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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
Lumibot strategies
7 documente
QuantRocket
7 documente
Awesome Quant
1 documente

Caută în bibliotecă

3,481 documente

BigQuant

The article presents a short-term stock screening pattern built around three sequential signals. First, it looks for at least five generally rising sessions, allowing one modest down day while the overall movement stays upward. Next, it requires a session…

AcțiuniStrăpungereIndicatori tehnici
BigQuant

This tutorial outlines a TensorFlow LSTM workflow for forecasting stock or index returns. It recommends transforming closing prices into returns, then arranging recent observations into sequential input groups with the next observations as prediction labels.…

Învățare automatăAcțiuniPiețele din ChinaTestare istorică
BigQuant

This research summary compares ordinary cross-validation with time-series cross-validation for model tuning on sequential data. Randomly assigning observations to folds can train a model on future observations and validate it on the past, violating the…

Învățare automatăStatisticăTestare istoricăAcțiuni
BigQuant

This competition update reports diagnostics for the ten highest-ranked anonymous factors and tracks a CSI 1000 enhancement portfolio. Factor profiles include average Barra style exposures and sector-level average Rank ICs, calculated from cross-sectional…

AcțiuniInvestiții bazate pe factoriConstruirea portofoliuluiTestare istorică
BigQuant

The document introduces Moving Average Envelopes (MAE), a technical indicator built from a moving average of closing prices. It describes two bands: a lower envelope set below the average by a specified percentage and an upper envelope set above it by…

Indicatori tehnici
BigQuant

This study proposes adding daily risk monitoring to a traditional monthly rebalancing schedule for multi-factor equity portfolios. It tracks the portfolio’s exposure to the size factor during the month and rebalances on the next trading day when that…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

The article summarizes research on whether forced mutual fund selling affects the prices and liquidity of related stocks. It describes a learning mechanism: investors may initially interpret a peer stock’s decline as evidence of negative information about…

AcțiuniMicrostructura piețeiStatisticăGestionarea riscului
BigQuant

The report describes an expanded Black-Litterman asset allocation model that combines subjective views with quantitative portfolio construction. It uses behavioral finance and historical trend information, including momentum and reversal effects, to form…

Active din mai multe claseConstruirea portofoliuluiMomentumRevenire la medie
BigQuant

This example tests whether market capitalization is associated with next-day stock returns using repeated cross-sectional ordinary least squares regressions. It joins Chinese stock factor data to daily bars, calculates forward returns from the next close,…

AcțiuniInvestiții bazate pe factoriStatisticăTestare istorică
BigQuant

This research summary examines the validity of mainland China Stock Connect ownership data and describes portfolio strategies built from investor holdings, sector allocations, style exposures, leading trades, and net capital flows. It reports persistent…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriMomentum
BigQuant

This study builds a monthly Chinese equity factor from stock returns and volatility around intraday volume surges. It defines a surge as a minute when the increase in trading volume exceeds that stock’s daily average increase by more than one standard…

AcțiuniInvestiții bazate pe factoriIndicatori tehniciTestare istorică
BigQuant

This Chinese market note proposes that China’s credit impulse may be turning upward and argues that it tends to lead A-share earnings growth. On that assumption, it recommends overweighting Chinese equities, favoring growth over value, and ranking…

Active din mai multe claseAcțiuniInstrumente cu venit fixPiețele din China
BigQuant

This reference catalogues TA-Lib functions and their input fields for calculating common trading indicators. It groups examples into overlapping price studies, volatility and volume-price measures, cycle functions, price transformations, momentum indicators,…

Indicatori tehniciStatisticăVolatilitateMomentum
BigQuant

This stock selection method turns a discretionary approach into a set of technical and liquidity filters, followed by periodic rebalancing. It looks for prices mostly above long-term moving averages, favorable short-term moving-average conditions, and moving…

Piețele din ChinaAcțiuniMomentumUrmărirea tendinței
BigQuant

This Chinese-language article discusses the proposed introduction of market making on China’s STAR Market. It explains that market makers continuously quote prices, sell inventory to buyers, and use their own capital to buy from sellers. They bear price risk…

AcțiuniMarket makingMicrostructura piețeiGestionarea riscului
BigQuant

This BigQuant forum post presents a daily-bar backtest example that submits several limit buy orders during a bar callback. Before each order, the callback reads available cash and frozen cash from the trading account. The reported output shows those values…

AcțiuniTestare istoricăExecuțieGestionarea riscului
BigQuant

The article summarizes research on whether active mutual fund managers adjust trading to anticipated stock-picking opportunities. It defines stock-selection timing as trading more when opportunities to hold positive-alpha stocks are greater and less when…

AcțiuniInvestiții bazate pe factoriStatisticăTestare istorică
BigQuant

This overview surveys unsupervised learning through two groups of methods: dimensionality reduction and clustering. For dimensionality reduction, it describes PCA as a way to project high-dimensional features into fewer dimensions using covariance-matrix…

Învățare automatăStatistică
BigQuant

This study compares conventional return labels with alternative labels based on information ratio and Calmar ratio for AI stock selection models. It argues that labels incorporating returns, volatility, and drawdowns across a period may convey more…

Piețele din ChinaAcțiuniÎnvățare automatăInvestiții bazate pe factori
BigQuant

This document outlines a simple KDJ-based trading strategy for the Chinese stock 605588, with the CSI 300 index named as a reference benchmark. It describes selling when K, D, and J are all in high territory, and buying when all three are in low territory.…

Piețele din ChinaAcțiuniIndicatori tehnici
BigQuant

This article presents three MACD-based warnings for managing long equity positions: the indicator moving below its zero line, bearish divergence as price makes higher highs while MACD peaks decline, and a bearish crossover above zero. It interprets these…

AcțiuniIndicatori tehniciMomentumGestionarea riscului
BigQuant

This article outlines a research approach for Chinese equities during sharp market-wide declines. It proposes identifying historical selloff days and studying two groups: stocks that hit their upper price limit despite the market drop, and stocks that hit…

AcțiuniBazat pe evenimenteTestare istoricăPiețele din China
BigQuant

This note summarizes the AlexNet deep convolutional neural network described in the ImageNet classification paper. It outlines the model’s use of multiple convolutional channels, five convolutional stages and three fully connected layers, and explains how…

Învățare automatăStatistică
BigQuant

This strategy applies a stock-ranking model to a filtered pool of Chinese A-shares, aiming to find stocks that may rebound from depressed levels and hold up better when the broader market weakens. The target universe is the small and medium-sized board. Its…

AcțiuniÎnvățare automatăRevenire la mediePiețele din China