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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
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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 BigQuant forum entry addresses an error caused by referencing a locally created stock list as though it were an attribute of the backtest context. Its central correction is to use the variable where it was defined, without the context prefix, unless the…

AksjerKinesiske markederTekniske indikatorerHistorisk testing
BigQuant

This study outlines an experiment applying a Transformer model to short-horizon stock selection in China’s A-share market. Its target is each stock’s return over the next five trading days, using daily market data from 2015 through 2021. The inputs begin…

AksjerKinesiske markederMaskinlæringFaktorinvestering
BigQuant

This Chinese-language excerpt summarizes a report about automatically generating trading strategies from time-series data. It frames conventional factor research, factor combinations, and rule combinations as labor-intensive manual work, then proposes using…

StatistikkFaktorinvesteringRisikostyring
BigQuant

This document summarizes a research paper on factor timing, connecting it to market timing and factor investing. Its theoretical starting point is that an optimal portfolio corresponds to the stochastic discount factor (SDF). Under a conditional factor model…

FaktorinvesteringPorteføljekonstruksjonStatistikk
BigQuant

The report summary explains a method for finding monthly indicators that may signal earnings changes in cyclical industries before standard financial statements are published. It covers real estate, autos, transport, coal, nonferrous metals, steel, building…

AksjerKinesiske markederStatistikkFaktorinvestering
BigQuant

This opinion article argues that traders should give more weight to market direction, entry timing, and risk discipline than to technical indicators alone. It warns against treating a falling price as automatically cheap, and favors aligning trades with…

AksjerTrendfølgende handelRisikostyringMomentum
BigQuant

This post documents an attempted BigQuant workflow for combining daily Chinese stock bars with prefactor and money-flow tables. The query joins records by date and instrument, selects price and valuation fields, calculates returns, and applies a non-null…

Kinesiske markederAksjerStatistikk
BigQuant

This research summary treats intraday stock prices as time series rather than isolated observations. It applies empirical mode decomposition (EMD) to separate trend-like price signals from noise, then uses their relative strength to form a signal-to-noise…

AksjerFaktorinvesteringTilbakevending mot gjennomsnittetStatistikk
BigQuant

This weekly report reviews style-factor signals across several Chinese equity universes and summarizes recent quantitative fund performance and market conditions. It reports that price-and-volume factors, especially beta, were relatively strong in the week…

Kinesiske markederAksjerFaktorinvesteringMomentum
BigQuant

The discussion describes a proposed rolling evaluation in which training and testing periods are kept separate. Its example uses successive annual training windows and test windows offset into later years, with each test period’s results concatenated to form…

Historisk testingStatistikkMaskinlæring
BigQuant

The question asks how to limit the capital assigned to any one stock to 20% of the portfolio. The response refers to a calculated weight for a stock and places an order targeting a percentage of portfolio value. This points to a useful portfolio-construction…

AksjerPosisjonsstørrelsePorteføljekonstruksjon
BigQuant

This brief Chinese-language support note addresses why a filter applied directly in a DataSource may not work as expected. It recommends using a dedicated data-filter module, filtering the resulting dataframe with boolean conditions, or passing a query…

StatistikkAksjer
BigQuant

The article questions whether asset pricing and investment can be understood only by predicting returns from company characteristics. It uses the example of rose prices on Valentine’s Day: prices rise when demand is concentrated and sellers are scarce, then…

Flere aktivaklasserMarkedsmikrostrukturStatistikk
BigQuant

The example builds a single-stock daily strategy from trend and momentum conditions. Entry requires the 21-period simple moving average to exceed the 60-period average, price to be above both averages, and daily and weekly MACD histograms to be positive. It…

AksjerTrendfølgende handelMomentumTekniske indikatorer
BigQuant

This article offers a checklist for choosing strategies from a trading community. It first asks investors to set their capital, acceptable drawdown, and return expectations, then distinguishes conservative from aggressive choices. The conservative profile…

AksjerRisikostyringHistorisk testingPorteføljekonstruksjon
BigQuant

This report studies whether stocks that suffer sustained, sharp declines later rebound, and how to identify such episodes. It uses rolling-window local extrema with a 15-period parameter to mark turning points, then applies pattern conditions to select 2,048…

Kinesiske markederAksjerTilbakevending mot gjennomsnittetHistorisk testing
BigQuant

This literature summary describes applying classification and regression trees (CART) to cross-sectional stock selection. The motivating advantage is that a tree can represent nonlinear relationships and interactions among variables, which linear models or…

AksjerMaskinlæringFaktorinvesteringStatistikk
BigQuant

This weekly Chinese equity research note tracks index-enhancement portfolios benchmarked to the CSI 300, CSI 500, and CSI 1000, and monitors single-factor portfolios within those indices and a public-fund-heavy-stock universe. Its portfolio construction is…

AksjerFaktorinvesteringMomentumPorteføljekonstruksjon
BigQuant

This article outlines an approach to improving a random forest stock-selection model through factor screening and parameter experimentation. It first limits the stock universe by excluding firms under special treatment and delisted stocks. Candidate…

AksjerMaskinlæringFaktorinvesteringStatistikk
BigQuant

This document summarizes experiments with a convolutional neural network (CNN) trading model called Deep Alpha. It reports that a seven-layer version performed better than a two-layer version in a same-period comparison, which the authors attribute to…

MaskinlæringStatistikkHistorisk testing
BigQuant

This brief response addresses preprocessing for deep-learning models applied to stock data, including Boolean values and infinite observations. Its direct recommendation for infinities is to remove them. For broader feature preparation, it lists outlier…

MaskinlæringAksjerStatistikk
BigQuant

This report compares AdaBoost, gradient boosting decision trees, and XGBoost as tools for selecting Chinese equities from factor data. Its workflow covers feature and label preparation, preprocessing, in-sample fitting, cross-validation, and out-of-sample…

MaskinlæringAksjerFaktorinvesteringHistorisk testing
BigQuant

This note shows how to construct a simple stock relative-return factor using a precomputed Chinese equity dataset. It identifies one field as each stock’s daily return and another as the CSI 300 index return, then defines relative return as the stock return…

AksjerKinesiske markederFaktorinvesteringStatistikk
BigQuant

This troubleshooting note addresses errors that arise after adding a rolling-training module to an AI strategy for convertible bonds. It points to a revised notebook and identifies two implementation changes associated with the fix. First, the rolling…

MaskinlæringHistorisk testingRentepapirerKinesiske markeder