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Zināšanu bibliotēka

Stratmill pētniecības aģenta sagatavoti kopsavilkumi un galvenās atziņas par grāmatām, pētījumiem, rakstiem un kodu, ko lasa mūsu MI aģenti. Katrā lapā ir saite uz oriģinālu.

Quant Q&A
Dokumentu skaits: 20,364
SuperMind
Dokumentu skaits: 12,226
OKX Learn
Dokumentu skaits: 8,431
Strategy library
Dokumentu skaits: 7,910
MQL5 code base
Dokumentu skaits: 7,090
BigQuant
Dokumentu skaits: 3,481
Bitget Academy
Dokumentu skaits: 3,298
MQL5 articles
Dokumentu skaits: 3,012
TradingView scripts
Dokumentu skaits: 1,976
ProRealCode
Dokumentu skaits: 1,507
Deribit Insights
Dokumentu skaits: 1,232
Machine Learning for Trading
Dokumentu skaits: 1,124
arXiv papers
Dokumentu skaits: 1,033
Amberdata research
Dokumentu skaits: 766
FMZ forum
Dokumentu skaits: 682
FMZ digest
Dokumentu skaits: 662
vn.py community
Dokumentu skaits: 560
QuantInsti blog
Dokumentu skaits: 511
Galaxy Research
Dokumentu skaits: 340
QuantStart
Dokumentu skaits: 246
Stratmill research code
Dokumentu skaits: 219
Robot Wealth
Dokumentu skaits: 195
NautilusTrader
Dokumentu skaits: 191
Hummingbot docs
Dokumentu skaits: 181
Paradigm research
Dokumentu skaits: 175
Lumibot
Dokumentu skaits: 164
Kraken Learn
Dokumentu skaits: 163
Kvantitatīvās tirdzniecības kursu bibliotēka
Dokumentu skaits: 157
OctoBot
Dokumentu skaits: 152
Cryptohopper blog
Dokumentu skaits: 144
Systematic trading blog (Rob Carver)
Dokumentu skaits: 132
Qlib
Dokumentu skaits: 116
TqSdk
Dokumentu skaits: 86
Quantpedia
Dokumentu skaits: 86
Hyperliquid docs
Dokumentu skaits: 79
Freqtrade
Dokumentu skaits: 68
Hudson & Thames
Dokumentu skaits: 62
Awesome Systematic Trading
Dokumentu skaits: 61
backtrader
Dokumentu skaits: 54
vn.py
Dokumentu skaits: 50
Binance API docs
Dokumentu skaits: 45
Quantopian lekcijas
Dokumentu skaits: 45
FMZ guides
Dokumentu skaits: 38
pysystemtrade
Dokumentu skaits: 34
Freqtrade docs
Dokumentu skaits: 32
quant-trading
Dokumentu skaits: 31
FinRL
Dokumentu skaits: 28
Zipline
Dokumentu skaits: 22
FMZ live strategies
Dokumentu skaits: 21
Jesse
Dokumentu skaits: 17
pyfolio
Dokumentu skaits: 16
WonderTrader
Dokumentu skaits: 14
Alphalens
Dokumentu skaits: 14
backtesting.py
Dokumentu skaits: 11
Technical Analysis
Dokumentu skaits: 9
QTPyLib
Dokumentu skaits: 8
QuantRocket
Dokumentu skaits: 7
Lumibot strategies
Dokumentu skaits: 7
Awesome Quant
Dokumentu skaits: 1

Meklēt bibliotēkā

Dokumentu skaits: 3,481

BigQuant

This research summary compares common ways to divide macroeconomic cycles: the growth-and-inflation framework often called the Merrill Lynch clock, monetary-credit cycles, and monetary-economic cycles. It argues that these approaches revolve around money,…

Vairāku aktīvu tirdzniecībaĶīnas tirgiStatistikaFiksēta ienākuma instrumenti
BigQuant

The post describes a problem loading a saved StockRanker model into a live trading environment. The author reports that prediction fails with a JSON parsing error, even though the model works on the platform in the development environment. This is a report…

MašīnmācīšanāsRīkojumu izpilde
BigQuant

This discussion explains why assets under management and quantitative strategy returns may be related without having a simple linear relationship. It argues that capacity depends on the strategy: too little capital may prevent a portfolio from meeting its…

Riska pārvaldībaPortfeļa veidošanaRīkojumu izpildeTirgus mikrostruktūra
BigQuant

The document describes Chris Camillo’s social-arbitrage approach: watch social media and everyday consumer behavior for emerging trends that may not yet be reflected in market expectations, then connect those signals to publicly traded companies. It…

AkcijasTirgus noskaņojumsUz notikumiem balstīta tirdzniecība
BigQuant

This research summary describes factors built from intraday snapshot data to measure the relationship between stock prices and trading volume. The reported premise is that price-volume divergence may precede higher returns whether prices are rising or…

AkcijasĶīnas tirgiFaktoru ieguldīšanaAugstas frekvences tirdzniecība
BigQuant

This report describes a stock-selection approach that optimizes portfolio weights while constraining market-cap, industry, and style exposures. Its goal is to retain exposure to alpha factors while limiting unwanted variation from other risk factors. The…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

The article challenges the idea that US regulators define high-frequency trading through a universal orders-per-second limit. It says figures repeated in public debate come from academic or market research definitions rather than an official regulatory…

Augstas frekvences tirdzniecībaTirgus mikrostruktūraRīkojumu izpildeRiska pārvaldība
BigQuant

This note outlines two ways to include industry or sector membership in stock factor research. One approach treats each industry or sector as a grouping variable and compares the cumulative returns of its constituent stocks, effectively studying group…

AkcijasFaktoru ieguldīšanaStatistikaPortfeļa veidošana
BigQuant

This 2020 research summary analyzes the Silver Hua CSI Innovative Drug Industry ETF and its underlying index. The index selects up to 50 listed companies associated with innovative drug research, ranked by market capitalization. The report discusses the…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The document introduces the case for identifying actively managed industry theme funds in China’s A-share market. It describes pronounced differences in industry returns and rotation over the prior decade, with some sectors producing strong gains even when…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This survey introduces supervised, unsupervised, semi-supervised, and reinforcement learning, then outlines common methods such as neural networks, support vector machines, nearest neighbors, clustering, and Bayesian networks. It places machine learning in…

MašīnmācīšanāsPortfeļa veidošanaRiska pārvaldībaStatistika
BigQuant

This survey introduces supervised, unsupervised, semi-supervised, and reinforcement learning, then outlines common methods such as neural networks, support vector machines, nearest neighbors, clustering, and Bayesian networks. It places machine learning in…

MašīnmācīšanāsPortfeļa veidošanaRiska pārvaldībaStatistika
BigQuant

This article explains a holdings-based panel regression approach to evaluating mutual fund performance. With stock fixed effects, a conventional performance measure can be separated into time-series ability (TSA), reflecting whether changing portfolio…

StatistikaFaktoru ieguldīšanaPortfeļa veidošanaAkcijas
BigQuant

This review examines whether published indicators of future mutual-fund performance retain their predictive power outside the periods in which they were studied. It compares high-minus-low portfolio alpha spreads and cross-sectional regression slopes across…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This paper guide summarizes research on convolutional networks, model efficiency, and image segmentation. It describes channel attention through feature recalibration, depthwise separable convolutions for reducing computation, and encoder-decoder or…

MašīnmācīšanāsStatistika
BigQuant

The article explains idiosyncratic volatility for individual stocks and extends the idea to residual volatility for indices and commodity futures. It argues that elevated volatility is more useful for anticipating the size of future moves or divergence from…

SvārstīgumsStatistikaTehniskie indikatoriSekošana tendencei
BigQuant

This brief forum exchange concerns an SQL error encountered after adding a z-score standardization function to factor data. The reported issue is that a query joining tables cannot determine which table supplies a column named date. The response recommends…

StatistikaMašīnmācīšanās
BigQuant

This tutorial outlines a Java client that connects to a streaming market data service over WebSocket, authenticates with an API key, and subscribes to selected instruments and data types. It explains the role of the Java endpoint annotations and session, and…

Valūtu tirgusRīkojumu izpildeTirgus mikrostruktūra
BigQuant

This research roundup summarizes two studies. The first compares the Fama–French five-factor model with a version that adds momentum, then considers replacing the standard value factor with a more immediate value measure. The summary says the six-factor…

Faktoru ieguldīšanaCenas impulssPortfeļa veidošanaVēsturisko datu pārbaude
BigQuant

This article describes four alleged ways quantitative traders affect or exploit short-term stock activity: creating false breakouts, pushing prices through clustered stop levels, trading aggressively near the close, and repeatedly capturing small spreads. It…

Ķīnas tirgiAkcijasAugstas frekvences tirdzniecībaRīkojumu izpilde
BigQuant

This research summary argues that broker stocks deserve separate modeling within CSI 300 enhancement portfolios because the sector has a meaningful index weight and tends to move differently from other industries. It tests stock-selection factors within the…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The document describes a Force indicator calculated from trading volume and the change in a moving average of closing prices. Its inputs are close, volume, and a configurable period. The indicator multiplies volume by the difference between the current…

Tehniskie indikatoriCenas impulssAkcijas
BigQuant

This Chinese-language competition update reports diagnostics for the ten highest-ranked anonymous factors and tracks a CSI 1000 index-enhancement portfolio. It describes average style exposures estimated through daily multivariate regressions and…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaStatistika
BigQuant

The document asks how to express a proposed factor in a platform's formula language. The factor is defined as the negative correlation, over a rolling interval, between a security's VWAP series and its volume series. In principle, this transforms the…

AkcijasStatistikaFaktoru ieguldīšanaTehniskie indikatori