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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
WonderTrader
14 dokumenter
Alphalens
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 Chinese-language post describes a stock-selection screen that ranks shares by a measure called capital strength, interpreted in the post as trading volume, and applies a condition on float concentration plus a limit on the price gain observed at 9:25.…

Kinesiske markederAksjerMomentumTekniske indikatorer
BigQuant

This short homework response defines quantitative investing as using scientific methods to seek profitable predictions. It identifies systematic analysis and statistical reasoning as strengths, then cautions that simulated results can create a false sense of…

StatistikkHistorisk testingMaskinlæring
BigQuant

The article presents five principles for trading: build decisions around one coherent set of rules, practice execution until it becomes habitual, judge performance over many trades, wait when conditions are noisy, and accept that strategies can fail. It…

RisikostyringStatistikkOrdreutførelseKinesiske markeder
BigQuant

This retrospective examines US exchange traded products and China’s ETF market during 2020, covering fund flows, product launches, fees, providers, regulation, and investor preferences. In the United States, bond ETF inflows were strong amid Federal Reserve…

Flere aktivaklasserAksjerRentepapirerKinesiske markeder
BigQuant

This short note highlights that feature importance can differ across predictive models, naming StockRanker and XGBoost as examples. It also points to changing the number of leaf nodes and the learning rate as model-tuning choices. These observations can help…

MaskinlæringAksjerStatistikkHistorisk testing
BigQuant

This report describes a high-frequency futures CTA approach that uses the relationship between traded prices and adjusted intraday open interest. It argues that open interest can provide information beyond trading volume, and derives directional signals from…

FuturesHøyfrekvenshandelTekniske indikatorerHistorisk testing
BigQuant

The document addresses a timing problem when using weekly indicators in a daily trading strategy. A weekly calculation may be absent or incomplete during the current week if the data pipeline only forms values after the week closes. Its example asks how to…

AksjerTekniske indikatorerStatistikkHistorisk testing
BigQuant

This essay groups chart-centered approaches such as candlestick analysis, Dow Theory, Elliott waves, Wyckoff methods, and Gann techniques under a broad category of pattern-based trading theories. It proposes examining whether any of their components have…

Tekniske indikatorerStatistikkRisikostyringAksjer
BigQuant

A platform user reports that a backtest transaction record can misstate realized profit and loss when a stock position passes through an ex-rights corporate action. The example compares transactions in Chinese equities before and after such an adjustment and…

AksjerHistorisk testingStatistikk
BigQuant

The post outlines an intended Chinese equity strategy that buys stocks when their price-to-earnings ratio ranks near the low end of its rolling history and sells when it reaches the high end, while restricting the universe to companies above a large…

AksjerKinesiske markederTilbakevending mot gjennomsnittetHistorisk testing
BigQuant

This article explains a GBDT-plus-logistic-regression method for classifying stock direction. Gradient-boosted decision trees capture nonlinear feature relationships; each tree maps observations to leaf nodes, which are encoded as indicator variables.…

MaskinlæringAksjerStatistikkHistorisk testing
BigQuant

This Chinese-language community post outlines a stock screening rule combining three technical conditions: daily amplitude above one percent, a newly formed KDJ golden cross, and three consecutive sessions in which the close is below the open. It frames high…

AksjerTekniske indikatorerMomentumKinesiske markeder
BigQuant

A user reports that a template stock strategy fails during model training after replacing its full A-share universe with a manually chosen set of only a few stocks. The response recommends expanding the sample pool, explaining that the template is designed…

AksjerMaskinlæringKinesiske markeder
BigQuant

This report introduces machine learning as data-driven learning and outlines its three broad forms: supervised, unsupervised, and reinforcement learning. It describes how computing advances, larger datasets, and accessible software libraries have helped make…

MaskinlæringStatistikkKinesiske markederHistorisk testing
BigQuant

This report reviews the weekly returns and directional changes of 16 equity factors. For the week covered, a three-month price momentum or reversal measure led with a reported return of 1.45%, while the 24-day Relative Strength Index returned 0.43%. All…

AksjerFaktorinvesteringMomentumTekniske indikatorer
BigQuant

This research summary investigates the A-share small-cap premium, comparing its strength across market-cap segments, industries, and global markets. It reports a pronounced size effect across the broader A-share market and within CSI 500 constituents, but a…

AksjerKinesiske markederFaktorinvesteringStatistikk
BigQuant

This research summary examines whether internet search attention can help anticipate rotations between Chinese large-cap and small-cap equity indices. It treats search-index changes related to the two market styles as a proxy for investor sentiment,…

AksjerKinesiske markederMarkedssentimentPorteføljekonstruksjon
BigQuant

This brief overview introduces genetic programming as an evolutionary approach to generating quantitative investment factors. It describes starting with basic data inputs and mathematical operators, then iteratively evolving candidate expressions through…

MaskinlæringFaktorinvesteringStatistikkAksjer
BigQuant

The article discusses translating plain-language trading rules into Python strategies for the QMT platform. Its example is a moving-average crossover on a named Chinese equity, with an all-in position rule and adjusted price data. The author reports that an…

MaskinlæringAksjerTrendfølgende handelHistorisk testing
BigQuant

This article advocates using the 20-day moving average as a simple guide to intermediate stock trends. It defines the line as the average closing price over the preceding 20 trading sessions, roughly a month of trading. The proposed reading combines the…

AksjerTekniske indikatorerTrendfølgende handel
BigQuant

The document outlines an end-to-end portfolio construction framework that embeds a differentiable convex optimization layer within a neural network. Raw asset data passes through the network for feature extraction and combination, then an optimization layer…

MaskinlæringPorteføljekonstruksjonHistorisk testingStatistikk
BigQuant

The document answers a data-preparation question: how to retain the time of day from a timestamp column while omitting its calendar date. It describes applying a datetime formatting operation to each value in a pandas column, creating a separate time field,…

FuturesStatistikkKinesiske markeder
BigQuant

This short forum exchange addresses how to use a custom benchmark for a strategy such as pair trading, where a portfolio of two stocks may be a more relevant comparison than a broad market index. The suggested approach is to construct an index from the two…

AksjerParhandelPorteføljekonstruksjon