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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

12,226 documents

SuperMind

This stock screen combines a positive MACD reading with a company-category filter and a history of high return on equity. The article describes the intended idea as finding companies with favorable fundamentals and technical trends. Its stated ROE threshold…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This Chinese-language post describes an equity screening rule that selects stocks with an amplitude threshold, prior-day main-fund control, and a rising DEA. It frames the conditions as combining price activity and fund-flow participation with a short-term…

EquitiesTechnical indicatorsMomentumStatistics
SuperMind

This Chinese equities screening recipe combines a volatility condition, positive large-order net volume, and a long-term trend filter. It selects stocks with amplitude above 1, large-order net volume above 0.05 for at least three consecutive days, and the…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

The report studies liquidity, volatility, and return distributions in the CSI 1000 near-month futures contract using minute bars over a specified 2024 sample. It examines log returns across several aggregation windows, compares full-session data with…

FuturesChina marketsStatisticsVolatility
SuperMind

The document describes a stock screen for the metaverse industry that combines a newly formed KDJ bullish crossover with three consecutively declining closing prices. It outlines the intended selection logic and gives formula and Python examples for applying…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This stock-selection example filters Chinese equities by turnover, listing year, and Bollinger Band position. It targets stocks with turnover between 3% and 12%, an IPO year of 2021, and a closing price above the Bollinger middle band but below the upper…

EquitiesChina marketsTechnical indicatorsVolatility
SuperMind

This document describes a Chinese stock-selection screen combining turnover with year-over-year growth in net profit attributable to parent-company shareholders. It specifies turnover between 3% and 12%, profit growth above 20% and no more than 100%, and an…

EquitiesChina marketsFactor investingStatistics
SuperMind

This note describes a Chinese equity screen that combines amplitude above 1, a prior-day proxy for main-investor control, and return on equity above 15% in each of five consecutive years. The proposed logic aims to pair active price movement and recent…

EquitiesChina marketsFactor investingTechnical indicators
SuperMind

This note presents a technical screen for Chinese stocks using daily amplitude above 1, a rounded or arc-like candle pattern, and a MACD reading below zero from two days earlier. It explains the amplitude condition as a way to find stocks with notable price…

EquitiesChina marketsTechnical indicatorsVolatility
SuperMind

This note describes a Chinese equity screen combining turnover, year-over-year net profit growth attributable to parent-company shareholders, and the ratio of external to internal trading volume. It selects stocks with turnover between 3% and 12%, profit…

EquitiesChina marketsMarket microstructureFactor investing
SuperMind

This post outlines an A-share stock screen using three conditions: a daily high-low range above a threshold, a nonempty convertible-bond name field, and a positive but capped return over ten days. It presents both an indicator-formula example and a Python…

EquitiesChina marketsVolatilityMomentum
SuperMind

This post describes an A-share screening recipe combining a price-range condition, exclusion of stocks marked ST, a five-session closing-high condition, and a limit on the stock’s price increase at 9:25. It also says to select stocks before 10:00. The…

EquitiesChina marketsBreakoutMomentum
SuperMind

The document describes a China-market stock screen that selects companies in the metaverse theme, with circulating market capitalization above 10 billion yuan and a 2019 dividend payout ratio above 25%. It defines payout ratio as dividends per share relative…

China marketsEquitiesFactor investingRisk management
SuperMind

The document presents a calculation for charting the annualized return of a fund over time. It retrieves daily closing prices for a specified fund and date range, then compares each later close with the first close to calculate cumulative return. It…

StatisticsBacktesting
SuperMind

The document proposes a China A-share screening rule focused on companies classified in the metaverse industry. Its initial filters include a limit on tradable share count and a positive MACD reading. A later version adds a market-cap ceiling and selects…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This brief community post proposes adding two filters to a small-cap stock strategy: rank companies from lower to higher debt ratio and require five-year average return on equity to exceed 10%. The title advertises an annualized return of 41% and a win rate…

EquitiesFactor investingChina markets
SuperMind

This stock-selection proposal filters for main-board shares with turnover between 3% and 12%, a daily gain above 1%, and a newly formed KDJ golden cross. The accompanying Python example checks recent K and D values for a crossover and adds a J-value…

EquitiesTechnical indicatorsMomentum
SuperMind

The document proposes a stock screen using three conditions: daily high-to-low amplitude above 1%, a K-line-related value below 20, and prior-day turnover above 8%. It frames amplitude as a volatility filter, the K-line threshold as a low-position condition,…

EquitiesTechnical indicatorsVolatility
SuperMind

The article explains that Ethereum’s mempool holds transactions awaiting inclusion in a block, and that fees and maximal extractable value can influence transaction priority. It distinguishes real-time pending-transaction data from historical records and…

CryptoOn-chain dataExecutionMarket microstructure
SuperMind

This A-share stock screen combines a volatility condition, a weekly moving-average crossover, and positive daily price action. It seeks stocks with amplitude above 1, a weekly MA5 crossing above MA10, and a daily gain greater than 1%, while restricting the…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The post describes a Chinese equity screening rule combining daily price movement, circulating market capitalization, and a rounded or arc-shaped chart pattern. Its stated conditions are amplitude above 1, circulating market value above 10 billion yuan, and…

EquitiesChina marketsTechnical indicatorsBacktesting
SuperMind

This Chinese stock-screening proposal combines three characteristics: at least five moving averages converging near one another, a preferred enterprise type, and at least two limit-up sessions within a 500-day lookback. The article interprets moving-average…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The document outlines a Chinese A-share screening idea focused on stocks classified in the metaverse theme. Its initial conditions are turnover above 8% on the previous day and an increase in the K value of the KDJ indicator. The final stated rule adds a…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This note presents a proposed screen for Shenzhen main-board shares that excludes Beijing-listed A-shares. Its initial conditions limit price-to-earnings and price-to-book ratios to stated ranges and require daily high-low amplitude above a threshold. An…

EquitiesChina marketsFactor investingTechnical indicators