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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 study examines the low-volatility effect in Chinese A-shares using price amplitude as a volatility proxy. The original amplitude factor has negative stock-selection ability, but its performance is unstable: the reported monthly IC and rank IC are…

Kinesiske markederAksjerFaktorinvesteringVolatilitet
BigQuant

The document summarizes research on how high-frequency trading in equities affects liquidity in options on those stocks. The study combines Nasdaq HFT records with options transaction data and other market data for 103 stocks, then uses instrumental-variable…

AksjerOpsjonerHøyfrekvenshandelMarkedsmikrostruktur
BigQuant

This account summarizes a 2021 conference talk on using machine learning to price stocks. A simple example feeds market-wide daily open, high, low, close, and average prices into an LSTM time-series model. The speaker stresses preparing noisy financial data,…

AksjerMaskinlæringHistorisk testingStatistikk
BigQuant

This Chinese-language forum exchange addresses a data handling issue that can arise after computing regression coefficients with an OLS factor expression. The questioner finds that beta values appear in DataFrame cells as array-like objects rather than as…

FaktorinvesteringStatistikk
BigQuant

The article introduces a stock-analysis framework built around candlesticks, trading volume, moving averages, and financial statements, then focuses on candlestick interpretation. Its central setup is a strong bullish candle breaking above a prolonged…

AksjerKursbruddMomentumTekniske indikatorer
BigQuant

This short Chinese-language forum exchange addresses how to preserve promising results from a quantitative strategy’s hyperparameter search. The example need is to save parameter sets whose score exceeds a chosen threshold. The poster reports that attempts…

Historisk testingStatistikk
BigQuant

This research explores shared analyst coverage as a way to represent links between Chinese A-share companies. It extends direct links to indirect, multi-step connections, arguing that these can reveal less visible information spillovers, especially for…

Kinesiske markederAksjerFaktorinvesteringTilbakevending mot gjennomsnittet
BigQuant

The document summarizes two strands of research. One examines analyst-run mutual funds across multiple fund companies, reporting that analyst-managed funds outperformed comparison funds on risk-adjusted measures and that stronger analyst skill was associated…

AksjerFaktorinvesteringStatistikkRisikostyring
BigQuant

The document describes two measures of active fund manager ability based on whether a manager’s holdings or portfolio changes resemble those of historically capable peers. The holdings measure assigns stocks a quality score from the weighted skill estimates…

AksjerFaktorinvesteringPorteføljekonstruksjonHistorisk testing
BigQuant

The document summarizes lifecycle fund design through human-capital theory and liability-driven dynamic asset allocation. Under stated assumptions, it says an optimal glide path can be derived analytically, and discusses how design factors affect the path.…

Flere aktivaklasserPorteføljekonstruksjonRisikostyringTrendfølgende handel
BigQuant

This brief research submission describes an experiment on stocks priced between zero and two yuan, using three five-day factors: momentum, reversal, and volatility. The author says the backtest results were weak and asks for ideas to improve the analysis.…

Kinesiske markederAksjerMomentumTilbakevending mot gjennomsnittet
BigQuant

This short BigQuant forum post concerns a modified XGBoost strategy whose equity curve and recent trade records appear unchanged despite adjustments to the data. The response offers two possible explanations: the backtest module may be reusing cached…

MaskinlæringHistorisk testingAksjer
BigQuant

The report extends a single-asset drawdown adjustment approach to portfolios of strategies. It describes selecting the control parameter gamma directly from the single-dimension result, avoiding Monte Carlo optimization for the multidimensional case. The…

AksjerRisikostyringPorteføljekonstruksjonPosisjonsstørrelse
BigQuant

The article explains recurrent neural networks and LSTM gates, then outlines an A-share stock-selection example that predicts five-day returns from sequences of seven factors. It describes missing-value handling, outlier filtering, standardization, and…

AksjerMaskinlæringFaktorinvesteringHistorisk testing
BigQuant

This article introduces a custom-coded stock selection strategy built around five rules, mainly seeking stocks that have fallen sharply and begun to stabilize, with some rules intended to capture possible strong buying. It contrasts rule-based strategies,…

AksjerTilbakevending mot gjennomsnittetHistorisk testingPosisjonsstørrelse
BigQuant

The article explains how risk-constrained Kelly sizing modifies the classic Kelly approach, which chooses a capital fraction to maximize long-run log growth but can produce large drawdowns. The constrained version adds a limit on the probability that wealth…

RisikostyringPosisjonsstørrelseHistorisk testingMaskinlæring
BigQuant

The document describes a China equity index enhancement approach that combines a multi-factor return forecast with portfolio constraints. Candidate factors span size, valuation, growth, profitability, technical signals, liquidity, and volatility. Symmetric…

AksjerFaktorinvesteringPorteføljekonstruksjonRisikostyring
BigQuant

The document outlines a framework for allocating across asset classes according to macroeconomic trends. It first identifies whether individual indicators are rising or falling, using historical averages or filtering, then compares asset returns over the…

Flere aktivaklasserPorteføljekonstruksjonHistorisk testingStatistikk
BigQuant

This overview surveys a broad set of quantitative approaches, describing their basic mechanisms and the market settings or investor capabilities they may suit. It covers trend following, machine learning, arbitrage and market neutral methods, factor…

Trendfølgende handelArbitrasjeFaktorinvesteringMaskinlæring
BigQuant

This Chinese-language research abstract describes a study of order sizes in stock-level tick data. It reports a Pareto-like pattern: small orders occur more often than large orders, with order-size frequencies declining according to a power law. The authors…

AksjerFaktorinvesteringStatistikkMarkedsmikrostruktur
BigQuant

This short platform discussion addresses whether a backtest can sell existing holdings at the open and use the proceeds to buy other stocks at that same open. The questioner reports that setting both order prices to the open did not make sale proceeds…

AksjerHistorisk testingOrdreutførelse
BigQuant

This study compares time-series and cross-sectional standardization of price and volume factors in an A-share stock-selection setting. It uses a dataset spanning 2011–2022 with 98 daily factors and a five-day return label, and compares LightGBM and deep…

AksjerKinesiske markederMaskinlæringFaktorinvestering
BigQuant

This Chinese-language forum post addresses two data needs in quantitative stock research: constructing weekly bars from daily observations and obtaining historical quarterly financial measures such as return on equity. It outlines grouping daily data by…

AksjerKinesiske markederStatistikkFaktorinvestering
BigQuant

This report summary describes a fund-selection approach that uses portfolio holdings to estimate managers’ stock-picking and market-timing abilities. Holdings provide information for attributing and forecasting performance that is not available from fund net…

FaktorinvesteringStatistikkPorteføljekonstruksjonKinesiske markeder