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 market commentary considers whether foreign investor outflows and a rise in shares becoming tradable would materially affect Chinese equities. It distinguishes short-term trading flows from longer-term institutional allocations, including new mutual…

Kinesiske markederAksjerMarkedssentimentHendelsesdrevet handel
BigQuant

This meetup summary covers several practical quant trading questions. It suggests examining order book behavior with Level 2 data when developing capital-flow signals, and describes using a Plotly heatmap to inspect correlations among factors. It also points…

MarkedsmikrostrukturFaktorinvesteringStatistikkRisikostyring
BigQuant

This review describes machine-learning approaches to predicting stock returns from accounting data and company characteristics. It compares linear methods, including ordinary least squares, LASSO, and ridge regression, with nonlinear models such as random…

AksjerKinesiske markederMaskinlæringFaktorinvestering
BigQuant

This submission reflects on a quantitative strategy test whose results changed substantially when the author replaced its default factors or broadened the stock universe to include main-board and ChiNext shares. The observation highlights two practical…

FaktorinvesteringMaskinlæringHistorisk testingKinesiske markeder
BigQuant

The article reviews research on whether individual commitment within US equity mutual fund management teams affects performance. It classifies managers as committed when they work only for one fund, and noncommitted when they also manage other funds. The…

AksjerFaktorinvesteringStatistikkPorteføljekonstruksjon
BigQuant

The report studies whether intraday return distributions contain stock selection information beyond conventional daily factors. Using one-minute stock prices, it constructs factors from intraday return variance, skewness, and kurtosis, then examines…

AksjerFaktorinvesteringHøyfrekvenshandelStatistikk
BigQuant

This guide describes a workflow for training an end-to-end stock model locally on compressed minute and multi-minute bar data, then submitting its weights and training code for cloud prediction. It explains that local files store prices and amounts as scaled…

AksjerMaskinlæringHistorisk testingOrdreutførelse
BigQuant

This note tests an A-share stock screen inspired by a “Beautiful 50” concept. It selects companies using three annual-report criteria: net profit growth above 15%, return on equity above 15% for three consecutive years, and a price-to-earnings ratio below…

AksjerFaktorinvesteringHistorisk testingKinesiske markeder
BigQuant

This article argues that a high percentage of winning trades does not guarantee profits. It emphasizes the relationship between win rate and the relative size of gains and losses, using examples to illustrate how a strategy with fewer winning trades may…

RisikostyringPosisjonsstørrelseMarkedssentimentAksjer
BigQuant

This overview introduces artificial intelligence and machine learning as tools for analyzing financial data and supporting investment decisions or trading operations. It groups methods by task: supervised learning for prediction and classification,…

MaskinlæringStatistikkRisikostyringPorteføljekonstruksjon
BigQuant

This note addresses a mismatch in factor analysis: a prediction set has been restricted by strategy conditions, so only a small number of stocks receive scores, while the analysis module still expects broad market coverage. The suggested response is to skip…

AksjerFaktorinvesteringHistorisk testing
BigQuant

This community post describes a discrepancy between a CNN-based stock strategy's backtest and simulated-trading signals. With a rolling window of five, both reportedly signal the same stock on January 4. With a window of ten, the simulation notification…

AksjerMaskinlæringHistorisk testingOrdreutførelse
BigQuant

The article argues that unusually high trading volume after a stock advance is an ambiguous event rather than an automatic sell signal. Every trade has both a buyer and a seller, so volume alone cannot reveal whether existing holders are distributing shares…

AksjerTekniske indikatorerMomentum
BigQuant

This educational overview introduces common inputs for quantitative stock selection, beginning with simple and exponential moving averages, RSI, Bollinger Bands, MACD, moving-average crossovers, VWAP, and historical volatility. It describes their usual…

AksjerTekniske indikatorerMaskinlæringStatistikk
BigQuant

This research summary revisits the capital asset pricing model by splitting daily stock returns into open-to-close intraday returns and close-to-open overnight returns. It reports that, in the US sample, beta is negatively related to intraday returns but…

AksjerStatistikkFaktorinvesteringAmerikanske markeder
BigQuant

The document outlines a Chinese stock screening and trading strategy that targets shares which closed at the daily limit-up on each of the previous two days. Candidates must trade above their five-day moving average, have a price-to-earnings ratio of roughly…

AksjerKinesiske markederMomentumKursbrudd
BigQuant

This article describes a modification to random forest models that lets an investor express factor preferences directly in the tree-building process. Selected factors are forced to drive splits near the top of each decision tree, increasing their influence.…

MaskinlæringFaktorinvesteringAksjerKinesiske markeder
BigQuant

The strategy selects Chinese stocks on the day they lose ST designation, buys qualifying names at the open, and holds them for three days. It describes closing trades at the end of the holding period and applying the rule to every stock that meets the event…

AksjerHendelsesdrevet handelKinesiske markeder
BigQuant

This research explores whether online search interest can serve as a timely measure of investor sentiment and help identify shifts between large-cap and small-cap Chinese equities. It treats search activity related to each style as a sentiment proxy,…

AksjerMarkedssentimentKinesiske markederHistorisk testing
BigQuant

This exchange asks whether MACD components and a short-window swing-volatility feature used in model training and prediction contain future information. The response says these factors are calculated from historical data, implying that the indicators…

Tekniske indikatorerMaskinlæringHistorisk testingStatistikk
BigQuant

This research note proposes an equity factor based on how each stock’s intraday volume pattern differs from the market’s. It starts from the common U-shaped market volume profile and argues that a stock with unusually concentrated or uneven trading may have…

AksjerFaktorinvesteringStatistikkMarkedsmikrostruktur
BigQuant

The document introduces stationarity and cointegration as the basis for pairs trading. It describes a stationary series as having a stable mean and variance, and presents a pair as cointegrated when the difference between their prices is stationary. Under…

FuturesParhandelTilbakevending mot gjennomsnittetStatistikk
BigQuant

This brief support note answers when a newly purchased position’s cost information appears in BigQuant. It distinguishes three operating modes: backtests and simulated trading update the value after the market close on the trade date, while live trading…

Historisk testingAksjer