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Zināšanu bibliotēka

Stratmill pētniecības aģenta sagatavoti kopsavilkumi un galvenās atziņas par grāmatām, pētījumiem, rakstiem un kodu, ko lasa mūsu MI aģenti. Katrā lapā ir saite uz oriģinālu.

Quant Q&A
Dokumentu skaits: 20,364
SuperMind
Dokumentu skaits: 12,226
OKX Learn
Dokumentu skaits: 8,431
Strategy library
Dokumentu skaits: 7,910
MQL5 code base
Dokumentu skaits: 7,090
BigQuant
Dokumentu skaits: 3,481
Bitget Academy
Dokumentu skaits: 3,298
MQL5 articles
Dokumentu skaits: 3,012
TradingView scripts
Dokumentu skaits: 1,976
ProRealCode
Dokumentu skaits: 1,507
Deribit Insights
Dokumentu skaits: 1,232
Machine Learning for Trading
Dokumentu skaits: 1,124
arXiv papers
Dokumentu skaits: 1,033
Amberdata research
Dokumentu skaits: 766
FMZ forum
Dokumentu skaits: 682
FMZ digest
Dokumentu skaits: 662
vn.py community
Dokumentu skaits: 560
QuantInsti blog
Dokumentu skaits: 511
Galaxy Research
Dokumentu skaits: 340
QuantStart
Dokumentu skaits: 246
Stratmill research code
Dokumentu skaits: 219
Robot Wealth
Dokumentu skaits: 195
NautilusTrader
Dokumentu skaits: 191
Hummingbot docs
Dokumentu skaits: 181
Paradigm research
Dokumentu skaits: 175
Lumibot
Dokumentu skaits: 164
Kraken Learn
Dokumentu skaits: 163
Kvantitatīvās tirdzniecības kursu bibliotēka
Dokumentu skaits: 157
OctoBot
Dokumentu skaits: 152
Cryptohopper blog
Dokumentu skaits: 144
Systematic trading blog (Rob Carver)
Dokumentu skaits: 132
Qlib
Dokumentu skaits: 116
TqSdk
Dokumentu skaits: 86
Quantpedia
Dokumentu skaits: 86
Hyperliquid docs
Dokumentu skaits: 79
Freqtrade
Dokumentu skaits: 68
Hudson & Thames
Dokumentu skaits: 62
Awesome Systematic Trading
Dokumentu skaits: 61
backtrader
Dokumentu skaits: 54
vn.py
Dokumentu skaits: 50
Binance API docs
Dokumentu skaits: 45
Quantopian lekcijas
Dokumentu skaits: 45
FMZ guides
Dokumentu skaits: 38
pysystemtrade
Dokumentu skaits: 34
Freqtrade docs
Dokumentu skaits: 32
quant-trading
Dokumentu skaits: 31
FinRL
Dokumentu skaits: 28
Zipline
Dokumentu skaits: 22
FMZ live strategies
Dokumentu skaits: 21
Jesse
Dokumentu skaits: 17
pyfolio
Dokumentu skaits: 16
WonderTrader
Dokumentu skaits: 14
Alphalens
Dokumentu skaits: 14
backtesting.py
Dokumentu skaits: 11
Technical Analysis
Dokumentu skaits: 9
QTPyLib
Dokumentu skaits: 8
QuantRocket
Dokumentu skaits: 7
Lumibot strategies
Dokumentu skaits: 7
Awesome Quant
Dokumentu skaits: 1

Meklēt bibliotēkā

Dokumentu skaits: 12,226

SuperMind

This article outlines a Chinese equity screening rule combining daily amplitude above one, a positive weekly MACD reading, and a high ranking for net large-order volume. It describes these conditions as a way to find volatile stocks with upward price…

AkcijasTehniskie indikatoriCenas impulssTirgus mikrostruktūra
SuperMind

This article sketches a Chinese stock-selection rule using MACD and price conditions. It describes screening for MACD above the zero line, a price below 12 yuan, and also states a share-price condition of 18.5 yuan. It says the scan is intended to run before…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
SuperMind

The proposed stock screen combines a technical condition with fundamental growth and company-size filters. It selects stocks with an RSI below 65, year-over-year net profit growth above 20% and at most 100%, and size of at least 200 million. The article…

Ķīnas tirgiAkcijasTehniskie indikatoriFaktoru ieguldīšana
SuperMind

This note outlines a basic screen for Chinese equities whose stock codes begin with 60, whose daily high-to-low range exceeds 1%, and whose closing price is 18.5 yuan. It also proposes sorting qualifying stocks by trading activity. The article illustrates…

AkcijasĶīnas tirgiSvārstīgumsTehniskie indikatori
SuperMind

This note describes a simple Chinese-equity screen combining daily price range, calendar year, and closing price. It selects stocks whose high-to-low range exceeds 1%, whose trading date falls in 2021, and whose closing price equals 18.5 yuan. The article…

AkcijasĶīnas tirgiSvārstīgumsTehniskie indikatori
SuperMind

This article describes an A-share screening rule combining three conditions: RSI below 65, seven consecutive sessions in which the close is below the open, and displayed best-bid volume greater than best-ask volume. It interprets the RSI and candle sequence…

AkcijasTehniskie indikatoriĶīnas tirgi
SuperMind

This post outlines a stock-selection rule using amplitude, three consecutive limit-up sessions on the previous day, and a historical limit-up condition tied to 2021. Its revised formulation specifies amplitude above 1.5% and requires a past limit-up price to…

AkcijasĶīnas tirgiCenas impulssCenas izrāviens
SuperMind

This Chinese A-share screening post describes a rule set combining amplitude above 1, a previous-day 9:15 matching price at the limit-down level, and a stated holder-concentration condition. It proposes selecting the first n stocks and provides…

AkcijasĶīnas tirgiSvārstīgumsTehniskie indikatori
SuperMind

This stock-selection screen combines three conditions: a relative strength index below 65, positive large-order net inflow in the afternoon, and more than two limit-up days in the recent ten-day period. The article presents the setup as a way to find…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
SuperMind

This Chinese stock selection proposal looks for shares with turnover between 3% and 12%, seven consecutive down days, and a Morning Star candlestick signal. Its rationale is to search for a possible rebound after a sustained short-term decline. The document…

AkcijasĶīnas tirgiTehniskie indikatoriAtgriešanās pie vidējās vērtības
SuperMind

This proposed Chinese equity screen combines a daily price move with company size and profitability. It selects stocks with amplitude above 1, a daily decline between 4% and 5%, market capitalization at or below 10 billion yuan, and no losses. The document…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

This survey outlines a broad set of systematic approaches across equities, currencies, futures, options, and fixed income. It describes cross-sectional signals such as price and earnings momentum, book-to-price value, volatility, and combinations of factors;…

Vairāku aktīvu tirdzniecībaFaktoru ieguldīšanaTehniskie indikatoriArbitrāža
SuperMind

The document describes a Chinese equity screen combining four conditions: membership in the metaverse sector, a prior-day close above the 250-day moving average, a robotics concept classification, and circulating market value below 10 billion yuan. It…

AkcijasĶīnas tirgiTehniskie indikatoriSekošana tendencei
SuperMind

This stock screen selects companies classified in the metaverse sector whose prior-day actual turnover is between 3% and 28%. It also requires the closing price to fall between the 20-period Bollinger middle band and upper band. The article describes the…

Ķīnas tirgiAkcijasTehniskie indikatoriSvārstīgums
SuperMind

This stock selection rule screens Chinese equities in the metaverse industry, limiting candidates to those with a circulating share count no greater than 5.5 billion and a high rank for large-order net flow. The accompanying Python example retrieves stock…

Ķīnas tirgiAkcijasTirgus noskaņojumsFaktoru ieguldīšana
SuperMind

This stock screen combines three conditions: amplitude above one, circulating market capitalization above 10 billion yuan, and a newly formed KDJ golden cross. The post frames the combination as a way to find relatively large, volatile stocks with a…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

The screen selects listed Chinese stocks with an amplitude above one, more than three years since listing, and closing prices that fell on each of the last three sessions. Its stated rationale is to seek actively traded shares while excluding newer listings,…

AkcijasĶīnas tirgiTehniskie indikatoriAtgriešanās pie vidējās vērtības
SuperMind

The document introduces hidden Markov models (HMMs) as a way to infer unobserved market regimes from observed asset returns. It explains the Markov assumption that the next state depends on the current state, and describes how regimes can change return…

StatistikaMašīnmācīšanāsRiska pārvaldība
SuperMind

This stock screen looks for Shenzhen Main Board companies with annual return on equity above 15% in each of five years, a price-to-earnings ratio from zero to 29.01, and a price-to-book ratio from zero to 3.11. It also requires the daily high-low range to be…

AkcijasFaktoru ieguldīšanaĶīnas tirgiStatistika
SuperMind

This Chinese stock selection rule combines three filters: rank stocks among the top 100 by volume ratio, keep those whose opening price is within five percent above or below the ten-day moving average, and look for a shortening histogram in the 15-minute…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
SuperMind

This document describes a daily stock screen that selects shares with turnover between 3% and 12%, a rising K value in the KDJ indicator, and an intraday low between 4% and 5% below the previous close. It provides equivalent selection logic in a…

AkcijasTehniskie indikatoriAtgriešanās pie vidējās vērtībasVēsturisko datu pārbaude
SuperMind

This guide outlines how to move a strategy from backtesting or simulation into Supermind’s live research-trading environment. It describes logging in to a simulated or funded account, creating live-trading code, and adapting a backtest template to initialize…

Rīkojumu izpildeVēsturisko datu pārbaude
SuperMind

This proposed stock screen looks for shares with a daily high-low range above 1%, trading activity above 60 million in the prior day, and a newly formed KDJ golden cross, defined as K moving above D after being below it. The article frames the range and…

Ķīnas tirgiAkcijasCenas impulssTehniskie indikatori
SuperMind

This proposed stock screen selects main board shares with turnover between 3% and 12% and circulating market value between 5 billion and 10 billion yuan. It also requires large-order net volume to exceed 0.05 for at least three consecutive days. The article…

Ķīnas tirgiAkcijasCenas impulssStatistika