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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 introductory article presents ten practical points about machine learning for non-specialists. It emphasizes that machine learning finds patterns in data, so data quality, accurate labels, representative samples, and careful transformations often matter…

MašīnmācīšanāsStatistikaRiska pārvaldība
BigQuant

The article explains how a long-only smart beta portfolio can combine equity factors to seek steadier returns than relying on one factor. It highlights value, low beta, profitability, investment, momentum, and size, arguing that their different return…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaCenas impulss
BigQuant

This report evaluates whether changes in listed companies’ employee compensation can help distinguish future stock returns. It compares growth in compensation payable, which represents amounts owed, with growth in compensation already paid, and also…

AkcijasFaktoru ieguldīšanaĶīnas tirgi
BigQuant

The post asks how to define groups for a learning-to-rank model in a stock selection setting. In ranking algorithms, each group contains observations that should be ranked against one another; the author proposes grouping stock samples by trading date so…

AkcijasMašīnmācīšanāsStatistika
BigQuant

This convertible-bond research note describes a relative-value approach based on the embedded option. It first compares implied volatility with the underlying stock’s historical volatility: relatively low implied volatility is treated as evidence that the…

SvārstīgumsAtvasināto instrumentu cenu noteikšanaVēsturisko datu pārbaudeFiksēta ienākuma instrumenti
BigQuant

FactorVAE is described as a probabilistic dynamic factor model that applies a variational autoencoder to noisy market data to forecast cross-sectional stock returns. In training, an encoder-decoder uses future returns to identify posterior factors that…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaStatistika
BigQuant

This introductory article explains Matplotlib’s role in Python data visualization and outlines the Figure and Axes objects, along with its state-based pyplot and object-oriented interfaces. It notes that the library supports chart types such as lines,…

AkcijasStatistika
BigQuant

The report develops stock selection factors from abnormal operating cash flow as proxies for real earnings management. Its premise is that firms can alter real business activity to make reported performance look stronger, potentially harming future…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This research summary discusses how asset prices can reflect economic expectations before official data is released. It proposes comparing year-over-year changes in a basket of asset prices with economic indicators, and comparing asset-price changes with…

Vairāku aktīvu tirdzniecībaStatistikaCenas impulss
BigQuant

This trading exercise translates a discretionary method into explicit stock-selection rules. A candidate must close above its five-day moving average for three consecutive days and remain within ten percent above its lowest price over the previous twenty…

AkcijasTehniskie indikatoriCenas impulssRiska pārvaldība
BigQuant

The report develops stock-selection factors from one-minute Chinese equity data, adding transaction counts to price and volume inputs. It examines average trade value and the direction of large-trade activity, then evaluates these signals both alone and…

AkcijasĶīnas tirgiFaktoru ieguldīšanaAugstas frekvences tirdzniecība
BigQuant

This platform guide distinguishes raw financial statement records from processed point-in-time (PIT) data and explains why the distinction matters in quantitative investing. Raw records are organized by instrument, report date, publication date, and change…

AkcijasĶīnas tirgiVēsturisko datu pārbaudeStatistika
BigQuant

This brief Chinese-language forum exchange concerns whether a user can retrieve feature importance from a random forest in a visual quantitative-strategy platform. The reply identifies the random forest module and suggests creating a visual strategy template…

MašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This monthly review compares equity factors using information coefficients (IC) and long-short returns. It reports that growth and turnover factors were relatively consistent over the preceding three months, while profitability and reversal results varied;…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This research summary studies whether lagged returns from related industries can help predict industry returns for sector rotation. It motivates the question by noting that such predictability would not be expected in an ideal frictionless market, while real…

AkcijasĶīnas tirgiMašīnmācīšanāsStatistika
BigQuant

This research summary examines low-volatility investing in overseas markets and mainland Chinese equities. It presents the low-volatility anomaly as a source of long-run risk-adjusted performance, with a behavioral explanation: speculative demand and…

AkcijasĶīnas tirgiFaktoru ieguldīšanaSvārstīgums
BigQuant

This report summary describes a Barra-style risk model for Hong Kong stocks, built to study factor returns and portfolio exposures. Its data foundation combines security details, prices, trading activity, Hang Seng industry classifications, valuation…

AkcijasFaktoru ieguldīšanaStatistikaĶīnas tirgi
BigQuant

The article outlines common aims of algorithmic execution: splitting a parent order to follow market volume, reducing market impact, and concealing trading intent. It also presents automation as a way to improve order-entry efficiency and reduce manual…

Rīkojumu izpildeTirgus mikrostruktūraAugstas frekvences tirdzniecībaMašīnmācīšanās
BigQuant

This note proposes screening Chinese equities associated with the metaverse theme, requiring a positive institutional-flow measure and a weekly crossover involving a 30-week reference. The intended rationale is to combine thematic exposure, evidence of…

AkcijasĶīnas tirgiSekošana tendenceiTehniskie indikatori
BigQuant

This overview explains Chinese depositary receipts (CDRs) as securities traded domestically that represent rights in foreign shares through a depositary institution. It summarizes the historical pilot eligibility criteria described in the document, says…

AkcijasĶīnas tirgiTirgus mikrostruktūraRiska pārvaldība
BigQuant

This forum post describes a reproducibility question in a quantitative modeling workflow. The author sets a learning-rate parameter to the same value in the model and in a hyperparameter-search module, and configures the search grid to contain only that…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

The document explains sliced inverse regression (SIR) as a supervised dimension-reduction method for financial prediction. Unlike principal component analysis, which summarizes variation in predictors without using the target, SIR uses the forecast variable…

AkcijasStatistikaFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This overview surveys models used to relate asset values or expected returns to risk and other inputs. It describes CAPM as linking expected return to the risk-free rate and market exposure, then introduces multifactor models such as Fama–French, which add…

Vairāku aktīvu tirdzniecībaFaktoru ieguldīšanaAtvasināto instrumentu cenu noteikšanaFiksēta ienākuma instrumenti