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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

The document describes a macro allocation framework built from six risk factors covering growth, inflation, rates, credit, foreign exchange, and term spreads. It combines momentum signals with cycle-phase estimates, maps the resulting states into…

Active din mai multe claseAcțiuniMărfuriMomentum
BigQuant

This research summary compares monthly factors built from price and volume data with quarterly factors built from financial statements. It reports that market data factors were more often effective in long-short portfolios, and examines their relationship…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriTestare istorică
BigQuant

This post describes a Chinese equity screen combining a daily amplitude threshold, exclusion of stocks that closed at a limit-up the prior day, and a positive MACD reading. The stated rationale is to favor stocks with room to move that also show positive…

AcțiuniPiețele din ChinaMomentumIndicatori tehnici
BigQuant

The report outlines a multi-asset allocation framework that links macroeconomic risks to broad asset classes, styles, and industries. It identifies economic growth, interest rates, exchange rates, inflation, and credit as five central risk factors. A…

Active din mai multe claseConstruirea portofoliuluiGestionarea risculuiStatistică
BigQuant

The research note studies tail dependence between individual stocks and the broader market. Using copulas, it estimates upper-tail and lower-tail correlation, which describe whether assets tend to move together during extreme outcomes. The reported…

AcțiuniInvestiții bazate pe factoriStatistică
BigQuant

The discussion explains a rolling sequence window as a way to assemble recent observations into a single training example. For instance, a model can receive a window of past factor values spanning several days, with the window advancing through time to…

Învățare automatăStatisticăAcțiuni
BigQuant

This tutorial introduces recurrent neural networks as models for sequential data, then explains long short-term memory networks as a design intended to address the difficulty ordinary RNNs have retaining information across long sequences. It describes the…

Învățare automatăAcțiuniStatisticăIndicatori tehnici
BigQuant

This strategy description applies a simple price rule to one Chinese stock. It defines a reference high as the highest closing price over the past 20 days and a reference low as the lowest closing price over the past 10 days. The stated entry condition is…

AcțiuniPiețele din ChinaUrmărirea tendințeiIndicatori tehnici
BigQuant

Average True Range (ATR), developed by J. Welles Wilder, measures price volatility rather than direction. The document defines true range as the greatest of the current high–low range and the distances from the previous close to the current high or low. ATR…

Indicatori tehniciVolatilitateGestionarea riscului
BigQuant

This Chinese equity screen selects stocks with amplitude above 1 and previous-day turnover above 8%, while excluding Beijing-listed shares. The article presents amplitude as a way to locate more volatile stocks and turnover as a sign of recent trading…

AcțiuniPiețele din ChinaVolatilitateExecuție
BigQuant

This Chinese-language strategy note tests a simple stock screen for mainland China A-shares. It selects companies using three annual-report criteria: net profit growth above a threshold, return on equity above a threshold for three consecutive years, and a…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriTestare istorică
BigQuant

This introductory tutorial explains machine learning concepts and linear regression with finance examples. It distinguishes features from labels, outlines training, validation, and test data, and contrasts supervised with unsupervised learning,…

Învățare automatăStatisticăInvestiții bazate pe factoriTestare istorică
BigQuant

This research review applies conditional generative adversarial networks (cGANs) to multi-asset allocation. It conditions the model on recent asset-return sequences and random inputs to generate possible future paths, then estimates expected returns and…

Active din mai multe claseÎnvățare automatăConstruirea portofoliuluiGestionarea riscului
BigQuant

The document raises a position-management idea for an equity holding: after a stock gains 10%, sell half the shares to secure some of the unrealized profit while leaving the rest invested for further gains. Its example starts with 1,000 shares and proposes…

AcțiuniDimensionarea pozițiilorGestionarea risculuiTestare istorică
BigQuant

This research summary describes two strategies using constituents of the Shanghai 50. The momentum version ranks stocks by their return over the previous three trading days, selects the three strongest, weights them equally, and holds each cohort for nine…

AcțiuniPiețele din ChinaMomentumRevenire la medie
BigQuant

This brief Chinese-language entry describes a lecture on deep reinforcement learning by researcher Matteo Hessel. It introduces general value functions (GVFs) as auxiliary tasks and mentions methods for handling scaling problems in reinforcement learning…

Învățare automată
BigQuant

This weekly report compares stock selection portfolios built with several artificial intelligence methods across broad Chinese A-share and index-constituent universes. It separates results for unconstrained selection, industry-neutral portfolios, and…

AcțiuniÎnvățare automatăTestare istoricăPiețele din China
BigQuant

This report evaluates momentum based sector allocation in Chinese equities. It compares ordinary lookback return ranking with risk-adjusted alternatives, including Sharpe ratio, information ratio, and a path-adjusted measure that divides return by the sum of…

AcțiuniMomentumInvestiții bazate pe factoriTestare istorică
BigQuant

This report summarizes quantitative approaches to enhancing returns over Chinese equity benchmarks while managing tracking error. It frames enhancement as a blend of active and passive investing and describes a top-down range of choices: market exposure,…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

This document outlines a monthly long-only stock selection strategy for the Chinese A-share market. It ranks the universe using a market-cap rank and a 20-day average of price changes, combines the two into a score, then holds 50 equally weighted stocks with…

Piețele din ChinaAcțiuniInvestiții bazate pe factoriMomentum
BigQuant

This research note explores additional technical factors for stock selection in the CSI 500 universe. It groups candidate measures into turnover behavior, illiquidity, and combined volume-price information. Turnover candidates include stability, coefficient…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriIndicatori tehnici
BigQuant

This report describes an initial step in building a multi-factor equity model: choosing which factors to combine. It proposes reducing the factor search space by including exposure relative to a benchmark as one screening criterion, alongside the strength of…

AcțiuniInvestiții bazate pe factoriConstruirea portofoliuluiStatistică
BigQuant

This example strategy searches Chinese A-shares for stocks that recently had a long run of consecutive limit-up sessions and then broke the streak. It further requires positive average returns over the three days after the break, excludes several board…

Piețele din ChinaAcțiuniMomentumTestare istorică
BigQuant

The page explains a practical issue in BigTrader’s simulated-trading workflow: strategy calculations run in a container on a daily schedule, so in-memory values may not carry over between runs. If a strategy needs to retain information such as its net asset…

ExecuțieGestionarea riscului