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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
WonderTrader
14 dokumenter
Alphalens
14 dokumenter
backtesting.py
11 dokumenter
Technical Analysis
9 dokumenter
QTPyLib
8 dokumenter
Lumibot strategies
7 dokumenter
QuantRocket
7 dokumenter
Awesome Quant
1 dokumenter

Søk i biblioteket

3,481 dokumenter

BigQuant

The article presents a short-term stock screening pattern built around three sequential signals. First, it looks for at least five generally rising sessions, allowing one modest down day while the overall movement stays upward. Next, it requires a session…

AksjerKursbruddTekniske indikatorer
BigQuant

This tutorial outlines a TensorFlow LSTM workflow for forecasting stock or index returns. It recommends transforming closing prices into returns, then arranging recent observations into sequential input groups with the next observations as prediction labels.…

MaskinlæringAksjerKinesiske markederHistorisk testing
BigQuant

This research summary compares ordinary cross-validation with time-series cross-validation for model tuning on sequential data. Randomly assigning observations to folds can train a model on future observations and validate it on the past, violating the…

MaskinlæringStatistikkHistorisk testingAksjer
BigQuant

This competition update reports diagnostics for the ten highest-ranked anonymous factors and tracks a CSI 1000 enhancement portfolio. Factor profiles include average Barra style exposures and sector-level average Rank ICs, calculated from cross-sectional…

AksjerFaktorinvesteringPorteføljekonstruksjonHistorisk testing
BigQuant

The document introduces Moving Average Envelopes (MAE), a technical indicator built from a moving average of closing prices. It describes two bands: a lower envelope set below the average by a specified percentage and an upper envelope set above it by…

Tekniske indikatorer
BigQuant

This study proposes adding daily risk monitoring to a traditional monthly rebalancing schedule for multi-factor equity portfolios. It tracks the portfolio’s exposure to the size factor during the month and rebalances on the next trading day when that…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

The article summarizes research on whether forced mutual fund selling affects the prices and liquidity of related stocks. It describes a learning mechanism: investors may initially interpret a peer stock’s decline as evidence of negative information about…

AksjerMarkedsmikrostrukturStatistikkRisikostyring
BigQuant

The report describes an expanded Black-Litterman asset allocation model that combines subjective views with quantitative portfolio construction. It uses behavioral finance and historical trend information, including momentum and reversal effects, to form…

Flere aktivaklasserPorteføljekonstruksjonMomentumTilbakevending mot gjennomsnittet
BigQuant

This example tests whether market capitalization is associated with next-day stock returns using repeated cross-sectional ordinary least squares regressions. It joins Chinese stock factor data to daily bars, calculates forward returns from the next close,…

AksjerFaktorinvesteringStatistikkHistorisk testing
BigQuant

This research summary examines the validity of mainland China Stock Connect ownership data and describes portfolio strategies built from investor holdings, sector allocations, style exposures, leading trades, and net capital flows. It reports persistent…

AksjerKinesiske markederFaktorinvesteringMomentum
BigQuant

This study builds a monthly Chinese equity factor from stock returns and volatility around intraday volume surges. It defines a surge as a minute when the increase in trading volume exceeds that stock’s daily average increase by more than one standard…

AksjerFaktorinvesteringTekniske indikatorerHistorisk testing
BigQuant

This Chinese market note proposes that China’s credit impulse may be turning upward and argues that it tends to lead A-share earnings growth. On that assumption, it recommends overweighting Chinese equities, favoring growth over value, and ranking…

Flere aktivaklasserAksjerRentepapirerKinesiske markeder
BigQuant

This reference catalogues TA-Lib functions and their input fields for calculating common trading indicators. It groups examples into overlapping price studies, volatility and volume-price measures, cycle functions, price transformations, momentum indicators,…

Tekniske indikatorerStatistikkVolatilitetMomentum
BigQuant

This stock selection method turns a discretionary approach into a set of technical and liquidity filters, followed by periodic rebalancing. It looks for prices mostly above long-term moving averages, favorable short-term moving-average conditions, and moving…

Kinesiske markederAksjerMomentumTrendfølgende handel
BigQuant

This Chinese-language article discusses the proposed introduction of market making on China’s STAR Market. It explains that market makers continuously quote prices, sell inventory to buyers, and use their own capital to buy from sellers. They bear price risk…

AksjerMarket makingMarkedsmikrostrukturRisikostyring
BigQuant

This BigQuant forum post presents a daily-bar backtest example that submits several limit buy orders during a bar callback. Before each order, the callback reads available cash and frozen cash from the trading account. The reported output shows those values…

AksjerHistorisk testingOrdreutførelseRisikostyring
BigQuant

The article summarizes research on whether active mutual fund managers adjust trading to anticipated stock-picking opportunities. It defines stock-selection timing as trading more when opportunities to hold positive-alpha stocks are greater and less when…

AksjerFaktorinvesteringStatistikkHistorisk testing
BigQuant

This overview surveys unsupervised learning through two groups of methods: dimensionality reduction and clustering. For dimensionality reduction, it describes PCA as a way to project high-dimensional features into fewer dimensions using covariance-matrix…

MaskinlæringStatistikk
BigQuant

This study compares conventional return labels with alternative labels based on information ratio and Calmar ratio for AI stock selection models. It argues that labels incorporating returns, volatility, and drawdowns across a period may convey more…

Kinesiske markederAksjerMaskinlæringFaktorinvestering
BigQuant

This document outlines a simple KDJ-based trading strategy for the Chinese stock 605588, with the CSI 300 index named as a reference benchmark. It describes selling when K, D, and J are all in high territory, and buying when all three are in low territory.…

Kinesiske markederAksjerTekniske indikatorer
BigQuant

This article presents three MACD-based warnings for managing long equity positions: the indicator moving below its zero line, bearish divergence as price makes higher highs while MACD peaks decline, and a bearish crossover above zero. It interprets these…

AksjerTekniske indikatorerMomentumRisikostyring
BigQuant

This article outlines a research approach for Chinese equities during sharp market-wide declines. It proposes identifying historical selloff days and studying two groups: stocks that hit their upper price limit despite the market drop, and stocks that hit…

AksjerHendelsesdrevet handelHistorisk testingKinesiske markeder
BigQuant

This note summarizes the AlexNet deep convolutional neural network described in the ImageNet classification paper. It outlines the model’s use of multiple convolutional channels, five convolutional stages and three fully connected layers, and explains how…

MaskinlæringStatistikk
BigQuant

This strategy applies a stock-ranking model to a filtered pool of Chinese A-shares, aiming to find stocks that may rebound from depressed levels and hold up better when the broader market weakens. The target universe is the small and medium-sized board. Its…

AksjerMaskinlæringTilbakevending mot gjennomsnittetKinesiske markeder