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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

The article explains a four-part decomposition of market beta based on whether market and individual stock returns are positive or negative together. The components distinguish joint losses, joint gains, and the two mixed-sign cases. Using realized…

AksjerStatistikkRisikostyringPorteføljekonstruksjon
BigQuant

This summary of a 2018 brokerage study examines whether capital-flow measures can help anticipate shifts among equity investment styles. It argues that raw fund-flow observations may add little beyond price and volume because those variables move together,…

AksjerFaktorinvesteringPorteføljekonstruksjonStatistikk
BigQuant

This article warns retail equity traders that fast algorithms may exploit order-flow speed differences, bursts of retail enthusiasm, and clustered stop levels. It illustrates possible behaviors: entering ahead of slower orders, buying into a sentiment-driven…

AksjerKinesiske markederMarkedsmikrostrukturOrdreutførelse
BigQuant

This study examines whether futures-curve shape can rank commodity markets. It explains backwardation, where deferred contracts are cheaper than nearer ones, and contango, where they are more expensive. The article connects these patterns to supply, demand,…

RåvarerFuturesCarry-avkastningFaktorinvestering
BigQuant

This platform support thread discusses how to preserve a model after rolling training and whether the final saved artifact represents the intended result. The replies characterize rolling training as a research-stage feature rather than a direct route to…

MaskinlæringHistorisk testing
BigQuant

The post contains a proposed monthly China A-share industry-rotation backtest. It ranks second-level industries by where current price-to-earnings ratios sit within their own rolling 60-session ranges, then selects six industries with the lowest relative…

Kinesiske markederAksjerFaktorinvesteringHistorisk testing
BigQuant

The article describes turning rolling OHLC windows into sparse, binary candlestick images for a convolutional neural network. Each sample uses 32 time steps, normalized and mapped onto a 128-by-128 grid; three columns represent each candle’s open, high-low…

AksjerMaskinlæringHistorisk testingTekniske indikatorer
BigQuant

The article presents a speculative outlook for China’s A-share market in 2026. It anticipates a gradual upward market with a spring positioning period, a stronger phase from May through September, and a sharp interim pullback that it interprets as a possible…

Kinesiske markederAksjerPorteføljekonstruksjonRisikostyring
BigQuant

This research digest summarizes two distinct topics. For multi-factor index construction, it contrasts a top-down approach, which combines single-factor indexes using assigned proportions, with a bottom-up approach, which optimizes holdings to increase…

FaktorinvesteringPorteføljekonstruksjonAksjerMarkedsmikrostruktur
BigQuant

This forum post describes an exploratory factor-construction exercise in the Chinese A-share market. The author says a small modification to a factor combination produced strong short-side results, then describes a revised version that removed the short…

Kinesiske markederAksjerFaktorinvesteringStatistikk
BigQuant

The document outlines three ways to explain portfolio results: style analysis using return regressions, benchmark-relative return decomposition, and multi-factor attribution. It describes the data each approach needs, including portfolio and benchmark…

FaktorinvesteringPorteføljekonstruksjonRisikostyringStatistikk
BigQuant

This opinion article argues that quantitative trading has different effects across market structures. It contrasts a US market it characterizes as institutionally dominated with China’s A-share market, which it portrays as retail-heavy and more driven by…

Kinesiske markederAksjerMarkedsmikrostrukturMarkedssentiment
BigQuant

This review summarizes research on whether skilled active U.S. equity fund managers add more value when prices are noisier. It measures skill mainly through portfolio selectivity, proxied by one minus a fund’s return-model R-squared, and also uses a…

AksjerMarkedssentimentStatistikkRisikostyring
BigQuant

This report summary describes revisions to AlphaNet, a neural network that learns stock-selection factors directly from raw price and volume data. AlphaNet v2 adds six ratio features, replaces pooling and dense layers with an LSTM to capture temporal…

AksjerKinesiske markederMaskinlæringFaktorinvestering
BigQuant

The document summarizes a study of four MSCI smart beta equity indexes—value weighted, equal weighted, momentum, and minimum volatility—against a market-cap-weighted benchmark. Using monthly returns over 1999–2014, the study applies principal component…

AksjerFaktorinvesteringStatistikkPorteføljekonstruksjon
BigQuant

The document explains a cost-concentration measure for the estimated purchase costs of a selected share of investors’ holdings. It defines the measure as the difference between the upper and lower bounds of that cost interval divided by their sum. A narrower…

AksjerStatistikkFaktorinvestering
BigQuant

This article outlines an event-driven strategy for Chinese equities, using quarterly reports as the trigger. It treats a published year-over-year increase of more than 30% in attributable net income as a positive event, buys qualifying stocks when reported,…

Kinesiske markederAksjerHendelsesdrevet handelHistorisk testing
BigQuant

This essay focuses on how fear can affect trading decisions, including hesitation after losses, reluctance to buy near highs, and difficulty acting during market weakness. Its central idea is that traders need not eliminate fear to act consistently; they can…

AksjerOrdreutførelseRisikostyringStatistikk
BigQuant

The report reviews the development of Smart Beta index funds in China and compares single-factor strategies in A-shares with counterparts in the US. It describes a Chinese market where offerings are relatively early-stage, commonly single-factor, and held…

AksjerFaktorinvesteringPorteføljekonstruksjonKinesiske markeder
BigQuant

This report summary describes a Chinese equity ETF rotation approach built from industry indices with matching exchange-traded funds. It selects 14 industries from the broader industry classification and rotates exposure among their ETFs. The strategy is…

AksjerKinesiske markederTrendfølgende handelFutures
BigQuant

This strategy ranks stocks across mainland China’s A-share market using a small-cap factor and a five-period momentum measure. It combines the market-cap rank with momentum into a composite score, selects the top 50 names, and assigns equal weights. The…

Kinesiske markederAksjerMomentumFaktorinvestering
BigQuant

The article argues that quantitative trading is an execution method, not a source of returns by itself. A trading system can automate orders and support consistent discipline, but it cannot make a weak or faulty strategy profitable. It also questions the…

OrdreutførelseRisikostyringHistorisk testingAksjer
BigQuant

The document reviews tactical and dynamic asset allocation, then focuses on predicting short-term movements in Chinese equity indices. It compares logistic regression, artificial neural networks, and support vector machines across different inputs and…

AksjerKinesiske markederMaskinlæringTrendfølgende handel