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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 article introduces discrete-time stochastic control as a method for dynamic asset allocation. Unlike a traditional mean-variance allocation made without an explicit sequence of future decisions, the approach models portfolio choices across multiple…

Flere aktivaklasserPorteføljekonstruksjonStatistikkKinesiske markeder
BigQuant

This brief support exchange explains why a MACD histogram value can shift substantially when the start date of a stock data list changes. The explanation points to the initialization of the exponential moving average: its first value is not fully smoothed,…

Tekniske indikatorerStatistikk
BigQuant

This document describes a Chinese stock strategy that combines classification and regression models to forecast intraday returns. It uses XGBoost and an ensemble of machine learning models, drawing on 15 features related to overnight returns, opening auction…

Kinesiske markederAksjerMaskinlæringHistorisk testing
BigQuant

This intraday system trades the CAC 40 on a 15-minute chart, taking both long and short positions during a defined daytime session. Long entries require the MACD line to cross above its signal line, RSI to remain below an upper threshold, and price to be…

AksjerTekniske indikatorerHistorisk testingOrdreutførelse
BigQuant

This report summary explores whether macroeconomic indicators can help select individual Chinese stocks, rather than serving only for asset allocation or industry rotation. It proposes estimating each stock’s sensitivity, or MacroBeta, to economic indicators…

Kinesiske markederAksjerFaktorinvesteringStatistikk
BigQuant

This brief BigQuant platform note explains how to use a factor written as a SQL function and field within a strategy workflow. It distinguishes platform expression factors, described as an operator paired with a field, from SQL factors, described as a…

FaktorinvesteringHistorisk testing
BigQuant

This research review describes a study of whether year-over-year changes in company filings contain information about future stock returns and business performance. It compares filings using four text-similarity measures, sorts stocks by similarity, and…

AksjerMarkedssentimentMaskinlæringFaktorinvestering
BigQuant

The report describes a weekly market-timing model based on a linear-chain conditional random field (CRF). It frames the task as inferring market direction states from observed features, with CRFs able to represent dependencies between observations and…

AksjerKinesiske markederMaskinlæringHistorisk testing
BigQuant

The document compares two ways to build multifactor equity portfolios. Integration ranks securities using a combined score for all target factors and selects a subset. Mixing first builds separate single-factor portfolios, then allocates among them. The…

AksjerFaktorinvesteringPorteføljekonstruksjonHistorisk testing
BigQuant

This overview presents a multi-factor model as a way to explain stock returns through shared risk exposures, factor returns, and stock-specific residual returns. It frames quantitative management as estimating factor behavior and managing portfolio…

AksjerFaktorinvesteringStatistikkPorteføljekonstruksjon
BigQuant

This article describes modifying OpenFE’s automated feature engineering workflow for futures research. It adds rolling mean, standard deviation, and sum operators across instrument groups, with preset window lengths, and explains how these operators are…

FuturesMaskinlæringFaktorinvesteringStatistikk
BigQuant

This personal account explains how an equity prediction model can appear strong in-sample yet fail when applied to unfamiliar stocks or later periods. The described experiment uses fundamental and technical features, including valuation, profitability,…

AksjerMaskinlæringStatistikkHistorisk testing
BigQuant

This example describes a short-horizon stock rotation strategy. It selects shares whose prior-day return was between 2% and 8% and whose turnover was between 3% and 8%, then removes special-treatment shares, halted stocks, and listings younger than the…

AksjerMomentumKinesiske markederOrdreutførelse
BigQuant

This report summary describes a method for refining traditional liquidity factors by incorporating intraday trading structure specific to China’s A-share market. The idea is to adapt a broad factor to market-specific trading patterns, with the report…

AksjerFaktorinvesteringMarkedsmikrostrukturStatistikk
BigQuant

The note turns a discretionary stock method into explicit rules: admit a stock to an initial universe when its daily low stays above 99% of the 34-day moving average for ten consecutive days, buy when its profitable-chip proportion exceeds 73%, and sell when…

AksjerTekniske indikatorerFaktorinvesteringHistorisk testing
BigQuant

This Chinese-language example sets up a test of whether stocks that have risen on each of the prior three days are more likely to rise the next day. It defines the condition as three consecutive daily price increases, each exceeding one percent, and labels…

AksjerMomentumStatistikk
BigQuant

This strategy combines model-based stock ranking with trend filters for a selected universe of China A-shares drawn from the CSI 150 and related large-cap core-asset stocks. It aims to rank candidates for potential five-day strength, then apply timing…

Kinesiske markederAksjerMaskinlæringMomentum
BigQuant

This research note examines an equity-selection factor built from differences in stock price behavior across the trading day. It reports that the original APM factor performed well in its in-sample period, weakened out of sample, and suffered a sustained…

AksjerFaktorinvesteringStatistikkHistorisk testing
BigQuant

This article is a catalog of beginner-oriented examples for learning TensorFlow and TFLearn. It lists introductory operations, nearest-neighbor and regression models, neural networks, autoencoders, model saving, visualization, and multi-GPU basics. The…

Maskinlæring
BigQuant

The document compares a traditional Chinese equity strategy that buys the smallest market-cap stocks with a StockRanker model that ranks stocks using market capitalization as its sole feature. Both are evaluated over 2015–2016, illustrating how a…

AksjerFaktorinvesteringMaskinlæringHistorisk testing
BigQuant

The article reviews the 2021 revision of China's Shenwan industry classification and its possible uses in quantitative investing. The revision expanded coverage to A-shares, Stock Connect shares, and selected Chinese firms listed overseas, added three…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

The document describes a daily process for building a short-term stock watchlist. It starts by ranking stocks by traded value and keeping the top 50. It then removes stocks that gained less than 5% that day, those whose 30-day moving average is flat or…

AksjerMomentumKursbruddTekniske indikatorer
BigQuant

The document surveys the development of enhanced ETFs in China alongside the expansion of actively managed ETFs in the United States. It presents enhanced ETFs as an effort to seek returns above a benchmark while retaining ETF features such as intraday…

AksjerPorteføljekonstruksjonOrdreutførelseRisikostyring
BigQuant

This 2020 China equity-sector note combines macroeconomic regime signals, earnings relative to valuation, trend signals, historical regime matching, and public-fund holdings to discuss sector allocation ahead of interim reports. It argues that weaker growth…

Kinesiske markederAksjerMomentumFaktorinvestering