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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

ProRealCode

The document describes an attempt to compress several Ichimoku comparisons into a separate histogram so that signals can be viewed without adding clutter to the main price chart. Five conditions compare the conversion and base lines, current and lagged…

Technical indicatorsTrend followingEquities
SuperMind

This stock-selection proposal combines three filters: membership in the metaverse theme, positive net buying attributed to major participants during the opening auction, and a close above the middle Bollinger Band but below its upper band. The article…

EquitiesChina marketsTechnical indicatorsSentiment
BigQuant

The article presents five principles for short-term stock trading: prominent stocks may attract liquidity despite looking expensive; near-term prices reflect the balance of buying and selling shaped by expectations and sentiment; traders should seek gaps…

EquitiesSentimentMomentumMarket microstructure
BigQuant

The study measures a fund’s risk shifting by comparing the volatility implied by its latest disclosed holdings with the fund’s realized volatility over the same rolling period. Using quarterly holdings and return data for actively managed US domestic equity…

EquitiesRisk managementFactor investing
SuperMind

This document presents a China A-share screen centered on turnover between 3% and 12%, a seven-day declining-price condition, current volume above 10,000 lots, and an opening price above the previous close. Its final proposed version adds market…

China marketsEquitiesMean reversionTechnical indicators
vn.py community

This brief forum exchange answers whether VeighNa, also known as vn.py, requires Tushare as the sole source of historical A-share data for backtesting. The response says the framework supports multiple data services and points readers to its documentation…

China marketsEquitiesBacktesting
SuperMind

This document describes a stock screen combining turnover, parent-company net profit growth, and a moving-average trend filter. It selects shares with turnover between 3% and 12%, year-over-year net profit growth above 20% and at most 100%, and a 20-day…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This Chinese-language post proposes screening stocks using three conditions: a large daily price range, prior-day turnover within a specified band, and a reversal candle pattern. It presents the combination as a way to find stocks with substantial price…

EquitiesTechnical indicatorsMean reversionChina markets
SuperMind

The document proposes selecting stocks with amplitude above 1, a share price of 18.5 yuan, and company size above 200 million. It frames the screen as a way to find active stocks with meaningful scale, then acknowledges that size alone cannot assess…

EquitiesVolatilityTechnical indicatorsRisk management
SuperMind

The article proposes a short-term equity screen based on amplitude above 1, three consecutive prior daily gains that are not limit-up moves, and large-order net inflow during the afternoon. It interprets amplitude as a sign of an active security and…

EquitiesMomentumVolatilityMarket microstructure
SuperMind

The document describes an equity screen requiring price amplitude above 1, return on equity above 15% for five consecutive years, and more than three years since listing. It presents these conditions as a way to combine active trading with a record of…

EquitiesFactor investingVolatilityRisk management
SuperMind

The article describes a short-term A-share screening rule combining three conditions: prior-session price amplitude above 1%, circulating shares no greater than 5.5 billion, and a stock code beginning with 60. Its sample implementation intersects these…

EquitiesChina marketsVolatilityTechnical indicators
Amberdata research

This research summary examines Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect, comparing northbound and southbound trading and describing the traits associated with northbound holdings. It reports that flows did not reliably anticipate market…

China marketsEquitiesFactor investingMarket microstructure
SuperMind

This strategy uses a market regime filter built from momentum across 11 sector and style ETFs. It measures each ETF against a 25-day moving average and treats the broad market as showing momentum when at least six ETFs qualify. The portfolio buys small-cap…

EquitiesChina marketsMomentumTrend following
SuperMind

This short-term A-share screen focuses on stocks classified in the metaverse theme. It seeks names with an opening gain below 6% and a current high equal to the highest high across two days, combining a thematic universe with a near-term price strength…

EquitiesChina marketsMomentumBreakout
SuperMind

This A-share stock screen combines a range expansion condition, a Bollinger Band location filter, and a historical dividend ratio threshold. It selects stocks whose daily high-low range exceeds its 20-day average, whose close lies between the middle and…

EquitiesChina marketsTechnical indicatorsVolatility
SuperMind

The document describes a daily stock screen for Chinese main-board shares. It selects non-ST stocks with turnover between 3% and 12%, a daily gain above 1%, and more than one year since listing. The stated rationale is to combine active trading and positive…

EquitiesChina marketsMomentumTechnical indicators
Qlib

This configuration defines a Qlib workflow for training a Temporal Fusion Transformer model on Alpha158 features for CSI 300 stocks. It sets Chinese market data from 2008 through mid-2020, using 2008–2014 for training, 2015–2016 for validation, and 2017–2020…

EquitiesChina marketsMachine learningBacktesting
SuperMind

This stock-selection rule combines a 14-period RSI below 65, positive daily return, and year-over-year growth in net profit attributable to parent shareholders above 20% and at most 100%. The article presents the combination as a way to find companies with…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This screen selects stocks in the metaverse sector that recorded a limit-up event within the previous 25 days and have year-over-year growth in net profit attributable to parent shareholders above 20% and no more than 100%. It also specifies selecting…

EquitiesChina marketsMomentumEvent-driven
SuperMind

This stock screen combines metaverse-sector membership, a prior-day turnover threshold above 8%, and a rising-bottom chart pattern. The article presents rising bottoms as a way to identify prices recovering from a low and potentially continuing upward, with…

EquitiesChina marketsTechnical indicatorsBreakout
SuperMind

The document outlines a Chinese equity screening rule that combines a metaverse sector filter, a closing price above the 250-day moving average, and a positive product of price change and a large-order net-volume measure. The stated interpretation is that…

China marketsEquitiesTechnical indicatorsMomentum
SuperMind

This short-term stock screen combines three signals: a high-low range greater than one, a shortening negative MACD histogram on a 15-minute chart, and at least one limit-up move during the past month. The document interprets the range as a sign of…

EquitiesChina marketsMomentumVolatility