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Kunnskapsbibliotek

Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.

Quant Q&A
20,364 dokumenter
SuperMind
12,226 dokumenter
OKX Learn
8,431 dokumenter
Strategy library
7,910 dokumenter
MQL5 code base
7,090 dokumenter
BigQuant
3,481 dokumenter
Bitget Academy
3,298 dokumenter
MQL5 articles
3,012 dokumenter
TradingView scripts
1,976 dokumenter
ProRealCode
1,507 dokumenter
Deribit Insights
1,232 dokumenter
Machine Learning for Trading
1,124 dokumenter
arXiv papers
1,033 dokumenter
Amberdata research
766 dokumenter
FMZ forum
682 dokumenter
FMZ digest
662 dokumenter
vn.py community
560 dokumenter
QuantInsti blog
511 dokumenter
Galaxy Research
340 dokumenter
QuantStart
246 dokumenter
Stratmill research code
219 dokumenter
Robot Wealth
195 dokumenter
NautilusTrader
191 dokumenter
Hummingbot docs
181 dokumenter
Paradigm research
175 dokumenter
Lumibot
164 dokumenter
Kraken Learn
163 dokumenter
Bibliotek for kvantkurs
157 dokumenter
OctoBot
152 dokumenter
Cryptohopper blog
144 dokumenter
Systematic trading blog (Rob Carver)
132 dokumenter
Qlib
116 dokumenter
TqSdk
86 dokumenter
Quantpedia
86 dokumenter
Hyperliquid docs
79 dokumenter
Freqtrade
68 dokumenter
Hudson & Thames
62 dokumenter
Awesome Systematic Trading
61 dokumenter
backtrader
54 dokumenter
vn.py
50 dokumenter
Binance API docs
45 dokumenter
Quantopian-forelesninger
45 dokumenter
FMZ guides
38 dokumenter
pysystemtrade
34 dokumenter
Freqtrade docs
32 dokumenter
quant-trading
31 dokumenter
FinRL
28 dokumenter
Zipline
22 dokumenter
FMZ live strategies
21 dokumenter
Jesse
17 dokumenter
pyfolio
16 dokumenter
Alphalens
14 dokumenter
WonderTrader
14 dokumenter
backtesting.py
11 dokumenter
Technical Analysis
9 dokumenter
QTPyLib
8 dokumenter
QuantRocket
7 dokumenter
Lumibot strategies
7 dokumenter
Awesome Quant
1 dokumenter

Søk i biblioteket

3,481 dokumenter

BigQuant

This report introduces environmental, social, and governance investing and outlines six approaches, including exclusions, positive screening, ESG integration, thematic investing, and shareholder engagement. It compares MSCI and Thomson Reuters rating methods…

AksjerFaktorinvesteringPorteføljekonstruksjonKinesiske markeder
BigQuant

This report overview describes how artificial intelligence may be applied across investment management, including portfolio research, investment decisions, trading, risk control, product development, and client marketing. It highlights information…

MaskinlæringFlere aktivaklasserRisikostyringOrdreutførelse
BigQuant

This weekly report surveys Chinese equity-selection factors built from intraday trading behavior. Examples include return skewness, downside-volatility share, buying interest and large-order net buying after the open, late-session trading share, average…

AksjerKinesiske markederHøyfrekvenshandelFaktorinvestering
BigQuant

This document describes an Expert Advisor built around the Adaptive Cyber Cycle indicator. It generates a signal when the indicator crosses its signal line at a bar close. The inputs also allow use of related Adaptive CG Oscillator, CyberCycle, or Adaptive…

ValutahandelTekniske indikatorerHistorisk testing
BigQuant

This article explains how to simplify factor research by converting many distinct raw values into a smaller number of bins. Its example is a daily return factor across thousands of stocks, where nearby values may be treated as equivalent for exploratory…

AksjerKinesiske markederStatistikkFaktorinvestering
BigQuant

This research summary reviews the 2021 commodity futures market and examines whether exchange increases in trading fees affect commodity prices and CTA strategies. It describes a year of broad price gains followed by sharp reversals amid market…

FuturesRåvarerTrendfølgende handelMarkedsmikrostruktur
BigQuant

The article explains how a backtest can look convincing while relying on conditions that may not hold in live markets. It examines three assumptions: orders fill immediately at the signal price, market data forms a continuous sequence, and prices from a…

Historisk testingOrdreutførelseMarkedsmikrostrukturRisikostyring
BigQuant

The document outlines a financial forum presentation on the use of artificial intelligence in quantitative investing. Its listed examples include a random forest approach to market timing and hedging, a pattern matching method for industry rotation, and a…

MaskinlæringPorteføljekonstruksjonPrising av derivater
BigQuant

This introductory tutorial explains machine learning concepts before focusing on linear regression. It defines features and labels, distinguishes supervised from unsupervised learning, and outlines classification, regression, clustering, and parameterized…

MaskinlæringStatistikkFaktorinvesteringHistorisk testing
BigQuant

This research summary examines whether analyst earnings forecast errors can help select stocks. It reports that realized forecast errors are strongly negatively associated with subsequent returns: stocks with smaller errors performed better in the described…

AksjerFaktorinvesteringStatistikk
BigQuant

This document summarizes a documentary series on the development of money and financial systems. Its topics include the origins and social role of credit and debt, the rise of securities markets and sovereign borrowing, insurance and the welfare state,…

Flere aktivaklasserRentepapirerAksjerRisikostyring
BigQuant

This weekly report presents diagnostics for the ten highest-ranked anonymous factors in a Chinese quantitative research competition. It describes average style exposures estimated with daily multivariate regressions and average industry-level Rank ICs, the…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

StockRanker is a tree-based learning-to-rank tool for ordering stocks from supplied features. Its listwise approach optimizes the quality of a stock list’s ranking, and the guide connects the method to the RankNet, LambdaRank, and LambdaMART line of work.…

AksjerMaskinlæringStatistikkHistorisk testing
BigQuant

The document addresses a backtest or trading-log puzzle: a buy order is calculated by rounding available cash down to a whole multiple of 100 shares, yet the recorded final purchase quantity is not a multiple of 100. The explanation given is that the system…

AksjerOrdreutførelseHistorisk testing
BigQuant

The document explains two differences between backtests and simulated or live trading in a notebook-based platform. In the described live modes, the initialization function runs during each trading period, whereas a backtest runs it once. It also says…

Historisk testingOrdreutførelseRisikostyring
BigQuant

This update log describes changes to a Chinese equity screening expression. The initial filters included a trailing price-to-earnings ceiling and a minimum listing age. The author then replaces a fixed floating-market-capitalization threshold with a…

AksjerFaktorinvesteringHistorisk testingKinesiske markeder
BigQuant

This literature digest covers two topics. The first evaluates asset-pricing factor models using the maximum squared Sharpe ratio. It describes comparisons among nested models, from CAPM through three-, five-, and six-factor specifications, and non-nested…

FaktorinvesteringPorteføljekonstruksjonRisikostyringAksjer
BigQuant

This report summarizes tests of equity factors across industries, covering cash flow, valuation, company size, momentum, liquidity, volatility, beta, and alpha. It compares factor performance using portfolio net value and ranking differences, and discusses…

AksjerFaktorinvesteringStatistikk
BigQuant

This review describes a model in which equity factors move through normal conditions, periods of value weakness, and subsequent reversals. The value drawdown may reflect a rally in growth stocks or a collapse in value stocks; reversals then differ according…

FaktorinvesteringAksjerMomentumStatistikk
BigQuant

The document discusses the rapid growth of China’s quantitative private fund industry since 2019. It attributes that expansion to growing volumes of data, better data processing, and market inefficiencies that may offer more alpha opportunities than mature…

Kinesiske markederAksjerFlere aktivaklasserRisikostyring
BigQuant

This research summary explains a model in which product-market competition and expected asset returns influence one another. Competition can reduce profit margins, increasing firms’ operating leverage and exposure to systematic shocks. Threats from new…

AksjerStatistikkFaktorinvesteringAmerikanske markeder
BigQuant

This Chinese-language research summary describes a top-down framework for institutional fund allocation that connects macro asset views with strategy and product selection. It argues that asset allocation recommendations need to flow through to specific…

Flere aktivaklasserFaktorinvesteringPorteføljekonstruksjonRisikostyring
BigQuant

The document introduces the Alligator technical indicator as three moving-average lines calculated from the midpoint of each bar's high and low. It names the lines Lips, Teeth, and Jaw, and describes each as a moving exponential average with a separate fast,…

Tekniske indikatorerAksjer
BigQuant

This summary of a May 2020 China A-share market-timing report combines sector performance, valuation, market breadth, fund positioning, and an intermediate-term model. It lists the strongest and weakest sectors, notes that valuations fell across several…

Kinesiske markederAksjerTekniske indikatorerStatistikk