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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
Prelegeri Quantopian
45 documente
Binance API docs
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 article introduces discrete-time stochastic control as a method for dynamic asset allocation. Unlike a traditional mean-variance allocation made without an explicit sequence of future decisions, the approach models portfolio choices across multiple…

Active din mai multe claseConstruirea portofoliuluiStatisticăPiețele din China
BigQuant

This brief support exchange explains why a MACD histogram value can shift substantially when the start date of a stock data list changes. The explanation points to the initialization of the exponential moving average: its first value is not fully smoothed,…

Indicatori tehniciStatistică
BigQuant

This document describes a Chinese stock strategy that combines classification and regression models to forecast intraday returns. It uses XGBoost and an ensemble of machine learning models, drawing on 15 features related to overnight returns, opening auction…

Piețele din ChinaAcțiuniÎnvățare automatăTestare istorică
BigQuant

This intraday system trades the CAC 40 on a 15-minute chart, taking both long and short positions during a defined daytime session. Long entries require the MACD line to cross above its signal line, RSI to remain below an upper threshold, and price to be…

AcțiuniIndicatori tehniciTestare istoricăExecuție
BigQuant

This report summary explores whether macroeconomic indicators can help select individual Chinese stocks, rather than serving only for asset allocation or industry rotation. It proposes estimating each stock’s sensitivity, or MacroBeta, to economic indicators…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriStatistică
BigQuant

This brief BigQuant platform note explains how to use a factor written as a SQL function and field within a strategy workflow. It distinguishes platform expression factors, described as an operator paired with a field, from SQL factors, described as a…

Investiții bazate pe factoriTestare istorică
BigQuant

This research review describes a study of whether year-over-year changes in company filings contain information about future stock returns and business performance. It compares filings using four text-similarity measures, sorts stocks by similarity, and…

AcțiuniSentimentÎnvățare automatăInvestiții bazate pe factori
BigQuant

The report describes a weekly market-timing model based on a linear-chain conditional random field (CRF). It frames the task as inferring market direction states from observed features, with CRFs able to represent dependencies between observations and…

AcțiuniPiețele din ChinaÎnvățare automatăTestare istorică
BigQuant

The document compares two ways to build multifactor equity portfolios. Integration ranks securities using a combined score for all target factors and selects a subset. Mixing first builds separate single-factor portfolios, then allocates among them. The…

AcțiuniInvestiții bazate pe factoriConstruirea portofoliuluiTestare istorică
BigQuant

This overview presents a multi-factor model as a way to explain stock returns through shared risk exposures, factor returns, and stock-specific residual returns. It frames quantitative management as estimating factor behavior and managing portfolio…

AcțiuniInvestiții bazate pe factoriStatisticăConstruirea portofoliului
BigQuant

This article describes modifying OpenFE’s automated feature engineering workflow for futures research. It adds rolling mean, standard deviation, and sum operators across instrument groups, with preset window lengths, and explains how these operators are…

Contracte futuresÎnvățare automatăInvestiții bazate pe factoriStatistică
BigQuant

This personal account explains how an equity prediction model can appear strong in-sample yet fail when applied to unfamiliar stocks or later periods. The described experiment uses fundamental and technical features, including valuation, profitability,…

AcțiuniÎnvățare automatăStatisticăTestare istorică
BigQuant

This example describes a short-horizon stock rotation strategy. It selects shares whose prior-day return was between 2% and 8% and whose turnover was between 3% and 8%, then removes special-treatment shares, halted stocks, and listings younger than the…

AcțiuniMomentumPiețele din ChinaExecuție
BigQuant

This report summary describes a method for refining traditional liquidity factors by incorporating intraday trading structure specific to China’s A-share market. The idea is to adapt a broad factor to market-specific trading patterns, with the report…

AcțiuniInvestiții bazate pe factoriMicrostructura piețeiStatistică
BigQuant

The note turns a discretionary stock method into explicit rules: admit a stock to an initial universe when its daily low stays above 99% of the 34-day moving average for ten consecutive days, buy when its profitable-chip proportion exceeds 73%, and sell when…

AcțiuniIndicatori tehniciInvestiții bazate pe factoriTestare istorică
BigQuant

This Chinese-language example sets up a test of whether stocks that have risen on each of the prior three days are more likely to rise the next day. It defines the condition as three consecutive daily price increases, each exceeding one percent, and labels…

AcțiuniMomentumStatistică
BigQuant

This strategy combines model-based stock ranking with trend filters for a selected universe of China A-shares drawn from the CSI 150 and related large-cap core-asset stocks. It aims to rank candidates for potential five-day strength, then apply timing…

Piețele din ChinaAcțiuniÎnvățare automatăMomentum
BigQuant

This research note examines an equity-selection factor built from differences in stock price behavior across the trading day. It reports that the original APM factor performed well in its in-sample period, weakened out of sample, and suffered a sustained…

AcțiuniInvestiții bazate pe factoriStatisticăTestare istorică
BigQuant

This article is a catalog of beginner-oriented examples for learning TensorFlow and TFLearn. It lists introductory operations, nearest-neighbor and regression models, neural networks, autoencoders, model saving, visualization, and multi-GPU basics. The…

Învățare automată
BigQuant

The document compares a traditional Chinese equity strategy that buys the smallest market-cap stocks with a StockRanker model that ranks stocks using market capitalization as its sole feature. Both are evaluated over 2015–2016, illustrating how a…

AcțiuniInvestiții bazate pe factoriÎnvățare automatăTestare istorică
BigQuant

The article reviews the 2021 revision of China's Shenwan industry classification and its possible uses in quantitative investing. The revision expanded coverage to A-shares, Stock Connect shares, and selected Chinese firms listed overseas, added three…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

The document describes a daily process for building a short-term stock watchlist. It starts by ranking stocks by traded value and keeping the top 50. It then removes stocks that gained less than 5% that day, those whose 30-day moving average is flat or…

AcțiuniMomentumStrăpungereIndicatori tehnici
BigQuant

The document surveys the development of enhanced ETFs in China alongside the expansion of actively managed ETFs in the United States. It presents enhanced ETFs as an effort to seek returns above a benchmark while retaining ETF features such as intraday…

AcțiuniConstruirea portofoliuluiExecuțieGestionarea riscului
BigQuant

This 2020 China equity-sector note combines macroeconomic regime signals, earnings relative to valuation, trend signals, historical regime matching, and public-fund holdings to discuss sector allocation ahead of interim reports. It argues that weaker growth…

Piețele din ChinaAcțiuniMomentumInvestiții bazate pe factori