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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
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

3,481 documents

BigQuant

This China equities screen targets stocks in the metaverse theme that had a limit-up move within the prior twenty-five days and more than two limit-up days in the latest ten-day window. The proposed ranking favors stocks with more limit-up occurrences and…

EquitiesChina marketsMomentumBreakout
BigQuant

This FAQ explains three data-processing tasks in BigQuant’s DAI SQL environment. It outlines how to define rolling aggregate macros and date-partitioned cross-sectional ranking or averaging macros, including a workaround for choosing sort direction when SQL…

EquitiesStatisticsFactor investingTechnical indicators
BigQuant

This example documents a manual, language-model-assisted factor-building workflow for equities over 2022–2024. It combines a stock’s price relative to its 20-day average with cross-sectional standardized price-to-earnings data, scales the composite by its…

EquitiesFactor investingMachine learningStatistics
BigQuant

The document answers a practical question about viewing the daily ranking of stocks produced by a machine learning selection process during a backtest. It says the prediction output can be understood as the model’s ordering of candidates, and recommends…

Machine learningEquitiesBacktesting
BigQuant

This forum post describes a data-pipeline problem encountered while merging stock and buy data on date and industry-code columns. The merge appeared to run, but the resulting data was not visible as expected and a later workflow node reported an error. The…

ExecutionStatistics
BigQuant

This post presents an A-share stock screen focused on companies described as connected to the metaverse. Its stated selection logic uses a turnover-rate range of 3% to 12% and excludes Beijing-listed A-shares. The discussion recommends examining companies'…

EquitiesChina marketsFactor investingRisk management
BigQuant

This report summary describes an industry-rotation strategy for Chinese A-shares based on medium-term momentum, measured by returns over the previous 250 days. It adds two crowding controls to address momentum reversals: a sector-level concentration measure…

China marketsEquitiesMomentumFactor investing
BigQuant

The document describes a stock screen focused on the metaverse sector. It looks for securities with a limit-up event during a recent lookback period and a price positioned in the lower part of its recent trading range, interpreting that combination as…

EquitiesChina marketsTechnical indicatorsMomentum
BigQuant

The proposed stock screen combines three daily conditions: price amplitude above one percent, afternoon large-order net inflow, and a closing price above the prior day’s low. It then selects the candidate with the highest gain. The article gives indicator…

EquitiesTechnical indicatorsMomentum
BigQuant

The document proposes a daily equity factor that combines three market measures: the gap between a 10-day average of amount per volume and the latest close, current volume relative to its 20-day average, and the latest close relative to its 20-day average.…

EquitiesFactor investingMomentumStatistics
BigQuant

This event-style strategy applies a dual moving average crossover to five named Chinese equities: Kweichow Moutai, China Mobile, ICBC, PetroChina, and Ping An Bank. It calculates both averages from daily opening prices, using a five-day average as the short…

EquitiesChina marketsTrend followingTechnical indicators
BigQuant

The document describes a tool for evaluating many equity factors in batches and saving overall and yearly long-side results. Users configure the factor source and names, stock universe, benchmark, optional smoothing, and output settings. For high-frequency…

Factor investingHigh-frequency tradingBacktestingExecution
BigQuant

The document answers a question about selecting the lowest closing price across five days for a market risk-control calculation. It explains that applying Python's built-in minimum directly to several pandas Series can be ambiguous: the intended comparison…

EquitiesRisk managementStatistics
BigQuant

The document summarizes research on long memory and fractal behavior in returns across global equity markets. It describes a multivariate wavelet approach intended to examine long-term dependence jointly across markets, rather than treating each market's…

EquitiesStatisticsMulti-asset
BigQuant

The note summarizes a study asking whether long-lived mutual funds’ ability to outperform the market reflects manager skill or luck. The researchers compare performance across different market states and use bootstrap sampling to assess whether stronger…

StatisticsFactor investingPortfolio construction
BigQuant

The document records a learner’s attempt to express past systematic futures trading in quantitative terms. Their prior methods were modified versions of Turtle trading and Aberration. They propose exploring a stock strategy that combines low…

EquitiesFuturesFactor investingTrend following
BigQuant

This short BigQuant forum exchange clarifies how an AI-generated portfolio template handles positions. The question asks whether a stated holding period means a ten-day accumulation window, why purchases are spread across days instead of completed…

EquitiesMachine learningPortfolio constructionPosition sizing
BigQuant

This research summary examines the volatility anomaly in Chinese equities and asks why high-volatility stocks often underperform in broad comparisons. It reports that the long-short effect is largely driven by the high-volatility short side; the…

EquitiesVolatilityFactor investingPortfolio construction
BigQuant

This research summary introduces China’s crude oil futures market and compares its medium sour crude benchmark with major international contracts. It describes renminbi denomination, a daily price limit, and potential currency and cross-market risks. The…

FuturesCommoditiesTrend followingMean reversion
BigQuant

This Chinese equity research describes a monthly stock-selection factor built from minute-level returns and changes in trading volume. For each stock and day, the authors regress returns on contemporaneous and lagged volume changes. They interpret the volume…

China marketsEquitiesFactor investingStatistics
BigQuant

The document summarizes a study that proposes a public-information reliance (RPI) measure as a way to assess active equity fund managers. It estimates how much changes in a fund’s stock holdings can be explained by changes in sell-side analyst…

EquitiesStatisticsFactor investingPortfolio construction
BigQuant

This research note develops an A-share strategy around post-earnings-announcement drift. It groups surprise signals by whether they compare analyst forecasts, reported results, or revised company guidance, then studies events such as unanimous analyst…

EquitiesChina marketsEvent-drivenMomentum