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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
WonderTrader
14 documents
Alphalens
14 documents
backtesting.py
11 documents
Technical Analysis
9 documents
QTPyLib
8 documents
Lumibot strategies
7 documents
QuantRocket
7 documents
Awesome Quant
1 documents

Search the library

29,558 documents

ProRealCode

This strategy looks for a possible reversal near the European market close, entering at 17:29 after a sustained moving-average trend. For a short, the faster average must have stayed above the slower one for a specified number of bars; the long signal uses…

EquitiesMean reversionTechnical indicatorsRisk management
MQL5 code base

This document explains how to install and display an MT4 indicator that adapts MetaQuotes’ iExposure tool to show open positions. It describes placing the indicator in a secondary chart window, adjusting column spacing and font weight, and interpreting its…

ForexTechnical indicatorsRisk management
SuperMind

The document explains impermanent loss for decentralized exchange liquidity providers. AMMs maintain pool balances through pricing formulas; when an asset’s market price moves relative to its pool partner, arbitrage trades restore alignment and change the…

CryptoDeFiMarket makingRisk management
MQL5 code base

This document describes an automated or manual pair-trading robot that opens positions in two symbols when their correlation meets a configured threshold. It distinguishes pairs, whose charts move similarly, from mirror symbols, whose charts move in opposite…

Pairs tradingForexStatisticsRisk management
SuperMind

This post describes a stock screen combining two signals: an increase in holdings above 5% and moving averages that diverge upward. It interprets the holdings measure as possible recent buying interest and the moving-average pattern as an upward trend. The…

EquitiesTechnical indicatorsMomentumRisk management
Hyperliquid docs

The document explains the maximum drawdown figure shown on Hyperliquid’s portfolio page and clarifies that it is a convenience metric displayed by the frontend. It does not affect margin requirements or other platform calculations. To reproduce the displayed…

StatisticsRisk management
SuperMind

This proposed Chinese stock screen selects companies classified in the metaverse theme, with prior-day actual turnover between 3% and 28%, and a listing or issuance date in 2021. The post treats the industry classification as a growth-oriented universe…

EquitiesChina marketsTechnical indicatorsRisk management
SuperMind

This proposed stock screen combines three filters: turnover between 3% and 12%, a newly formed KDJ bullish cross, and a positive institutional net-purchase measure. The post frames turnover as a liquidity and activity filter, the KDJ signal as a trend or…

EquitiesChina marketsTechnical indicatorsMomentum
SuperMind

This proposed Chinese equity screen focuses on stocks classified in the metaverse theme. It combines a price condition involving the five-day moving average with a directional filter: the product of the stock’s daily return and a measure of net large-order…

EquitiesChina marketsMomentumTechnical indicators
SuperMind

The document proposes a stock screen combining intraday amplitude above 1, excluding ST-designated stocks, selection before 10 a.m., a named five-step limit-up method, and floating shares no greater than 5.5 billion. It describes float as a measure related…

EquitiesBreakoutTechnical indicatorsRisk management
BigQuant

This educational article explains a crypto bubble as a rapid price rise driven by investment enthusiasm and expectations of quick gains, followed by the possibility of a sharp decline. It attributes bubble formation to new investors, fear of missing out,…

CryptoSentimentRisk managementPortfolio construction
SuperMind

This Chinese equity screening strategy selects stocks with turnover between 3% and 12%, three consecutive declining sessions, and return on equity above 15% for five consecutive years. The article presents the ROE filter as a way to favor firms with…

EquitiesFactor investingTechnical indicatorsRisk management
SuperMind

This document describes a Chinese equity screening rule combining daily amplitude above a threshold, higher successive lows, and upward separation among short, medium, and longer moving averages. Its indicator example compares five-, ten-, and twenty-period…

EquitiesTechnical indicatorsTrend followingRisk management
MQL5 code base

This description identifies Aeron as an Expert Advisor using a martingale technique and says its parameters are customizable. It offers a sizing illustration based on the broker’s minimum lot, the margin needed to open that lot at 100:1 leverage, and a…

ForexPosition sizingRisk management
MQL5 code base

This document describes a trading robot that sets position volume and stop loss using market volatility. The trader can choose the volatility lookback period, smoothing method, and whether volatility is measured from open and close prices or from the high…

VolatilityRisk managementBacktesting
Cryptohopper blog

The article surveys common cryptocurrency frauds, grouping them into attempts to gain access to a wallet and schemes that persuade victims to transfer assets. Examples include fake websites and apps, phishing, pump-and-dump promotions, romance and Ponzi…

CryptoRisk managementOn-chain data
SuperMind

This proposed Chinese stock screen targets companies classified in the metaverse theme, with market capitalization below 10 billion yuan, positive earnings, and an indicated gain below 6% at 9:25. The post frames the theme as a growth opportunity and…

China marketsEquitiesEvent-drivenRisk management
MQL5 code base

The document gives parameter settings for a EURGBP strategy that plots a channel over a specified bar period and uses an operating time window and a stop loss scaled to channel width. It reports a five-minute historical test spanning 2006 to 2009, with 2,523…

ForexTechnical indicatorsBacktestingRisk management
MQL5 code base

This document outlines an expert advisor (EA) template for applying user-defined long and short entry signals while managing open trades. Users supply entry logic through separate signal functions. The template is described as placing one buy or sell order…

ExecutionRisk managementTechnical indicators
SuperMind

The proposed screen selects stocks associated with the metaverse theme when the prior day’s actual turnover is between 3% and 28%, and the current day’s position increase share exceeds 5%. The article interprets the turnover band as a possible sign of…

China marketsEquitiesTechnical indicatorsRisk management
FinRL

This FAQ describes the scope and practical use of an educational financial reinforcement learning library. It covers supported data sources, feature inputs such as sentiment, training options, reward functions, hyperparameter tuning, and algorithm choices.…

Machine learningBacktestingStatisticsRisk management
Stratmill research code

The document contains reusable strategy calculations for price returns, volatility scaling, trend following, and MACD signals. Its intermediate trend strategy combines the signs of one-month and one-year returns, weighted by a parameter, and applies that…

Trend followingTechnical indicatorsVolatilityRisk management
Lumibot

This documentation entry directs coding agents to start from complete LumiBot examples for either AI-based or ordinary Python strategies. It describes the strategy lifecycle at a high level: create agents during initialization and invoke them during each…

Machine learningBacktestingExecutionRisk management