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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 report summary explains diffusion indicators as measures of how broadly index constituents participate in an advance or decline. Using the CSI 300 and its constituents, it compares moving-average and rate-of-change versions, equal weighting with…

Ķīnas tirgiAkcijasTehniskie indikatoriVēsturisko datu pārbaude
BigQuant

The report proposes using Benford’s law, the uneven distribution of leading digits found in many datasets, to study stock minute-volume data. From those statistics, it constructs an “institutional footprint” measure: higher values are interpreted as stronger…

AkcijasStatistikaFaktoru ieguldīšanaTirgus mikrostruktūra
BigQuant

This study examines how Chinese and US equity markets move together, with a focus on whether movements in one market help explain later movements in the other. It uses Granger causality tests on market returns and volatility, reporting evidence of two-way…

AkcijasStatistikaĶīnas tirgiASV tirgi
BigQuant

This Chinese A-share example builds a daily stock-ranking strategy using LightGBM regression. Its features combine market capitalization, recent price and turnover averages, dividend yield and price-to-earnings ranks, plus two custom factors. The target is a…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This discussion raises a data-reconciliation question: why historical prices retrieved from a Chinese equity data platform still differ from observed market prices after dividing open, high, low, and close by an adjustment factor. The example queries daily…

AkcijasĶīnas tirgiStatistika
BigQuant

This research note describes two revisions to AlphaNet, a neural model that learns stock selection factors from raw price and volume data. Version two adds ratio features, replaces pooling and dense layers with an LSTM to capture temporal patterns, and gives…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This meetup page collects questions about quantitative trading on the BigQuant platform. Topics include searching for holding-period parameters in a default stock-ranking template, defining reusable Python modules, and building a workflow for developing…

AkcijasMašīnmācīšanāsVēsturisko datu pārbaudeStatistika
BigQuant

This article proposes a defensive equity strategy that seeks oversold rebounds or bounces after a pullback. It draws inspiration from research on money-flow factors, including inflow, outflow, net institutional flow, and opening net flow, and proposes…

AkcijasAtgriešanās pie vidējās vērtībasFaktoru ieguldīšanaMašīnmācīšanās
BigQuant

This research summary describes factors derived from operating financial statements and reports selected long-short results. It identifies changes in operating current liabilities as a notable factor, with a reported Sharpe ratio of 2.62 and annualized…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This research summary proposes stock-selection factors built from daily highs, lows, opens, and average traded prices, arguing that closing-price indicators alone miss information in price movement. It evaluates opening-price spikes, rebounds from intraday…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTehniskie indikatori
BigQuant

This support exchange concerns warnings from BigQuant’s feature extractor that it cannot find the open, high, low, close, and volume fields in its field mapping. The logs show the warnings recurring across multiple years while basic feature extraction still…

AkcijasTehniskie indikatori
BigQuant

The document describes a method for testing factor effectiveness dynamically and selecting stocks within industries. It examines whether differences in style-factor exposure relate to differences in stock returns, then uses the results to form industry-based…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This guide describes how a BigAlpha competition participant can build equity factors using BigQuant’s DAI data engine. The specified universe is the historical membership of the CSI 1000, and the listed inputs include one-minute bars and order-book…

AkcijasĶīnas tirgiFaktoru ieguldīšanaStatistika
BigQuant

This overview explains the main stages of a machine-learning workflow for quantitative investing, using a fruit-selection analogy to introduce training data, labels, features, prediction, and validation. It recommends defining the market and stock universe,…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This research summary examines stock selection factors derived from operating financial statement items, especially changes in operating current liabilities. It reports that these factors showed selection ability, with the strongest cited result for a…

AkcijasFaktoru ieguldīšanaĶīnas tirgiVēsturisko datu pārbaude
BigQuant

This research overview examines risk parity within the broader development of portfolio allocation methods. It describes several risk measures and risk-allocation principles, emphasizing Euler allocation to define each asset’s contribution to portfolio risk.…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRiska pārvaldībaVēsturisko datu pārbaude
BigQuant

This short forum post gives a data access pattern for retrieving historical benchmark or stock data from a trade module. The example requests closing prices and volume for a benchmark symbol over a specified lookback, using daily frequency, and assigns the…

Vēsturisko datu pārbaudeAkcijasNākotnes līgumi
BigQuant

The report describes a stock-selection strategy that predicts the future usefulness of seven style factors and adjusts their portfolio weights over time. It uses historical factor information coefficients (ICs), macroeconomic variables, and market variables…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaMašīnmācīšanās
BigQuant

This Chinese-language support exchange addresses a quantitative research notebook that restarts automatically after two features are added and feature extraction begins. The user reports that the visible CPU and memory figures have not reached their…

MašīnmācīšanāsRiska pārvaldībaStatistika
BigQuant

This article collects learning materials for applying machine learning to algorithmic trading, grouped into books, blogs, research papers, videos, and podcasts. The topics span neural networks, structured data, regression, clustering, nearest-neighbor…

MašīnmācīšanāsAkcijasVēsturisko datu pārbaudeStatistika
BigQuant

This study considers whether a company’s decision to capitalize research and development spending conveys information about future project profitability. Because accounting rules allow judgment in deciding whether development costs should be capitalized, the…

AkcijasĶīnas tirgiUz notikumiem balstīta tirdzniecībaFaktoru ieguldīšana
BigQuant

This study turns unusual intraday stock behavior into a measurable event signal. It describes days when a stock repeatedly moves against the direction of the broader index, then uses correlation to screen for these cases. The resulting event samples are…

AkcijasĶīnas tirgiUz notikumiem balstīta tirdzniecībaStatistika
BigQuant

This study examines whether managers of equity-focused and mixed equity funds can anticipate shifts between market styles defined by company size, and whether any apparent skill persists. It identifies funds that ranked near the top around past style…

AkcijasĶīnas tirgiStatistikaFaktoru ieguldīšana
BigQuant

The document presents a SQL approach to estimating annualized variance for Chinese stocks. It first calculates daily close-to-close returns for each instrument, then applies a rolling 20-observation standard deviation, squares that value, and multiplies by…

AkcijasStatistikaSvārstīgums