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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
Quantopian-forelesninger
45 dokumenter
Binance API docs
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 forum post asks how to choose among three paid equity strategies using platform rankings and reported performance figures. The examples contrast a highly ranked strategy with large cumulative gains but a substantial drawdown, a smoother strategy with a…

AksjerHistorisk testingRisikostyring
BigQuant

The document summarizes a study that derives a listed-company default probability factor, MPD, from the Black–Scholes–Merton option-pricing framework under stated assumptions. It evaluates MPD as a measure of corporate credit risk and examines how portfolios…

AksjerFaktorinvesteringRisikostyringKinesiske markeder
BigQuant

This post introduces the Accumulation Swing Index (ASI), also called the oscillating swing index, and lists open, high, low, and close prices as its inputs. It presents pseudocode that compares current prices with prior close, low, and open values, builds…

Tekniske indikatorerAksjer
BigQuant

The discussion explains that weights in a multi-strategy portfolio can represent each strategy’s share of the combined allocation and can also be used to calculate a weighted portfolio return. Its example divides capital evenly among three strategies,…

PorteføljekonstruksjonHistorisk testingPosisjonsstørrelse
BigQuant

The article explains stock price changes through macroeconomic forces such as growth, interest rates, inflation, policy, and global capital flows, alongside company performance, supply and demand, news, and investor sentiment. It describes how retail and…

AksjerTekniske indikatorerMomentumVolatilitet
BigQuant

This study summary describes using recurrent neural networks, including long short-term memory cells, to forecast stock movements separately by industry. The rationale is that firms in one sector may share common influences, while industries can differ in…

AksjerMaskinlæringFaktorinvesteringStatistikk
BigQuant

This report summary describes using gene expression programming, a heuristic inspired by biological evolution, to discover price-volume factors from short-horizon data. Because many candidate factors had weakly monotonic group returns or no excess return in…

AksjerFaktorinvesteringMaskinlæringStatistikk
BigQuant

This research summary presents a framework for classifying industry and thematic funds and using the resulting categories to form candidate investment pools. Fund contracts’ investment requirements serve as a core eligibility basis. The framework begins with…

Kinesiske markederAksjerPorteføljekonstruksjonFaktorinvestering
BigQuant

This analysis proposes six dimensions for assessing Chinese brokerage firms’ monthly stock recommendations: absolute returns, return relative to volatility, sector selection, stock-level excess returns, ability to identify emerging winners, and selection…

AksjerStatistikkRisikostyringFaktorinvestering
BigQuant

This review of Asness and Frazzini examines how price timing affects the book-to-price measure used to form the HML value factor. The standard Fama–French approach pairs accounting book value with a prior fiscal year-end price, then updates portfolios…

AksjerFaktorinvesteringMomentumPorteføljekonstruksjon
BigQuant

This brief platform support exchange answers where to find the dividend_yield_ratio field after a user cannot locate it in the dividend information table. The response points to a precomputed stock factors table, explaining that the requested field is…

AksjerKinesiske markederFaktorinvestering
BigQuant

This article explains how to turn future returns into labels for supervised trading models, arguing that the label should match the instrument, trading horizon, liquidity, and costs of the intended strategy. It first describes using an n-period return as a…

MaskinlæringStatistikkHistorisk testingRisikostyring
BigQuant

This summary of a securities research report examines alternative data, focusing on company job postings. It describes a hiring-count factor tested over 2017–2021, reporting that preprocessing and filtering job listings improved its backtest performance. The…

AksjerFaktorinvesteringHendelsesdrevet handelHistorisk testing
BigQuant

The article compares two Chinese equity approaches built around market capitalization. The traditional strategy selects the smallest-cap stocks for a portfolio. The AI version uses a StockRanker ranking model with market capitalization as its sole feature.…

AksjerFaktorinvesteringMaskinlæringHistorisk testing
BigQuant

This report forecasts additions to and removals from the CSI 300, CSI 500, and SSE 50 indexes. It uses index construction rules together with stock trading and financial data to estimate which securities may enter or leave at a scheduled review. The document…

AksjerKinesiske markederHendelsesdrevet handelStatistikk
BigQuant

This research note outlines a machine-learning framework for timing equity factors. It discusses candidate signals such as valuation spreads between factors and correlations within factor pairs, then examines how those measures relate to subsequent factor…

AksjerFaktorinvesteringMaskinlæringHistorisk testing
BigQuant

This article describes three sources of perceived information advantages for quantitative funds: alternative data such as satellite imagery, online sales and shipping activity; automated monitoring of retail investor discussion and sentiment; and rapid…

AksjerMaskinlæringMarkedssentimentOrdreutførelse
BigQuant

This research review presents a model in which active fund managers trade more when they see greater profit opportunities, then examines whether turnover predicts later benchmark-adjusted performance. Evidence from a historical sample of US active equity…

AksjerStatistikkMarkedsmikrostrukturMarkedssentiment
BigQuant

This support note explains why entering an index symbol in a visual strategy builder’s code-list module does not select the index constituents. The module expects individual stock codes, or can be left empty to include all A-share stocks. As a result, using…

Kinesiske markederAksjerHistorisk testing
BigQuant

This post outlines a beginner workflow for developing a rule-based stock selection strategy on a quantitative research platform. It describes combining five selection rules, mainly aimed at finding stocks that have fallen and begun to stabilize, with some…

AksjerTilbakevending mot gjennomsnittetHistorisk testingPosisjonsstørrelse
BigQuant

The article explains the Deep Momentum Network (DMN) approach to multi-asset trend following. Rather than predict returns and determine portfolio weights separately, the model outputs each asset’s next-period position, bounded between long and short…

FuturesTrendfølgende handelMaskinlæringPorteføljekonstruksjon
BigQuant

This research summary explains feature selection as a way to reduce a model’s input set before training. It outlines filter, wrapper, and embedded approaches, with filter criteria including variance or entropy, statistical measures such as F-scores and…

Kinesiske markederAksjerMaskinlæringFaktorinvestering
BigQuant

This research summary compares a basic reversal factor based on roughly one month of returns with volume-weighted high-frequency and structured reversal factors. The structured version incorporates both momentum and reversal behavior in high-frequency data.…

AksjerTilbakevending mot gjennomsnittetMomentumFaktorinvestering
BigQuant

The article argues that two features of China’s A-share market may hinder a durable, gradual bull market: quantitative trading and what it calls excessive corporate financing. It attributes the impact of quantitative strategies to their technological and…

Kinesiske markederAksjerMarkedsmikrostrukturMarkedssentiment