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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 research summary describes a bull-bear indicator built from changes in equity-market volatility and turnover. It classifies conditions according to whether each measure is rising or falling. Rising volatility with falling turnover is presented as a…

AcțiuniPiețele din ChinaVolatilitateIndicatori tehnici
BigQuant

The article surveys execution algorithms by their main objective: reducing market impact, controlling total trading costs, or adapting execution to favorable market conditions. It describes TWAP as time-based order slicing, VWAP as matching execution to…

ExecuțieMicrostructura piețeiGestionarea riscului
BigQuant

This study summary examines an improvement to a one-sided volatility-difference timing model. It reports that bullish signal accuracy rises with trading amount, while bearish signal win rates show no similar relationship. The proposed change uses relative…

Piețele din ChinaAcțiuniIndicatori tehniciTestare istorică
BigQuant

This research summary argues that low valuation alone can select weak businesses, so investors should assess company quality before deciding whether its price is reasonable. It evaluates A-share companies using indicators related to profitability, growth,…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

This research summary compares five classifiers for predicting stock returns from historical equity factors: multinomial logistic regression, linear support vector machines, random forests, XGBoost, and deep neural networks. It distinguishes the linear…

AcțiuniInvestiții bazate pe factoriÎnvățare automatăStatistică
BigQuant

The document reports a mismatch between a portfolio strategy’s expected rebalance schedule and its simulated trading activity. The author says the backtest rebalances every five days or after a take-profit trigger, while the simulation records trades on many…

Testare istoricăExecuțieConstruirea portofoliuluiPiețele din China
BigQuant

The document summarizes an analysis of Chinese A-share investor research visits, using public company announcements to identify when institutions visit firms. These events are usually targeted visits involving five or fewer institutions, and announcements…

AcțiuniBazat pe evenimenteStatisticăPiețele din China
BigQuant

This note diagnoses a backtest error in BigQuant position-condition functions. Both the opening and closing condition functions attempt to filter a DataFrame using a column named “fantanbili.” The author points out that the prediction output supplied by an…

Testare istoricăStatistică
BigQuant

The document outlines a systematic stock-selection model built from valuation, profitability, growth, operating-efficiency, and technical factors. It describes a scoring approach: choose candidate factors, test their predictive usefulness, remove redundant…

AcțiuniInvestiții bazate pe factoriStatisticăTestare istorică
BigQuant

This summary examines whether frequent constituent changes weaken returns for a portfolio tracking the CSI 300. It reports a Wilcoxon test finding short-term price effects around index adjustments: additions tended to rise and removals to fall, with the…

AcțiuniPiețele din ChinaConstruirea portofoliuluiTestare istorică
BigQuant

The article explains “price desensitization”: a cyclical company’s shares stop advancing, or weaken, while the underlying commodity price continues to rise. Its proposed rationale is that equity prices anticipate earnings and may have priced in future gains…

AcțiuniMărfuriMomentumGestionarea riscului
BigQuant

This Chinese-language manual explains a portfolio optimizer for constructing benchmark-aware equity portfolios. It describes initializing the optimizer with a dated stock universe and benchmark, refreshing factor data for each trading date, and then…

AcțiuniConstruirea portofoliuluiInvestiții bazate pe factoriGestionarea riscului
BigQuant

This research digest summarizes two studies on equity signals. The first trains a deep neural network on five years of fundamental data to forecast future fundamentals. The summary reports that, relative to a simple forecast assuming current data persist,…

AcțiuniÎnvățare automatăInvestiții bazate pe factoriSentiment
BigQuant

This document summarizes a framework for deciding whether and how to combine single-factor smart beta strategies with multifactor portfolios. It represents each candidate strategy as exposures to a small set of investable elementary smart betas—initially…

Investiții bazate pe factoriConstruirea portofoliuluiGestionarea risculuiAcțiuni
BigQuant

This introductory guide surveys algorithmic trading, from the history and terminology of financial markets to the roles of quants, traders, and market makers. It describes common benefits of automation, such as speed and scalability, alongside constraints…

Testare istoricăGestionarea risculuiMomentumRevenire la medie
BigQuant

This strategy seeks to outperform the CSI 1000 while controlling industry, style, and individual-stock exposures. It proposes combining value, growth, size, quality, volatility or liquidity, and alternative signals. Factors are cleaned for outliers,…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

The document summarizes a study of a composite fundamental factor based on the Piotroski framework and its stock-selection value in the Chinese A-share market. It reports that stocks in the highest factor decile outperformed those in the lowest by an average…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriStatistică
BigQuant

A user asks how to express a specific six-session price pattern in a platform’s daily factor-expression syntax. The condition is that the latest close exceeds each of the previous five closes, while the close from five sessions earlier also exceeds the…

AcțiuniIndicatori tehniciInvestiții bazate pe factori
BigQuant

This example equity strategy selects stocks using valuation and trading activity. At each 30-trading-day interval, it buys at the open shares with positive price-to-book ratios below 1.5, price-to-earnings ratios below 15, and volume between 1.5 and 2.5…

AcțiuniPiețele din ChinaIndicatori tehniciTestare istorică
BigQuant

The document presents asset allocation as a combination of choosing assets and timing the market, with portfolio risk and expected return as the objectives. It explains that a hidden Markov model infers unobservable market states from observable data, then…

Învățare automatăStatisticăConstruirea portofoliuluiPiețele din China
BigQuant

This study describes a revised deep Q-network approach for timing individual Chinese stocks. The earlier version used daily OHLCV data and several common factors as its state. The revision instead takes a configurable window of recent closing prices and…

AcțiuniÎnvățare automatăTestare istoricăDimensionarea pozițiilor
BigQuant

This forum discussion asks how a visual stock-selection strategy combines multiple factors when no explicit weights are visible, and how the same logic might be represented in SQL. The example lists dividend yield, price-to-book, return on equity, and profit…

AcțiuniInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

The document explains why a stock filter set to the CSI 500 can return fewer securities than the index membership count. In the cited example, the filter is applied to a particular date, and some constituents were suspended, leaving their five-day return…

AcțiuniPiețele din ChinaStatistică