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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

SuperMind

This stock screen selects companies associated with the metaverse concept, then applies a price condition and a relative-volume band. It requires the close to exceed the previous session’s low and volume relative to its five-session average to be above 1.5…

EquitiesChina marketsTechnical indicatorsRisk management
SuperMind

This Chinese equity screen combines three conditions: at least five moving averages are described as converging, the tradable share float is no more than 5.5 billion shares, and the ten-day return is positive but below 35%. The article frames this…

EquitiesMomentumTechnical indicatorsChina markets
SuperMind

This stock screen combines turnover between 3% and 12% with seven consecutive sessions in which the closing price falls, then filters for a daily price change below 2.6% and above -5%. The article presents the rule as a way to find stocks after a sustained…

EquitiesMean reversionTechnical indicatorsChina markets
SuperMind

This stock screen combines three daily price conditions: amplitude greater than 1%, an opening price within 5% of the 10-day moving average, and a current low below the previous day's low. The document includes formula and Python examples for calculating…

EquitiesTechnical indicatorsVolatilityChina markets
SuperMind

This equity screen targets stocks in the metaverse sector that recorded a limit-up move within the prior 25 days and whose opening price falls between 2% below and 5% above the reference close. The post describes selecting candidates before 10 a.m. for…

EquitiesChina marketsMomentumBreakout
SuperMind

This stock screen combines a daily turnover rate between 3% and 12%, a positive change in the KDJ K value, and an indicator intended to identify institutional accumulation. The document provides formula and Python examples, with the Python version also…

EquitiesTechnical indicatorsMomentumChina markets
BigQuant

The document introduces a moving-average arrangement scoring model, or MASS, that assesses market direction and trend strength from the relative ordering of multiple moving averages. It aims to combine the smoothness of longer averages with the quicker…

EquitiesTrend followingMomentumTechnical indicators
SuperMind

The document proposes a stock screen combining three conditions: relatively large price amplitude, upward divergence in the day’s moving averages, and a limit-down price at the prior session’s 9:15 matching stage. The stated rationale is to find volatile…

China marketsEquitiesTechnical indicatorsMean reversion
SuperMind

This Chinese A-share stock screen selects shares with a daily high-low range above a stated threshold, while excluding Beijing-listed stocks and specified board categories. The article also describes a refinement that keeps prices close to a 60-period moving…

EquitiesChina marketsTechnical indicatorsRisk management
Qlib

This paper description presents a learnable scheduler for sequence-learning problems with related prediction tasks, such as forecasting returns at different future horizons. During training, the scheduler chooses an auxiliary task based on the current model…

Machine learningEquitiesChina marketsBacktesting
BigQuant

This tutorial explains how to use Seaborn to explore financial data through matrix plots, plot grids, regression plots, and style settings. It uses stock financial statement data to demonstrate correlation heatmaps, including annotations and color maps, and…

EquitiesStatisticsTechnical indicators
BigQuant

This Chinese-language question and answer explains why a strategy’s apparently strong later years in a long backtest may not reproduce the same pattern when tested over those years alone. It identifies several possible causes rather than prescribing a single…

BacktestingStatisticsEquities
SuperMind

The document describes a Chinese stock selection screen that combines a 14-period RSI below 65, the product of percentage price change and an oversized-order net inflow measure above 1, and a circulating market capitalization between 5 billion and 10 billion…

EquitiesChina marketsTechnical indicatorsMean reversion
SuperMind

This document describes a Chinese equity screen that looks for stocks with turnover between 3% and 12% and circulating market value between 5 billion and 10 billion yuan. Within those limits, it selects stocks whose closing price has fallen on each of the…

EquitiesChina marketsMean reversionTechnical indicators
SuperMind

The document describes a Chinese equity screening rule that starts with stocks classified in the metaverse theme, applies a minimum threshold for circulating market capitalization, then ranks candidates by the day’s auction amount and selects the five…

EquitiesChina marketsMomentumBacktesting
ProRealCode

The document defines a simple candlestick signal that labels a bar as bullish, bearish, or neutral. A bullish signal requires the current candle to close above its open and above the previous high, while opening below the previous low. A bearish signal…

Technical indicatorsEquities
SuperMind

This A-share screening idea focuses on stocks in the metaverse industry. It looks for a fresh KDJ bullish crossover alongside a pattern described as rising lows, with selection limited to before 10:00 and to stocks marked as tradable. The supplied examples…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This Chinese A-share stock screen selects shares with current turnover between 3% and 12%, excludes Beijing-listed stocks, and requires the previous day's turnover to exceed 8%. The rationale is to find liquid stocks that have recently attracted trading…

EquitiesChina marketsTechnical indicators
SuperMind

This document presents a Chinese A-share screening idea combining technical signals with a basic profitability filter. The stated criteria include RSI below 65, an external-to-internal trading volume ratio above 1.3, and at least five moving averages…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

The document presents a stock-screening idea that combines MACD above its zero line, upward-diverging daily moving averages, and an external-to-internal trading volume ratio above a stated threshold. It then extends the screen with fundamental filters:…

EquitiesTechnical indicatorsMomentumFactor investing
BigQuant

The page reports a user’s concern that the Chinese stock 600256 had incorrect values for the total-liabilities factor fs_total_liability_0 over a historical interval in 2021. The user says values for other periods agreed with Eastmoney data, while the…

EquitiesChina marketsStatistics
SuperMind

This note describes a screen for metaverse-related equities using two signals: prior-day actual turnover between 3% and 28%, and large-order net volume above 0.05 for at least three consecutive days. The article interprets the turnover band as evidence of…

EquitiesChina marketsTechnical indicatorsMomentum
Amberdata research

This note proposes screening equities for intraday amplitude above 1, prior-day actual turnover between 3% and 28%, and positive net large-order flow during the afternoon. The combined filters aim to find shares showing both price movement and trading…

EquitiesChina marketsTechnical indicatorsMarket microstructure
SuperMind

This note describes a short-term screen for metaverse-related equities. It selects stocks that had at least one limit-up session during the prior 25 days and whose current opening price is near the 10-day moving average. The stated rationale is that recent…

EquitiesChina marketsTechnical indicatorsMomentum