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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

BigQuant

This essay argues that traders can develop intuition about leading stocks by participating in their price moves rather than relying only on charts or retrospective analysis. It frames market feel as accumulated experience with price rhythm, capital flows,…

EquitiesSentimentRisk managementPosition sizing
SuperMind

The document outlines a Chinese equity screen combining company size, recent trading activity, and an indicator described as capital strength. Its headline criteria are companies valued below 10 billion yuan, no losses, and previous-day turnover above 8%,…

EquitiesChina marketsMarket microstructureRisk management
BigQuant

The report overview describes MSCI’s CNE6 equity risk model for China A-shares. It organizes the model into descriptors, basic factors, and style factors, with style factors formed through two weighting stages. The model adds quality, sentiment, and dividend…

EquitiesFactor investingStatisticsChina markets
BigQuant

This study examines stocks that attract investors seeking rare, extreme gains. It defines lottery-like stocks as those in the highest group when stocks are sorted into ten groups by their largest daily gain over the prior month. The proposed behavioral…

EquitiesMomentumFactor investingStatistics
SuperMind

This stock screen selects companies classified in the metaverse industry whose opening price is near the 10-day moving average and whose recent 10-day return is positive but below 35%. The article frames proximity to the moving average as a way to assess…

EquitiesChina marketsTechnical indicatorsMomentum
BigQuant

This report studies whether boosted tree models can extract stock return signals from analyst expectation factors that linear models may miss. The authors note that these factors can be complex, have substantial missing data, and correlate with earnings and…

EquitiesMachine learningFactor investingBacktesting
SuperMind

This short-term stock screen selects shares with an amplitude above 1 that appeared on the previous day’s trading leaderboard and whose first-level buy volume exceeds first-level sell volume. The document presents the combination as a way to identify stocks…

EquitiesChina marketsMarket microstructureTechnical indicators
SuperMind

This A-share screening idea combines three conditions: RSI below 65, a ratio of outside-market to inside-market volume above 1.3, and an opening price near the 10-day moving average. The accompanying example translates these filters into a…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This Chinese stock-screening post describes selecting shares priced below 12 yuan, with turnover between 3% and 12%, and a pattern it calls a reversal or “wraparound” candle. Its indicator conditions include a low price threshold and an RC value no greater…

EquitiesTechnical indicatorsChina marketsBacktesting
SuperMind

The post proposes screening Chinese stocks for MACD above zero, upward separation of short moving averages, and a prior-day auction turnover condition. It presents the combination as a way to select for positive momentum while using trading activity as a…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This stock-selection note describes a short-term screen for Chinese equities based on three conditions: turnover between 3% and 12%, a ratio of outside to inside trading volume above 1.3, and a closing price above the previous day’s low. The combination is…

China marketsEquitiesTechnical indicatorsMarket microstructure
BigQuant

This 2019 strategy review discusses how Chinese equity quant investors might respond to a weak market and changing asset-management conditions. It questions the near-term outlook for multi-factor approaches, noting that technical factors could remain under…

China marketsEquitiesFactor investingBreakout
BigQuant

This research note examines shifts between large-cap and small-cap leadership in Chinese equities, using the CSI 300 and ChiNext indices as proxies. It argues that rotation can reflect macroeconomic conditions, industry trends, and market sentiment, and that…

China marketsEquitiesMachine learningStatistics
BigQuant

This review summarizes research spanning equity market microstructure, institutional holdings, crypto momentum, dynamic-graph forecasting, and reinforcement learning for limit-order-book trading. In equities, one study groups trades by their timing relative…

EquitiesCryptoMomentumMachine learning
Amberdata research

This research roundup describes several quantitative finance studies. One classifies equity trades by their short-term co-occurrence with other trades and standardizes associated order imbalances into conditional order imbalance measures. These measures…

EquitiesCryptoMomentumMachine learning
SuperMind

The post proposes a Chinese equity screen combining daily price amplitude, afternoon large-order net inflow, and a record of high return on equity over five years. It frames the criteria as a mix of price activity, money flow, and company profitability, and…

China marketsEquitiesFactor investingSentiment
BigQuant

The document shows a Chinese A-share strategy that ranks stocks using market capitalization, float capitalization, and closing price, then selects a small target list. It filters for smaller float capitalization, sufficient listing history, and nonnegative…

China marketsEquitiesFactor investingExecution
SuperMind

The article proposes a Chinese-equity screen that combines a price-range condition, a named morning-star signal, and a historical dividend payout ratio above a stated threshold for 2019. It frames the combination as a way to identify shares with short-term…

EquitiesTechnical indicatorsFactor investing
MQL5 code base

This Expert Advisor pairs trend-change signals from an Iin_MA_Signal indicator with position sizing that responds to recent trade outcomes. Signals are acted on after a bar closes when the indicator shows a trend change. Buy and sell histories are evaluated…

EquitiesTrend followingPosition sizingRisk management
SuperMind

This brief A-share stock-screening note combines three conditions: turnover between 3% and 12%, first-level bid volume greater than first-level ask volume, and a reversal pattern described as a counterattack or engulfing move. The conditions aim to identify…

EquitiesTechnical indicatorsMarket microstructureChina markets
SuperMind

This Chinese-language post describes a daily premarket stock screen that combines MACD above its zero line, a share price below 12 yuan, and evidence of institutional accumulation. It presents the screen as a blend of technical, price, and capital-flow…

EquitiesTechnical indicatorsChina marketsMomentum
SuperMind

This article describes a stock selection screen combining three conditions: membership in the metaverse industry, a rounded-bottom chart pattern, and a 20-day moving average above the 120-day moving average. It presents the moving-average relationship as a…

EquitiesChina marketsTechnical indicatorsTrend following
SuperMind

The post describes a technical stock screen combining daily price amplitude above a threshold, a positive weekly MACD reading, and detection of a bullish morning star candlestick pattern. It presents the morning star as a three-candle reversal signal and…

EquitiesTechnical indicatorsMomentumBacktesting