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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

22,592 documents

SuperMind

The screen selects listed Chinese stocks with an amplitude above one, more than three years since listing, and closing prices that fell on each of the last three sessions. Its stated rationale is to seek actively traded shares while excluding newer listings,…

EquitiesChina marketsTechnical indicatorsMean reversion
MQL5 code base

DayRSI is an RSI indicator whose calculation period adjusts automatically according to the number of bars in the current day. The description says it is intended for intraday decision-making and supports chart timeframes from one-minute to daily. This makes…

Technical indicatorsEquities
SuperMind

This stock screen looks for Shenzhen Main Board companies with annual return on equity above 15% in each of five years, a price-to-earnings ratio from zero to 29.01, and a price-to-book ratio from zero to 3.11. It also requires the daily high-low range to be…

EquitiesFactor investingChina marketsStatistics
SuperMind

This Chinese stock selection rule combines three filters: rank stocks among the top 100 by volume ratio, keep those whose opening price is within five percent above or below the ten-day moving average, and look for a shortening histogram in the 15-minute…

EquitiesTechnical indicatorsMomentumChina markets
backtrader

This Backtrader example combines two entry and exit signals on a selected data feed: a short and long simple moving average crossover, and a MACD line crossing its signal line. Each bullish crossover adds half of the strategy's target stake, while bearish…

EquitiesTechnical indicatorsTrend followingBacktesting
SuperMind

This document describes a daily stock screen that selects shares with turnover between 3% and 12%, a rising K value in the KDJ indicator, and an intraday low between 4% and 5% below the previous close. It provides equivalent selection logic in a…

EquitiesTechnical indicatorsMean reversionBacktesting
SuperMind

This proposed stock screen looks for shares with a daily high-low range above 1%, trading activity above 60 million in the prior day, and a newly formed KDJ golden cross, defined as K moving above D after being below it. The article frames the range and…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This proposed stock screen selects main board shares with turnover between 3% and 12% and circulating market value between 5 billion and 10 billion yuan. It also requires large-order net volume to exceed 0.05 for at least three consecutive days. The article…

China marketsEquitiesMomentumStatistics
SuperMind

This Chinese stock-selection approach narrows its universe to metaverse-related companies, then applies a positive institutional-flow condition and a threshold for the ratio of external to internal trading volume. The article’s prose gives a ratio threshold…

EquitiesChina marketsSentimentMarket microstructure
SuperMind

This Chinese equity screen combines current turnover, year-over-year net profit growth attributable to parent-company shareholders, and the previous day’s turnover. It selects stocks with turnover from 3% through 12%, profit growth above 20% and no more than…

EquitiesChina marketsMomentumFactor investing
SuperMind

The document presents a Chinese equity screening idea focused on metaverse-related stocks. Its initial criteria combine a volume ratio between 1.5 and 6 with a stated prior-year return threshold. The post then proposes adding a market-cap floor, a forecast…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This document describes a Chinese stock screen that selects shares with turnover between 3% and 12%, a listing year of 2021, and a MACD value below zero two trading days earlier. It presents the conditions as a way to combine a liquidity filter with a…

EquitiesChina marketsTechnical indicators
BigQuant

This BigQuant forum entry addresses an error caused by referencing a locally created stock list as though it were an attribute of the backtest context. Its central correction is to use the variable where it was defined, without the context prefix, unless the…

EquitiesChina marketsTechnical indicatorsBacktesting
SuperMind

This document describes a Chinese stock screen that combines an intraday price range filter with a 15-minute MACD histogram condition, limited to 2021. The accompanying examples implement the filters in indicator and Python workflows. The intended use is to…

EquitiesChina marketsTechnical indicatorsVolatility
SuperMind

This stock screen selects companies with strong money-flow readings, share prices below 12 yuan, market capitalization under 10 billion yuan, and no reported losses. The proposed ranking orders candidates from strongest to weakest by money-flow intensity.…

EquitiesChina marketsMomentumFactor investing
BigQuant

This study outlines an experiment applying a Transformer model to short-horizon stock selection in China’s A-share market. Its target is each stock’s return over the next five trading days, using daily market data from 2015 through 2021. The inputs begin…

EquitiesChina marketsMachine learningFactor investing
MQL5 code base

This indicator description is based on a method attributed to Markos Katsanos and a 2018 magazine article. It describes average penetration bars and adds configurable choices for how penetration is measured: the original method uses closing prices, while…

Technical indicatorsTrend followingEquities
SuperMind

This A-share stock screen combines three technical conditions: a 14-period RSI below 65, MACD above its zero line, and a rising 30-day moving average. The stated rationale is to find stocks with moderate momentum and an upward price trend. The article also…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This post describes a stock-selection rule focused on the metaverse sector. It screens for positive returns and prior-day auction turnover above a stated threshold, then proposes adding a positive return over the preceding six months. The rationale is that…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This post outlines an A-share stock screen combining a metaverse-sector classification, positive recent returns, and positive auction turnover. It proposes an additional six-month positive-return filter, and later suggests valuation and trend conditions such…

EquitiesChina marketsMomentumTechnical indicators
BigQuant

The report summary explains a method for finding monthly indicators that may signal earnings changes in cyclical industries before standard financial statements are published. It covers real estate, autos, transport, coal, nonferrous metals, steel, building…

EquitiesChina marketsStatisticsFactor investing
SuperMind

The document proposes screening Chinese stocks for three conditions: association with the metaverse theme, a rounded chart pattern, and company scale above 200 million yuan. It frames the rounded pattern as a technical signal and company size as a rough…

EquitiesChina marketsTechnical indicatorsRisk management