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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 research surveys how several forms of Chinese Level 2 market data can support equity signals: minute bars, order-book snapshots and queues, and transaction-level records. It describes factors based on intraday return shape, downside variation,…

AksjerHøyfrekvenshandelFaktorinvesteringMarkedsmikrostruktur
BigQuant

This research describes a bond-fund selection method built around return attribution. It expands the Campisi framework—which separates income, government-rate, credit-spread, and security-selection effects—with convertible-bond and monetary-policy effects.…

RentepapirerFaktorinvesteringPorteføljekonstruksjonHistorisk testing
BigQuant

This 2018 weekly report reviews a sharp post-holiday decline in Chinese equities, noting that large-cap leaders held up better than smaller companies. It interprets price structure, valuation, and long-term support as signs that the market was in a potential…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This discussion explains a mismatch in which a simulated trading run produces no signal even though a backtest does. The reported cause is a SQL query using a one-row lead on closing prices. At date t, that field requires the closing price from t+1, which is…

Historisk testingOrdreutførelseStatistikk
BigQuant

This post describes a revised rolling machine-learning training workflow, reporting that its code was reorganized for clarity, model parameters were adjusted, and memory monitoring was added. The author says the parameter changes increased backtest speed…

MaskinlæringHistorisk testingStatistikk
BigQuant

The article argues that individual investors should not expect consumer AI tools to compete with professional high-frequency trading. It points to differences in computing location, market data access, and technical resources, and describes an alleged…

AksjerHøyfrekvenshandelMaskinlæringRisikostyring
BigQuant

The document explains how to generate moving-average features in BigQuant when only selected lookback windows are wanted. It contrasts a range-based list comprehension, which the platform accepts, with a list of chosen values, which it says is unsupported in…

Tekniske indikatorerStatistikk
BigQuant

This guide outlines a workflow for preparing a factor research report for a quantitative trading competition. It recommends generating factor data, then using BigQuant’s FactorLens v4 to calculate single-factor results. The platform returns ranking metrics…

FaktorinvesteringStatistikkHistorisk testing
BigQuant

This text introduces market efficiency as a contested idea whose meaning shapes how investors approach investing and valuation. It says the chapter offers a basic definition and considers what efficient markets would imply for investors. It also points…

StatistikkHistorisk testingAksjer
BigQuant

This short forum post asks whether a linear equity strategy can compare a stock’s ranking when purchased with its current ranking and sell after sufficient deterioration. The example uses a small-capitalization strategy holding ten stocks: a stock bought at…

AksjerFaktorinvesteringOrdreutførelseHistorisk testing
BigQuant

This monthly review evaluates equity factors using information coefficient (IC) relationships with subsequent prices and market- and industry-neutral long-short returns. It reports that growth and turnover factors were relatively consistent over the latest…

AksjerKinesiske markederFaktorinvesteringStatistikk
BigQuant

The document presents volatility of volatility (VoV) as a proxy for uncertainty about an asset’s probability distribution, distinct from ordinary risk. It argues that investors tend to avoid stocks with greater ambiguity and may favor stocks whose prospects…

AksjerVolatilitetFaktorinvesteringHøyfrekvenshandel
BigQuant

This report reviews China’s digital finance industry as user growth matures and competition shifts toward retaining and serving customers and merchants. It compares finance apps across user scale, growth, market concentration, and engagement, and describes…

Flere aktivaklasserKinesiske markederAksjer
BigQuant

This article explains risk parity as an allocation approach that assigns comparable risk contributions across assets or risk factors, unlike capital-weighted mixes such as a conventional stock and bond portfolio. It lays out assumptions behind the method,…

Flere aktivaklasserPorteføljekonstruksjonRisikostyringVolatilitet
BigQuant

This research summary examines whether trading behavior can serve as a proxy for speculative intensity in Chinese A-shares. It studies four measures: idiosyncratic volatility, idiosyncrasy, price delay, and size-adjusted turnover. The proposed intuition is…

AksjerFaktorinvesteringStatistikkKinesiske markeder
BigQuant

The document describes how to build daily return data for level-two industries and use it in stock selection. It proposes joining stock industry classifications with daily returns and float market capitalizations, then grouping by industry and date. Each…

AksjerMaskinlæringMomentumPorteføljekonstruksjon
BigQuant

This brief summary of a 2018 Chinese new-share market review reports that IPO issuance slowed while subscription winning rates remained stable. It also says that new-share subscription returns differed by investor category: A- and B-class investors…

AksjerKinesiske markederHendelsesdrevet handel
BigQuant

This overview explains active learning as a way to reduce the cost of building supervised or semi-supervised models when expert labels are scarce. A model repeatedly identifies candidate examples for human review, incorporates the resulting labels through…

MaskinlæringStatistikk
BigQuant

This forum post raises a factor-construction question about accessing older financial statement observations beyond a platform's precomputed factors. The example is operating revenue: the author understands the suffix-zero field to represent the latest…

FaktorinvesteringAksjerStatistikk
BigQuant

This Chinese research note examines the common practice of relating price-to-earnings ratios to expected earnings growth, including the assumption that a PEG ratio of one indicates fair value. Its hypothetical comparison shows that companies with PE and…

AksjerFaktorinvesteringStatistikkKinesiske markeder
BigQuant

This overview surveys empirical research on pricing stock-index options, focusing on how systematic stochastic volatility and jump risk affect option values and returns. It describes the evolution from Black–Scholes–Merton assumptions, in which the…

OpsjonerVolatilitetPrising av derivaterStatistikk
BigQuant

This research summary describes using machine learning to predict equity returns from alpha factors. It compares LASSO, support vector machines, boosted decision trees, and random forests, selecting random forests for their relatively simple structure,…

AksjerKinesiske markederMaskinlæringFaktorinvestering
BigQuant

This research summary describes equity signals built from timestamped order submissions and cancellations, which can reveal more of the trading process than completed trades alone. It focuses mainly on Shenzhen exchange data because that venue had a longer…

AksjerFaktorinvesteringMarkedsmikrostrukturKinesiske markeder
BigQuant

The document presents a pairs-trading question about two stocks believed to have a long-run cointegrating relationship. It describes fitting a linear relationship between their prices, then standardizing a series associated with that relationship using a…

ParhandelTilbakevending mot gjennomsnittetStatistikkAksjer