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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 market note summarizes a difficult week for major Chinese equity indexes and sectors, then reports changes in factor behavior. It says quality and analyst sentiment factors strengthened relative to the prior week, while value, reversal, and alternative…

Kinesiske markederAksjerFaktorinvesteringMomentum
BigQuant

This Chinese language literature digest summarizes research on return predictability and portfolio covariance estimation. One study examines stock prices and dividends in the Netherlands, the United Kingdom, and the United States over nearly four centuries.…

AksjerFaktorinvesteringStatistikkPorteføljekonstruksjon
BigQuant

This research summary examines how broad equity factors behaved across Chinese A-share industries from July 2009 through November 2018. It links differences in factor effectiveness to industry characteristics: small-cap effects were stronger in industries…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This tutorial introduces two strategy-building approaches on BigQuant: an AI-driven workflow that trains a model from selected factors and filters, and a user-coded approach with explicit selection rules. It focuses on the latter, outlining how to clone a…

AksjerKinesiske markederHistorisk testingFaktorinvestering
BigQuant

This BigQuant example assembles a China A-share workflow from stock selection through backtesting. It selects eligible main board, STAR Market, and ChiNext stocks associated with the CSI 1000, then calculates current and lagged volume relative to a five-day…

AksjerKinesiske markederTekniske indikatorerHistorisk testing
BigQuant

This meetup note explains factor exposure as an asset’s sensitivity to factor returns. It sketches a way to estimate that sensitivity: form a low-versus-high valuation long-short portfolio, treat its return series as the factor return, and regress an asset’s…

AksjerFaktorinvesteringStatistikkPorteføljekonstruksjon
BigQuant

This legacy platform example shows how to model a stock purchase at a specified price relative to the day’s open. A custom slippage model sets buy orders at 98% of the opening price, while sell orders use the current close. A daily order check compares the…

AksjerOrdreutførelseHistorisk testingMarkedsmikrostruktur
BigQuant

This discussion examines how to exclude Chinese convertible bonds after an issuer announces early redemption. The author found that the platform’s redemption-terms table showed one row each for two example bonds, including contractual trigger conditions,…

RentepapirerKinesiske markederHendelsesdrevet handelHistorisk testing
BigQuant

This article discusses practical choices when tuning a short-term stock-selection strategy: starting capital, number of holdings, capital allocated per holding, holding period, and factor selection. Its author argues that the number of names and per-name…

AksjerFaktorinvesteringPosisjonsstørrelseHistorisk testing
BigQuant

The document examines whether rolling Fama-French five-factor (FF5) alphas can guide U.S. sector rotation. It compares FF5 with the three-factor model (FF3), using monthly returns for ten U.S. industry portfolios from 1964 to 2014. The rotation signal is…

FaktorinvesteringAksjerPorteføljekonstruksjonHistorisk testing
BigQuant

This Chinese-language assignment turns a discretionary trading idea into a simple rule for constituents of the CSI 300: buy when the five-day moving average crosses above the ten-day average and MACD is positive, and sell otherwise. It also sketches a…

Kinesiske markederAksjerTekniske indikatorerMomentum
BigQuant

This research summary tests whether negative sentiment in posts on Xueqiu, a Chinese investing forum, can help time major Chinese equity indexes. It describes using machine learning to classify post sentiment and building a weekly negative-sentiment ratio…

MarkedssentimentMaskinlæringAksjerKinesiske markeder
BigQuant

The article summarizes research on how speculative stocks perform around earnings announcements. It measures speculative characteristics using proxies such as maximum daily return, idiosyncratic volatility, price, expected skewness, and jackpot probability,…

AksjerHendelsesdrevet handelMarkedssentimentFaktorinvestering
BigQuant

This brief support exchange asks how the numbers displayed after decision branches in a visual stockRanker model are calculated. The response points the reader toward material on tree models and information entropy, implying that the displayed values relate…

MaskinlæringStatistikk
BigQuant

The article introduces quantitative trading as the use of mathematical models, statistical analysis, historical and real-time market data, and algorithms to guide or automate decisions. It describes stock screening and rule-based execution as common…

AksjerMaskinlæringFaktorinvesteringHistorisk testing
BigQuant

This brief support exchange addresses a user’s report that a hyperparameter module raises a key error even though the key appears in the visual chart and can be found in code-mode parameters. The response attributes the issue to an error in the…

MaskinlæringHistorisk testingStatistikk
BigQuant

This forum post describes an attempted 12–1 momentum strategy on S&P 500 constituents and reports two backtest problems: target-percent orders repeatedly encounter null prices, and a visual workflow produces no instruments or trades. The strategy ranks…

AksjerAmerikanske markederMomentumHistorisk testing
BigQuant

This brief describes a deep neural network approach intended for quantitative stock selection. Its stated implementation goals are to run on systems with limited memory, use GPU acceleration when available, and adapt the number of input features. It also…

AksjerMaskinlæringHistorisk testing
BigQuant

The article explains five behavioral traps that it associates with retail trading losses: confirmation bias, reacting to noisy opening-auction signals, trying to recover losses through riskier trades, loss aversion that delays selling, and greed that delays…

RisikostyringMarkedssentimentAksjerKinesiske markeder
BigQuant

This meetup Q&A surveys several quantitative trading topics: using Level 2 order-book data, reproducing a legacy strategy, allocating idle capital to a gold ETF, factor decomposition, building CNN strategies, and portfolio optimization. Most answers point…

PorteføljekonstruksjonRisikostyringMaskinlæringMarkedsmikrostruktur
BigQuant

This forum post records an attempt to create an A-share factor from securities appearing on the trading ranking list, using a transaction category and several broker department codes. The submitted expression tests whether the category matches a specified…

AksjerKinesiske markederFaktorinvesteringStatistikk
BigQuant

The article presents a scenario analysis of how sustained high crude prices could affect energy-importing and energy-exporting economies. It argues that higher energy costs may shift income toward resource exporters, raise manufacturing expenses in importing…

RåvarerVolatilitetFlere aktivaklasser
BigQuant

The document introduces the task of developing a complete strategy from daily factors extracted with a high-frequency expression engine. It identifies strategy development as the topic of a meetup presentation and points readers to a related recording, but…

FaktorinvesteringMaskinlæringHistorisk testing
BigQuant

This report examines listed companies’ interest-bearing debt as a quantitative stock-selection and risk-screening measure. It defines the debt measure around short-term and long-term borrowings and bonds, and observes that debt ratios tend to reflect…

AksjerRisikostyringFaktorinvesteringHistorisk testing