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Kunnskapsbibliotek

Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.

Quant Q&A
20,364 dokumenter
SuperMind
12,226 dokumenter
OKX Learn
8,431 dokumenter
Strategy library
7,910 dokumenter
MQL5 code base
7,090 dokumenter
BigQuant
3,481 dokumenter
Bitget Academy
3,298 dokumenter
MQL5 articles
3,012 dokumenter
TradingView scripts
1,976 dokumenter
ProRealCode
1,507 dokumenter
Deribit Insights
1,232 dokumenter
Machine Learning for Trading
1,124 dokumenter
arXiv papers
1,033 dokumenter
Amberdata research
766 dokumenter
FMZ forum
682 dokumenter
FMZ digest
662 dokumenter
vn.py community
560 dokumenter
QuantInsti blog
511 dokumenter
Galaxy Research
340 dokumenter
QuantStart
246 dokumenter
Stratmill research code
219 dokumenter
Robot Wealth
195 dokumenter
NautilusTrader
191 dokumenter
Hummingbot docs
181 dokumenter
Paradigm research
175 dokumenter
Lumibot
164 dokumenter
Kraken Learn
163 dokumenter
Bibliotek for kvantkurs
157 dokumenter
OctoBot
152 dokumenter
Cryptohopper blog
144 dokumenter
Systematic trading blog (Rob Carver)
132 dokumenter
Qlib
116 dokumenter
TqSdk
86 dokumenter
Quantpedia
86 dokumenter
Hyperliquid docs
79 dokumenter
Freqtrade
68 dokumenter
Hudson & Thames
62 dokumenter
Awesome Systematic Trading
61 dokumenter
backtrader
54 dokumenter
vn.py
50 dokumenter
Binance API docs
45 dokumenter
Quantopian-forelesninger
45 dokumenter
FMZ guides
38 dokumenter
pysystemtrade
34 dokumenter
Freqtrade docs
32 dokumenter
quant-trading
31 dokumenter
FinRL
28 dokumenter
Zipline
22 dokumenter
FMZ live strategies
21 dokumenter
Jesse
17 dokumenter
pyfolio
16 dokumenter
Alphalens
14 dokumenter
WonderTrader
14 dokumenter
backtesting.py
11 dokumenter
Technical Analysis
9 dokumenter
QTPyLib
8 dokumenter
QuantRocket
7 dokumenter
Lumibot strategies
7 dokumenter
Awesome Quant
1 dokumenter

Søk i biblioteket

3,481 dokumenter

BigQuant

This forum post presents a Chinese stock strategy that reportedly produced backtest trades but no live signals. Its example queries daily stock factors for non-ST, non-suspended companies outside specified listing sectors, then applies filters for minimum…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This tutorial explains XGBoost through the broader framework of supervised learning and gradient-boosted decision trees. It introduces loss functions for regression and classification, then describes an ensemble as the sum of tree outputs. Training adds one…

MaskinlæringStatistikk
BigQuant

This Chinese-language listing introduces a talk by the CEO of quantitative investment firm High-Flyer. The described presentation surveys what artificial intelligence is, how it has developed and been applied, examples of AI in investment, the firm’s work in…

MaskinlæringAksjerPorteføljekonstruksjon
BigQuant

This document summarizes a study of whether slow updating of beliefs about future earnings can account for the profitability anomaly: the tendency for more profitable stocks to outperform. In its model, expectations combine prior beliefs with rational…

AksjerFaktorinvesteringStatistikkMomentum
BigQuant

A forum question presents Python code that tests combinations of rolling volume windows and percentile thresholds on a Chinese stock index series. The script creates a signal when average volume over a chosen window exceeds a five-year rolling percentile,…

AksjerKinesiske markederStatistikkHistorisk testing
BigQuant

This note presents a simplified Turtle trend-following system and applies it to a rubber futures contract. It frames the approach around seeking positive long-run expectancy, controlling risk, following rules consistently, and favoring simplicity. The…

FuturesRåvarerTrendfølgende handelKursbrudd
BigQuant

This research examines whether large and small order money flows predict cross-sectional stock returns. It standardizes net inflow by trading value to form separate factors, then compares stocks grouped by factor strength. The reported results show a…

AksjerFaktorinvesteringStatistikkKinesiske markeder
BigQuant

This introductory overview explains neural networks through the progression from a single perceptron to deeper architectures. It describes weighted inputs, activation functions, loss minimization, and gradient descent, noting how learning rate affects…

MaskinlæringStatistikkAksjerKinesiske markeder
BigQuant

The study tests whether hedge funds can time returns in particular industries and whether measured timing ability predicts later fund outcomes. It first removes market exposure from industry returns, then estimates each fund’s industry timing coefficient…

StatistikkHendelsesdrevet handelAksjerFaktorinvestering
BigQuant

The document discusses pricing path-dependent snowball structured notes by decomposing their payoff scenarios into exotic option components and valuing them with Monte Carlo simulation or partial differential equations. It also considers how equity index…

OpsjonerPrising av derivaterVolatilitetRisikostyring
BigQuant

The article presents a discretionary short-term stock selection approach for Chinese A-shares. It recommends tracking themes with broad limit-up participation and persistence, then considering entries after follow-through is confirmed. Within a strong theme,…

Kinesiske markederAksjerMomentumKursbrudd
BigQuant

This document summarizes a method for evaluating mutual fund managers while accounting for both factor exposures and the characteristics of the stocks held. Its premise is that standard factor models, including the Carhart four-factor model, may leave return…

AksjerFaktorinvesteringPorteføljekonstruksjonStatistikk
BigQuant

This meetup summary collects questions and answers on quantitative research, strategy development, and live trading. It points readers toward factor analysis, information coefficient interpretation, portfolio performance assessment, and resampling data to…

AksjerKryptoFaktorinvesteringHistorisk testing
BigQuant

This research summary describes a Chinese equity approach that first screens stocks within broad industry groups using selected fundamental indicators, then combines the resulting preferred-stock universe with a multi-factor framework. It discusses a…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This overview explains feature engineering as the work of turning raw market and company data into useful inputs for quantitative strategies and machine-learning models. It uses a dual moving-average crossover to show that a trading rule can be understood…

MaskinlæringStatistikkFaktorinvesteringTekniske indikatorer
BigQuant

The document explains how small-cap and value premiums in China’s A-share market may vary over time. It frames stock valuation as depending on expected future cash flows and the discount rates applied to them. A two-beta model separates a stock’s sensitivity…

AksjerFaktorinvesteringKinesiske markederPorteføljekonstruksjon
BigQuant

The document describes a rolling ordinary least squares function that takes a requested result type, an input series, a dependent variable, and a window length. It says the function can return residuals, an intercept, or a coefficient from the rolling…

StatistikkTekniske indikatorer
BigQuant

The document introduces the challenge of forecasting intraday volatility across financial assets. It notes that researchers and practitioners have developed many approaches, including intraday estimators, GARCH models, and continuous-time models. Volatility…

VolatilitetMaskinlæringStatistikk
BigQuant

This student response translates a discretionary ETF approach into simple trading conditions. It describes a dual moving-average breakout idea using the 150-period or 30-period average alongside prior highs and lows: a move above the relevant average and a…

BigQuant

The document presents a brief summary of a research report on identifying the investment style of equity funds. It argues that fund style is closely related to fund returns and should be assessed before selecting a fund. The proposed approach is to identify…

AksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This tutorial introduces Seaborn, a visualization library built on Matplotlib, using Chinese stock financial statement data. It demonstrates distribution and rug plots for return on equity, joint and pair plots for examining relationships among financial…

AksjerStatistikk
BigQuant

The document outlines a process for forecasting dividends for index constituents, aggregating those estimates into an index-level total, and predicting when the dividends will be paid. Historical distributions are described as concentrated mainly in May…

AksjerFuturesKinesiske markederCarry-avkastning
BigQuant

This brief discussion asks whether a strategy’s return calculation horizon should match its holding period. The response says the answer depends on the trading logic, but that the two are generally kept consistent so a backtest represents the intended trade.…

Historisk testingMaskinlæringStatistikk
BigQuant

This July 2020 market note attributes the bull market to falling risk-free rates and increased allocations to equities. It argues that a more diverse investor base may reduce the chance of a sudden collapse, while rising participation and short-term…

Kinesiske markederAksjerFaktorinvesteringVolatilitet