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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

The article describes a framework that links macroeconomic and style factors with traditional asset-class allocation. It proceeds from selecting factors and estimating asset exposures to building factor-mimicking portfolios, forecasting their returns,…

Flere aktivaklasserFaktorinvesteringPorteføljekonstruksjonHistorisk testing
BigQuant

The article compares long and short training windows for an AI stock selection strategy and recommends evaluating each window against a fixed validation period. In its rolling experiments, extending the sample from 2005 to 2021 changed labels, factor…

AksjerMaskinlæringHistorisk testingStatistikk
BigQuant

This submission outlines an intraday stock-selection idea for a day when a market theme is breaking out. It proposes identifying a popular sector early, using large orders that hold at the daily price limit as a sign of a clear direction, then ranking…

AksjerKinesiske markederOrdreutførelseMarkedsmikrostruktur
BigQuant

This Chinese post compiles a selection of 65 titles from a much larger collection of publicly released Springer books, focusing on data and machine learning. The bibliography spans foundations such as algebra, probability, statistics, optimization, and time…

MaskinlæringStatistikkAksjer
BigQuant

This research summary reviews several quantitative approaches to interpreting China’s 2018 equity market. It reports historical analysis of rebounds in the CSI 1000, saying smaller-cap indexes tended to outperform larger ones during rebounds, while sectors…

AksjerKinesiske markederStatistikkFaktorinvestering
BigQuant

This BigQuant framework describes a workflow for computing and evaluating minute-frequency stock factors. Researchers define factors in SQL against a specialized derived minute-bar table, assign an output table name, and run the program to calculate and…

AksjerMaskinlæringHistorisk testingStatistikk
BigQuant

This document is a brief outline of a presentation on machine learning in finance. It names four application areas: Lasso regression for commodity futures price prediction, decision trees for detecting possible financial fraud, logistic regression for…

MaskinlæringRåvarerFuturesAksjer
BigQuant

The document introduces fund-of-funds structures and distinguishes four types according to whether the parent and underlying funds are actively or passively managed. It focuses on an actively managed parent investing in passive sector ETFs, and describes an…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This brief strategy description proposes checking stocks one by one to identify which has produced the highest return under an event strategy, then buying that stock. It specifies daily Chinese stock-bar data, a backtest beginning in 2020 and running through…

AksjerHendelsesdrevet handelHistorisk testingKinesiske markeder
BigQuant

This older Chinese-equity strategy looks for stocks that rally to the daily limit, pull back, and later break to a new high. It defines a pullback as any post-limit-up close below the earlier limit-up price. After the pullback, a new high triggers a purchase…

Kinesiske markederAksjerKursbruddMomentum
BigQuant

This Chinese-language conference excerpt introduces how artificial intelligence is being adopted by global asset managers. It frames technology as one response to falling margins per unit of managed assets, alongside efforts to grow assets under management.…

MaskinlæringFaktorinvesteringPorteføljekonstruksjonMarkedssentiment
BigQuant

The document discusses how to judge excess returns in an index enhancement strategy. It argues that alpha should be evaluated by its persistence and consistency against a benchmark, rather than by the size of gains in a brief period. It describes monthly…

AksjerStatistikkRisikostyringPorteføljekonstruksjon
BigQuant

This brief BigQuant support exchange concerns an error triggered after a user changed features in a beginner template. The response identifies a formatting issue in the feature list: comments or notes should be placed on separate lines rather than appended…

MaskinlæringHistorisk testingStatistikk
BigQuant

This document outlines a Turtle style trend following strategy for stocks. It buys when the close crosses above the prior 20 trading days’ high and exits when the close crosses below the prior 10 trading days’ low. The examples define signals using current…

AksjerTrendfølgende handelKursbruddHistorisk testing
BigQuant

The document uses national input-output tables to map intermediate flows between industries and represent those relationships as networks. It compares China across several historical snapshots with the United States in a later snapshot, using network…

AksjerKinesiske markederStatistikk
BigQuant

The post asks how to calculate, for each instrument, the rolling five-day count of sessions when a stock value exceeds an index value, then assign those counts back to the original dataframe. The author reports that the grouped calculation produces the…

AksjerStatistikk
BigQuant

The document examines a top-ranked strategy that selects the smallest stocks in the CSI 1000 and reports unusually strong historical performance. Its central lesson is that a backtest must use index constituents as they were known at each historical date.…

Kinesiske markederAksjerHistorisk testingRisikostyring
BigQuant

This equity factor study measures buying and selling pressure through the amount of time a stock spends at different positions within its recent price range. It first normalizes price between the interval’s high and low to obtain relative price position…

Kinesiske markederAksjerFaktorinvesteringTekniske indikatorer
BigQuant

This Chinese equity research note develops industry-rotation signals from several types of institutional money flow. It treats a flow source as useful when its derived signals show a reasonably orderly relationship across ranked groups and a tolerable…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This Chinese-language post introduces a custom-coded stock selection strategy on the BigQuant platform. It says the strategy combines five selection rules, mainly seeking stocks that have fallen sharply and begun to stabilize, while also attempting to…

AksjerKinesiske markederFaktorinvesteringHistorisk testing
BigQuant

This Chinese-language post describes a stock selection screen combining three conditions: RSI below 65, the day’s volume above 1.05 times the prior day’s volume, and the absolute move from the previous close to the opening price below 6%. It frames the…

AksjerTekniske indikatorerMomentumKinesiske markeder
BigQuant

The note recasts a past trading approach as a daily double moving average strategy, using crossovers as signals. The author reports that this approach produced losses over several consecutive years, offering a cautionary personal outcome rather than a…

Tekniske indikatorerTrendfølgende handelHistorisk testing
BigQuant

This short Chinese-language Q&A explains the delayed-entry setting in factor analysis. The setting shifts the point at which stocks are purchased based on factor values by a specified number of days. The question concerns a one-day rebalance cycle and asks…

FaktorinvesteringHistorisk testing
BigQuant

The document describes adding a custom Rank Information Coefficient module to a stock-ranking strategy. The module reports average RankIC separately for the training set and the test set, providing a way to assess how well a model’s rankings align with…

AksjerMaskinlæringStatistikkHistorisk testing