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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

A brief forum exchange addresses a user whose stock strategy appears not to run. The response suggests two checks: use English names for features, and print the daily buy and sell candidate lists to see whether any stocks meet the strategy’s conditions. The…

AksjerOrdreutførelse
BigQuant

This research summary proposes combining price-to-book ratio (PB) with return on equity (ROE) to find companies with stronger fundamentals and lower valuations in China’s A-share market. It treats ROE and other operating measures as indicators of value…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This market-monitoring report summarizes Chinese trading conditions for July 13, 2022. It reviews broad index and sector performance, then gauges equity sentiment using limit-up and limit-down counts, next-day returns for stocks that had hit either limit,…

AksjerFuturesMarkedssentimentMarkedsmikrostruktur
BigQuant

This student submission translates a Chinese “dragon returning” trading approach into a factor-based stock strategy. It first identifies strong sectors with sector momentum, then selects leading stocks within them using stock momentum and price-volume…

Kinesiske markederAksjerMomentumTilbakevending mot gjennomsnittet
BigQuant

The document summarizes a study of dividend-focused Smart Beta strategies in Chinese equities. It argues that dividend factors show a consistent ranking pattern across the CSI 300, CSI 500, and broader market universe, with higher-dividend portfolios…

AksjerFaktorinvesteringKinesiske markederHistorisk testing
BigQuant

The document surveys a Chinese securities research team’s work on applying artificial intelligence to quantitative investing. It organizes that research around model evaluation, factor discovery, overfitting controls, synthetic data, and methods intended to…

MaskinlæringFaktorinvesteringAksjerPorteføljekonstruksjon
BigQuant

This document summarizes a securities research report on constructing factors from high-frequency data, with a focus on combining intraday and day-level information. It frames the choice of calculation method around whether price and volume signals retain…

AksjerHøyfrekvenshandelFaktorinvesteringMomentum
BigQuant

This Chinese-language post discusses connecting BigQuant research with Guojin Securities’ QMT platform for automated live trading. Its concrete example is a stock strategy that first processes daily data to select a watchlist, then monitors those names and…

Kinesiske markederAksjerKursbruddHøyfrekvenshandel
BigQuant

This equity research note examines suppliers of radio-frequency components for active phased-array radar amid anticipated military equipment upgrades in China. It explains that active arrays connect a separate transmit/receive module to each radiating…

Kinesiske markederAksjer
BigQuant

This report tests the ratio of research and development spending to revenue as an equity-selection factor across industries. Single-factor tests find some effectiveness in technology-oriented sectors, including pharmaceuticals, electronics, communications,…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

The note answers whether factors shown in BigQuant’s factor dashboard can be referenced directly. It says they cannot be called directly from the dashboard; users need to open a factor’s detail page and extract its expression. When available, the displayed…

Faktorinvestering
BigQuant

The document gives a brief historical overview of quantitative investing. It describes how advances in computing made it practical to store and process large amounts of historical data, supporting the use of statistical and mathematical models in investment…

StatistikkMaskinlæringHistorisk testing
BigQuant

A BigQuant user raises a timing problem involving premarket data processing in backtests. In the example, a signal generated on one day leads to an order for the next day; premarket history in the backtest appears to expose that day’s open and close. Such…

Historisk testingOrdreutførelseMarkedsmikrostrukturAksjer
BigQuant

This educational article introduces support vector machines (SVMs) as classification models, with examples framed around separating two classes using features. It explains the maximum-margin objective: choose a decision boundary that stays as far as possible…

MaskinlæringStatistikk
BigQuant

This report challenges mean-variance optimization assumptions that returns are normally distributed, volatility captures risk symmetrically, and portfolios should maximize return per unit of risk. It instead frames investor concerns as preserving principal…

Flere aktivaklasserPorteføljekonstruksjonRisikostyringStatistikk
BigQuant

This article explains a Dual Thrust trend-following method and its application to a basket of nickel, rebar, and coking coal futures. It defines a range from historical highs, lows, and closes, then sets upper and lower breakout thresholds around the current…

FuturesRåvarerTrendfølgende handelKursbrudd
BigQuant

The article evaluates whether a stock’s overnight return, measured from the prior close to the next open, can proxy for firm-level investor sentiment. The rationale is that retail investors may place orders outside regular market hours, concentrating demand…

AksjerMarkedssentimentTilbakevending mot gjennomsnittetStatistikk
BigQuant

This reference describes several candidate fitness objectives for genetic programming that produces equity factors. It defines IC information ratio as the mean information coefficient divided by its standard deviation, with the information coefficient…

AksjerFaktorinvesteringMaskinlæringStatistikk
BigQuant

This research roundup summarizes three studies. The first examines whether unexpected US monetary policy announcements affected hedge fund alpha after the financial crisis, using event studies, structural-break tests, and Markov-switching models. It reports…

Flere aktivaklasserPorteføljekonstruksjonHendelsesdrevet handelMarkedssentiment
BigQuant

This report examines shortcomings in the Henriksson–Merton (HM) and Treynor–Mazuy (TM) models for assessing fund managers’ market and style timing. TM represents beta adjustment as a gradual quadratic pattern, while HM assumes a two-state exposure shift;…

StatistikkFaktorinvesteringPorteføljekonstruksjon
BigQuant

This report overview defines smart beta as a systematic, rules based way to obtain exposure to selected investment factors. It compares the United States and China through their ETF markets, describing differences in product scale, factor coverage, and index…

FaktorinvesteringAksjerKinesiske markederAmerikanske markeder
BigQuant

The document outlines the five factors used to explain differences in stock returns: market excess return, company size, book-to-market value, profitability, and investment. It describes each as a comparison between groups of stocks, such as small versus…

AksjerFaktorinvesteringStatistikkPorteføljekonstruksjon
BigQuant

This BigQuant example builds a daily Chinese-stock portfolio by ranking eligible shares on 30-day turnover variability relative to their industry group. It filters out risk-warning stocks and applies price and listing-age conditions, then selects five names…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This market note reviews a modest rebound in Chinese equities and discusses the forces behind it. It attributes the recovery partly to expectations of improved second-quarter corporate earnings and reduced global risk aversion. It also cautions that…

AksjerKinesiske markederPorteføljekonstruksjonRisikostyring