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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 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
BigQuant

This explanation defines an asset’s exposure to a factor as its sensitivity to that factor’s returns. It illustrates the idea with a value factor: filter a broad A-share universe, sort stocks by price-to-earnings ratio, and form a long-short portfolio from…

EquitiesFactor investingStatisticsPortfolio construction
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
BigQuant

This BigQuant help exchange explains how to select a fixed number of records after ranking a stock universe. The question concerns approximating a CSI 1000 universe when a direct index-membership filter is unavailable and usable daily constituent data is…

EquitiesChina marketsPortfolio construction
SuperMind

This note describes a Chinese A-share screen requiring an RSI below 65 and seven consecutive sessions in which the close is no higher than the open, followed by a ranking based on a stock popularity measure. It outlines the intended use of technical…

EquitiesChina marketsTechnical indicatorsSentiment
SuperMind

The author describes a monthly stock-selection approach that buys the ten lowest price-to-book stocks in the CSI 300 universe, then combines it with a market-timing signal based on a hidden Markov model. The starting idea is a single-factor value strategy,…

EquitiesFactor investingMean reversionMachine learning
BigQuant

The report compares ordinary K-fold cross-validation with time-series cross-validation for tuning machine-learning models on sequential data. Random folds can place later observations in training while earlier ones are used for validation, creating leakage…

Machine learningStatisticsBacktestingChina markets
Qlib

This configuration describes a Qlib experiment for CSI 300 equities using a LightGBM regression model. The dataset combines daily features with intraday one-minute data resampled to a daily frequency through a custom handler. The configured label frequency…

EquitiesChina marketsMachine learningBacktesting
FMZ forum

The essay argues that traders should accept uncertainty instead of relying on confident market forecasts. It treats technical and fundamental interpretations as fallible explanations that may sometimes coincide with subsequent price moves, but cannot…

EquitiesRisk managementPosition sizingStatistics
SuperMind

This Chinese-language post outlines a daily stock selection screen based on three filters: rank stocks by trading volume or a stated measure of capital strength, keep those whose gain at 9:25 is below 6%, and require at least two limit-up sessions in the…

EquitiesChina marketsMomentumBreakout
SuperMind

This stock-selection proposal combines three filters: a large high-low range, at least one daily gain of 10% or more in the preceding 25 trading days, and a ranking by capital or fund-flow strength. The intended effect is to find volatile stocks that have…

EquitiesMomentumTechnical indicatorsFactor investing
SuperMind

The post describes a Chinese A-share selection rule combining membership in a metaverse-related category, high recent turnover, and a recent limit-up event. Its stated intent is to find actively traded stocks attracting attention and showing evidence of…

China marketsEquitiesMomentumTechnical indicators
BigQuant

This research summary outlines a strategic allocation model that maps Chinese equities and bonds to underlying macroeconomic risks. It uses principal component analysis to define five factors: economic growth, interest rates, credit, term spread, and…

Multi-assetEquitiesFixed incomeFactor investing
SuperMind

This document describes a Chinese A-share screening rule that combines daily price movement, a moving-average trend filter, and reported revenue growth. It selects stocks whose amplitude exceeds a threshold, whose 20-day moving average is above the 120-day…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The post proposes a Chinese equity screening approach that combines stocks with clustered moving averages, afternoon net inflows from large orders, and multiple limit-up sessions over a historical window. It interprets these conditions as possible signs of…

China marketsEquitiesTechnical indicatorsMomentum
BigQuant

The document summarizes research on the disposition effect among actively managed U.S. equity mutual funds: managers may sell winning stocks too readily while holding losing stocks. It describes a measurement based on quarterly changes in reported holdings…

EquitiesStatisticsFactor investing
SuperMind

The document describes a Chinese stock screen that combines a daily increase in position share above 5%, Shenzhen main-board valuation limits, and more than two limit-up sessions within ten days. Its rationale treats the position-share measure as a possible…

EquitiesMomentumTechnical indicatorsChina markets
SuperMind

This document outlines a screen for Shenzhen main-board stocks combining a technical condition with valuation, size, and profitability filters. Candidates must have RSI below 65, a price-to-earnings ratio between zero and 29.01, a price-to-book ratio between…

EquitiesFactor investingTechnical indicatorsChina markets
SuperMind

The document describes a technical screen for stocks using three conditions: daily amplitude above 1%, weekly MACD above zero, and an opening gain between -5% and 2.6%. It interprets amplitude and the gain range as measures of market activity, while weekly…

EquitiesTechnical indicatorsMomentumRisk management
FinRL

The tutorial outlines an end-to-end FinRL workflow for training and comparing deep reinforcement learning agents on Dow 30 equities. It describes downloading and preprocessing market data, adding technical indicators plus VIX and a turbulence measure,…

EquitiesMachine learningTechnical indicatorsBacktesting
SuperMind

This Chinese equity screen combines a non-ST requirement, a minimum intraday range, a five-session closing-price high, and a circulating market capitalization between 5 billion and 10 billion yuan. The article frames the conditions as a way to find active…

China marketsEquitiesBreakoutTechnical indicators
SuperMind

This Chinese equity screening idea selects stocks with RSI below 65, more than two limit-up sessions during the preceding ten days, and no limit-up on the prior day. Its final version adds a closing-price ceiling of 60 yuan and ranks candidates by stock…

China marketsEquitiesMomentumTechnical indicators
BigQuant

This essay presents a set of survival rules for trading Chinese equities during overheated bull markets, using the 2007 and 2015 market peaks as cautionary examples. It describes a recurring pattern of shakeouts, rapid speculative advances, and liquidity…

China marketsEquitiesRisk managementPosition sizing
BigQuant

This brief presents a market review for Chinese A-shares during March 23–27, 2020, alongside a report title stating that the strategic long stance remained unchanged. It lists the strongest and weakest industry groups for the week and tracks several breadth…

EquitiesChina marketsTechnical indicatorsPortfolio construction