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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 document introduces a quantitative way to assess an individual stock before deciding whether to buy it and how much capital to allocate. Rather than forecasting which stocks will be attractive, the proposed diagnostic approach looks backward at how the…

AksjerStatistikkPosisjonsstørrelseKinesiske markeder
BigQuant

This Chinese equity strategy screens for stocks whose opening price is above the previous close and whose five-day closing-price average is above its ten-day average. It ranks qualifying candidates by price-to-earnings ratio in ascending order and selects…

AksjerKinesiske markederTekniske indikatorerMomentum
BigQuant

This research note summarizes evidence that changes in the wording and structure of company filings can contain information about future operating performance and equity returns. The underlying study compares the text of SEC annual and quarterly reports with…

AksjerAmerikanske markederMarkedssentimentStatistikk
BigQuant

This report summary examines how market and style conditions may affect equity market-neutral strategies. Such strategies select stocks using fundamental or price-and-volume signals, then hedge broad market exposure with index futures or short selling. Their…

AksjerFaktorinvesteringPorteføljekonstruksjonRisikostyring
BigQuant

The article explains why a factor’s strong historical returns may come from an unintended exposure to market capitalization or liquidity. It uses large-order sell value as an example: because the measure is an absolute amount, it tends to be larger for…

FaktorinvesteringAksjerHistorisk testingStatistikk
BigQuant

This research summary describes an equity selection process built from daily price and volume data. Genetic programming searches for candidate factors, using RankIC to favor linear relationships or mutual information to seek nonlinear ones. To focus on…

AksjerMaskinlæringFaktorinvesteringHistorisk testing
BigQuant

This exchange explains how to handle a stock that remains in a backtest portfolio after it leaves the benchmark universe. The example concerns a strategy restricted to CSI 300 constituents: a stock bought while it belonged to the index may still be held…

AksjerHistorisk testingKinesiske markederPorteføljekonstruksjon
BigQuant

This brief explanation addresses why applying a one-period lag to closing prices can return a missing value. If the available data begins on the current day, the prior day’s close is outside the dataset, so there is no value for the lag operation to…

Statistikk
BigQuant

This forum excerpt concerns constructing training labels for a Chinese stock dataset and asks why label extraction produces an empty training set. The displayed expression derives a future return from a later close divided by a later open, assigns a…

Kinesiske markederAksjerMaskinlæringStatistikk
BigQuant

This article examines how the length of an AI model’s training window can affect an equity strategy. It compares manually rolled long windows spanning several years with shorter windows ranging from a month to a few years, and recommends evaluating each…

MaskinlæringHistorisk testingAksjerFaktorinvestering
BigQuant

This brief Chinese market report summarizes A-share sector performance, index and sector valuations, market breadth, fund positioning, and an intermediate-term LLT model. It reports the strongest and weakest sectors, notes declining valuations across several…

AksjerKinesiske markederTekniske indikatorerTrendfølgende handel
BigQuant

This research summary examines whether market conditions and macro variables can predict the next month’s long-short reversal-factor returns in Chinese equities. It identifies market funding sensitivity, turnover, volatility, and bid-ask spread as…

AksjerKinesiske markederTilbakevending mot gjennomsnittetFaktorinvestering
BigQuant

This BigQuant forum post describes a factor-research problem: detect a recent stock event, then later compare price with a level from the event day. The example defines an event using volume more than twice the prior value and a return above a stated…

AksjerStatistikkTekniske indikatorer
BigQuant

This brief BigQuant support exchange addresses a simulation error reported as having no data remaining after missing rows are dropped. The user had already tried extending the feature-extraction lookback by several different periods, without resolving the…

Historisk testingMaskinlæringStatistikk
BigQuant

The post asks why a daily stock-ranking strategy sells positions in backtests but appears only to buy them in simulation. Its code includes a trailing stop based on a percentage below the highest observed price since purchase, then manages capital and ranks…

AksjerMaskinlæringRisikostyringPosisjonsstørrelse
BigQuant

This brief BigQuant support exchange explains an error raised when a time-series minimum function receives a window argument that is not an integer. The example first derives a value from the position of the highest high within a fixed lookback, then passes…

StatistikkMaskinlæring
BigQuant

The document explains a data-processing approach for high-frequency factors that are first aggregated from intraday observations to daily values and then smoothed over multiple days. Instead of loading the full intraday history needed for the rolling window…

HøyfrekvenshandelTekniske indikatorerStatistikk
BigQuant

This note describes a single-security, multi-day approach to constructing intraday volume factors. For each trading day, the proposed measures calculate the share of full-day volume transacted within selected intervals. The intervals cover early trading,…

HøyfrekvenshandelMarkedsmikrostrukturStatistikk
BigQuant

This short exchange addresses how to sell stock shares according to the age of each entry. The example strategy opens separate lots in the same stock on consecutive days, then intends to close each lot after a specified holding period. That requires tracking…

AksjerOrdreutførelsePosisjonsstørrelseRisikostyring
BigQuant

This note describes a reproduction of a Guosen Securities quantitative research report on daily momentum factors that combine individual stock and industry returns. Its central idea is to use the relationship between a stock’s performance and its industry’s…

AksjerMomentumFaktorinvesteringHistorisk testing
BigQuant

This April 2020 market note combines a long-term trend assessment, short-term liquidity view, sector preferences, and tracking of A-share fund flows. It says a market breadth model had turned bearish and describes weakening turnover and a declining stock of…

Kinesiske markederAksjerStatistikkRisikostyring
BigQuant

The report proposes combining company-level stock selection with industry allocation for Chinese equities. Its coupling model uses each stock’s historical beta to its industry as a dynamic weight: more industry-sensitive stocks receive more influence from…

Kinesiske markederAksjerFaktorinvesteringPorteføljekonstruksjon
BigQuant

This summary of a Changjiang Securities study explains a stock-selection factor based on the balance between active and passive buying. Active buying occurs when an investor’s order trades against displayed sell orders; passive buying places a limit order…

AksjerMarkedssentimentFaktorinvesteringStatistikk
BigQuant

This research summary argues that factor signals weaken over time, so holding a monthly portfolio unchanged can leave later-period capital exposed to less informative signals. It proposes measuring a factor’s decay speed through the half-life of its…

FaktorinvesteringHistorisk testingPorteføljekonstruksjonOrdreutførelse