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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

12,226 dokumenter

SuperMind

This document presents an equity selection rule that combines a positive MACD signal, a 20-day moving average above the 120-day moving average, and year-over-year parent-company net profit growth above 20% and up to 100%. Its example implementation scans…

AksjerKinesiske markederMomentumTekniske indikatorer
SuperMind

This document describes a Chinese equity screening rule for beverage and alcohol import-export companies. It selects stocks with turnover between 3% and 12% and year-over-year parent-company net profit growth above 20% and up to 100%. The article also gives…

AksjerKinesiske markederFaktorinvesteringRisikostyring
SuperMind

This note outlines a daily stock screen combining a positive MACD reading, a share price below a stated ceiling, and a measure of trading activity based on the prior day’s turnover rate and the ratio of current auction volume to the previous day’s volume. It…

AksjerTekniske indikatorerMomentum
SuperMind

This note describes a stock screen based on three chart conditions: amplitude above a threshold, a rising price base, and a rounded-bottom pattern. It presents the pattern as a way to find stocks whose lows are gradually moving higher, while filtering out…

AksjerTekniske indikatorerHistorisk testing
SuperMind

This brief template note describes a stock-screening strategy that selects companies using natural-language queries. Its example combines a small-market-cap screen with recent net inflows from large investors, a prior period of contracting volume, and a…

AksjerMomentumTekniske indikatorerRisikostyring
SuperMind

The document outlines a stock screen that selects companies in a designated metaverse category, requires a positive recent return, and excludes firms listed for less than a chosen period. Its rationale is to combine thematic exposure and recent performance…

AksjerMomentumTekniske indikatorer
SuperMind

This post describes a rule-based screen for Chinese main-board stocks. It selects shares with turnover between 3% and 12%, a daily gain above 1%, and a circulating share count no greater than 5.5 billion. The stated intent is to combine trading activity,…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

This Chinese-language stock screen combines an amplitude threshold above 1, a ranking among the five highest stocks by the day’s auction amount, and a rising K value from the KDJ indicator. Its rationale is to find stocks with larger price movement and…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

This Chinese-language screening note describes selecting stocks with turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and a reversal pattern called “fan bao.” The stated rationale is to find active stocks where buying-side…

AksjerKinesiske markederTekniske indikatorerTilbakevending mot gjennomsnittet
SuperMind

This stock-selection post proposes combining three filters: an increase in holdings share above a stated threshold, weekly MACD above its zero line, and at least two limit-up sessions within a stated lookback period. The accompanying explanation treats these…

AksjerMomentumTekniske indikatorerFaktorinvestering
SuperMind

This post presents a Chinese stock-selection screen using three main conditions: RSI below 65, float market capitalization between 5 billion and 10 billion yuan, and positive net buying attributed to major participants during the opening auction. Its Python…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

This post describes a Chinese A-share stock screen combining daily price range, company size, profitability, turnover activity, and a weekday filter. It selects stocks with a daily high-to-low range of at least 1%, market capitalization up to 10 billion…

AksjerKinesiske markederTekniske indikatorerRisikostyring
SuperMind

The proposed Chinese equity screen focuses on stocks classified in the metaverse sector. It ranks them by the day’s auction amount, keeps the top five, and requires the prior day’s main-fund-flow measure to be positive. The post presents this as a way to…

AksjerKinesiske markederMarkedsmikrostrukturOrdreutførelse
SuperMind

This stock-selection method screens Chinese equities using three filters: a 14-period RSI below 65, a ten-day price gain above zero but below 35%, and a ranking by individual-stock popularity. The accompanying Python example additionally considers stocks…

AksjerTekniske indikatorerMomentumKinesiske markeder
SuperMind

A forum post asks how to retrieve popular industry indices through iWencai and pass them into a variable. The author describes a query combining industry indices with weekly KDJ conditions and recent main-fund-flow filters, then reports that a Python query…

Kinesiske markederAksjerHistorisk testing
SuperMind

This tutorial explains how support vector machines classify data by finding a boundary with a wide margin, and how slack variables allow some classification errors in noisy data. It introduces kernel methods as a way to handle nonlinear boundaries by…

AksjerMaskinlæringStatistikkHistorisk testing
SuperMind

This note describes a daily stock screen combining MACD above zero, a share price below 12 yuan, and three consecutive declines in closing price. It interprets the MACD condition as a positive trend context and the recent losses as a possible short-term…

AksjerTekniske indikatorerTilbakevending mot gjennomsnittet
SuperMind

This stock-selection rule combines three filters: daily price range greater than 1%, closing price below 20, and prior-day trading value above 60 million. The article interprets the range as a sign of price movement and the trading-value threshold as a…

AksjerKinesiske markederTekniske indikatorerRisikostyring
SuperMind

This Chinese equity screen looks for stocks in the metaverse sector whose prior-day price is above the 250-day moving average while MACD was below zero two days earlier. The intended combination is sector exposure, a long-term price trend filter, and a…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

This stock screen combines a turnover range of 3% to 12%, appearance on the prior day’s market leaderboard, and a stated intraday drawdown between 4% and 5%. The article frames turnover as a liquidity filter, leaderboard presence as a sign of market…

AksjerKinesiske markederTekniske indikatorerRisikostyring
SuperMind

This note proposes a short-term Chinese equity screen using three conditions: a price-range measure above 1, a 9:25 price rise below 6%, and a previous-day 9:15 matched price associated with a limit-down event. It frames the filters as a combination of…

AksjerTekniske indikatorerMarkedssentimentKinesiske markeder
SuperMind

This note describes a Chinese stock selection screen that combines a daily price-range condition, five consecutive years of return on equity above 15%, and a limit on tradable share count. It presents the range condition as a measure of volatility, the ROE…

AksjerFaktorinvesteringVolatilitetKinesiske markeder
SuperMind

This Chinese equity screen combines three conditions: daily high-low range relative to the opening price above 1%, a ten-day return above zero and below 35%, and bullish technical filters. The accompanying Python example calculates moving averages of the…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

The document introduces a 2017 Guotai Junan research report that constructed 191 short-cycle alpha factors from price and volume characteristics and combined them in a stock-selection system. It notes that the factor expressions resemble those in…

AksjerKinesiske markederFaktorinvesteringHistorisk testing