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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 article argues that China’s A-share T+1 settlement rule can constrain retail traders who buy shares during a session, while some institutions may approximate intraday trading through previously held inventory or short selling. It describes two proposed…

AksjerKinesiske markederMarkedsmikrostrukturRisikostyring
BigQuant

This weekly competition report presents diagnostic summaries for the ten highest-ranked submitted factors and tracks a China 1000 index-enhancement portfolio. Factor diagnostics include average exposures to BARRA style factors and sector-level average Rank…

AksjerFaktorinvesteringStatistikkHistorisk testing
BigQuant

This introductory note describes quantitative investing as turning research, asset selection, timing, and position decisions into explicit rules that can be tested and executed by a computer. It distinguishes cross-sectional stock selection, which compares…

FaktorinvesteringHistorisk testingMomentumTilbakevending mot gjennomsnittet
BigQuant

This tutorial describes assembling a matrix of daily closing prices for Hong Kong stocks using BigQuant data sources and pandas. It first retrieves unique instrument identifiers, then loops through them to collect date and close fields over a specified…

AksjerKinesiske markederStatistikk
BigQuant

This report reviews the relative performance of Chinese quantitative equity funds and considers what rising assets under management mean for strategy design. It attributes the period’s advantage for index-enhanced and quantitative long products largely to…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

The document introduces reinforcement learning through the interaction of an agent and an environment, where actions produce feedback and the objective is to maximize discounted future rewards. It frames the problem as a Markov decision process, explaining…

MaskinlæringAksjerKinesiske markederHistorisk testing
BigQuant

This research summary presents asset allocation as a continuing process: define investor objectives, set an investment strategy, implement it, and review performance. Objectives specify the desired return, acceptable risk, and other investor needs. The…

Flere aktivaklasserPorteføljekonstruksjonHistorisk testingRisikostyring
BigQuant

This overview describes several ways quantitative trading attempts to identify and capture market opportunities. It presents historical data analysis and backtesting as tools for assessing a strategy, then discusses automated execution, momentum examples,…

Historisk testingMomentumArbitrasjeRisikostyring
BigQuant

A BigQuant forum question asks how to select the ten stocks with the strongest return over the previous calendar year on the first trading day of a new year. The user’s current approximation compares prices about 250 trading days apart, but they want a…

AksjerKinesiske markederStatistikkPorteføljekonstruksjon
BigQuant

A BigQuant forum exchange discusses why feature extraction can return multiple records for a stock on a given day, even when one record is expected. The suggested cause is that the feature input stage reads across tables; joining data with multiple matching…

AksjerStatistikk
BigQuant

This report presents an A-share stock-selection framework that applies StockRanker, a gradient-boosted learning-to-rank model, to fundamental, financial, and trading factors. It tests 282 individual factors across short, medium, and long holding horizons,…

AksjerKinesiske markederMaskinlæringFaktorinvestering
BigQuant

The document explains how to adapt a StockRanker multi-factor stock-selection workflow to Hong Kong and US equities on BigQuant. The backtest uses BigTrader’s custom historical data input, which must include date, instrument, and daily open, high, low,…

AksjerKinesiske markederAmerikanske markederMaskinlæring
BigQuant

This report reviews Chinese market and exchange-traded product developments for the week of March 30 to April 3, 2020. It summarizes moves in domestic equity indexes, bond yields, precious metals, currencies, and traded ETP categories. Equity and…

AksjerRentepapirerRåvarerFlere aktivaklasser
BigQuant

The document explains how BigQuant’s factor research platform handles user-created factors and downstream dependencies. A researcher can combine platform factors A and B into a derived factor C, submit C to the platform, and let other users with access clone…

FaktorinvesteringStatistikk
BigQuant

This research summary compares common stock-selection factors in the U.S. and Chinese markets, with a test of seven factor groups and 24 signals in the Russell 3000 universe. It reports that profitability, valuation, and growth measures were among the…

AksjerFaktorinvesteringStatistikkMomentum
BigQuant

This entry introduces two overseas research topics: persistent bias in analyst expectations and holiday effects in stock markets. The first paper is described as examining habitual distortions in conventional analyst expectation factors and developing a…

AksjerFaktorinvesteringHendelsesdrevet handel
BigQuant

This weekly report summarizes factor returns across broad Chinese equity universes and several index or liquidity-based stock pools. It describes positive performance for profitability and book-to-price factors in the full market, while size, beta, and…

AksjerKinesiske markederFaktorinvesteringMomentum
BigQuant

A stock screen for a moving average crossover can return no matches if its query loads only the target date. The note explains that time-series indicators need earlier observations to calculate their rolling values, so the data query should begin before the…

AksjerTekniske indikatorerStatistikk
BigQuant

This Chinese-language research summary describes equity industry-rotation models for cyclical sectors, non-cyclical sectors, and the full market. It ranks industries using composite scores built from different factor groups: historical and expected…

AksjerFaktorinvestering
BigQuant

This post describes a scheduling problem in a daily China-stock simulation: a strategy launched at 5 a.m. after a weekend or holiday may use the previous calendar day as both its start and end date. Because that date has no market session, the run fails. The…

AksjerKinesiske markederHistorisk testing
BigQuant

This note presents a behavioral-finance explanation for a possible link between World Cup final outcomes and investor activity. A national team’s victory may lift local sentiment and trading, while defeat may discourage participation. The analysis compares…

AksjerMarkedssentimentHendelsesdrevet handelStatistikk
BigQuant

The document explains how to decide whether to remove sector exposures from equity factor portfolios. It splits a stock characteristic into a within-sector component, which ranks companies against peers, and an across-sector component, which captures…

AksjerFaktorinvesteringPorteføljekonstruksjonStatistikk
BigQuant

This article presents five discretionary rules for interpreting stock turnover, support and resistance, trend direction, and price-volume behavior. It treats unusually high turnover near price lows as possible accumulation and high turnover after a sharp…

AksjerTrendfølgende handelTekniske indikatorerRisikostyring