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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
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

The article answers practical questions about machine-learning stock strategies. It says the platform separates training and test periods by time, with the test interval serving as the backtest, and describes a setup without a separate validation set. It…

MaskinlæringHistorisk testingStatistikkAksjer
BigQuant

This research note reviews the CSI Dividend Index and the Bosera CSI Dividend ETF, which tracks it. The index draws 100 Shanghai and Shenzhen A-shares from stocks with high average after-tax cash dividend yields over the prior two years, then weights them by…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This monitoring framework tracks valuation measures and fund holdings across major Chinese equity indexes, industry groups, and selected large companies. It covers price-to-earnings, growth-adjusted valuation, price-to-book, dividend yields, and fund…

AksjerKinesiske markederStatistikkFaktorinvestering
BigQuant

This research note studies value-based stock selection in Hong Kong equities, using the Hang Seng Composite as its universe. Because Hong Kong main-board companies are not required to publish quarterly reports and may change their fiscal year-end, the…

AksjerFaktorinvesteringStatistikkHistorisk testing
BigQuant

The discussion asks how to detect whether a value, such as a closing price or trading volume, has risen or fallen continuously over a chosen number of days. It points to a pair of time-series functions: one counts consecutive rises, and a corresponding…

Tekniske indikatorerStatistikk
BigQuant

The discussion explains how to interpret a Chinese stock daily-bar dataset that stores fully back-adjusted prices. It says the dataset includes a back-adjustment factor and describes recovering the underlying market price by dividing the adjusted close by…

AksjerKinesiske markederHistorisk testing
BigQuant

This report summary examines Chinese public mutual funds around the second quarter of 2018, focusing on actively managed equity funds. It describes falling scale and share counts for active equity products, differences in reported returns across fund types,…

AksjerKinesiske markederPorteføljekonstruksjonStatistikk
BigQuant

This report summary examines Chinese public mutual funds around the second quarter of 2018, focusing on actively managed equity funds. It describes falling scale and share counts for active equity products, differences in reported returns across fund types,…

AksjerKinesiske markederPorteføljekonstruksjonStatistikk
BigQuant

The document presents eight discretionary rules for interpreting stock price action. It treats steady small advances as possible accumulation and repeated sharp gains as a potential overextension. It also compares each stock’s behavior with the broad market:…

AksjerTekniske indikatorerMomentumMarkedsmikrostruktur
BigQuant

The document groups common Chinese A-share factors into style, technical, and fundamental categories. Style factors include market, size, and value; technical factors include reversal, low turnover, and low volatility; fundamental factors cover profitability…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

The document introduces the Commodity Channel Index (CCI), a technical indicator calculated from high, low, and close prices. It first derives typical price as the average of those three values, then compares typical price with its moving average and scales…

Tekniske indikatorerStatistikk
BigQuant

This project outlines a mean-reversion pairs strategy for Chinese futures. It screens candidate contract pairs for cointegration with an Augmented Dickey-Fuller test, estimates a changing hedge ratio with a Kalman filter, and measures spread behavior with…

FuturesKinesiske markederParhandelTilbakevending mot gjennomsnittet
BigQuant

This short description presents TEMACandle, an indicator that displays a price series as candlesticks after processing it with the Triple Exponential Moving Average (TEMA) algorithm. The transformation applies TEMA smoothing to the underlying price data and…

Tekniske indikatorerAksjer
BigQuant

This research summary describes a multi-factor investment workflow that combines alpha forecasts, a risk model, transaction-cost estimates, portfolio optimization, and performance attribution. It argues that simple score-based selection or sector and…

AksjerPorteføljekonstruksjonRisikostyringStatistikk
BigQuant

This brief Chinese-language support note explains a prediction-data problem traced to missing values in a rolling price-range factor. The example factor compares a short moving average of the high-low range with a longer moving average. According to the…

MaskinlæringStatistikkTekniske indikatorerHistorisk testing
BigQuant

The report asks whether exposure to macroeconomic policy uncertainty is priced in Chinese A-shares. It describes a news-based economic policy uncertainty index and estimates each stock’s EPU beta through time-series regression. Stocks are sorted into five…

AksjerKinesiske markederFaktorinvesteringMarkedssentiment
BigQuant

This study examines whether price-to-earnings ratios, cyclically adjusted P/E (CAPE), and the bond-stock earnings yield measure (BSEYD) can signal major downturns in the Shanghai and Shenzhen composite indexes. It also describes return features in the two…

AksjerKinesiske markederStatistikkHistorisk testing
BigQuant

This troubleshooting note describes a SQL binder error that occurs when a normalization function is used in a query joining two stock-factor tables. Both tables contain a date column, so passing an unqualified date reference to the function leaves the…

AksjerFaktorinvesteringTekniske indikatorer
BigQuant

This discussion examines why published quantitative findings and attractive backtests may fail to reproduce or translate into tradable returns. It explains how publication incentives, selective reporting, repeated testing, flexible data choices, and…

StatistikkHistorisk testingRisikostyringFaktorinvestering
BigQuant

The document explains how to construct factors grouped by Chinese stock market concepts when a built-in grouping expression does not recognize the concept field. For standard industry groupings, it gives a group-mean example using a level-two industry…

AksjerFaktorinvesteringStatistikkKinesiske markeder
BigQuant

The document describes a problem encountered when using an initialization module to set a day counter and prevent duplicate daily trades. In backtests, the counter reportedly advanced across several days, but in simulated trading the same first-day trade ran…

OrdreutførelseHistorisk testingRisikostyring
BigQuant

This short forum post asks how to add market-timing conditions to a strategy backtest. The author has seen strategies reduce or alter exposure after several consecutive market declines and is interested in alternatives based on whether the Shanghai Composite…

AksjerTekniske indikatorerHistorisk testingKinesiske markeder
BigQuant

This brief introductory response defines quantitative investing as finding patterns in historical data, translating them into a model and strategy, and using software to execute trades according to the resulting rules. It highlights discipline as a benefit:…

StatistikkMaskinlæringHistorisk testingRisikostyring