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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
Lumibot strategies
7 documents
QuantRocket
7 documents
Awesome Quant
1 documents

Search the library

4,194 documents

SuperMind

This A-share selection method combines moderate trading activity and company size with a profitability filter. It selects companies with a turnover rate from 3% to 12%, market value below 10 billion yuan, no losses, and return on equity above 15% in each of…

EquitiesFactor investingChina marketsRisk management
BigQuant

This research note presents a framework for explaining bond fund returns and identifying portfolio style. It decomposes returns into income, government yield-curve movements, credit spread effects, convexity, and bond roll-down. Income is further divided…

Fixed incomeStatisticsFactor investingPortfolio construction
BigQuant

The document presents a workflow for evaluating a stock factor with Chinese A-share data. It builds daily stock returns and industry returns, represents industry membership with indicator variables, and subtracts each stock’s industry return to form an…

EquitiesFactor investingStatisticsChina markets
BigQuant

This research summary presents a method for assessing analyst earnings forecast errors and whether investors give analyst consensus too much weight. Instead of fitting historical forecast errors to company characteristics, the method predicts future earnings…

EquitiesFactor investingStatisticsBacktesting
BigQuant

This reference compiles expressions for the Alpha191 stock-factor set, attributed in the document to a 2017 research report on short-horizon price and volume characteristics. The expressions draw on open, high, low, close, volume, turnover value, VWAP,…

China marketsEquitiesFactor investingTechnical indicators
BigQuant

This article explains a cross-sectional futures strategy that ranks commodity contracts by roll yield, going long markets in backwardation and short markets in contango. It distinguishes weekly signal-based direction changes from contract rolls: the former…

FuturesCommoditiesCarryFactor investing
BigQuant

This report outlines a method for rotating among equity style factors as the macroeconomic environment changes. It proposes two signals: identify macro events historically associated with stronger performance in particular styles, and compare current macro…

EquitiesFactor investingEvent-drivenStatistics
BigQuant

The article describes a Chinese-equity factor that looks for unusually large intraday increases in trading amount, then aggregates information around those moments into daily observations. Its rationale is that a volume surge accompanying modest, orderly…

EquitiesFactor investingHigh-frequency tradingVolatility
SuperMind

This Chinese equity screen combines three criteria: a dividend payout ratio above 25% in 2019, at least one limit-up session in the prior 25 days, and ranking by capital strength from high to low. The post interprets capital strength as a measure of inflows…

EquitiesChina marketsMomentumEvent-driven
BigQuant

This report tests thirteen momentum and modified-momentum signals in China’s A-share market. The signals include returns over several lookback periods, turnover-weighted returns, exponentially weighted turnover-adjusted returns, and a long-horizon regression…

EquitiesChina marketsMomentumFactor investing
BigQuant

This account describes extending a backtest of a Chinese small-cap strategy and finding that returns before 2024 were negative. It attributes the later high returns largely to growth in the Beijing Stock Exchange, whose smaller companies strongly affected a…

China marketsEquitiesFactor investingBacktesting
BigQuant

This research summary explores whether statistics computed from intraday stock data can add new signals to established multi-factor selection approaches in China’s A-share market. It evaluates realized volatility, realized skewness, and realized kurtosis as…

EquitiesFactor investingStatisticsHigh-frequency trading
SuperMind

This post presents an A-share stock screen combining a daily price-range condition, an opening price near the ten-day moving average, and a dividend measure above a stated threshold for 2019. It supplies example formulas and Python-style logic for…

EquitiesFactor investingTechnical indicatorsChina markets
SuperMind

This Chinese-language post proposes an equity screen combining price movement, profitability, and revenue growth. It selects stocks with a high-low range exceeding the 14-period ATR, return on equity above 15% for five consecutive years, and revenue growth…

EquitiesFactor investingTechnical indicatorsChina markets
BigQuant

The document explains alpha as the return remaining after comparing a portfolio’s actual return with the return predicted by the Capital Asset Pricing Model. It gives the formula using portfolio return, the risk-free rate, portfolio beta, and benchmark…

Factor investingStatisticsPortfolio construction
BigQuant

This meetup listing raises two quantitative research questions: why factors with strong information coefficients and information ratios may produce weaker Sharpe ratios, and why combining individually strong Sharpe-ratio factors can reduce portfolio…

Factor investingPortfolio constructionTechnical indicatorsStatistics
BigQuant

This research summary examines whether macroeconomic and market indicators can predict returns to equity factors, with particular attention to the size and reversal factors in Chinese stocks. It uses IVX regression, presented as a response to endogeneity and…

EquitiesFactor investingStatisticsChina markets
BigQuant

This summary of a Chinese securities research presentation describes several equity research approaches. It outlines an event-driven system intended to assess whether market events have an effective price impact, alongside a multi-factor risk framework and…

EquitiesChina marketsEvent-drivenFactor investing
SuperMind

This document outlines a Chinese A-share stock screen combining price trends and valuation. It selects Shenzhen main-board stocks with price-to-earnings ratios from 0 to 29.01 and price-to-book ratios from 0 to 3.11, requires the 30-day moving average to be…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

The proposed Chinese equity screen looks for companies with positive earnings, market capitalization within a stated ceiling, daily price amplitude above a threshold, and revenue growth between two specified years. The article frames the size and revenue…

EquitiesChina marketsFactor investingStatistics
BigQuant

The document presents factor analysis as a core research step for multi-factor, index-enhancement, and market-neutral strategies. It frames the analyst’s work as evaluating candidate factors before strategy researchers use them, with the quality of that…

Factor investingStatisticsBacktesting
BigQuant

The report proposes refining the book-to-price (BP) valuation factor by splitting book value into retained earnings and invested capital. It forms two market-value ratios: REP for retained earnings and CCP for invested capital. The stated motivation is that…

EquitiesFactor investingChina marketsBacktesting
SuperMind

This Chinese equity screening idea selects companies with MACD above zero, nonnegative profit, and market capitalization below a stated ceiling. The positive MACD condition is intended to favor upward price momentum, while profitability and the size limit…

China marketsEquitiesMomentumTechnical indicators