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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
Alphalens
Dokumentu skaits: 14
WonderTrader
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 translated research review examines seven U.S. equity sentiment signals built from analyst revisions and surprises, short-selling data, news, options, consumer web content, corporate filings, and insider trades. It describes how the signals are…

AkcijasTirgus noskaņojumsFaktoru ieguldīšana
BigQuant

This brief Chinese-language research note concerns a replication of a study on the micro-level sources of reversal effects in China’s A-share market. It says the replication lacked tick-by-tick data and therefore used minute-level data as an approximation.…

AkcijasAtgriešanās pie vidējās vērtībasStatistikaĶīnas tirgi
BigQuant

This research review describes a link between employee movement and subsequent stock performance. It summarizes a study that estimates company hiring and departures from employee résumé histories, then tests whether net outflows predict returns. In event…

AkcijasFaktoru ieguldīšanaStatistikaASV tirgi
BigQuant

This research summary describes a unified framework for explaining and testing generalized linear models in multi-factor stock selection. It compares how these models can combine factors into a composite signal, then evaluates the signal through grouped…

AkcijasĶīnas tirgiMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

The tutorial explains how Brinson performance attribution separates a portfolio’s actual return from its benchmark return and decomposes active performance into timing, security selection, and an interaction component. The benchmark reflects planned asset…

AkcijasPortfeļa veidošanaVēsturisko datu pārbaudeĶīnas tirgi
BigQuant

The study summary describes a dynamic contextual alpha model designed to address a weakness in conventional multi-factor scoring: applying the same factor evaluation across the entire market. It groups A-share stocks by characteristics such as size,…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The document reports a BigQuant user’s problem: the visualization module was set to a latest strategy date of November 1, 2022, while the generated data appeared to begin on July 15, 2018. The response suggests checking the beta factor and points to a…

AkcijasStatistika
BigQuant

This article distinguishes overfitting from overtraining and explains why cross-validation does not, by itself, prevent a model from overfitting. Validation estimates predictive performance, including through out-of-sample data and averaged errors across…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

This research summary examines how to estimate factor premia for return prediction when market conditions change. It notes that a fixed estimation window creates a trade-off: a long window can lag shifts in the market, while a short one can be dominated by…

Faktoru ieguldīšanaStatistikaSvārstīgumsRiska pārvaldība
BigQuant

This example combines three cross-sectional stock measures into a composite score: market capitalization rank, turnover rank, and the rank of the latest close-to-prior-close ratio. It filters out risk-warning stocks, recently listed stocks, certain listing…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This short platform discussion addresses whether a strategy initialization routine runs once in backtests but each day in simulated live trading. The response explains that live trading parameters are reloaded each trading day, so simulation follows that…

Vēsturisko datu pārbaudeRīkojumu izpilde
BigQuant

The document explains a basic result in convex optimization: for a differentiable convex function, any point where the gradient is zero is a global minimizer. It argues this by applying the first-order inequality for convex functions, which bounds the…

StatistikaMašīnmācīšanās
BigQuant

This weekly competition report profiles the ten highest-ranked ModelScore factors and tracks a CSI 1000 index-enhancement portfolio. It summarizes average style exposures from daily multivariate regressions and reports average within-industry Rank ICs, the…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This discussion examines how growth in assets can affect a quantitative strategy’s returns and stability. It describes a trade-off: as a strategy takes more capital, returns may decline and volatility may rise. It distinguishes broad-capacity equity…

Riska pārvaldībaPortfeļa veidošanaAkcijasNākotnes līgumi
BigQuant

The document asks how to define an earnings stability factor as the mean of four years of trailing-twelve-month gross profit divided by its standard deviation. It considers calculating this from daily financial-indicator data using a rolling window, and asks…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This study examines how neural network hidden-layer configurations affect reinforcement-learning performance in stock portfolio optimization. Using the FinRL framework, agents receive open, high, low, close, and volume data and seek to improve portfolio…

AkcijasMašīnmācīšanāsPortfeļa veidošanaVēsturisko datu pārbaude
BigQuant

This paper summary presents a method for evaluating active mutual funds when their stated benchmark may not match their actual style. It argues that omitted risk factors can make factor-model alpha estimates too high, while benchmark mismatch can also…

AkcijasStatistikaRiska pārvaldībaPortfeļa veidošana
BigQuant

This study examines whether differences in buy and sell limit order strength contain information about short-term price moves and longer-horizon stock returns in Chinese equities. Its Spread measure aggregates order book strength at each tick. Across…

AkcijasAugstas frekvences tirdzniecībaTirgus mikrostruktūraFaktoru ieguldīšana
BigQuant

This overview organizes machine learning algorithms in two ways: by learning setup and by functional family. It distinguishes supervised learning with labeled examples, unsupervised learning that finds structure in unlabeled data, and semi-supervised…

MašīnmācīšanāsStatistika
BigQuant

This report describes a regularized fund-evaluation model used to rank Chinese equity funds and form fund-of-funds portfolios. It compares funds selected for overall ability, stock-selection ability, and market-timing ability, then reviews their holdings and…

Faktoru ieguldīšanaPortfeļa veidošanaVēsturisko datu pārbaudeStatistika
BigQuant

This explanation clarifies why a stock strategy may send orders for shares it already owns. It separates current holdings into positions with sell signals, new buy candidates, and existing positions that still qualify to be held. The strategy sells the first…

AkcijasTehniskie indikatoriPozīcijas apjoma noteikšanaPortfeļa veidošana
BigQuant

This article explains how to allocate weights in a multi-factor equity portfolio using a structured risk model. It decomposes stock returns into factor exposures, factor returns, and stock-specific returns, then describes industry and style neutrality…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This report summary outlines a risk-budgeting approach to domestic multi-asset allocation conditioned on economic growth and inflation regimes. It describes statistical comparisons of asset behavior across those states and reports rotation patterns: bonds…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRiska pārvaldībaVēsturisko datu pārbaude
BigQuant

This research note describes a China A-share fund designed to enhance returns over the CSI 500 while managing benchmark-relative risk. It attributes the strategy’s excess returns to quantitative multifactor stock selection, sector allocation, trading…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaRiska pārvaldība