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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
Quantpedia
Dokumentu skaits: 86
TqSdk
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
Quantopian lekcijas
Dokumentu skaits: 45
Binance API docs
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 study describes active risk parity as a portfolio built around a risk-parity allocation, with controlled deviations based on investment views. In its two-asset example, Chinese equities and corporate bonds are rebalanced using rolling covariance…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRiska pārvaldībaCenas impulss
BigQuant

This opinion piece argues that, in a sideways market, capital may rotate from large companies whose prior gains have reduced their perceived upside toward lower-priced, smaller companies. It distinguishes catalyst-driven sector rotation from buying weak…

Ķīnas tirgiAkcijasUz notikumiem balstīta tirdzniecība
BigQuant

This April 2020 market-monitoring report combines valuation, trading activity, sentiment, sector rotation, capital concentration, and style-factor observations for Chinese equities. It describes sentiment as having recovered to an optimistic range after a…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaCenas impulss
BigQuant

This research summary describes stock-selection factors built from tick-level aggressive buy and sell amounts. It applies an informed-trading probability model to filter noisy order flow, then forms informed sell share, informed buy share, and net informed…

AkcijasFaktoru ieguldīšanaTirgus mikrostruktūraStatistika
BigQuant

This overview contrasts efficient markets, where prices are assumed to incorporate available information, with inefficient markets, where prices may diverge from estimated value. It outlines the weak, semi-strong, and strong forms of the efficient market…

StatistikaArbitrāžaPāru tirdzniecībaTirgus noskaņojums
BigQuant

This research summary examines factor weighting, orthogonalization, and timing through the relationship between composite information-coefficient weighting and Fama–MacBeth regressions. Under the stated condition that returns and factors are z-scored, it…

Faktoru ieguldīšanaStatistikaPortfeļa veidošana
BigQuant

The article describes adding a trailing stop to a stock selection and rotation workflow. Its example sells a holding when the latest price falls more than 10% from the highest price observed since purchase. It recommends tracking stop-triggered stocks,…

AkcijasRiska pārvaldībaRīkojumu izpilde
BigQuant

This personal account discusses trading strong Chinese stocks during a broad market decline. The author describes using stock-ranking models to pursue strong stocks, next-day rebounds after pullbacks, and limit-up leaders, while emphasizing that these…

AkcijasĶīnas tirgiCenas impulssRiska pārvaldība
BigQuant

This meetup compilation surveys practical topics in quantitative investing, including factor design, machine learning, strategy validation, portfolio sizing, live execution, and sector rotation. It recommends building a factor library by collecting and…

Faktoru ieguldīšanaMašīnmācīšanāsVēsturisko datu pārbaudePortfeļa veidošana
BigQuant

The document describes a library that packages deep learning models for quantitative research, including feedforward networks, LSTMs, Transformers, TabNet, and TRA. Its quick-start workflow covers selecting a model, configuring an optimizer and loss…

MašīnmācīšanāsAkcijasVēsturisko datu pārbaude
BigQuant

This A-share strategy narrows a stock universe in three stages: rank industry or concept indices by short- and medium-term momentum, select stocks with a current limit-up and a recent prior limit-up, then confirm strength after the open before buying.…

AkcijasĶīnas tirgiCenas impulssCenas izrāviens
BigQuant

This advice article presents five principles for surviving in stock trading: use technical analysis as context, follow established trends, keep short-term trades distinct from long-term investments, assess broad market direction, and protect capital by…

AkcijasSekošana tendenceiTehniskie indikatoriRiska pārvaldība
BigQuant

This overview explains machine learning as a way to model patterns in data and describes how that capability may be applied to investment decisions. It outlines a workflow spanning data collection, feature creation, preprocessing, model training, model…

MašīnmācīšanāsStatistikaAkcijas
BigQuant

This note describes a reproduction of a sell-side factor based on overnight stock returns, intended to capture information advantages held by informed traders. It says the research defines the factor in stages, from a traditional overnight-return measure to…

AkcijasĶīnas tirgiFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

The article describes a research workflow that pairs a language model’s reading of crypto news with live exchange data. The agent identifies token mentions, retrieves current prices and four-hour candles, and can consult order books when warranted. It then…

KriptoaktīviTirgus noskaņojumsTehniskie indikatoriTirgus mikrostruktūra
BigQuant

This 2020 market note reviews Chinese equity style performance and outlines a short-term outlook. It reports that small-cap reversal strengthened, value began to recover, and earnings-growth exposure showed a brief rebound. Its recommendations draw on…

AkcijasĶīnas tirgiAtgriešanās pie vidējās vērtībasFaktoru ieguldīšana
BigQuant

This discussion distinguishes two approaches to Chinese A-share limit-up trading: a daily model that forecasts which stocks may hit the limit the next day, and an intraday model that uses current market information to identify candidates for the remainder of…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
BigQuant

A BigQuant user reports an apparent issue when a strategy sells before buying back to close a position. In the user's tests, buybacks above half of the initial capital fail, while smaller buybacks work. The report describes varying the amount used for the…

Ķīnas tirgiRīkojumu izpildePozīcijas apjoma noteikšana
BigQuant

This article summarizes a study of U.S. mutual funds that links SEC N-SAR options disclosures with fund returns and characteristics. The sample covers 1998 through November 2013 and includes 106,357 filings. Cross-sectional and panel analyses compare funds…

OpcijasAkcijasRiska pārvaldībaStatistika
BigQuant

The article explains why standard stochastic gradient descent can remain inefficient when training Word2Vec on a large vocabulary. Even when an update concerns only a few words, calculating gradients across the full parameter matrices wastes work, and the…

MašīnmācīšanāsStatistika
BigQuant

This brief support exchange addresses whether a strategy’s data is standardized anew each day during simulated trading. The response says that when the strategy uses parameters bound for live trading, simulated trading standardizes that day’s data. A…

StatistikaVēsturisko datu pārbaude
BigQuant

This overview explains XGBoost regression as an ensemble of decision trees built sequentially to reduce a loss. Each new tree uses gradient information from prior predictions, while complexity controls and regularization are intended to limit overfitting.…

MašīnmācīšanāsAkcijasStatistikaVēsturisko datu pārbaude
BigQuant

This document explains a reverse-doubling strategy designed for an older crypto-margined futures venue. It focuses on the strategy’s control flow: open a position, place take-profit orders, monitor both profit-taking and reverse stop triggers, cancel…

KriptoaktīviNākotnes līgumiRiska pārvaldībaPozīcijas apjoma noteikšana