…

EquitiesTechnical indicatorsMomentumFactor investing