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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
Quantpedia
86 documente
TqSdk
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 study describes active risk parity as a portfolio built around a risk-parity allocation, with controlled deviations based on investment views. In its two-asset example, Chinese equities and corporate bonds are rebalanced using rolling covariance…

Active din mai multe claseConstruirea portofoliuluiGestionarea risculuiMomentum
BigQuant

This April 2020 market-monitoring report combines valuation, trading activity, sentiment, sector rotation, capital concentration, and style-factor observations for Chinese equities. It describes sentiment as having recovered to an optimistic range after a…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriMomentum
BigQuant

This research summary describes stock-selection factors built from tick-level aggressive buy and sell amounts. It applies an informed-trading probability model to filter noisy order flow, then forms informed sell share, informed buy share, and net informed…

AcțiuniInvestiții bazate pe factoriMicrostructura piețeiStatistică
BigQuant

This overview contrasts efficient markets, where prices are assumed to incorporate available information, with inefficient markets, where prices may diverge from estimated value. It outlines the weak, semi-strong, and strong forms of the efficient market…

StatisticăArbitrajTranzacționarea perechilorSentiment
BigQuant

This research summary examines factor weighting, orthogonalization, and timing through the relationship between composite information-coefficient weighting and Fama–MacBeth regressions. Under the stated condition that returns and factors are z-scored, it…

Investiții bazate pe factoriStatisticăConstruirea portofoliului
BigQuant

The article describes adding a trailing stop to a stock selection and rotation workflow. Its example sells a holding when the latest price falls more than 10% from the highest price observed since purchase. It recommends tracking stop-triggered stocks,…

AcțiuniGestionarea risculuiExecuție
BigQuant

This personal account discusses trading strong Chinese stocks during a broad market decline. The author describes using stock-ranking models to pursue strong stocks, next-day rebounds after pullbacks, and limit-up leaders, while emphasizing that these…

AcțiuniPiețele din ChinaMomentumGestionarea riscului
BigQuant

This meetup compilation surveys practical topics in quantitative investing, including factor design, machine learning, strategy validation, portfolio sizing, live execution, and sector rotation. It recommends building a factor library by collecting and…

Investiții bazate pe factoriÎnvățare automatăTestare istoricăConstruirea portofoliului
BigQuant

The document describes a library that packages deep learning models for quantitative research, including feedforward networks, LSTMs, Transformers, TabNet, and TRA. Its quick-start workflow covers selecting a model, configuring an optimizer and loss…

Învățare automatăAcțiuniTestare istorică
BigQuant

This A-share strategy narrows a stock universe in three stages: rank industry or concept indices by short- and medium-term momentum, select stocks with a current limit-up and a recent prior limit-up, then confirm strength after the open before buying.…

AcțiuniPiețele din ChinaMomentumStrăpungere
BigQuant

This advice article presents five principles for surviving in stock trading: use technical analysis as context, follow established trends, keep short-term trades distinct from long-term investments, assess broad market direction, and protect capital by…

AcțiuniUrmărirea tendințeiIndicatori tehniciGestionarea riscului
BigQuant

This overview explains machine learning as a way to model patterns in data and describes how that capability may be applied to investment decisions. It outlines a workflow spanning data collection, feature creation, preprocessing, model training, model…

Învățare automatăStatisticăAcțiuni
BigQuant

This note describes a reproduction of a sell-side factor based on overnight stock returns, intended to capture information advantages held by informed traders. It says the research defines the factor in stages, from a traditional overnight-return measure to…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriTestare istorică
BigQuant

The article describes a research workflow that pairs a language model’s reading of crypto news with live exchange data. The agent identifies token mentions, retrieves current prices and four-hour candles, and can consult order books when warranted. It then…

CriptoSentimentIndicatori tehniciMicrostructura pieței
BigQuant

This 2020 market note reviews Chinese equity style performance and outlines a short-term outlook. It reports that small-cap reversal strengthened, value began to recover, and earnings-growth exposure showed a brief rebound. Its recommendations draw on…

AcțiuniPiețele din ChinaRevenire la medieInvestiții bazate pe factori
BigQuant

This discussion distinguishes two approaches to Chinese A-share limit-up trading: a daily model that forecasts which stocks may hit the limit the next day, and an intraday model that uses current market information to identify candidates for the remainder of…

AcțiuniPiețele din ChinaMomentumIndicatori tehnici
BigQuant

A BigQuant user reports an apparent issue when a strategy sells before buying back to close a position. In the user's tests, buybacks above half of the initial capital fail, while smaller buybacks work. The report describes varying the amount used for the…

Piețele din ChinaExecuțieDimensionarea pozițiilor
BigQuant

This article summarizes a study of U.S. mutual funds that links SEC N-SAR options disclosures with fund returns and characteristics. The sample covers 1998 through November 2013 and includes 106,357 filings. Cross-sectional and panel analyses compare funds…

OpțiuniAcțiuniGestionarea risculuiStatistică
BigQuant

The article explains why standard stochastic gradient descent can remain inefficient when training Word2Vec on a large vocabulary. Even when an update concerns only a few words, calculating gradients across the full parameter matrices wastes work, and the…

Învățare automatăStatistică
BigQuant

This brief support exchange addresses whether a strategy’s data is standardized anew each day during simulated trading. The response says that when the strategy uses parameters bound for live trading, simulated trading standardizes that day’s data. A…

StatisticăTestare istorică
BigQuant

This overview explains XGBoost regression as an ensemble of decision trees built sequentially to reduce a loss. Each new tree uses gradient information from prior predictions, while complexity controls and regularization are intended to limit overfitting.…

Învățare automatăAcțiuniStatisticăTestare istorică
BigQuant

This document explains a reverse-doubling strategy designed for an older crypto-margined futures venue. It focuses on the strategy’s control flow: open a position, place take-profit orders, monitor both profit-taking and reverse stop triggers, cancel…

CriptoContracte futuresGestionarea risculuiDimensionarea pozițiilor