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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
Quantpedia
86 dokumenter
TqSdk
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 note describes a stock risk factor based on the Sharpe ratio over a 20-day period. It defines the measure as annualized stock return minus a risk-free rate, divided by the stock return standard deviation. The stated risk-free rate is 0.04. The factor is…

AksjerFaktorinvesteringRisikostyringStatistikk
BigQuant

The document introduces the True Strength Index (TSI), a price momentum indicator calculated from closing prices. It first measures the close-to-close change and its absolute value, then applies two exponential moving average smooths to each series. The…

Tekniske indikatorerMomentumAksjer
BigQuant

This research explores whether intraday price and volume relationships can improve stock selection. It builds three signals from price–volume correlation: its average level, its stability over time, and its trend. The average signal adjusts conventional…

AksjerKinesiske markederFaktorinvesteringTekniske indikatorer
BigQuant

This report examines institutional investors’ holdings and style preferences, focusing on Chinese equities and summarizing US institutional ownership for context. It outlines four possible data sources for monitoring institutions: macro indicators, trading…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This report describes ways to study institutional investors’ market footprint and preferences in Chinese equities. Because many institutions do not disclose complete holdings, it proposes classifying shareholder accounts from listed companies’ public…

AksjerKinesiske markederFaktorinvesteringPorteføljekonstruksjon
BigQuant

This report explains how to isolate a desired equity style factor while controlling exposure to other factors. It contrasts full replication, which reproduces modeled pure-factor returns but does not control ex ante risk, with constrained mean-variance…

AksjerFaktorinvesteringPorteføljekonstruksjonRisikostyring
BigQuant

This review summarizes research on whether anchoring to a stock’s 52-week high helps explain why prices respond slowly to news about economically connected firms. Its central hypothesis is that investors underreact to good news about a customer when a…

AksjerMomentumFaktorinvestering
BigQuant

This article proposes a visual exit technique combining a moving average with a horizontal line drawn from a recent swing high or low. For a long position, the trader marks a visible high, extends a horizontal reference, and treats the bar vertically aligned…

AksjerTekniske indikatorerTrendfølgende handelRisikostyring
BigQuant

This Chinese-language strategy note surveys ways to connect macro conditions, company fundamentals, valuation, and sector performance. It describes a credit and earnings framework for market regimes, a rolling stock-bond yield spread as a relative valuation…

AksjerKinesiske markederFaktorinvesteringStatistikk
BigQuant

This research summary presents factor timing as a risk-control problem rather than solely as a return-forecasting problem. It proposes adjusting the upper and lower bounds on factor exposures in a portfolio, with those limits informed by factor risk,…

FaktorinvesteringRisikostyringPorteføljekonstruksjonHistorisk testing
BigQuant

This Chinese-language brokerage research summary examines advanced driver-assistance systems in China, focusing on adoption, technology requirements, supplier roles, and domestic competition. It describes ADAS as a chain of perception, planning, decision,…

AksjerKinesiske markederMaskinlæring
BigQuant

This platform guide describes two ways to organize data preparation and simulated trading. In one workflow, a separate task cleans and stores factors, then a trading task consumes its output. In the other, one task first prepares and saves the data and then…

OrdreutførelseHistorisk testing
BigQuant

The article discusses three reported Chinese equity-market changes: wider daily price limits for risk-warning stocks, expanded after-hours fixed-price trading, and revised ChiNext listing standards that place more weight on growth and research investment. It…

Kinesiske markederAksjerOrdreutførelseMarkedsmikrostruktur
BigQuant

This overview introduces the Fama-French five-factor asset pricing model as an extension of the three-factor model. It describes the market risk premium, size, value, profitability, and investment-style factors, and explains each as a return spread between…

AksjerFaktorinvesteringStatistikkPorteføljekonstruksjon
BigQuant

This strategy selects stocks that open above the previous close and have a five-day closing-price average above the ten-day average. Among qualifying stocks, it ranks by price-to-earnings ratio in ascending order and chooses the first ten; purchases are…

AksjerMomentumTekniske indikatorerHistorisk testing
BigQuant

This document summarizes the SEC’s 2019 adoption of Rule 6c-11, which lets many ETFs operate under the Investment Company Act of 1940 and list without seeking an individual exemptive order. It frames the rule around regulatory exemptions, custom creation and…

AksjerAmerikanske markederMarkedsmikrostruktur
BigQuant

This student submission translates a Chinese stock trading pattern into explicit screening and trading rules. The setup looks for a declining session after a limit-up day, with current volume twice the prior day’s volume. The stock is then watched, with…

AksjerKursbruddMomentumTekniske indikatorer
BigQuant

The document presents a SQL query for calculating a stock’s five-day price return and ranking that return against other stocks on the same date. It uses a window function to retrieve each instrument’s close from five trading rows earlier, then computes the…

AksjerStatistikkKinesiske markeder
BigQuant

The document explains how to restrict a Chinese stock machine-learning dataset to shares that closed at their daily price limit, or to shares that hit the limit on the previous trading day. It describes filtering factor data by limit-status value and using a…

AksjerKinesiske markederMaskinlæringHistorisk testing
BigQuant

This guide explains how to monitor a StockRanker model’s validation performance during training. It recommends preparing a validation dataset, either from a separate data module or by reusing the training data, and connecting it to the model’s validation…

MaskinlæringStatistikkHistorisk testing
BigQuant

This short indicator reference defines a Keltner Channel using high, low, and close price data alongside three parameters: the moving-average period, the ATR period, and a multiplier. It first calculates an average of true range over the selected ATR period.…

Tekniske indikatorerVolatilitet
BigQuant

This document outlines a two-stock pairs strategy based on a presumed long-term cointegrating relationship. It fits a linear relationship between the stocks’ prices, calculates the residual, and standardizes that residual over a rolling history. When the…

AksjerParhandelTilbakevending mot gjennomsnittetHistorisk testing
BigQuant

This article outlines a workflow for adding intraday monitoring and automated execution to a BigQuant equity strategy. Its premise is that factor calculations may produce a next-day trading plan only after the market closes, leaving no mechanism to react to…

AksjerOrdreutførelseMarkedsmikrostrukturKinesiske markeder
BigQuant

This essay uses an unfinished introduction to a supposed futures reversal method to illustrate the appeal and danger of searching for secret signals. It does not describe a specific entry rule or demonstrate that any reversal strategy works. Instead, it…

FuturesRisikostyringPosisjonsstørrelse