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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
Quantopian lectures
45 documents
Binance API docs
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

79,386 documents

SuperMind

This Chinese stock-selection approach narrows its universe to metaverse-related companies, then applies a positive institutional-flow condition and a threshold for the ratio of external to internal trading volume. The article’s prose gives a ratio threshold…

EquitiesChina marketsSentimentMarket microstructure
MQL5 code base

ZoneUS30 is an Expert Advisor for the US30 index that opens short positions when its internal conditions detect an upward price extension that may be followed by a correction. The approach combines counter-trend mean reversion with the possibility of…

Mean reversionCarryRisk managementPosition sizing
SuperMind

This Chinese equity screen combines current turnover, year-over-year net profit growth attributable to parent-company shareholders, and the previous day’s turnover. It selects stocks with turnover from 3% through 12%, profit growth above 20% and no more than…

EquitiesChina marketsMomentumFactor investing
MQL5 code base

The indicator presents CandlesticksBW color signals from ten timeframes as separate lines, allowing a trader to compare trend readings across multiple horizons in one display. Each line represents one instance of the underlying indicator, and its colors…

Technical indicators
SuperMind

The document presents a Chinese equity screening idea focused on metaverse-related stocks. Its initial criteria combine a volume ratio between 1.5 and 6 with a stated prior-year return threshold. The post then proposes adding a market-cap floor, a forecast…

EquitiesChina marketsMomentumTechnical indicators
ProRealCode

The document describes a MACD variant that replaces its fast, slow, and signal moving averages with Arnaud Legoux Moving Averages (ALMA). It calculates weighted averages using a Gaussian-shaped weighting scheme, then subtracts the slow average from the fast…

Technical indicatorsTrend followingBacktesting
MQL5 code base

This document outlines an Expert Advisor that trades when the KWAN_NRP indicator changes its direction, represented by a color change. The signal is evaluated when a bar closes, making the bar close the decision point for detecting a change and generating a…

ForexTechnical indicatorsBacktesting
SuperMind

This document describes a Chinese stock screen that selects shares with turnover between 3% and 12%, a listing year of 2021, and a MACD value below zero two trading days earlier. It presents the conditions as a way to combine a liquidity filter with a…

EquitiesChina marketsTechnical indicators
BigQuant

This BigQuant forum entry addresses an error caused by referencing a locally created stock list as though it were an attribute of the backtest context. Its central correction is to use the variable where it was defined, without the context prefix, unless the…

EquitiesChina marketsTechnical indicatorsBacktesting
Amberdata research

This weekly market commentary reviews Bitcoin and Ether options positioning before a US inflation release in February 2023. It connects macro uncertainty and recent spot declines to implied volatility, realized volatility, term structure, and risk reversals.…

CryptoOptionsVolatilityDerivatives pricing
SuperMind

This document describes a Chinese stock screen that combines an intraday price range filter with a 15-minute MACD histogram condition, limited to 2021. The accompanying examples implement the filters in indicator and Python workflows. The intended use is to…

EquitiesChina marketsTechnical indicatorsVolatility
SuperMind

This stock screen selects companies with strong money-flow readings, share prices below 12 yuan, market capitalization under 10 billion yuan, and no reported losses. The proposed ranking orders candidates from strongest to weakest by money-flow intensity.…

EquitiesChina marketsMomentumFactor investing
Hyperliquid docs

This document explains the conditions for enabling a token as a spot quote asset on Hyperliquid. Requirements include specified token precision, no deployer fee share, a USD peg mechanism, and liquidity thresholds across the quote token’s USDC and HYPE…

CryptoSpot marketsMarket microstructureRisk management
BigQuant

This study outlines an experiment applying a Transformer model to short-horizon stock selection in China’s A-share market. Its target is each stock’s return over the next five trading days, using daily market data from 2015 through 2021. The inputs begin…

EquitiesChina marketsMachine learningFactor investing
Amberdata research

This recap reviews Bitcoin and Ether options and volatility conditions in early April 2023. It tracks realized volatility, term structure, put and call skew, options flow, and dealer gamma positioning as a compact framework for reading the derivatives…

CryptoOptionsVolatilityDerivatives pricing
BigQuant

This Chinese-language excerpt summarizes a report about automatically generating trading strategies from time-series data. It frames conventional factor research, factor combinations, and rule combinations as labor-intensive manual work, then proposes using…

StatisticsFactor investingRisk management
MQL5 code base

This indicator description is based on a method attributed to Markos Katsanos and a 2018 magazine article. It describes average penetration bars and adds configurable choices for how penetration is measured: the original method uses closing prices, while…

Technical indicatorsTrend followingEquities
SuperMind

This A-share stock screen combines three technical conditions: a 14-period RSI below 65, MACD above its zero line, and a rising 30-day moving average. The stated rationale is to find stocks with moderate momentum and an upward price trend. The article also…

EquitiesChina marketsTechnical indicatorsMomentum
BigQuant

This document summarizes a research paper on factor timing, connecting it to market timing and factor investing. Its theoretical starting point is that an optimal portfolio corresponds to the stochastic discount factor (SDF). Under a conditional factor model…

Factor investingPortfolio constructionStatistics
MQL5 code base

This document describes an indicator for detecting harmonic chart patterns. These patterns use five price turning points and constraints on their relative proportions, which can be difficult to assess consistently by eye. The tool uses a ZigZag indicator to…

Technical indicatorsMarket microstructureRisk management
SuperMind

This post describes a stock-selection rule focused on the metaverse sector. It screens for positive returns and prior-day auction turnover above a stated threshold, then proposes adding a positive return over the preceding six months. The rationale is that…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

This post outlines an A-share stock screen combining a metaverse-sector classification, positive recent returns, and positive auction turnover. It proposes an additional six-month positive-return filter, and later suggests valuation and trend conditions such…

EquitiesChina marketsMomentumTechnical indicators