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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

A trader reports that a stock-selection strategy using TA-Lib indicators produces different results each time it is run. The response points to an SQL query in one module that lacks an explicit sort order as the likely cause. When rows arrive in an…

Testare istoricăStatistică
BigQuant

This article proposes a compact framework for reviewing the equity market by sector rather than inspecting every stock individually. To gauge sector persistence, it counts stocks moving from an initial limit-up session to a second consecutive limit-up…

AcțiuniMomentumGestionarea risculuiPiețele din China
BigQuant

This research summary describes a study of retail investor attention and asymmetric volatility in China’s A-share market. The authors use the balance of positive, negative, and neutral posts on online stock message boards to construct a proxy for asymmetry…

AcțiuniPiețele din ChinaSentimentVolatilitate
BigQuant

This user post describes rolling model training for stock forecasts using two approaches. The StockRanker example updates the model annually, fitting on one year of data and predicting over the following year. The XGBoost example instead constructs monthly…

AcțiuniÎnvățare automatăTestare istorică
BigQuant

This Chinese equity research excerpt analyzes the beer industry’s shift from pursuing output scale toward improving profitability through premium products. It separates the outlook into volume and price per ton: production depends on the number of operating…

AcțiuniPiețele din ChinaInvestiții bazate pe factori
BigQuant

This article examines how training-window length affects an AI stock-selection model. It compares long windows, expanded year by year from 2005 to 2021, with shorter windows ranging from a month to a few years. The author recommends checking each window…

Învățare automatăTestare istoricăInvestiții bazate pe factoriStatistică
BigQuant

This discussion addresses how to use factors from BigQuant's factor library as model features. The question asks whether a factor identifier can be used directly or whether users must copy its expression, and whether factors associated with a brokerage are…

Investiții bazate pe factoriÎnvățare automatăAcțiuni
BigQuant

This article introduces Granger causality and transfer entropy as ways to study directional predictive relationships in time series. Granger causality tests whether a source series improves forecasts of a target beyond the target’s own history, commonly…

StatisticăAcțiuniÎnvățare automatăSentiment
BigQuant

This article explains a proposed arbitrage mechanism involving Chinese A-shares, stock purchases, securities borrowing, and short sales. Its example describes buying shares to push the price higher, borrowing shares to sell at that higher price, and later…

AcțiuniPiețele din ChinaArbitrajMicrostructura pieței
BigQuant

This Chinese-language note describes a stock prediction exercise using four cross-sectional inputs: total market capitalization, turnover, trailing price-to-earnings, and quarterly net-profit year-over-year growth. It compares linear regression, XGBoost, and…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriÎnvățare automată
BigQuant

This document summarizes an introductory treatment of graph neural networks (GNNs), which learn from data represented as entities and their relationships. It explains how convolutional ideas used with images can be generalized to graph structures, whose…

Învățare automatăStatistică
BigQuant

The article reviews the long decline in mutual fund and ETF expense ratios and links it to investor choices, competition, and changes in financial advice and retirement plans. It distinguishes asset-weighted averages, which reflect where investors put their…

StatisticăConstruirea portofoliuluiPiețele din SUA
BigQuant

This research review summarizes a study of manager changes at Chinese equity funds, distinguishing internal transfers, moves to another public fund, moves to a private fund, and exits from the fund industry. The study compares benchmark-adjusted buy-and-hold…

AcțiuniPiețele din ChinaStatisticăGestionarea riscului
BigQuant

The article introduces a custom coded stock selection strategy built from five rules, mainly intended to identify stocks that have fallen sharply and begun to stabilize, with some rules meant to capture potential strong advances. It contrasts AI generated…

AcțiuniInvestiții bazate pe factoriTestare istoricăDimensionarea pozițiilor
BigQuant

The document asks whether minute or second level price and volume data can be aggregated into daily net flow measures for very large, large, medium, and small trades. It points readers to two implementations: a general set of high frequency factors and a…

AcțiuniTranzacționare de înaltă frecvențăInvestiții bazate pe factoriMicrostructura pieței
BigQuant

The document explains gold contracts for difference as cash-settled derivatives that provide exposure to gold price changes without holding bullion or futures. It outlines features such as leverage, smaller contract sizes, the ability to take long or short…

MărfuriEvaluarea derivatelorGestionarea riscului
BigQuant

This research review examines momentum in Chinese equities and compares several ways to measure it. It reports that simple one-year returns, with a recent period excluded, did not predict returns reliably across the full A-share market in the tested sample.…

AcțiuniMomentumInvestiții bazate pe factoriStatistică
BigQuant

This research summary examines the difference between monthly stock and industry behavior in Chinese equities: individual stocks are described as showing reversal, while industry indices show momentum. It proposes an industry-momentum factor based on the…

AcțiuniPiețele din ChinaMomentumRevenire la medie
BigQuant

This event description focuses on why quantitative strategy results in backtests can differ from live trading performance, a concern it identifies specifically for Chinese A-share strategies. It presents the topic as a practical challenge for traders and…

AcțiuniTestare istoricăStatistică
BigQuant

This article recommends a three-part process for improving trading discipline: record each trade’s rationale, timing, size, planned risk and reward, and emotional state; review those records regularly for rule violations and emotional decisions; then analyze…

Gestionarea risculuiDimensionarea pozițiilorStatistică
BigQuant

The article argues that trading researchers should judge machine learning papers by simplicity, reproducibility, and generality, alongside reported empirical results. A method that performs well on a benchmark may not transfer to financial prediction, and a…

Învățare automatăStatisticăTestare istoricăInvestiții bazate pe factori
BigQuant

This study compares ARIMA, a multilayer perceptron, an LSTM, and an ARIMA-GARCH combination for next-day NVIDIA share-price forecasts. It describes ARIMA as a model for linear time-series structure, neural networks as ways to capture nonlinear patterns, and…

AcțiuniÎnvățare automatăStatisticăVolatilitate
BigQuant

This interview discusses how artificial intelligence, large datasets, and cloud computing support one another: AI methods analyze data, while cloud infrastructure supplies scalable storage and computing. It maps the data industry across collection, storage,…

AcțiuniPiețele din ChinaÎnvățare automatăConstruirea portofoliului
BigQuant

The report proposes improving traditional liquidity factors for Chinese A-share stock selection by incorporating information about intraday trading structure. It frames the approach as a reusable factor research method: identify market information omitted by…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriMicrostructura pieței