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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

12,226 documents

SuperMind

This post describes a Chinese stock selection screen combining price amplitude, a measure labeled today’s control, and a recent large daily gain. Its written logic adds a free-float market capitalization floor. The article also provides example formulas for…

EquitiesChina marketsTechnical indicatorsRisk management
SuperMind

This post proposes a technical screen for Chinese A-share stocks. It selects shares with RSI below 65, seven consecutive sessions in which the close is no higher than the open, and a KDJ stochastic K reading below 20. The accompanying code sketch describes…

China marketsEquitiesTechnical indicatorsMean reversion
SuperMind

This Chinese stock selection proposal combines turnover between 3% and 12%, an opening price near the 10-day moving average, and a prior-day signal described as main-force control. The formula reference adds conditions requiring the close to exceed both the…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This stock-selection proposal targets main-board shares associated with robotics. It filters for turnover between three and twelve percent, circulating market value below 10 billion yuan, and a weekly five-period moving average crossing above the ten-period…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This Chinese-language post proposes a stock screen combining three signals: today’s increase in holdings above five percent, an institutional buying indication, and shortening green MACD bars on a 15-minute chart. It interprets the first two as evidence of…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This note outlines a chart-based stock screen using at least five converging moving averages, a rounded or arc-shaped price pattern, and a focus on stocks associated with 2021. It illustrates possible moving-average periods of 5, 10, 20, 50, and 200 days,…

EquitiesTechnical indicatorsTrend followingBacktesting
SuperMind

This note presents a Chinese equity screen combining turnover between 3% and 12%, market capitalization below 10 billion yuan, positive profitability, and a closing price above the previous day’s low. It includes example formula and Python references for…

EquitiesChina marketsTechnical indicatorsRisk management
SuperMind

This note describes an A-share stock screen combining three conditions: membership in the metaverse industry, a positive return based on the latest close versus the prior close, and circulating market capitalization below 10 billion yuan. It also gives…

EquitiesChina marketsMomentumRisk management
SuperMind

This note proposes screening metaverse-related stocks for elevated recent turnover and a low KDJ reading, then adds valuation and leverage constraints. Its final selection logic combines sector membership, a turnover condition, KDJ below a threshold,…

EquitiesTechnical indicatorsMomentumRisk management
SuperMind

This note outlines a stock screen for companies in the metaverse theme whose average price is above its five-day moving average and that show a rebound after a recent decline. It frames the setup as a way to find stocks recovering from weakness, but does not…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This document outlines a stock screen requiring price amplitude above a threshold, a current-day control measure above a threshold, and a MACD value below zero from two days earlier. It frames the inputs as measures of price movement, trading control or…

EquitiesTechnical indicatorsChina markets
SuperMind

This document describes an equity screening rule combining a metaverse industry label, a close above the previous day’s low, and a KDJ %K reading at or below 20. Its stated rationale is that the industry label identifies potential growth exposure, the price…

EquitiesTechnical indicatorsChina markets
SuperMind

The document proposes screening stocks using amplitude above one, a shortening negative MACD histogram on a 15-minute chart, and MACD at or above zero. It explains the intended rationale as combining price movement with a momentum indicator to identify…

China marketsEquitiesMomentumTechnical indicators
SuperMind

The document outlines a Chinese-equity screening idea that combines daily turnover between 3% and 12%, a positive product of price change and net large-order volume, and a rounded or arc-like candlestick condition. It presents sample formula and Python…

China marketsEquitiesTechnical indicatorsMarket microstructure
SuperMind

The document describes an A-share screening rule for companies assigned to the metaverse industry. It combines a float-size ceiling with an ownership-concentration ceiling, then returns qualifying stock codes and names. The accompanying Python example…

EquitiesChina marketsFactor investing
SuperMind

This stock-selection rule filters for a high–low range above 1% of the high, a closing price of exactly 18.5 yuan, and circulating market value of at least 10 billion yuan. The document includes example selection logic and sorts qualifying names by trading…

EquitiesChina marketsTechnical indicatorsFactor investing
SuperMind

This stock screen combines a daily range condition with sustained profitability and a momentum indicator. It selects shares whose high–low range exceeds an ATR-based threshold, whose return on equity stayed above 15% across five years, and whose 14-period…

EquitiesFactor investingTechnical indicatorsRisk management
SuperMind

This post proposes a Chinese equity screen combining three conditions: daily amplitude above a threshold, at least two limit-up events within a 500-trading-day window, and a reversal or engulfing-style candle condition. It gives formula references and an…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The proposed screen selects main-board stocks with circulating market value between 5 billion and 10 billion yuan, turnover between 3% and 12%, and a stated daily maximum decline between 4% and 5%. The article presents the filters as a way to find stocks…

China marketsEquitiesTechnical indicatorsRisk management
SuperMind

This stock-selection post describes a screen using three short-term conditions: a positive ten-day return below 35%, daily amplitude above 1%, and a stated concentration measure below 20%. It frames these filters as a way to identify stocks with recent gains…

China marketsEquitiesTechnical indicatorsRisk management
SuperMind

This community post proposes a Chinese stock screen within the metaverse industry. It filters for prior-session actual turnover between 3% and 28%, an opening-auction price move ranked among the strongest in the market, and combined net buying by very large…

China marketsEquitiesMarket microstructureExecution
SuperMind

This article reviews global equity factor performance in the third quarter of 2021 and compares defensive factor positioning. It reports that momentum and low residual volatility led the pure factor results, while liquidity lagged. Regional index outcomes…

EquitiesFactor investingMomentumVolatility
SuperMind

This Chinese-language article describes an equity screen for Shenzhen main-board stocks associated with the metaverse theme. It selects stocks with a positive return, positive price-to-earnings and price-to-book ratios, and upper bounds on both valuation…

EquitiesChina marketsFactor investing
SuperMind

This document outlines a stock-selection screen that ranks securities by capital strength and requires weekly MACD to be above zero, alongside a year-based filter referring to 2021. It explains capital strength through trading volume and value, and describes…

China marketsEquitiesMomentumTechnical indicators