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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

This document describes a cross-sectional equity reversal factor that separates recent returns according to trading activity. For each stock, it looks back over 20 sessions, calculates average transaction value per trade each day, ranks the sessions by that…

AksjerTilbakevending mot gjennomsnittetFaktorinvesteringMarkedsmikrostruktur
BigQuant

The report examines Chinese analyst earnings forecasts, ratings, and target prices as inputs to equity selection. It compares six ways to combine analyst estimates and finds that weighting forecasts by analysts’ past accuracy performs better than several…

AksjerFaktorinvesteringStatistikkKinesiske markeder
BigQuant

The document reports a pandas error raised when a factor-analysis workflow formats the minimum date with strftime. The shown failing expression takes the minimum of a date column and immediately formats it; pandas raises the error when that value is NaT, its…

StatistikkAksjer
BigQuant

The article introduces XGBoost as a boosting ensemble and contrasts its sequential fitting of weak learners with the parallel training used in bagging. It explains residual-based improvement, complexity penalties, sample and feature subsampling, and…

MaskinlæringFaktorinvesteringAksjerHistorisk testing
BigQuant

This summary of a Changjiang Securities study examines whether the weak side of equity style factors can help improve an index-enhancement portfolio. It reports historical measurements from 2007 through 2019, identifying negative excess returns for…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This report summary outlines a method for inferring informed trading from minute-level data. It first applies a “smartness” measure to identify trades attributed to smart money, then aggregates their relative execution prices into sentiment factor Q. Higher…

AksjerKinesiske markederFaktorinvesteringMarkedssentiment
BigQuant

This short assignment response describes quantitative investing as using data to check market observations and identify patterns. It frames the goal as building strategies that can be backtested and evaluated for stability over time, then using software to…

StatistikkHistorisk testingOrdreutførelse
BigQuant

The example constructs price-based stock factors from recent daily observations. For each half-month sampling date, it retrieves a recent window of daily high, open, low, close, and average price data for the stock universe. It calculates three mean…

AksjerFaktorinvesteringTekniske indikatorerKinesiske markeder
BigQuant

This research note proposes that stock price changes reflect market forces, firm-specific short-term information, longer-term fundamentals, and noise. It uses daily minute data and cross-lag regressions of returns on changes in trading volume to estimate…

AksjerKinesiske markederFaktorinvesteringStatistikk
BigQuant

This brief report summary reviews changes in Chinese public mutual fund holdings during the fourth quarter of 2017. It says fund exposure rose in several sectors, led by food and beverage and home appliances, with increases also reported for electrical…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

The document explains a portfolio approach built around StockRanker, a model that ranks stocks using historical data. Each day, the strategy buys the highest-ranked candidates and sells or replaces holdings that fall toward the bottom of the ranking. The…

AksjerMaskinlæringPorteføljekonstruksjonPosisjonsstørrelse
BigQuant

The post outlines a low-frequency model for timing Chinese A-share exposure and selecting trading targets. It describes using daily Wind data and a large set of predetermined spreadsheet formulas to generate bullish or bearish signals and identify an…

AksjerTekniske indikatorerHistorisk testingKinesiske markeder
BigQuant

This brief BigQuant support exchange addresses a question about a backtest whose logs and apparent trade signals stop before the requested end date. The user says the run was set through the 23rd, exchange data stopped at the 17th, and the output log stopped…

Historisk testingRisikostyringStatistikk
BigQuant

This forum post raises a practical backtesting question: whether strategy data indexed by the current date can be used to trade at that same day’s open without introducing lookahead bias. The example ranks stocks using market capitalization, tradable market…

Historisk testingOrdreutførelseAksjerRisikostyring
BigQuant

This dated Chinese equity market report combines market activity, sentiment, sector flows, concentration measures, and style-factor performance. It describes subdued overall valuations, rising consumer-sector turnover, a recovery in TMT activity, and strong…

AksjerKinesiske markederFaktorinvesteringMomentum
BigQuant

This review examines whether controlling for quality changes the apparent size premium in equity markets and whether small-cap exposure can strengthen other factors. It compares size-factor returns with quality and related factor exposures using time-series…

AksjerFaktorinvesteringStatistikkAmerikanske markeder
BigQuant

The article reviews empirical research comparing commodity futures momentum based on total returns with momentum based on residual, or idiosyncratic, returns after accounting for selected systematic factors. The study forms ranked portfolios across…

RåvarerFuturesMomentumFaktorinvestering
BigQuant

This short exchange explains how to assign groups when training a learning-to-rank model on stock observations. The questioner expects each trading date to form a group, so the model ranks stocks against other stocks observed on that date, and asks where…

AksjerMaskinlæringStatistikk
BigQuant

This brief assignment reflects on translating discretionary investment ideas into a quantitative process. The author cites assessing future demand for a company’s product, with examples involving a stablecoin business in the context of US Treasury pressures…

AksjerHistorisk testingRisikostyringFaktorinvestering
BigQuant

This Chinese-language research digest summarizes two separate studies. The first considers adding trend-following strategies to target-date funds in defined-contribution retirement plans. It argues that these funds may concentrate too much risk in equities…

Trendfølgende handelPorteføljekonstruksjonAksjerStatistikk
BigQuant

This Chinese-language tutorial describes implementing a stock-ranking strategy in BigTrader by passing StockRanker predictions directly to the trading engine and coding the trade logic there. It allocates purchases among the top-ranked stocks using weights…

AksjerMaskinlæringPosisjonsstørrelsePorteføljekonstruksjon
BigQuant

This study mines short-horizon price and volume factors for Chinese equities, then examines how transaction costs and rebalance frequency affect portfolio results. It uses genetic programming to generate candidate daily factors from conventional price and…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This BigQuant note explains how to change a strategy template’s initialization settings from a declining weight schedule to equal weights. The existing example assigns larger allocations to earlier stocks using weights proportional to the inverse logarithm…

AksjerPorteføljekonstruksjonPosisjonsstørrelse