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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 stock-screening idea combines an amplitude threshold above 1, a prior-day 9:15 matched price at the limit-down level, and an increasing K value from the KDJ indicator. The post interprets large price ranges as opportunity, the auction price condition as…

EquitiesTechnical indicatorsMomentumRisk management
BigQuant

This research summary describes using an imitation portfolio to infer the holdings of three Chinese mutual funds. The motivation is that public disclosures arrive with a delay and represent holdings at reporting dates, making them an imperfect guide to a…

EquitiesPortfolio constructionStatisticsChina markets
MQL5 code base

The document defines an Average Modified Moving Average indicator with two inputs: a calculation period and the price series to apply. Each new value combines the previous indicator value, weighted by the period minus two, with the current and prior prices,…

Technical indicatorsEquitiesStatistics
BigQuant

This research summary presents a framework for timing equity factors whose returns have become less reliable. It reviews indicators such as valuation spreads and pairwise correlations, and examines their relationship with future factor returns. A random…

Factor investingMachine learningEquitiesPortfolio construction
SuperMind

This Chinese-language post outlines a short-term A-share screening rule. It selects stocks classified in the metaverse theme that appeared on the previous day’s trading rankings, then applies a five-day moving-average condition. The post offers formula and…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This Chinese-language post describes an equity screen requiring turnover between 3% and 12%, a 2021 listing year, and rising price lows. It interprets successively higher lows as a sign of improving rebound strength and possible momentum or trend. The post…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This stock-selection idea screens for companies with market capitalization below a stated threshold, positive profit, and daily price amplitude above one percent, then ranks eligible names by a measure of stock attention. It presents the approach as a way to…

EquitiesChina marketsTechnical indicatorsFactor investing
BigQuant

This study analyzes information transmission among 28 first-level Chinese equity sectors using daily closing prices from 2000 through 2017. It uses transfer entropy to estimate the strength and direction of information flows between sector time series, then…

EquitiesChina marketsStatisticsMarket microstructure
BigQuant

This market outlook examines whether a narrowing or inverted spread between long and short US Treasury yields reliably signals an approaching recession. It argues that historical examples of a true ten-year versus one-year inversion are limited, and that…

Fixed incomeEquitiesCommoditiesUS markets
SuperMind

This proposed equity screen combines a daily amplitude threshold with a ratio involving the prior day's turnover and current auction volume relative to prior-day volume. It also uses a rising 30-day moving average as a trend filter. The discussion describes…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This stock screen selects companies in the beverage and alcohol import-export industry with a 14-period RSI below 65 and an appearance on the prior day's market activity list. It combines a technical indicator with industry membership and a signal of recent…

China marketsEquitiesTechnical indicatorsSentiment
SuperMind

This document describes a Chinese equity screen combining daily turnover with growth in net profit attributable to the parent company. It selects stocks with turnover between 3% and 12%, profit growth above 20% and up to 100%, and an earnings forecast for…

EquitiesChina marketsFactor investingBacktesting
SuperMind

The post proposes a Chinese equity selection rule combining turnover, order book imbalance, and a weekly chart signal. It screens for stocks with turnover between 3% and 12%, bid volume at the best price greater than ask volume, and a weekly close crossing…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This document outlines a stock-selection screen requiring MACD above zero, upward-moving averages, and at least one limit-up event during the preceding 25 days. The conditions are framed as signals of positive trend and recent market attention. It includes…

EquitiesMomentumTechnical indicatorsBacktesting
BigQuant

This practitioner note compares very high-return, moderate-return, and lower-return backtests for short-horizon equity models, focusing on whether their apparent performance persists out of sample. The author reports that aggressive timing filters and models…

EquitiesMachine learningFactor investingBacktesting
SuperMind

This document describes a short-term stock screen combining prior-session amplitude above 1%, an opening price within roughly 5% of the 10-day moving average, and prior actual turnover between 3% and 28%. It presents these conditions as a way to find shares…

EquitiesVolatilityTechnical indicatorsRisk management
SuperMind

This stock-selection idea searches within the metaverse industry for shares where the KDJ indicator has just formed a bullish crossover and the day’s K value is below 20. It selects during the trading day before 10 a.m. and requires the stock to be trading.…

EquitiesTechnical indicatorsMean reversionChina markets
SuperMind

This Chinese stock-screening note combines a price-range filter, exclusion of ST-designated shares, a five-period trend condition, and large or extra-large buy orders during the opening auction. It also limits selection to before 10 a.m. The example…

EquitiesMomentumTechnical indicatorsMarket microstructure
SuperMind

This China equity screening idea selects stocks with amplitude above 1 whose prior-day 9:15 matched auction price met a limit-down condition, then ranks candidates by a measure of stock popularity and takes the leading names. Its rationale combines price…

EquitiesChina marketsVolatilitySentiment
SuperMind

This China-focused equity screen combines daily price amplitude with a turnover and opening-auction volume measure, then requires large-order net buying to remain above a threshold for several consecutive days. The stated selection rule uses amplitude above…

EquitiesChina marketsTechnical indicatorsMarket microstructure
SuperMind

This A-share screening rule places stocks in a candidate pool when they have a daily high-low range above 1%, at least one limit-up day in the preceding 25 sessions, and circulating shares no greater than 5.5 billion. The author interprets these conditions…

EquitiesBreakoutVolatilityChina markets
SuperMind

This article proposes screening stocks with turnover between 3% and 12%, a K value below 20, and circulating market capitalization between 5 billion and 10 billion yuan. It describes the combination as a starting point for finding stocks with potential, then…

EquitiesChina marketsTechnical indicatorsRisk management