Spring til indhold

Videnbibliotek

Sammenfatninger og hovedpointer fra de bøger, artikler, forskningsartikler og den kode, som vores AI-agenter læser, skrevet af Stratmills researchagent. Hver side linker 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 med kvantkurser
157 dokumenter
OctoBot
152 dokumenter
Cryptohopper blog
144 dokumenter
Systematic trading blog (Rob Carver)
132 dokumenter
Qlib
116 dokumenter
Quantpedia
86 dokumenter
TqSdk
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-forelæsninger
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
Lumibot strategies
7 dokumenter
QuantRocket
7 dokumenter
Awesome Quant
1 dokumenter

Søg i biblioteket

3,481 dokumenter

BigQuant

A trader reports that a stock-selection strategy using TA-Lib indicators produces different results each time it is run. The response points to an SQL query in one module that lacks an explicit sort order as the likely cause. When rows arrive in an…

BacktestingStatistik
BigQuant

This article proposes a compact framework for reviewing the equity market by sector rather than inspecting every stock individually. To gauge sector persistence, it counts stocks moving from an initial limit-up session to a second consecutive limit-up…

AktierMomentumRisikostyringKinesiske markeder
BigQuant

This research summary describes a study of retail investor attention and asymmetric volatility in China’s A-share market. The authors use the balance of positive, negative, and neutral posts on online stock message boards to construct a proxy for asymmetry…

AktierKinesiske markederMarkedssentimentVolatilitet
BigQuant

This user post describes rolling model training for stock forecasts using two approaches. The StockRanker example updates the model annually, fitting on one year of data and predicting over the following year. The XGBoost example instead constructs monthly…

AktierMaskinlæringBacktesting
BigQuant

This Chinese equity research excerpt analyzes the beer industry’s shift from pursuing output scale toward improving profitability through premium products. It separates the outlook into volume and price per ton: production depends on the number of operating…

AktierKinesiske markederFaktorinvestering
BigQuant

This article examines how training-window length affects an AI stock-selection model. It compares long windows, expanded year by year from 2005 to 2021, with shorter windows ranging from a month to a few years. The author recommends checking each window…

MaskinlæringBacktestingFaktorinvesteringStatistik
BigQuant

This discussion addresses how to use factors from BigQuant's factor library as model features. The question asks whether a factor identifier can be used directly or whether users must copy its expression, and whether factors associated with a brokerage are…

FaktorinvesteringMaskinlæringAktier
BigQuant

This article introduces Granger causality and transfer entropy as ways to study directional predictive relationships in time series. Granger causality tests whether a source series improves forecasts of a target beyond the target’s own history, commonly…

StatistikAktierMaskinlæringMarkedssentiment
BigQuant

This article explains a proposed arbitrage mechanism involving Chinese A-shares, stock purchases, securities borrowing, and short sales. Its example describes buying shares to push the price higher, borrowing shares to sell at that higher price, and later…

AktierKinesiske markederArbitrageMarkedsmikrostruktur
BigQuant

This Chinese-language note describes a stock prediction exercise using four cross-sectional inputs: total market capitalization, turnover, trailing price-to-earnings, and quarterly net-profit year-over-year growth. It compares linear regression, XGBoost, and…

Kinesiske markederAktierFaktorinvesteringMaskinlæring
BigQuant

This document summarizes an introductory treatment of graph neural networks (GNNs), which learn from data represented as entities and their relationships. It explains how convolutional ideas used with images can be generalized to graph structures, whose…

MaskinlæringStatistik
BigQuant

The article reviews the long decline in mutual fund and ETF expense ratios and links it to investor choices, competition, and changes in financial advice and retirement plans. It distinguishes asset-weighted averages, which reflect where investors put their…

StatistikPorteføljekonstruktionAmerikanske markeder
BigQuant

This research review summarizes a study of manager changes at Chinese equity funds, distinguishing internal transfers, moves to another public fund, moves to a private fund, and exits from the fund industry. The study compares benchmark-adjusted buy-and-hold…

AktierKinesiske markederStatistikRisikostyring
BigQuant

The article introduces a custom coded stock selection strategy built from five rules, mainly intended to identify stocks that have fallen sharply and begun to stabilize, with some rules meant to capture potential strong advances. It contrasts AI generated…

AktierFaktorinvesteringBacktestingPositionsstørrelse
BigQuant

The document asks whether minute or second level price and volume data can be aggregated into daily net flow measures for very large, large, medium, and small trades. It points readers to two implementations: a general set of high frequency factors and a…

AktierHøjfrekvenshandelFaktorinvesteringMarkedsmikrostruktur
BigQuant

The document explains gold contracts for difference as cash-settled derivatives that provide exposure to gold price changes without holding bullion or futures. It outlines features such as leverage, smaller contract sizes, the ability to take long or short…

RåvarerPrisfastsættelse af derivaterRisikostyring
BigQuant

This research summary examines the difference between monthly stock and industry behavior in Chinese equities: individual stocks are described as showing reversal, while industry indices show momentum. It proposes an industry-momentum factor based on the…

AktierKinesiske markederMomentumTilbagevenden til gennemsnittet
BigQuant

This event description focuses on why quantitative strategy results in backtests can differ from live trading performance, a concern it identifies specifically for Chinese A-share strategies. It presents the topic as a practical challenge for traders and…

AktierBacktestingStatistik
BigQuant

This article recommends a three-part process for improving trading discipline: record each trade’s rationale, timing, size, planned risk and reward, and emotional state; review those records regularly for rule violations and emotional decisions; then analyze…

RisikostyringPositionsstørrelseStatistik
BigQuant

The article argues that trading researchers should judge machine learning papers by simplicity, reproducibility, and generality, alongside reported empirical results. A method that performs well on a benchmark may not transfer to financial prediction, and a…

MaskinlæringStatistikBacktestingFaktorinvestering
BigQuant

This study compares ARIMA, a multilayer perceptron, an LSTM, and an ARIMA-GARCH combination for next-day NVIDIA share-price forecasts. It describes ARIMA as a model for linear time-series structure, neural networks as ways to capture nonlinear patterns, and…

AktierMaskinlæringStatistikVolatilitet
BigQuant

This interview discusses how artificial intelligence, large datasets, and cloud computing support one another: AI methods analyze data, while cloud infrastructure supplies scalable storage and computing. It maps the data industry across collection, storage,…

AktierKinesiske markederMaskinlæringPorteføljekonstruktion
BigQuant

The report proposes improving traditional liquidity factors for Chinese A-share stock selection by incorporating information about intraday trading structure. It frames the approach as a reusable factor research method: identify market information omitted by…

Kinesiske markederAktierFaktorinvesteringMarkedsmikrostruktur