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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

This report discusses using optimization methods for asset allocation and portfolio construction, especially when objectives are complex, high dimensional, discontinuous, or heavily constrained. It argues that the in-sample optimum may not remain optimal in…

Construirea portofoliuluiTestare istoricăActive din mai multe claseStatistică
BigQuant

This Chinese-language document outlines how asset allocation models and fund portfolios can support wealth management. It frames portfolio design as a way to make investment choices more accessible to clients facing gaps in financial knowledge, behavioral…

Construirea portofoliuluiActive din mai multe clasePiețele din China
BigQuant

This report extends a factor attribution framework for equity funds from quantitative strategies to actively managed funds. It argues that the factor set should reflect the fund’s likely investment approach and risk exposures. Active managers may share…

AcțiuniInvestiții bazate pe factoriConstruirea portofoliuluiStatistică
BigQuant

The article describes training BigQuant’s StockRanker machine-learning model using an overnight-return factor, following earlier factor research that links this signal to informed trading. Training uses all A-share stocks from 2013 through the start of 2021.…

AcțiuniPiețele din ChinaÎnvățare automatăInvestiții bazate pe factori
BigQuant

This research summary examines how market-cap style relates to trader behavior and trading activity in Chinese equities. It argues that shifts between large- and small-cap stocks involve changes in capital flows, and that market capitalization is closely…

AcțiuniInvestiții bazate pe factoriStatisticăPiețele din China
BigQuant

The report summary describes a strategy that varies fundamental factor exposures according to recurring seasons around company financial reports. It groups the calendar into four windows: annual-report disclosure, post-report performance trading,…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

The document introduces an index-enhancement research report about building longer-horizon price and volume factors from mid- and high-frequency data. Its central motivation is a mismatch between data frequency and forecast horizon: as sampling frequency…

AcțiuniMicrostructura piețeiInvestiții bazate pe factoriStatistică
BigQuant

This overview explains how environmental, social, and governance considerations can enter investment decisions alongside conventional financial analysis. It surveys approaches including positive and negative screening, thematic allocation, impact investing,…

AcțiuniActive din mai multe claseInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

This brief strategy entry describes an A-share stock-selection example built around a k-nearest neighbors classifier. The accompanying discussion clarifies that the target is binary: a stock is labeled positive when its five-day return is above zero and…

AcțiuniPiețele din ChinaÎnvățare automatăStatistică
BigQuant

This overview explains structured snowball options, focusing on how their coupons and risks arise. It describes coupon sources as exposure to the underlying asset's volatility, futures basis convergence for products linked to the China 500 index, and returns…

OpțiuniEvaluarea derivatelorVolatilitateGestionarea riscului
BigQuant

This research summary tests stock selection factors built from return and trading-volume data, using moments from the first through fourth orders. It places these measures in a broader taxonomy: factors can use one data dimension, combine dimensions at one…

AcțiuniTranzacționare de înaltă frecvențăInvestiții bazate pe factoriStatistică
BigQuant

The article argues that traders should avoid entering sectors after a short burst of broad attention and instead look for less crowded themes. It proposes treating three consecutive strong sessions and a sharp cumulative rise in leading stocks as a possible…

AcțiuniMomentumStrăpungereSentiment
BigQuant

The discussion addresses how to combine daily observations with data from a longer period, such as the previous month, when constructing factors. Its suggested workflow is to convert daily market data into weekly or monthly bars with Python resampling, then…

Investiții bazate pe factoriStatisticăTestare istorică
BigQuant

This Chinese-language survey discusses fundamental allocation, mean-variance optimization, Kelly-CVaR, Black-Litterman, and risk-parity approaches. Fundamental methods include mapping economic-cycle phases to asset weights and using macroeconomic factors to…

Active din mai multe claseConstruirea portofoliuluiGestionarea risculuiTestare istorică
BigQuant

This Chinese research abstract proposes using sales data from Pinduoduo to follow consumption patterns, particularly among consumers in China’s lower tier cities. It frames the analysis against rising household leverage, high housing costs, and slowing…

Piețele din ChinaStatisticăAcțiuni
BigQuant

This brief discussion shows how to retrieve financial factor data through a data-access module using an SQL query. Its example selects dates, instrument identifiers, and a trailing-twelve-month operating-profit growth field from a Chinese stock…

Investiții bazate pe factoriAcțiuniÎnvățare automatăTestare istorică
BigQuant

This short strategy note describes an iterative experiment in which initial parameter adjustments appeared unpromising. The author then added a weight based on closing-price ranking and reports that the resulting Sharpe ratio exceeded 1.5. The takeaway is…

AcțiuniInvestiții bazate pe factoriTestare istoricăIndicatori tehnici
BigQuant

The document outlines a dynamic scenario alpha model (DCAM), which layers scenario classification over a conventional static alpha model. It argues that scenarios are useful when their subdivisions correspond to meaningfully different drivers or importance…

AcțiuniInvestiții bazate pe factoriStatisticăConstruirea portofoliului
BigQuant

This research overview explains how knowledge graphs represent entities and their relationships, then outlines a construction process: gather data, preprocess it, identify entities and links, reconcile information from different sources, form relationship…

AcțiuniBazat pe evenimenteInvestiții bazate pe factoriÎnvățare automată
BigQuant

This research summary reviews a study of whether mutual fund managers’ personal investments in their own funds are related to fund risk. Using disclosures of investment ranges, fund holdings, and risk measures for actively managed U.S. equity funds from 2007…

AcțiuniGestionarea risculuiConstruirea portofoliuluiStatistică
BigQuant

This article summarizes research on how mutual funds’ trading decisions can lead other funds and how this herding relates to fund returns. It defines buy-leading and sell-leading funds according to whether peers follow their stock purchases or sales in…

AcțiuniStatisticăMicrostructura piețeiPiețele din SUA
BigQuant

This research note adapts the four phase investment clock to Chinese equities. It describes recovery, overheating, stagflation, and recession as distinct macroeconomic environments, and explains the traditional asset preferences associated with each. Because…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriConstruirea portofoliului
BigQuant

The document describes an integrated model for estimating how crowded equity factor strategies may be. It combines five standardized signals: valuation spreads between high and low factor-ranked stocks, short-interest spreads, within-group return…

AcțiuniInvestiții bazate pe factoriGestionarea risculuiVolatilitate
BigQuant

The document describes an Expert Advisor that uses a single-layer neural network, identified as a perceptron, to classify the direction of quote movement. The implementation draws on the iAC indicator and bar opening-time history. It also reports a testing…

Învățare automatăForexTestare istorică