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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
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

This research summary studies Chinese home appliance industry conditions using revenue and net profit growth as proxies, arguing that calculations on comparable constituents reduce distortions caused by changes in the historical stock universe. It frames…

AksjerKinesiske markederStatistikkHistorisk testing
BigQuant

This weekly review tracks the returns and direction changes of 16 equity factors in the Chinese market during the 25th week of 2018. It reports that sales net margin had the strongest weekly performance, while valuation factors remained weak and the size…

Kinesiske markederAksjerFaktorinvesteringMomentum
BigQuant

The document describes a deep learning model intended to estimate a company’s next-quarter operating revenue year-over-year growth. It frames the forecast as a potential input to value-oriented investment decisions and says the target corresponds to roughly…

AksjerMaskinlæringStatistikk
BigQuant

This document is a brief summary of a research presentation on changes in quantitative investing. It outlines a model that combines a linear factor component with a nonlinear function of factor exposures, leaving a residual term. It says that residual…

MaskinlæringFaktorinvesteringRisikostyringStatistikk
BigQuant

This forum post questions the target-return calculation in a one-day equity strategy’s labeling code. The strategy is described as buying near the open on one day and selling at the close of the next, yet the label reportedly divides a later session’s high…

AksjerMaskinlæringHistorisk testingStatistikk
BigQuant

This forum post raises operational problems encountered after submitting previously successful stock strategies for simulated trading. The author reports that some buy instructions appear a day early and are later marked invalid, that incomplete purchases…

AksjerOrdreutførelseHistorisk testingPorteføljekonstruksjon
BigQuant

This report summary describes an index-timing model that applies wavelet analysis to filter price series before using a support vector machine to predict direction. It compares daily and weekly data and different model settings for the Shanghai Composite and…

Kinesiske markederAksjerMaskinlæringStatistikk
BigQuant

This research summary introduces the “Huayin Linjian” factor as a way to identify A-shares that may be undervalued because their prices have not been strongly driven by information-related trading pressure. Its stated premise separates price movements into…

AksjerKinesiske markederFaktorinvesteringStatistikk
BigQuant

This brief strategy outline combines a machine-learning stock ranking model with market-level and individual-stock exit rules. It proposes training a StockRanker on price, volume, and valuation factors, then applying three conditions to the CSI 300 index as…

AksjerMaskinlæringTrendfølgende handelRisikostyring
BigQuant

This tutorial demonstrates a workflow for evaluating WorldQuant Alpha#100 on Chinese equities. It builds the factor from price and volume signals, including the close’s position within the daily range, the relationship between closing price and ranked…

AksjerFaktorinvesteringHistorisk testingStatistikk
BigQuant

The report reviews how to assess pure bond funds using disclosed portfolio data, covering asset allocation and leverage, bond-type exposure, concentration, duration, and turnover. It then explains bond performance attribution, contrasting Campisi with…

RentepapirerStatistikkRisikostyringPorteføljekonstruksjon
BigQuant

This essay challenges the idea that time in the market reliably produces trading skill. It argues that experience can reinforce poor habits, while trading decisions are better treated as probabilistic choices whose quality depends on having a statistical…

StatistikkRisikostyringOrdreutførelse
BigQuant

The strategy ranks CSI 300 constituents by their return over the first three trading days of each month, measured from the first day's open to the third day's close. If the month has at least four trading days, it buys the ten strongest eligible stocks at…

AksjerKinesiske markederMomentumPosisjonsstørrelse
BigQuant

This report reviews the issuance and performance of Chinese private CTA products in the first quarter of 2018, then discusses equity index futures, government bond futures, and commodity markets. It links strategy ideas to changes in volatility, trading…

FuturesRåvarerRentepapirerTrendfølgende handel
BigQuant

The report compares machine learning approaches with traditional linear methods for predicting stock-level excess returns from alpha factors. Rather than predict excess return directly, it forecasts return dispersion with an AR(1) model and predicts…

AksjerFaktorinvesteringMaskinlæringHistorisk testing
BigQuant

This article argues that some quantitative strategies in China’s A-share market focus on exploiting short-term price fluctuations rather than company fundamentals. It portrays rapid trading as a source of high turnover and a potential conflict with the…

Kinesiske markederAksjerHøyfrekvenshandelVolatilitet
BigQuant

This weekly note assesses the Chinese equity market using trend, liquidity, options sentiment, macroeconomic data, calendar patterns, and factor crowding. Its timing framework treats the market as bullish when the broad-market index is above its 20-day…

AksjerKinesiske markederTrendfølgende handelMomentum
BigQuant

This summary of a Huatai Securities research report discusses using alternative target labels and ensemble learning to improve machine-learning stock selection. It reports comparing models built with different labels across multiple training-window lengths.…

Kinesiske markederAksjerMaskinlæringHistorisk testing
BigQuant

This Chinese equity research report builds a stock-selection factor from how each stock’s trading value co-moves with the rest of the market under two conditions. When a stock is relatively high in its recent intraday range, stronger co-movement is…

Kinesiske markederAksjerFaktorinvesteringMarkedsmikrostruktur
BigQuant

This meetup Q&A discusses several practical questions in quantitative research. It explains that traders can label chart patterns for AI training in principle, but visual labels may vary with chart scale and between annotators. A model can only learn useful…

MaskinlæringTekniske indikatorerFaktorinvesteringStatistikk
BigQuant

The document explains why a portfolio weight constraint routine can fail when it caps bond exposure and then normalizes all weights. Normalization may push the bond allocation above its intended limit; the example describes a bond weight of 20% becoming 40%…

Flere aktivaklasserPorteføljekonstruksjonRisikostyring
BigQuant

This lecture listing introduces policy-gradient methods, which learn a policy directly, and actor-critic methods, which pair a policy learner with a value estimator. The description highlights the actor-critic combination as a way to use value predictions to…

MaskinlæringStatistikk
BigQuant

The post asks why a BigQuant strategy raises a parser error near FROM. Its factor expression defines several intraday features, including a bid-ask spread measure, weighted order-book volume imbalance, early-session price movement, and a volume ratio, then…

AksjerMarkedsmikrostrukturTekniske indikatorerOrdreutførelse
BigQuant

This post reports a suspected mismatch in BigTrader v35 minute backtests after an ETF share split. The author says the strategy-visible position fields—quantity, cost price, last price, and market value—appeared to retain their pre-split values, while a…

AksjerHistorisk testingOrdreutførelseRisikostyring