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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

A user asks why backtest results change when the number of historical days fetched before a test is increased, even when the strategy calculates a moving average such as a ten-period average. The question assumes that fetching at least the indicator’s…

Testare istoricăAcțiuniStatistică
BigQuant

This post describes a simple stock-selection screen for Chinese equities. It seeks stocks with a daily trading range above a threshold, upward movement in short-term moving averages, and circulating market capitalization above a stated floor. The…

AcțiuniPiețele din ChinaIndicatori tehniciMomentum
BigQuant

This article explains Reflex and Trendflex, two price indicators attributed to John Ehlers. Both first smooth prices with a SuperSmoother low-pass filter, then measure average distance between the smoothed price path and a reference line. The distance is…

Contracte futuresCriptoUrmărirea tendințeiIndicatori tehnici
BigQuant

This research report develops equity-selection factors from four types of intraday information: price behavior, price-volume relationships, pre-open auction data, and selected trading periods such as the close or high-volume intervals. It evaluates 46…

AcțiuniPiețele din ChinaIndicatori tehniciMicrostructura pieței
BigQuant

This article compares neural networks, decision trees, random forests, support vector machines, and a stock-ranking algorithm for equity selection. It describes broad trade-offs: neural networks can model nonlinear relationships and varied data structures,…

AcțiuniÎnvățare automatăTestare istoricăPiețele din China
BigQuant

This report examines corporate tax burdens in China at the economy, industry, and listed-company levels. It distinguishes taxes from mandatory non-tax charges, describes differences across industries, and notes that the shift from business tax to value-added…

AcțiuniPiețele din ChinaStatistică
BigQuant

This overview explains how factor models can identify portfolio risk drivers and how factor strategies can target their associated return premia. It describes equity factors such as value, size, momentum, volatility, quality, yield, growth, and liquidity,…

Investiții bazate pe factoriActive din mai multe claseGestionarea risculuiConstruirea portofoliului
BigQuant

This educational-session overview introduces machine-learning and deep-learning approaches to quantitative stock selection. It lists core workflow topics: defining prediction labels, engineering features, separating training, validation, and test sets, and…

Învățare automatăAcțiuniStatisticăTestare istorică
BigQuant

The document answers a programming question about applying a per-stock price-pattern test to stocks first selected by daily market filters. The suggested workflow reads hourly bars for the candidate instruments in one batch, then passes the shared data frame…

AcțiuniIndicatori tehniciExecuție
BigQuant

The document explains the price-to-earnings ratio (P/E) as a way to relate a stock’s price to the company’s earnings per share. Comparing two stocks at the same price but with different earnings shows how the ratio distinguishes their valuations: the stock…

AcțiuniInvestiții bazate pe factoriStatistică
BigQuant

The report explains how option prices can be used to construct volatility and skew measures for the Chinese equity market. VIX represents expected volatility over a forward period, while SKEW captures the market’s pricing of asymmetric returns and tail risk.…

OpțiuniVolatilitateEvaluarea derivatelorPiețele din China
BigQuant

The article presents a discretionary momentum setup for Chinese equities that avoids chasing rapid advances and instead looks for a shift from prior weakness to renewed strength. It characterizes a previous session with heavy trading and a contested limit-up…

Piețele din ChinaAcțiuniMomentumStrăpungere
BigQuant

This short post introduces a template for monitoring a personally selected stock universe, aimed at individual investors who may trade manually rather than buy many stocks in batches. The stated use is to track dozens or hundreds of shares and view their…

AcțiuniPiețele din China
BigQuant

This summary of a Haitong Securities study examines whether the cyclically adjusted price-to-earnings ratio can predict long-run US equity returns. Historically, high CAPE readings tended to accompany weaker subsequent ten-year returns, and low readings…

AcțiuniPiețele din SUARevenire la medieStatistică
BigQuant

This study compares loss functions for neural models that forecast five-day cumulative returns for selected Chinese A-shares. It uses a DeepAlpha DNN as the main model and compares mean squared error with absolute error, Pseudo-Huber, negative information…

Învățare automatăAcțiuniPiețele din ChinaTestare istorică
BigQuant

This research summary explains why predictive machine-learning models can mistake unstable associations for causal relationships, especially when selection bias or confounding drives observed patterns. It presents propensity-score analysis as a way to make…

AcțiuniÎnvățare automatăStatisticăPiețele din China
BigQuant

This lesson explains binary classification through logistic regression, using the example of predicting whether a stock will rise. It describes encoding outcomes as zero and one, interpreting the model output as a probability, and applying a 0.5 cutoff to…

Învățare automatăStatisticăAcțiuni
BigQuant

The document introduces a video about applying “做T” trading—buying and selling within a trading period—to a value stock, using China Yangtze Power as its example. Its stated aim is to explore whether this approach can generate returns beyond simply holding…

AcțiuniPiețele din China
BigQuant

The article argues that algorithmic trading can exploit retail investors’ predictable reactions to price action and news in China’s A-share market. It illustrates this with two anecdotal patterns: a sharp late-session reversal in high-flying consumer stocks…

AcțiuniPiețele din ChinaMicrostructura piețeiSentiment
BigQuant

This research update describes two A-share stock-selection approaches. The product-price strategy tracks changes in listed companies’ main product prices and highlights cases where prices have risen but share prices may not yet reflect the development. The…

AcțiuniPiețele din ChinaBazat pe evenimenteSentiment
BigQuant

The report builds a composite trading-heat signal from stock-specificity and market-cap-adjusted turnover, using these trading-behavior measures as proxies for speculative activity. It argues that the two inputs capture most of the useful information in a…

AcțiuniInvestiții bazate pe factoriPiețele din ChinaTestare istorică
BigQuant

The document explains how members access BigQuant market data through its Python SDK. It outlines installation and login, describes checking table permissions, and demonstrates querying selected daily stock fields over a date range. It also shows how to…

Piețele din ChinaAcțiuniIndicatori tehniciStatistică
BigQuant

This overview explains how quantitative strategies use factors to express hypotheses about future returns. It describes candidate signal sources such as prices, fundamentals, macroeconomic data, and text, then outlines a research process: translate an…

Investiții bazate pe factoriStatisticăTestare istoricăGestionarea riscului
BigQuant

A BigQuant support discussion addresses a user’s difficulty training a StockRanker model for individual-stock prediction. The replies recommend moving to the newer StockRanker template because the older module is no longer actively maintained. A second…

AcțiuniÎnvățare automatăPiețele din China