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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 stock-screening approach selects securities with MACD above zero and a weekly close crossing above its 30-week moving average, then ranks candidates by market attention or heat. It runs after each trading day’s close. The stated rationale is to pair a…

EquitiesMomentumTrend followingTechnical indicators
SuperMind

This document presents a Chinese equity screen that combines a turnover band with seven consecutive declining sessions and a nonempty name for an outstanding convertible bond. It then proposes adding fundamental filters: market capitalization above the…

EquitiesChina marketsTechnical indicatorsStatistics
SuperMind

This stock-selection rule combines three filters: a high-to-low daily range above 1%, a daily decline between 4% and 5%, and a positive price-to-earnings ratio. The article offers formula and Python examples, although the displayed formula does not appear to…

EquitiesTechnical indicatorsStatisticsBacktesting
SuperMind

This stock-selection idea screens shares whose codes begin with 60, whose high-to-low daily range exceeds 1%, and that meet a reversal or “wraparound” candlestick condition. The article gives formula and Python examples, but the definitions are not fully…

EquitiesTechnical indicatorsChina marketsBacktesting
SuperMind

This screening method selects stocks with market capitalization below 10 billion yuan, positive earnings, and an RSI below 65, then orders candidates by money-flow strength. It describes the money-flow measure as Chaikin Money Flow and gives a 20-period…

EquitiesTechnical indicatorsMomentumFactor investing
SuperMind

This stock screen combines three conditions: daily amplitude above 1, at least one limit-up event within the past month, and a 20-day moving average above the 120-day moving average. The moving-average relationship is intended to identify an upward trend,…

EquitiesTrend followingTechnical indicatorsChina markets
SuperMind

This Chinese stock-screening idea ranks candidates by volume ratio and filters for turnover between 2% and 9% and a positive ten-day gain below 35%. The article interprets stronger volume ratio as greater investor attention, the turnover range as a sign of…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This stock-selection rule combines a turnover filter, a beverage and alcohol industry screen, and a weekly moving-average crossover. It selects stocks with turnover between 3% and 12% when the weekly five-period moving average crosses above the ten-period…

EquitiesChina marketsTechnical indicators
SuperMind

The post describes a stock-selection screen based on three technical conditions: price amplitude above a threshold, a rounded price shape, and a rising DEA indicator. It interprets the amplitude condition as a way to select active stocks without excessive…

EquitiesTechnical indicatorsMomentum
SuperMind

This stock-selection rule combines a 14-period RSI below 65 with three consecutive declining sessions and a condition intended to identify upward divergence between short- and longer-term moving averages. The post presents the rule as a way to screen for…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This stock-selection proposal combines a price-amplitude filter with a turnover and auction-volume condition, then requires return on equity (ROE) to exceed a stated threshold over five years. The central liquidity-style condition is yesterday's turnover…

China marketsEquitiesFactor investingTechnical indicators
SuperMind

This Chinese-language community discussion asks how to calculate a market measure commonly translated as main-force net inflow. The post proposes defining it as the sum of actively initiated buy orders classified as large and extra-large. This frames the…

EquitiesMarket microstructureStatistics
SuperMind

This post proposes a short-term China A-share screen combining three conditions: prior-session amplitude above 1%, tradable shares no greater than 5.5 billion, and more than two limit-up days within ten sessions. The rationale is to find active, relatively…

China marketsEquitiesMomentumTechnical indicators
SuperMind

This stock-selection screen combines trading activity, company size, market value, and profitability. Its final criteria require turnover of up to 12%, circulating market value between 10 and 100 billion yuan, circulating share count no greater than 5.5…

China marketsEquitiesFactor investingPosition sizing
SuperMind

The document describes an A-share stock screen for companies associated with robotics. Its stated filters include turnover between 3% and 12%, total market value below 10 billion yuan, positive net income, and circulating market value below 10 billion yuan.…

EquitiesChina marketsFactor investing
SuperMind

This document presents an A-share stock selection idea that combines turnover between 3% and 12%, a K indicator below 20, and three consecutive days of falling closing prices. It describes the signal as a technical screen for stocks that might merit…

EquitiesTechnical indicatorsMean reversion
SuperMind

This document outlines an equity screen beginning with stocks whose daily price range exceeds 1%, market capitalization is at most 10 billion yuan, and average net profit is positive, while excluding stocks that hit the daily limit on the prior session. Its…

EquitiesTechnical indicatorsFactor investingRisk management
SuperMind

This document presents an equity selection rule using a turnover range of 3% to 12%, seven consecutive down days, and a pattern described as the start of a strong advance. It gives indicator-formula and Python examples intended to identify the setup,…

EquitiesTechnical indicatorsMomentumBreakout
SuperMind

This document describes an equities screening rule combining a turnover range, beverage and alcohol industry classification, and a comparison between displayed best bid and ask quantities. It presents the intended screen as selecting stocks with turnover…

EquitiesMarket microstructureTechnical indicatorsBacktesting
SuperMind

This document describes a short-term Chinese equity screen using three conditions: a share price below 12, more than two limit-up sessions within ten days, and amplitude above one. It frames repeated limit-ups as a possible sign of continued upside and…

EquitiesMomentumBreakoutChina markets
SuperMind

This stock-screening idea combines three conditions: a rising 30-day moving average, three consecutive declining sessions, and a reported increase in position or capital inflow greater than five percent. The rationale pairs a positive longer-term trend with…

EquitiesTechnical indicatorsMean reversionMomentum
SuperMind

This reference catalog groups Python libraries used across quantitative finance. It covers strategy research and backtesting, technical indicators, risk and portfolio analysis, scientific computing, derivatives pricing, factor and time-series analysis,…

BacktestingTechnical indicatorsRisk managementPortfolio construction
SuperMind

This Chinese equity screen requires turnover between 3% and 12%, a rising DEA condition, and a reversal pattern described as a rebound after a short pullback. The note frames turnover as a measure of trading activity and rising DEA as evidence of an upward…

EquitiesTechnical indicatorsMomentumChina markets
SuperMind

This Chinese stock-selection rule combines three filters: rank stocks among the top 100 by volume ratio, rank the top 100 by the absolute difference between the previous day’s limit-down price and opening price, and require at least two limit-up days within…

EquitiesMomentumVolatilityChina markets