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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

4,510 documents

SuperMind

This note describes a Chinese equity screening rule combining a 14-period RSI below 65, greater buy-side than sell-side volume, and a volume ratio between 1.5 and 6. It presents the rule as a way to find stocks with a relatively favorable technical reading…

EquitiesChina marketsTechnical indicatorsSentiment
SuperMind

This Chinese stock screening proposal combines three filters: membership in the metaverse theme, positive net buying attributed to major participants during the auction, and a listing date in 2021. It frames the screen as a way to focus on newer companies in…

EquitiesChina marketsSentimentEvent-driven
BigQuant

This note proposes screening Chinese equities associated with the metaverse theme, requiring a positive institutional-flow measure and a weekly crossover involving a 30-week reference. The intended rationale is to combine thematic exposure, evidence of…

EquitiesChina marketsTrend followingTechnical indicators
SuperMind

The proposed stock screen combines three signals: daily amplitude above one percent, a reversal or engulfing-style pattern appearing within the recent three-session window, and pre-open auction buying volume from large and very large orders above seven…

EquitiesChina marketsTechnical indicatorsMarket microstructure
SuperMind

This proposed A-share screen selects stocks whose turnover rate is between three and twelve percent, whose codes begin with 60, and whose previous-day turnover exceeds eight percent. The code example retrieves daily basic data, applies these filters, then…

EquitiesChina marketsMarket microstructureSentiment
SuperMind

This proposed Chinese equity screen combines three filters: daily turnover between 3% and 12%, an appearance on the previous day’s 龙虎榜 (a list highlighting unusual trading activity), and external volume exceeding internal volume by a ratio above 1.3. The…

EquitiesChina marketsTechnical indicatorsSentiment
BigQuant

This research summary explains a monthly stock-selection factor built from news sentiment. It combines the direction of news evaluations with their volume by summing sentiment scores for each stock over the month. The factor is refined by retaining news…

EquitiesSentimentFactor investingBacktesting
SuperMind

This Chinese-language post outlines an A-share screening idea focused on metaverse stocks. Its initial filters are prior-day turnover above 8% and a measure intended to identify afternoon large-order net inflows. The author discusses dependence on…

EquitiesChina marketsSentimentTechnical indicators
SuperMind

This document outlines a Chinese equity screening rule that combines daily price amplitude, a minimum circulating market capitalization, and ranking by large-order net volume. Its stated rationale is to focus on stocks with meaningful price movement,…

EquitiesChina marketsTechnical indicatorsSentiment
SuperMind

This post presents a stock-selection idea combining three filters: RSI below 65, a favorable company-type classification, and more than two limit-up sessions within a ten-day window. The author frames repeated limit-ups as a way to identify stocks attracting…

EquitiesTechnical indicatorsMomentumSentiment
SuperMind

The article describes a Chinese stock-selection screen using an RSI below 65, a ratio of external to internal trading volume above 1.3, and a ranking by stock popularity. It presents these filters as a combination of technical conditions and measures of…

EquitiesChina marketsTechnical indicatorsSentiment
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 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
FMZ forum

The article distinguishes following a visible trend from trying to anticipate how large participants may use crowded positioning. It argues that traders should first assess the broader trend, while recognizing that directions can differ across chart time…

Trend followingSentimentForexCommodities
BigQuant

This opinion article argues that quantitative trading has become entrenched in China’s A-share market despite retail investor criticism. It attributes its perceived advantage to rapid automated execution, access to data and signals, and institutional tools…

China marketsEquitiesHigh-frequency tradingMarket microstructure
Amberdata research

This market snapshot examines the February 2024 crypto rally through spot-market activity, ETF flows, decentralized exchange volumes, lending deposits, miner supply behavior, and Bitcoin net unrealized profit and loss (NUPL). It presents the view that ETF…

CryptoOn-chain dataSentimentMarket microstructure
BigQuant

This weekly Chinese equity market note combines market timing, sentiment, fund positioning, capital flows, and sector rotation. Its timing discussion uses an RSRS model on the Shanghai Composite, SSE 50, and ChiNext indexes. The model issued no new signals…

China marketsEquitiesMomentumTechnical indicators
Amberdata research

This overview explains a set of Ethereum indicators grouped around market tops and bottoms, sentiment, and user activity. Price measures include the 50-day and 200-day moving averages, a 200-week average, and the Pi Cycle comparison between the 111-day…

CryptoOn-chain dataTechnical indicatorsSentiment
SuperMind

The document outlines a Chinese stock screen requiring daily high-low amplitude above 1%, a positive measure of prior-day main-fund flow, and RSI below 65, with the formula reference specifying a three-period RSI. It presents these conditions as a way to…

China marketsEquitiesTechnical indicatorsMean reversion
SuperMind

The document proposes screening Chinese stocks with RSI below 65, seven consecutive down sessions, and large-order net flow above 0.05 for at least three consecutive days. Its final version adds a market-capitalization range of 5–10 billion yuan, while the…

China marketsEquitiesTechnical indicatorsStatistics
SuperMind

This proposed Chinese equity screen selects stocks with a 14-period RSI below 65 and first-level buy volume greater than first-level sell volume, while excluding Beijing-listed shares. The article interprets RSI as a price-condition indicator and the…

EquitiesChina marketsTechnical indicatorsSentiment
SuperMind

This note describes a short-term screen for metaverse-themed Chinese stocks that appeared on the prior day’s Dragon-Tiger list and whose daily price change multiplied by the net volume of very large orders is positive. The intended signal combines a public…

China marketsEquitiesMarket microstructureSentiment
SuperMind

This post describes a Chinese equity screen for metaverse-related stocks. It combines a positive institutional-flow measure with positive net buying attributed to major participants during the opening auction. The proposed selection rule is intended to…

EquitiesChina marketsMarket microstructureSentiment
Amberdata research

The document explains how to use spot market volume data to assess activity, liquidity, exchange concentration, and trading interest across tokens and quote currencies. It describes comparing dollar volume over time, reviewing each exchange’s share, tracking…

CryptoSpot marketsMarket microstructureSentiment