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
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-forelæsninger
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

This document introduces two self-balancing binary search trees and explains how their balancing rules support efficient lookup, insertion, and deletion. For AVL trees, it defines node height and balance factor, then describes single and double rotations and…

Statistik
BigQuant

The article describes an architecture that combines iTick market feeds, other economic and alternative data, and DeepSeek-R1 to support quantitative research workflows. It proposes attention-based data fusion, automated strategy variant generation,…

MaskinlæringStatistikRisikostyringFlere aktivklasser
BigQuant

The document summarizes U.S. retirement accounts, especially 401(k) plans and IRAs, as flexible account structures that offer a broad range of investments. It reports that mutual funds represent about half of assets and describes equity funds as the largest…

Flere aktivklasserPorteføljekonstruktionRisikostyringAmerikanske markeder
BigQuant

The document considers whether quantitative trading in China has a durable future as the field becomes more crowded. It argues that more participants using similar strategies can compress the limited excess returns available in an A-share market that is not…

AktierKinesiske markederRisikostyring
BigQuant

This strategy ranks constituents of the CSI Dividend Index using a combined score based on dividend yield and return on equity (ROE). Dividend yield represents distributions relative to share price, while ROE is used as a measure of how effectively a company…

AktierKinesiske markederFaktorinvesteringPorteføljekonstruktion
BigQuant

This report introduces intraday high-frequency trading in the Chinese securities market, framing it as an attempt to capture short-lived market inefficiencies caused by investor behavior or delayed reactions to information. It defines the strategies under…

HøjfrekvenshandelMarkedsmikrostrukturOrdreudførelseRisikostyring
BigQuant

This report studies whether rotating among industry and thematic exchange-traded funds can support an absolute-return portfolio. It selects 14 industries with matching funds from a broader set of Chinese industry groups, then compares a rotation portfolio…

AktierKinesiske markederMomentumPorteføljekonstruktion
BigQuant

This research report explains reinforcement learning concepts, including Markov decision processes, state and action values, reward design, and value-based and policy-based algorithms. Its applied example uses a deep Q-network to time long positions in the…

MaskinlæringAktierTekniske indikatorerBacktesting
BigQuant

This broad practical guide introduces machine learning in quantitative investing, covering supervised, unsupervised, and reinforcement learning. It explains data preparation, missing values, categorical features, scaling, train and test splits,…

MaskinlæringAktierMomentumTrendfølgning
BigQuant

This brief indicator note defines the moving average, abbreviated MA, as a calculation using closing prices and a lag or lookback parameter. It gives a compact formula reference for producing an MA value from those inputs. The entry identifies the indicator…

Tekniske indikatorerStatistik
BigQuant

The document describes a Chinese stock-selection example that represents daily buy signals as a factor value of one and sell signals as zero. It proposes processing those values and using them to filter and rank stocks. The stated data source is daily stock…

AktierFaktorinvesteringBegivenhedsdrevetBacktesting
BigQuant

This assignment outlines a workflow for creating a visual machine learning stock strategy using BigQuant’s StockRanker course and strategy template. The participant is asked to modify the model’s input features by adding personal factors, then backtest the…

MaskinlæringAktierBacktestingFaktorinvestering
BigQuant

The post describes a feature-data issue in a quantitative research workflow. A request failed with an error indicating that no observations remained after missing values were removed. Changing the feature lookback period, as suggested by common…

AktierStatistikBacktesting
BigQuant

The document introduces convex functions through their geometric shape and explains a first-order characterization for differentiable convex functions on an open convex domain. At any point, the function’s value at another point is at least as large as the…

StatistikMaskinlæring
BigQuant

The document examines how benchmark choice affects mutual fund performance estimates. It contrasts return-based evaluation, which is sensitive to benchmark selection, with holdings-based evaluation, which can be more precise but depends on infrequently…

AktierStatistikFaktorinvesteringRisikostyring
BigQuant

This discretionary trend-following framework aims to join an established advance rather than predict a market bottom. It lists three possible entry cues: a bullish engulfing candle after a pullback to support, a retest near the short moving-average cluster…

AktierTrendfølgningTekniske indikatorerRisikostyring
BigQuant

This report outlines the first step in a multi-factor model research process: selecting which factors to combine. It proposes using a hedging perspective and benchmark factor exposures as part of the screening criteria, with the stated aim of reducing the…

FaktorinvesteringPorteføljekonstruktionAktierStatistik
BigQuant

This brief platform report describes a suspected stock-price data problem in a backtest using TWAP-based adjustment prices. A newly added position in Ping An Bank appeared to generate a sudden large profit because its recorded average holding price was…

BacktestingOrdreudførelseAktierKinesiske markeder
BigQuant

This research summary presents labor leverage as a company characteristic that may help explain differences in expected stock returns. It defines labor leverage through the relative size and rigidity of labor expenses, which shape how operating profits…

AktierFaktorinvesteringStatistikAmerikanske markeder
BigQuant

This report summary presents an investment case for Chinese technology equities and the China Securities Technology 50 Strategy Index. It argues that sharp market declines may offer opportunities to add exposure, based on a claimed review of 13 major…

AktierKinesiske markederFaktorinvesteringPorteføljekonstruktion
BigQuant

This tutorial outlines a workflow for modeling Chinese steel futures using high frequency market data. It starts with tick snapshots for the RB2305 contract, explains that futures may trade during night sessions, and resamples the raw observations into ten…

FuturesRåvarerHøjfrekvenshandelMaskinlæring
BigQuant

This introductory assignment defines quantitative investing as using computer programs and historical market data to identify assets and apply predefined buying and selling rules. It highlights the ability to scan a broad universe more quickly than a human…

BacktestingMaskinlæringRisikostyring
BigQuant

This study examines whether active U.S. equity mutual funds that increase portfolio concentration subsequently achieve better risk-adjusted performance. Its rationale is a tradeoff: concentrating holdings can give greater weight to the manager’s strongest…

AktierPorteføljekonstruktionRisikostyringStatistik
BigQuant

This document describes a competition built around Deep Alpha-CNN, an equity prediction model using a seven-layer one-dimensional convolutional neural network with residual connections. The report’s stated findings favor the deeper network over a two-layer…

AktierMaskinlæringBacktestingStatistik