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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
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SuperMind
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OKX Learn
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Strategy library
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MQL5 code base
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BigQuant
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Bitget Academy
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MQL5 articles
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TradingView scripts
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ProRealCode
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Deribit Insights
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Machine Learning for Trading
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arXiv papers
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Amberdata research
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FMZ forum
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FMZ digest
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vn.py community
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QuantInsti blog
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Galaxy Research
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QuantStart
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Stratmill research code
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Robot Wealth
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NautilusTrader
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Hummingbot docs
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Paradigm research
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Lumibot
Dokumentu skaits: 164
Kraken Learn
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Kvantitatīvās tirdzniecības kursu bibliotēka
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OctoBot
Dokumentu skaits: 152
Cryptohopper blog
Dokumentu skaits: 144
Systematic trading blog (Rob Carver)
Dokumentu skaits: 132
Qlib
Dokumentu skaits: 116
Quantpedia
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TqSdk
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Hyperliquid docs
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Freqtrade
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Hudson & Thames
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Awesome Systematic Trading
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backtrader
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vn.py
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Quantopian lekcijas
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Binance API docs
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FMZ guides
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pysystemtrade
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Freqtrade docs
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quant-trading
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FinRL
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Zipline
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FMZ live strategies
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Jesse
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pyfolio
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Alphalens
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WonderTrader
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backtesting.py
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Technical Analysis
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QTPyLib
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QuantRocket
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Lumibot strategies
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Awesome Quant
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Meklēt bibliotēkā

Dokumentu skaits: 3,481

BigQuant

This note describes a stock-selection and rotation approach built around buying shares judged to be at relatively low price levels. A scoring system ranks candidates; the strategy buys higher-ranked stocks and holds five positions. As a holding rises, its…

AkcijasAtgriešanās pie vidējās vērtībasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This troubleshooting note describes a simulation failure in a single-stock AI ranking strategy: the pipeline reports that no data remains after missing values are removed, even though the strategy completes a backtest. It suggests checking whether the data…

MašīnmācīšanāsVēsturisko datu pārbaudeRīkojumu izpilde
BigQuant

The document describes a buy-sentiment factor built by distinguishing aggressive purchases, which execute against sell orders already on the book, from passive purchases, which wait as limit orders for incoming sells. It argues that finer-grained market data…

AkcijasTirgus noskaņojumsTirgus mikrostruktūraFaktoru ieguldīšana
BigQuant

This research review describes a dynamic, out-of-sample process for selecting US active equity mutual funds from observable fund characteristics. It compares elastic net, random forests, gradient boosting, and linear regression. Each year, models are trained…

MašīnmācīšanāsPortfeļa veidošanaFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

The post argues that factor research can use information from later minutes within the same trading day when constructing a daily-frequency factor, provided the factor does not use data from the following day. It proposes examining relationships between…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This study tests whether investors can improve a diversified global equity factor portfolio by adjusting factor weights with time-series signals or cross-sectional characteristics. It applies parameterized portfolio policies to twenty long-short equity…

AkcijasFaktoru ieguldīšanaCenas impulssPortfeļa veidošana
BigQuant

This research summary compares seven ways to identify active mutual funds with positive, negative, or zero risk-adjusted performance. It explains a factor-model benchmark and methods that adjust for multiple testing, including bootstrap and false discovery…

AkcijasStatistikaFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

The article presents market feel as a learned pattern-recognition skill, built by repeatedly observing price structure, volume changes, and trading behavior. It recommends focusing on three intraday periods, watching whether moves attract volume and support,…

AkcijasTehniskie indikatoriStatistikaĶīnas tirgi
BigQuant

The report explains how the MSCI China A50 Connect Index is constructed and why its futures contract could help overseas investors manage A-share exposure. The index selects large free-float-adjusted stocks across GICS sectors, then adjusts weights to match…

Nākotnes līgumiAkcijasĶīnas tirgiRiska pārvaldība
BigQuant

This brief summary of a multi-factor stock-selection report describes three ways to refine traditional factors using high-frequency returns. High-frequency return variance is presented as a closer measure of volatility than variance calculated from daily…

AkcijasFaktoru ieguldīšanaStatistikaSvārstīgums
BigQuant

This tutorial presents a workflow for evaluating a factor on Chinese equities, using the lower Bollinger Band as an example. It introduces factor analysis as a way to summarize variables and derive composite scores, then describes building daily factor data,…

Faktoru ieguldīšanaAkcijasĶīnas tirgiStatistika
BigQuant

This research summary tests whether splitting stock volatility into components yields useful selection signals from high-frequency returns. The reported system-versus-idiosyncratic volatility split performs poorly at the one-minute sampling interval; its…

AkcijasFaktoru ieguldīšanaSvārstīgumsStatistika
BigQuant

This BigQuant help response clarifies which data fields are available in its visual automatic stock-labeling module. It says the module directly supports only the standard open, high, low, and close price fields. A user asking about unavailable documentation…

AkcijasMašīnmācīšanāsStatistika
BigQuant

This research digest summarizes two studies. The first revisits the claim that small-cap stocks earn higher average returns than large-cap stocks, reviewing competing views about the size effect and describing simple tests with public data intended to…

AkcijasFaktoru ieguldīšanaStatistika
BigQuant

The document asks how to use a user-uploaded file as a feature in an LSTM trading strategy. The suggested approach is to pass the uploaded data through a data-source module and join it with the strategy’s existing data, making the added values available…

MašīnmācīšanāsAkcijas
BigQuant

The discussion addresses missing values from a rolling maximum over a fixed 1,000-day window when a stock has been listed for less than the full period. It proposes making the window length depend on the stock's available listing history: use the smaller of…

AkcijasStatistikaTehniskie indikatori
BigQuant

This report examines listed Chinese securities firms as a cyclical, high-beta equity sector and presents a securities-company index as a way to track them. It describes the index as market-cap weighted, concentrated in its largest constituents, and composed…

Ķīnas tirgiAkcijasVēsturisko datu pārbaude
BigQuant

This research summary examines valuation factors for industry allocation and explains why raw valuation measures can be difficult to compare across sectors. It reviews five common measures, including earnings, book, sales, and operating-cash-flow based…

Faktoru ieguldīšanaPortfeļa veidošanaCenas impulssStatistika
BigQuant

This platform discussion shows how to count Chinese stocks whose total market capitalization is below three billion yuan on each trading date. It queries daily stock-factor data for a selected date range, filters the rows by market capitalization, and groups…

AkcijasĶīnas tirgiStatistika
BigQuant

A user reports that custom normalization code works on its own but produces no result when run as a module. The response explains that the normalization step operates on cross-sectional data: it compares observations across securities at a given point in…

StatistikaAkcijas
BigQuant

This reading note summarizes two quantitative research papers. The first applies the thermal optimal path method, a nonparametric approach motivated by statistical physics, to study how stock indices and their futures lead or lag one another over time. The…

Nākotnes līgumiAkcijasStatistikaMašīnmācīšanās
BigQuant

The supplied summary describes research on alternative data and a hiring-count signal for Chinese equities. It says overseas use of alternative data is still developing, with most applications aimed at short-term opportunities, and identifies six…

AkcijasFaktoru ieguldīšanaUz notikumiem balstīta tirdzniecībaVēsturisko datu pārbaude
BigQuant

The discussion examines a sequence-window feature pipeline for stock data. If rows are filtered before a rolling window is formed, dates can disappear; a nominal three-row window may then combine observations that are not three consecutive days. The answer…

AkcijasMašīnmācīšanāsStatistika