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Biblioteca de cunoștințe

Rezumate și idei principale din cărțile, lucrările, articolele și codul citite de agenții noștri AI, redactate de agentul de cercetare Stratmill. Fiecare pagină trimite la sursa originală.

Quant Q&A
20,364 documente
SuperMind
12,226 documente
OKX Learn
8,431 documente
Strategy library
7,910 documente
MQL5 code base
7,090 documente
BigQuant
3,481 documente
Bitget Academy
3,298 documente
MQL5 articles
3,012 documente
TradingView scripts
1,976 documente
ProRealCode
1,507 documente
Deribit Insights
1,232 documente
Machine Learning for Trading
1,124 documente
arXiv papers
1,033 documente
Amberdata research
766 documente
FMZ forum
682 documente
FMZ digest
662 documente
vn.py community
560 documente
QuantInsti blog
511 documente
Galaxy Research
340 documente
QuantStart
246 documente
Stratmill research code
219 documente
Robot Wealth
195 documente
NautilusTrader
191 documente
Hummingbot docs
181 documente
Paradigm research
175 documente
Lumibot
164 documente
Kraken Learn
163 documente
Biblioteca cursurilor cuantitative
157 documente
OctoBot
152 documente
Cryptohopper blog
144 documente
Systematic trading blog (Rob Carver)
132 documente
Qlib
116 documente
TqSdk
86 documente
Quantpedia
86 documente
Hyperliquid docs
79 documente
Freqtrade
68 documente
Hudson & Thames
62 documente
Awesome Systematic Trading
61 documente
backtrader
54 documente
vn.py
50 documente
Binance API docs
45 documente
Prelegeri Quantopian
45 documente
FMZ guides
38 documente
pysystemtrade
34 documente
Freqtrade docs
32 documente
quant-trading
31 documente
FinRL
28 documente
Zipline
22 documente
FMZ live strategies
21 documente
Jesse
17 documente
pyfolio
16 documente
WonderTrader
14 documente
Alphalens
14 documente
backtesting.py
11 documente
Technical Analysis
9 documente
QTPyLib
8 documente
QuantRocket
7 documente
Lumibot strategies
7 documente
Awesome Quant
1 documente

Caută în bibliotecă

3,481 documente

BigQuant

The document presents a framework for deciding whether a candidate multifactor strategy adds value beyond a small set of investable reference factors, called elementary smart betas. It models each candidate through its exposures to those factors and its…

Investiții bazate pe factoriConstruirea portofoliuluiGestionarea risculuiActive din mai multe clase
BigQuant

This commentary reviews the Chinese quantitative-investing environment in 2022 and presents a private manager’s expectations for 2023. It attributes a difficult path to excess returns to weak trading activity and rapid shifts in market style, alongside a…

Piețele din ChinaActive din mai multe claseÎnvățare automatăGestionarea riscului
BigQuant

This weekly report compares artificial intelligence stock-selection portfolios across broad A-share universes, industry-neutral portfolios, and portfolios restricted to the CSI 300 or CSI 500. It names XGBoost, support vector machines, random forests,…

Piețele din ChinaAcțiuniÎnvățare automatăTestare istorică
BigQuant

The document presents a Chinese A-share backtest question: after selling a holding at the open, a daily-frequency strategy places a purchase near the close but buys only a small amount. The author wonders whether the simulator updates available cash only on…

AcțiuniTestare istoricăExecuțieDimensionarea pozițiilor
BigQuant

The study defines an earnings surprise by comparing reported results from forecasts, flash reports, or periodic filings with analyst consensus net profit estimates from the day before the announcement. For nonannual reports, it estimates full-period…

AcțiuniBazat pe evenimenteStatisticăTestare istorică
BigQuant

This brief literature note introduces a VaR-augmented Black-Litterman approach for constructing an absolute-return fund-of-funds portfolio with market-risk controls. The described formulation incorporates value-at-risk alongside practical trading…

Active din mai multe claseConstruirea portofoliuluiGestionarea risculuiStatistică
BigQuant

This research review examines whether mutual-fund outflows provide a clean measure of nonfundamental pressure on stock prices. It decomposes the MFFlow measure into components tied to the inverse of returns, the inverse of turnover, and relative fund selling…

AcțiuniStatisticăMicrostructura piețeiGestionarea riscului
BigQuant

The document explains that exchange-traded funds cannot be processed with the platform’s basic feature extraction module. The reported run shows the extractor failing to find a close-price field for the selected instruments, producing no feature rows and…

AcțiuniPiețele din China
BigQuant

This Chinese-language report summary reviews the history and practice of machine learning in quantitative investing. It says that machine learning was used in the field during the early-1990s boom and that its applications continued in algorithmic trading…

Învățare automatăStatisticăTestare istoricăExecuție
BigQuant

This roundtable transcript gathers views from Chinese investment managers, researchers, and futures professionals on the development of quantitative investing. Participants discuss the tension among scale, returns, and risk; the challenge of declining or…

Active din mai multe claseInvestiții bazate pe factoriÎnvățare automatăMicrostructura pieței
BigQuant

This study develops two equity factors from the relationship between intraday volatility and the return-to-volatility ratio. The “rebuilding” factor uses the daily covariance between that ratio and volatility to represent cases where volatility rises without…

AcțiuniVolatilitateInvestiții bazate pe factoriIndicatori tehnici
BigQuant

This note discusses market style and public mutual fund positioning in Chinese equities. It compares the share of CSI 300 constituents within the broader CSI 800 universe, reporting that the share was 81% at the time of the note. It also cites two recent…

AcțiuniPiețele din ChinaConstruirea portofoliului
BigQuant

The document introduces multiple linear regression as a model relating one dependent variable to several explanatory variables, contrasting it with simple regression. It connects the method’s broad use in quantitative finance with the progression from the…

StatisticăInvestiții bazate pe factoriActive din mai multe clase
BigQuant

This brief request proposes a factor based on the correlation between a benchmark’s returns and an individual stock’s returns over a three-day window. It names the intended inputs and lookback, but provides no code, worked example, explanation of how to…

AcțiuniStatisticăInvestiții bazate pe factori
BigQuant

This April 2020 market note reviews a sharp decline in Chinese equities amid weakening fundamentals and falling investor sentiment. It reports that large- and small-cap indexes had similar declines, while consumer and utility sectors held up better and…

AcțiuniPiețele din ChinaConstruirea portofoliuluiGestionarea riscului
BigQuant

This note explains how to handle low-frequency financial or analyst factors alongside daily price and volume factors when constructing nonlinear models. One approach is to merge monthly or quarterly fundamentals with daily market data and carry the latest…

Învățare automatăInvestiții bazate pe factoriStatisticăTestare istorică
BigQuant

This brief note describes a feature-selection exercise based on a random forest template. The author says they ranked 37 stock factors by random forest importance and retained the nine factors with the highest importance scores. The document does not…

AcțiuniInvestiții bazate pe factoriÎnvățare automatăStatistică
BigQuant

This Chinese-language research summary discusses forecasting stock-selection factor returns after commonly used factors became more volatile. It focuses on screening timing variables with Lasso and Elastic Net regression, then extending factor timing with…

AcțiuniInvestiții bazate pe factoriÎnvățare automatăTestare istorică
BigQuant

The post raises a backtesting concern: a user observes that a stock delisted on one date still appears sellable on a later scheduled rebalance date. A reply explains the platform behavior as a special backtest-engine rule: when a strategy does not handle a…

Testare istoricăAcțiuniGestionarea riscului
BigQuant

The document explains how to find the highest and lowest closing prices over roughly two years using rolling time-series maximum and minimum functions. It treats two years as about 400 trading days and applies those functions to the closing-price series,…

AcțiuniIndicatori tehniciStatistică
BigQuant

This research summary proposes measuring capital flows between large-cap and small-cap styles to identify when style investors are shifting allocations. It treats flow intensity as a signal of changing conviction and risk: when intensity is high, investors…

AcțiuniInvestiții bazate pe factoriStatisticăGestionarea riscului
BigQuant

This short weekly report summarizes a divided Chinese equity market, with major indices moving in different directions. It compares the reported changes in the Shanghai Composite, CSI 500, ChiNext, and CSI 300, then notes that most of the covered long…

AcțiuniPiețele din ChinaMomentumConstruirea portofoliului
BigQuant

This brief market note reviews Chinese 50ETF options conditions for the week ending November 2. It reports that the ETF closed at 2.594 after gaining 3.1% for the week, while the trading-value put-call ratio fell from 0.754 on October 26 to 0.575. The note…

OpțiuniVolatilitateSentimentPiețele din China
BigQuant

This weekly market commentary reviews Chinese equity index moves and sector themes for August 8–14, 2022. It highlights Chiplet-related stocks and financial firms, linking the former to semiconductor packaging developments and the latter to expectations…

AcțiuniPiețele din ChinaBazat pe evenimenteMărfuri