Hopp til innhold

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 event announcement describes a strategy framework that uses macroeconomic timing to adjust a multi-asset portfolio as markets and economic conditions change. The stated approach is to monitor macro indicators for turning points in the economic cycle,…

Flere aktivaklasserPorteføljekonstruksjonStatistikkRisikostyring
BigQuant

This introductory article explains Python’s built-in data types, distinguishing ordered sequences such as lists, tuples, and strings from sets and dictionaries. Its main focus is the list: an ordered, resizable collection that can contain objects of…

Statistikk
BigQuant

This report summary presents a strategic allocation framework that shifts the budgeting unit from asset classes to macroeconomic drivers, aiming to reduce exposure to changing correlations among assets. Principal component analysis is used to derive five…

Flere aktivaklasserFaktorinvesteringPorteføljekonstruksjonRisikostyring
BigQuant

This brief strategy example uses two simple moving averages of closing prices to generate entry and exit signals. It buys when the five-day average rises above the ten-day average, placing the order at the following session’s open. After entering, it sells…

AksjerTrendfølgende handelTekniske indikatorerOrdreutførelse
BigQuant

The discussion considers how to evaluate a Chinese stock-market tactic that buys during the session as a stock approaches its first daily price-limit rise, then sells on the following day. Because the entry decision happens intraday, it suggests using…

AksjerKinesiske markederHistorisk testingKursbrudd
BigQuant

The document reports a factor built from the standardized ratio of closing price to its 20-period average, divided by price-to-book, then normalized by its 60-period standard deviation. It gives an information coefficient of -0.02 and reports cumulative…

AksjerFaktorinvesteringStatistikkHistorisk testing
BigQuant

This research replication constructs a Chinese equity factor called “Flowers in the Forest” by calculating three component factors and combining them with equal weights. The source describes deriving the daily factor from high-frequency data, a…

Kinesiske markederAksjerFaktorinvesteringStatistikk
BigQuant

The document introduces volume-synchronized probability of informed trading (VPIN) as a practical way to assess order flow toxicity. It frames toxicity as informed traders’ ability to trade against liquidity providers, potentially causing market makers to…

HøyfrekvenshandelMarkedsmikrostrukturRisikostyringStatistikk
BigQuant

This short description covers an indicator that monitors candlesticks for breaks through a dot-dash channel. When a break occurs, it can issue an alert and send email or push notifications. The accompanying captions refer to a channel break on the first bar…

Tekniske indikatorerKursbruddOrdreutførelse
BigQuant

The document lists fitness measures for a genetic programming module that evolves stock factors. It describes ICIR as the mean information coefficient divided by its standard deviation, with the information coefficient measuring the relationship between…

AksjerFaktorinvesteringMaskinlæringStatistikk
BigQuant

This tutorial describes generating synthetic multivariate financial time series with SDV’s PAR synthesizer, a conditional autoregressive GAN approach. It fits sequences using asset identifiers and stable context fields, with returns and volume as modeled…

MaskinlæringStatistikkHistorisk testingAksjer
BigQuant

This report tracks three China-focused active equity quant portfolios against an active mutual fund benchmark: an enhancement portfolio built from selected fund holdings, a portfolio that filters earnings-surprise stocks, and an enhancement portfolio based…

AksjerKinesiske markederFaktorinvesteringHendelsesdrevet handel
BigQuant

This study examines whether unusual changes in industry returns can help anticipate moves in the CSI 300 and CSI 500. It treats extreme long-short industry returns and subsequent trend breakouts as signals, comparing monthly and weekly observations across…

AksjerKinesiske markederKursbruddMomentum
BigQuant

This brief Chinese-language support thread concerns a rolling-training error reported as “NaTType does not support.” A response attributes the failure to a module whose output lacks a date column, and says the rolling-training process needs that field. The…

MaskinlæringStatistikkHistorisk testing
BigQuant

This study summary describes a low-risk portfolio approach for South Korean equities. It forecasts KOSPI 200 constituent volatility with Gaussian process regression, support vector regression, an artificial neural network, and GARCH, then ranks stocks into…

AksjerMaskinlæringVolatilitetPorteføljekonstruksjon
BigQuant

This brief summary introduces Markowitz modern portfolio theory as a framework for choosing asset weights by balancing expected return and risk. It describes mean and variance as ways to represent asset return characteristics and investor preferences, then…

PorteføljekonstruksjonStatistikkRisikostyring
BigQuant

This brief platform support exchange answers whether BigQuant backtest performance reflects commissions and stamp tax. The response says the displayed results include these trading costs, and that their settings can be adjusted during strategy…

Historisk testingOrdreutførelseKinesiske markeder
BigQuant

This competition update reports diagnostics for the highest-ranked factors and tracks a CSI 1000 index enhancement portfolio. Factor descriptions are anonymized: the report gives average style exposures estimated through daily multivariate regressions and…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This note describes a stock-selection screen combining daily price amplitude above one percent, current volume above ten thousand lots, a higher open, and prior-day turnover above eight percent. It interprets amplitude and volume as signs of active trading,…

Kinesiske markederAksjerVolatilitetTekniske indikatorer
BigQuant

This discussion post argues that a quantitative trading operation may need to work across high, medium, and low frequencies. It characterizes high-frequency trading as a field with relatively clear technical aims, including reducing latency and improving…

HøyfrekvenshandelPorteføljekonstruksjonOrdreutførelse
BigQuant

This Chinese-language overview argues that entry and exit timing within a short holding period can affect results, even when a broad market is flat or rising. It contrasts a common schedule of buying at the open and selling at the following close with the…

AksjerOrdreutførelseStatistikkHistorisk testing
BigQuant

The document describes a stock-price forecasting study that compares a Kalman filter, XGBoost, and a hybrid that uses XGBoost to select features before passing them to a Kalman filter. Inputs include price and volume data, accounting measures, and quarterly…

AksjerMaskinlæringStatistikkMarkedssentiment
BigQuant

This brief answer addresses how to keep multiple StockRanker models available for reuse. It recommends following a separate guide’s approach to load the models, then placing them together in a dictionary for storage. The intended benefit is faster future…

Maskinlæring
BigQuant

This article introduces dense neural networks and backpropagation, then outlines an A-share stock selection experiment. A DNN uses connected layers of neurons; training sends predictions forward and propagates errors backward to update weights. The example…

Kinesiske markederAksjerMaskinlæringFaktorinvestering