Hopp til innhold

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
QuantRocket
7 dokumenter
Lumibot strategies
7 dokumenter
Awesome Quant
1 dokumenter

Søk i biblioteket

3,481 dokumenter

BigQuant

This report studies trend-based allocation across CITIC Securities’ first-level industry indexes. It frames the process as choosing assets, measuring momentum, selecting industries, assigning weights, and managing portfolio tracking. The analysis describes…

AksjerTrendfølgende handelMomentumPorteføljekonstruksjon
BigQuant

This brief exchange explains an apparent one-day mismatch between backtest output logs and the trade details table. The stated cause is the difference between a signal and its execution: the log records when a price bar arrives and triggers the signal, while…

Historisk testingOrdreutførelse
BigQuant

The document asks why a historical price-to-earnings series for China’s pharmaceutical industry differs from a series calculated in code. The example selects constituents classified under the 2021 Shenwan industry scheme, joins them to daily stock valuation…

AksjerKinesiske markederStatistikk
BigQuant

The document summarizes a strategy that combines the relative strength index, exponential moving average, volume-weighted average price, and moving average convergence divergence to generate trading signals. It describes this as a “Weapon Candle” approach…

AksjerTekniske indikatorerMomentumHistorisk testing
BigQuant

The article describes a discretionary review process for short-term stock theme trading. It begins by scanning the ten highest-turnover names, then compares sectors by the number of stocks advancing from a first limit-up day to a second. Next, it checks the…

AksjerMomentumKursbruddMarkedsmikrostruktur
BigQuant

The article argues that widely used price-based stop rules can cluster orders around obvious levels, where sudden moves may trigger many exits. It recommends shifting attention from a fixed loss percentage on each position to a preset total loss budget, with…

RisikostyringPosisjonsstørrelseAksjer
BigQuant

This short forum exchange reports a TypeError encountered when opening a backtesting module. The response attributes it to a strategy parameter-name list containing an item that is not a string, which prevents the framework from using that item as an…

Historisk testing
BigQuant

This review summarizes research on U.S. actively managed equity mutual funds during the acute COVID-19 market crisis. The study compares net fund returns with broad-market, style-matched, prospectus, and factor-model benchmarks, and examines investor flows…

AksjerAmerikanske markederFaktorinvesteringStatistikk
BigQuant

A user reports that a price-ratio buy condition never triggers during a year-long backtest, although the sell condition does. The response attributes the training error to StockRanker receiving only two factors, with a Boolean factor causing the model to…

AksjerMaskinlæringHistorisk testing
BigQuant

The document explains abnormal idiosyncratic volatility (AIV), a price based measure of information risk around earnings announcements. It compares each stock’s residual volatility during the five trading days before earnings announcements with its residual…

AksjerVolatilitetFaktorinvesteringHendelsesdrevet handel
BigQuant

The document describes pure factor portfolios as a way to test whether a given equity factor has explanatory or investment value while controlling exposure to other factors. A pure portfolio is constructed to have unit exposure to the factor under…

FaktorinvesteringPorteføljekonstruksjonStatistikkAksjer
BigQuant

The document introduces the Commodity Selection Index (CSI), specifying high, low, close, and a lookback length as inputs. Its pseudocode builds an average true range measure from intraday range and gaps relative to the previous close. It also estimates…

RåvarerFuturesTekniske indikatorerTrendfølgende handel
BigQuant

This short project note outlines a workflow for preparing data and fitting a predictive model. It divides SQL data construction into a base dataset, a normalized dataset, and a final feature-and-label stage. The note mentions cross-sectional factor ranking…

MaskinlæringStatistikkHistorisk testing
BigQuant

This weekly report compares three Huatai fundamental stock-selection portfolios during a market pullback: a high-dividend-yield model, a low price-to-sales model, and a relative price-to-earnings model. It reports absolute returns, benchmark-relative…

AksjerFaktorinvesteringPorteføljekonstruksjonHistorisk testing
BigQuant

The article offers four proposed reasons Chinese quantitative funds may find A-shares attractive relative to US equities: a large listed universe and volatile small-cap stocks, lower trading costs, a higher retail share of trading that may create mispricing,…

Kinesiske markederAmerikanske markederAksjerMarkedsmikrostruktur
BigQuant

This excerpt argues that risk control and position sizing deserve priority alongside stock selection and timing. Its proposed framework smooths risk decisions with a moving-average tracking system and calculates a daily score from market or stock…

RisikostyringPosisjonsstørrelseTrendfølgende handelTekniske indikatorer
BigQuant

This report applies the Fama-French three-factor framework to China’s A-share market, examining monthly evidence from April 2010 through October 2022. It describes the market excess return factor, SMB for company size, and HML for book-to-market, with SMB…

AksjerFaktorinvesteringStatistikkKinesiske markeder
BigQuant

This monthly allocation report describes a strategic portfolio framework that maps broad asset holdings to underlying macroeconomic risks. It uses principal component analysis to define five factors—economic growth, interest rates, credit, term spread, and…

Flere aktivaklasserAksjerRentepapirerFaktorinvestering
BigQuant

This Chinese-language strategy note combines two China A-share stock-selection approaches: a small-cap, low-turnover portfolio and a dividend-focused portfolio that excludes the smallest stocks. The first ranks eligible stocks using market capitalization and…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This report summary describes a growth-stock selection approach for Chinese equities that pairs an earnings-surprise signal with the next-day price gap after an earnings announcement. Rather than comparing reported earnings with a numeric consensus estimate,…

Kinesiske markederAksjerHendelsesdrevet handelMomentum
BigQuant

The document explains how to calculate a 14-period Relative Strength Index from closing prices. It separates price changes into gains and losses, averages the first period simply, then smooths later averages by carrying forward the prior value. The ratio of…

AksjerTekniske indikatorerMomentumTrendfølgende handel
BigQuant

This document presents the China Securities Innovative Drug Industry Index as a way to track listed companies whose main businesses involve innovative drug research and development. It describes the index’s exposure to pharmaceutical subsectors, company…

AksjerKinesiske markederPorteføljekonstruksjon
BigQuant

This report introduces gcForest, or multi-grained cascade forest, as a tree-based deep learning model proposed as an alternative to deep neural networks. It argues that the method may suit financial data because it is described as more robust to…

MaskinlæringHistorisk testingStatistikkPorteføljekonstruksjon
BigQuant

This report evaluates risk parity, an asset allocation approach that aims to equalize the risk contributions of assets in a portfolio. It tests the approach using two historical data window designs: rolling windows and expanding recursive windows. For each…

Flere aktivaklasserPorteføljekonstruksjonRisikostyringHistorisk testing