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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 article argues that China’s A-share T+1 settlement rule can constrain retail traders who buy shares during a session, while some institutions may approximate intraday trading through previously held inventory or short selling. It describes two proposed…

AkcijasĶīnas tirgiTirgus mikrostruktūraRiska pārvaldība
BigQuant

This weekly competition report presents diagnostic summaries for the ten highest-ranked submitted factors and tracks a China 1000 index-enhancement portfolio. Factor diagnostics include average exposures to BARRA style factors and sector-level average Rank…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This introductory note describes quantitative investing as turning research, asset selection, timing, and position decisions into explicit rules that can be tested and executed by a computer. It distinguishes cross-sectional stock selection, which compares…

Faktoru ieguldīšanaVēsturisko datu pārbaudeCenas impulssAtgriešanās pie vidējās vērtības
BigQuant

This tutorial describes assembling a matrix of daily closing prices for Hong Kong stocks using BigQuant data sources and pandas. It first retrieves unique instrument identifiers, then loops through them to collect date and close fields over a specified…

AkcijasĶīnas tirgiStatistika
BigQuant

This report reviews the relative performance of Chinese quantitative equity funds and considers what rising assets under management mean for strategy design. It attributes the period’s advantage for index-enhanced and quantitative long products largely to…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The document introduces reinforcement learning through the interaction of an agent and an environment, where actions produce feedback and the objective is to maximize discounted future rewards. It frames the problem as a Markov decision process, explaining…

MašīnmācīšanāsAkcijasĶīnas tirgiVēsturisko datu pārbaude
BigQuant

This research summary presents asset allocation as a continuing process: define investor objectives, set an investment strategy, implement it, and review performance. Objectives specify the desired return, acceptable risk, and other investor needs. The…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaVēsturisko datu pārbaudeRiska pārvaldība
BigQuant

This overview describes several ways quantitative trading attempts to identify and capture market opportunities. It presents historical data analysis and backtesting as tools for assessing a strategy, then discusses automated execution, momentum examples,…

Vēsturisko datu pārbaudeCenas impulssArbitrāžaRiska pārvaldība
BigQuant

A BigQuant forum question asks how to select the ten stocks with the strongest return over the previous calendar year on the first trading day of a new year. The user’s current approximation compares prices about 250 trading days apart, but they want a…

AkcijasĶīnas tirgiStatistikaPortfeļa veidošana
BigQuant

A BigQuant forum exchange discusses why feature extraction can return multiple records for a stock on a given day, even when one record is expected. The suggested cause is that the feature input stage reads across tables; joining data with multiple matching…

AkcijasStatistika
BigQuant

This report presents an A-share stock-selection framework that applies StockRanker, a gradient-boosted learning-to-rank model, to fundamental, financial, and trading factors. It tests 282 individual factors across short, medium, and long holding horizons,…

AkcijasĶīnas tirgiMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

The document explains how to adapt a StockRanker multi-factor stock-selection workflow to Hong Kong and US equities on BigQuant. The backtest uses BigTrader’s custom historical data input, which must include date, instrument, and daily open, high, low,…

AkcijasĶīnas tirgiASV tirgiMašīnmācīšanās
BigQuant

This report reviews Chinese market and exchange-traded product developments for the week of March 30 to April 3, 2020. It summarizes moves in domestic equity indexes, bond yields, precious metals, currencies, and traded ETP categories. Equity and…

AkcijasFiksēta ienākuma instrumentiIzejvielasVairāku aktīvu tirdzniecība
BigQuant

The document explains how BigQuant’s factor research platform handles user-created factors and downstream dependencies. A researcher can combine platform factors A and B into a derived factor C, submit C to the platform, and let other users with access clone…

Faktoru ieguldīšanaStatistika
BigQuant

This research summary compares common stock-selection factors in the U.S. and Chinese markets, with a test of seven factor groups and 24 signals in the Russell 3000 universe. It reports that profitability, valuation, and growth measures were among the…

AkcijasFaktoru ieguldīšanaStatistikaCenas impulss
BigQuant

This entry introduces two overseas research topics: persistent bias in analyst expectations and holiday effects in stock markets. The first paper is described as examining habitual distortions in conventional analyst expectation factors and developing a…

AkcijasFaktoru ieguldīšanaUz notikumiem balstīta tirdzniecība
BigQuant

This weekly report summarizes factor returns across broad Chinese equity universes and several index or liquidity-based stock pools. It describes positive performance for profitability and book-to-price factors in the full market, while size, beta, and…

AkcijasĶīnas tirgiFaktoru ieguldīšanaCenas impulss
BigQuant

A stock screen for a moving average crossover can return no matches if its query loads only the target date. The note explains that time-series indicators need earlier observations to calculate their rolling values, so the data query should begin before the…

AkcijasTehniskie indikatoriStatistika
BigQuant

This Chinese-language research summary describes equity industry-rotation models for cyclical sectors, non-cyclical sectors, and the full market. It ranks industries using composite scores built from different factor groups: historical and expected…

AkcijasFaktoru ieguldīšana
BigQuant

This post describes a scheduling problem in a daily China-stock simulation: a strategy launched at 5 a.m. after a weekend or holiday may use the previous calendar day as both its start and end date. Because that date has no market session, the run fails. The…

AkcijasĶīnas tirgiVēsturisko datu pārbaude
BigQuant

This note presents a behavioral-finance explanation for a possible link between World Cup final outcomes and investor activity. A national team’s victory may lift local sentiment and trading, while defeat may discourage participation. The analysis compares…

AkcijasTirgus noskaņojumsUz notikumiem balstīta tirdzniecībaStatistika
BigQuant

The document explains how to decide whether to remove sector exposures from equity factor portfolios. It splits a stock characteristic into a within-sector component, which ranks companies against peers, and an across-sector component, which captures…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaStatistika
BigQuant

This article presents five discretionary rules for interpreting stock turnover, support and resistance, trend direction, and price-volume behavior. It treats unusually high turnover near price lows as possible accumulation and high turnover after a sharp…

AkcijasSekošana tendenceiTehniskie indikatoriRiska pārvaldība