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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 forum post documents a failure in a stock strategy’s trailing-stop routine. The routine reads each position’s last sale date, subtracts it from the current date to estimate holding days, and uses that value as the bar count when requesting historical…

EquitiesRisk managementExecution
SuperMind

This note describes an equity screen that ranks stocks by a capital-strength measure, such as turnover or volume ratio, and filters for turnover between 2% and 9%. It also requires the 20-day moving average to be above the 120-day moving average, using the…

EquitiesTrend followingTechnical indicatorsRisk management
MQL5 code base

This brief description introduces a two moving average crossover strategy. A trader chooses a shorter and a longer averaging period, then uses their intersection to identify a possible position entry. It gives a 50-day and 200-day pair as an example, but…

Technical indicatorsTrend followingEquitiesRisk management
SuperMind

The described screen combines technical, valuation, and recent-return conditions for Shenzhen mainboard stocks. It seeks shares with at least five moving averages aligned or clustered, a price-to-earnings ratio from 0 to about 29, a price-to-book ratio from…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This proposed stock screen excludes Beijing-listed shares, keeps stocks whose high-to-low amplitude exceeds 1%, and ranks the remaining names by that day’s opening-auction traded value. It selects the top five, making auction activity the ranking signal and…

EquitiesChina marketsVolatilityMarket microstructure
SuperMind

This stock-selection rule targets mainland Chinese mainboard equities with turnover between 3% and 12%, first-level bid volume greater than first-level ask volume, and a daily gain above 1%. The proposed screen combines a turnover range intended to capture…

EquitiesChina marketsMomentumMarket microstructure
BigQuant

The document addresses a conflict between stop-loss logic and a machine-learning strategy's next-day buy list. A stock may be sold when it reaches a trailing stop, then selected again by the model on the following trading day. The proposed fix is to track…

Machine learningRisk managementEquities
SuperMind

The document describes a Chinese equity screen for companies in the metaverse theme, with a minimum circulating market capitalization and positive price-to-earnings ratio. It presents the positive PE condition as a basic profitability filter and the size…

EquitiesChina marketsFactor investingStatistics
SuperMind

The document proposes screening Chinese stocks in the metaverse theme for a circulating market capitalization above 10 billion yuan and a prior-day actual turnover rate within a stated range. Its final rule specifies turnover between 5% and 20%, while an…

EquitiesChina marketsMarket microstructureStatistics
SuperMind

The document describes a Chinese stock selection rule combining three filters: RSI below 65, positive large-order net inflow in the afternoon, and a dividend ratio above 25% for 2019. It also gives example indicator and Python logic for applying these…

EquitiesTechnical indicatorsChina marketsFactor investing
SuperMind

This stock-selection method screens for shares priced below 12 yuan, with MACD above its zero line and more than two limit-up days in the preceding ten days. The post says the screen runs before 10 a.m. each trading day. It frames the combination as a way to…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This Chinese stock-selection note describes a screen combining turnover, circulating share capital, and a concentration measure. Its stated final rule keeps stocks with turnover at or below 12%, circulating market capitalization at or below 5.5 billion…

EquitiesChina marketsTechnical indicators
ProRealCode

This indicator combines an ATR-distance trailing stop with a candle-based estimate of buying and selling volume. It allocates bar volume according to candle range and open-close structure, averages the estimated buy and sell components, and normalizes their…

Technical indicatorsTrend followingVolatilityRisk management
SuperMind

This Chinese-market stock screen combines turnover, a reversal-style candle condition, and a daily price-decline band. It selects stocks with turnover between 3% and 12%, applies a reversal filter, and keeps candidates whose daily percentage change is…

China marketsEquitiesTechnical indicatorsMean reversion
BigQuant

This short research note discusses how machine learning might classify stock price patterns. It uses a triple-bottom formation to show why chart labels can be ambiguous: after a decline and consolidation with three lows, an upward break may confirm a…

EquitiesMachine learningTechnical indicatorsBacktesting
SuperMind

This document outlines an intraday Chinese stock selection idea. It screens the metaverse industry for non-ST shares, ranks candidates by the day’s opening auction amount, keeps the top five, and applies a named five-stage limit-up method before 10 a.m. The…

EquitiesMomentumBreakoutExecution
BigQuant

This document describes a Chinese equity strategy research approach that uses supervised neural networks to generate many weak alpha factors from a set of input factors. It proposes transforming those outputs into low-correlation components, then weighting…

EquitiesFactor investingMachine learningPortfolio construction
SuperMind

This stock-selection idea screens for Chinese beverage and alcohol-related companies, with turnover between 3% and 12% and a buying-versus-selling activity condition described as external volume exceeding internal volume by a factor greater than 1.3. The…

EquitiesChina marketsMomentumTechnical indicators
MQL5 code base

This research summary examines whether trading volume, represented by turnover, can distinguish stronger from weaker stock momentum signals. It separates intraday price changes from overnight returns and reports different price-volume patterns for each.…

EquitiesChina marketsMomentumFactor investing
SuperMind

This proposed Chinese equity screen selects stocks with a daily high-low range of at least 1%, a nonempty name for an outstanding convertible bond, and a closing price above the previous day’s low. The article frames these filters as a combination of price…

China marketsEquitiesVolatilityTechnical indicators
SuperMind

This proposed Chinese stock screen looks for an RSI below 65, three consecutive bearish sessions, current trading volume above 10,000 lots, and an opening price more than 2% above the previous close. It pairs a momentum oscillator and recent price weakness…

China marketsEquitiesMean reversionTechnical indicators
SuperMind

This Chinese stock-screening proposal combines a minimum daily price range with at least two limit-up events over a 500-day lookback and signs of large buying orders during the opening auction. It also specifies a threshold for combined very-large-order…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This Chinese equity screening idea combines a prior-day price-amplitude threshold, appearance on the previous day’s trading activity list, and at least one limit-up event within the preceding month. The document frames these filters as a way to find shares…

EquitiesChina marketsVolatilityMomentum