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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
Quantpedia
86 dokumenter
TqSdk
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
Quantopian-forelesninger
45 dokumenter
Binance API docs
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

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…

Historisk testingAksjerStatistikk
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…

AksjerKinesiske markederTekniske indikatorerMomentum
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…

FuturesKryptoTrendfølgende handelTekniske indikatorer
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…

AksjerKinesiske markederTekniske indikatorerMarkedsmikrostruktur
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,…

AksjerMaskinlæringHistorisk testingKinesiske markeder
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…

AksjerKinesiske markederStatistikk
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,…

FaktorinvesteringFlere aktivaklasserRisikostyringPorteføljekonstruksjon
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…

MaskinlæringAksjerStatistikkHistorisk testing
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…

AksjerTekniske indikatorerOrdreutførelse
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…

AksjerFaktorinvesteringStatistikk
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.…

OpsjonerVolatilitetPrising av derivaterKinesiske markeder
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…

Kinesiske markederAksjerMomentumKursbrudd
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…

AksjerKinesiske markeder
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…

AksjerAmerikanske markederTilbakevending mot gjennomsnittetStatistikk
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…

MaskinlæringAksjerKinesiske markederHistorisk testing
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…

AksjerMaskinlæringStatistikkKinesiske markeder
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…

MaskinlæringStatistikkAksjer
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…

AksjerKinesiske markeder
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…

AksjerKinesiske markederMarkedsmikrostrukturMarkedssentiment
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…

AksjerKinesiske markederHendelsesdrevet handelMarkedssentiment
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…

AksjerFaktorinvesteringKinesiske markederHistorisk testing
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…

Kinesiske markederAksjerTekniske indikatorerStatistikk
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…

FaktorinvesteringStatistikkHistorisk testingRisikostyring
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…

AksjerMaskinlæringKinesiske markeder