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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 describes a Chinese stock screen combining turnover, a robot-industry theme, tradable market value, price change, and large-order flow. Its stated target is robot-related stocks with turnover between 3% and 12%, tradable capitalization below 10…

AksjerKinesiske markederMomentumMarkedsmikrostruktur
SuperMind

This document describes a Chinese stock screen combining daily price range, market capitalization, profitability, and a rising-price condition. It selects stocks with an intraday high-low range of at least 1%, market capitalization under 10 billion yuan,…

AksjerKinesiske markederTekniske indikatorerTrendfølgende handel
SuperMind

This A-share screening proposal combines daily price amplitude above 1%, an outstanding convertible-bond name, and a large reversal-style candle. The example formula treats a session as qualifying when the close is at least 5% above the open or the open is…

AksjerKinesiske markederTekniske indikatorerVolatilitet
SuperMind

The article proposes screening A-shares for daily amplitude of at least 1%, market capitalization under 10 billion yuan, positive net profit, and a weekly five-period moving average crossing above the ten-period average. It presents formula and Python…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

The document presents an A-share screening idea combining metaverse industry classification with relative trading volume between 1.5 and 6 times a reference average. It also uses a closing-price ceiling of 18.5 and, in its final version, a…

AksjerKinesiske markederTekniske indikatorerStatistikk
SuperMind

The post outlines a proposed stock screen combining company size with a daily increase in positions. It first describes selecting stocks with market capitalization from 2 billion to below 10 billion, then narrows the set to stocks listed during 2021. A daily…

AksjerKinesiske markederMomentum
SuperMind

This BigQuant forum answer explains how to use a different feature set in each iteration of a rolling model workflow. The example loop updates training and test start and end dates for each rolling window, disables the backtest module during model runs, and…

MaskinlæringHistorisk testingStatistikk
SuperMind

This post describes a short-term A-share screening approach that combines daily price range, robotics-related business exposure, a circulating market-cap ceiling, and a positive recent return filter. It also proposes adding industry and valuation or…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

This proposed A-share screen combines a daily price-amplitude threshold, a morning-star pattern, and a company-type filter. The post frames it as a blend of technical and fundamental considerations, while its example defines eligible company types through…

AksjerTekniske indikatorerRisikostyring
SuperMind

This A-share stock screen combines a positive MACD reading, positive trailing P/E, and a control-position ratio above 21. It is intended to run before the market opens each trading day. The document explains the MACD condition as a way to identify strength,…

AksjerTekniske indikatorerMomentum
SuperMind

The document presents a Chinese stock-screening idea that combines three technical conditions: at least five moving averages converge, the KDJ indicator has just formed a bullish crossover, and the 30-day average is rising or flat. It interprets clustered…

AksjerTekniske indikatorerMomentum
SuperMind

This post describes an A-share screening rule based on turnover between 3% and 12%, an opening price within 5% of the 10-day moving average, and a stated control-strength condition above 21. It presents the screen as a combination of trading activity,…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

This post presents an A-share stock screen combining a daily price-range threshold, a nonempty convertible-bond name field, and a high ranking by large-order net volume. Its stated final selection rule takes the top 30 stocks by daily large-order net volume,…

AksjerKinesiske markederMarkedsmikrostrukturTekniske indikatorer
SuperMind

This post outlines a Chinese equity screen combining a daily range above 1%, dates in 2021, and a current low below the prior day's low. Its final stated selection also adds valuation filters: a price-to-earnings ratio below 30 and a price-to-book ratio…

AksjerKinesiske markederVolatilitetTekniske indikatorer
SuperMind

This note describes a simple technical screen for Chinese stocks: select observations from 2021 where the daily high-low range exceeds 1% and the current high equals the highest high over the current and prior day. The author frames a large range as a sign…

AksjerKinesiske markederVolatilitetTekniske indikatorer
SuperMind

The article outlines an A-share stock selection screen combining turnover, company size, profitability, and past price-limit events. It selects shares with turnover between 3% and 12%, market capitalization below 10 billion yuan, no reported loss, and at…

AksjerKinesiske markederMomentumFaktorinvestering
SuperMind

This post combines a stock universe selected through an intelligent query with a dual moving average rule. The proposed rule buys when the five-day average is above the twenty-day average and sells when it falls below, while the universe is screened for low…

AksjerTekniske indikatorerTrendfølgende handelKinesiske markeder
SuperMind

This post describes a Chinese equity screen combining daily price amplitude, dividend yield, and a large-order net-flow ranking. It presents the factors as signals for volatility, company profitability, and market interest, then shows example implementations…

AksjerKinesiske markederFaktorinvesteringTekniske indikatorer
SuperMind

The post proposes a stock screen that combines a recent increase in institutional buying, weekly MACD above its zero line, and a high historical dividend payout ratio. It interprets the buying measure as evidence of institutional interest, weekly MACD as a…

Kinesiske markederAksjerTekniske indikatorerMomentum
SuperMind

This overview surveys recurring problems in quantitative trading, including unreliable or incomplete data, model risk, slippage and fees, parameter selection, real-time monitoring, and processing speed. It also discusses choosing machine-learning methods and…

RisikostyringHistorisk testingOrdreutførelseMaskinlæring
SuperMind

This document describes a daily Chinese equity screen combining three conditions: turnover between 3% and 12%, the product of price change and net large-order volume above zero, and MACD above zero. It presents the rule as a way to select stocks with…

AksjerKinesiske markederTekniske indikatorerMomentum
SuperMind

This A-share stock screen combines a 14-period RSI below 65 with a positive prior-day main-fund net inflow. The stated rationale is that a relatively lower RSI may indicate rebound potential, while the fund-flow condition adds a capital-flow filter. The…

Kinesiske markederAksjerTekniske indikatorerMomentum
SuperMind

This Chinese equity screening idea combines three conditions: the 20-day moving average must exceed the 120-day moving average, the stock must have recorded at least two limit-up sessions in the prior 500 days, and its volume ratio must rank among the top…

AksjerKinesiske markederMomentumTekniske indikatorer
SuperMind

This Chinese equity screen combines a turnover rate between 3% and 12%, an outside-to-inside trading volume ratio above 1.3, and displayed buy volume greater than sell volume. The article presents the filters as a way to find actively traded stocks with…

AksjerKinesiske markederMarkedsmikrostrukturTekniske indikatorer