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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
Alphalens
14 documente
WonderTrader
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

This forum post raises a data-preparation problem in an LSTM trading strategy. The author reports that filtering rows before rolling sequence windows can remove observations that should remain part of the temporal context, causing later observations to shift…

Învățare automatăAcțiuniStatisticăTestare istorică
BigQuant

The article compares two ways to build equity strategies: train a predictive model on chosen factors and a defined stock universe, or specify screening conditions directly. The AI approach can be quicker to assemble, but its behavior may be hard to diagnose…

AcțiuniÎnvățare automatăInvestiții bazate pe factoriTestare istorică
BigQuant

The report reassesses valuation, growth, technical, and risk factors in Chinese A-shares after removing industry and style influences. Its core method is to adjust factor data for exposures such as industry, market capitalization, or beta, then test whether…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriTestare istorică
BigQuant

Fundamental quantitative investing applies statistical methods and algorithms to company accounts, industry information, and macroeconomic data to rank stocks by estimated value or quality. The document describes analyzing profitability, financial strength,…

AcțiuniInvestiții bazate pe factoriConstruirea portofoliuluiStatistică
BigQuant

The paper examines how five emotions expressed on Sina Weibo—anger, disgust, fear, joy, and sadness—relate to Chinese stock returns. It uses machine learning to classify posts and wavelet analysis to study how the relationship changes over time and across…

Piețele din ChinaAcțiuniSentimentÎnvățare automată
BigQuant

This competition entry modifies a Deep Alpha convolutional neural network by supplying five time slices for each of 98 factors, rather than using a single slice. The network structure and most parameters remain close to the original model because training…

AcțiuniÎnvățare automatăInvestiții bazate pe factoriTestare istorică
BigQuant

This report examines changes in concentrated holdings among Chinese active equity funds using quarterly disclosures and a daily estimate of fund exposure to a core group of crowded stocks. It compares the core group’s holdings over time, separates changes in…

AcțiuniPiețele din ChinaStatisticăInvestiții bazate pe factori
BigQuant

This support exchange concerns an IndexError that occurs when a Chinese convertible bond data table is read during a backtest. The same table can be read outside the backtest, and comparable reads of other tables within the backtest reportedly succeed. That…

Testare istoricăStatisticăPiețele din China
BigQuant

This short BigQuant assignment describes a daily stock-ranking approach built from a combination of total equity and trailing-twelve-month average return on equity. Each day, the strategy holds stocks with comparatively high values of the combined factor. It…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

This overview introduces three types of company data associated with FactSet: a six-level product and service based industry classification, company-to-company supply-chain relationships, and geographic revenue exposure. The classification is presented as a…

AcțiuniInvestiții bazate pe factoriBazat pe evenimenteConstruirea portofoliului
BigQuant

This forum exchange explains why a sell order can disappear in an event-driven backtest when the stock is suspended on the scheduled execution day. The order was generated on one day, but the attempted match on the next day failed because trading was halted;…

AcțiuniTestare istoricăExecuție
BigQuant

This weekly report describes two quantitative equity approaches. The first builds on holdings of successful active funds, then applies quantitative selection to create an enhanced portfolio benchmarked against active equity funds rather than broad market…

AcțiuniInvestiții bazate pe factoriBazat pe evenimenteIndicatori tehnici
BigQuant

This note explains beta as a measure of an asset’s market risk exposure and introduces the Fama–French three-factor model as a way to capture size and value style exposures. It describes how to quantify style performance with long-short portfolios: small-cap…

AcțiuniInvestiții bazate pe factoriStatisticăPiețele din China
BigQuant

This research proposes a stock selection factor that estimates similarity between companies using ten financial measures spanning profitability, solvency, operating efficiency, and growth. After cross-sectional normalization, it uses cosine similarity to…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriMomentum
BigQuant

This post sketches an experimental model for identifying leading stocks, using gradient boosted regression to score shares within a rolling window. The proposed label gives higher scores to stocks that rise more, experience smaller drawdowns, and break out…

AcțiuniÎnvățare automatăStrăpungereGestionarea riscului
BigQuant

This China equity strategy ranks stocks cross-sectionally using circulating market capitalization and earnings per share, combining the rankings into a total score and sorting from low to high. It excludes special-treatment and suspended stocks, requires…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriTestare istorică
BigQuant

The article argues that retail traders often sell during fear and re-enter during excitement, while institutions may accumulate during declines and distribute into renewed demand. It recommends treating oneself as a follower, watching capital flows, and…

SentimentMicrostructura piețeiGestionarea riscului
BigQuant

This review of research on equity factor investing explains why low unconditional correlations between factors may not provide protection when macroeconomic conditions deteriorate. It describes selecting fast-moving state variables that reflect expectations…

AcțiuniInvestiții bazate pe factoriConstruirea portofoliuluiGestionarea riscului
BigQuant

This report examines why investment portfolios turn over, how to manage that turnover, and how turnover control affects portfolio outcomes. It frames the drivers across the research and trading process: alpha changes influence target holdings, optimization…

Construirea portofoliuluiExecuțieGestionarea risculuiAcțiuni
BigQuant

The document describes a hybrid approach that combines ARMA-GARCH models with several neural network types to identify market patterns and predict the direction of shocks in defense equities and foreign exchange. ARMA-GARCH models capture linear time-series…

Învățare automatăStatisticăVolatilitateAcțiuni
BigQuant

This note describes a daily liquidity signal built from intraday order book snapshots. It sums bid and ask volumes across the first five levels, then measures their normalized imbalance as the difference divided by the total. Separately, it calculates…

Investiții bazate pe factoriMicrostructura piețeiStatistică
BigQuant

The document briefly introduces a multi-factor investment platform, focusing on how to estimate stock and portfolio factor exposures. Stock exposures are derived from database values such as price-to-earnings and price-to-book ratios, then cleaned and…

Investiții bazate pe factoriConstruirea portofoliuluiStatistică