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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
Alphalens
Dokumentu skaits: 14
WonderTrader
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 stock screen combines a positive MACD reading with a company-category filter and a history of high return on equity. The article describes the intended idea as finding companies with favorable fundamentals and technical trends. Its stated ROE threshold…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
SuperMind

This Chinese-language post describes an equity screening rule that selects stocks with an amplitude threshold, prior-day main-fund control, and a rising DEA. It frames the conditions as combining price activity and fund-flow participation with a short-term…

AkcijasTehniskie indikatoriCenas impulssStatistika
SuperMind

This Chinese equities screening recipe combines a volatility condition, positive large-order net volume, and a long-term trend filter. It selects stocks with amplitude above 1, large-order net volume above 0.05 for at least three consecutive days, and the…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
SuperMind

The report studies liquidity, volatility, and return distributions in the CSI 1000 near-month futures contract using minute bars over a specified 2024 sample. It examines log returns across several aggregation windows, compares full-session data with…

Nākotnes līgumiĶīnas tirgiStatistikaSvārstīgums
SuperMind

The document describes a stock screen for the metaverse industry that combines a newly formed KDJ bullish crossover with three consecutively declining closing prices. It outlines the intended selection logic and gives formula and Python examples for applying…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
SuperMind

This stock-selection example filters Chinese equities by turnover, listing year, and Bollinger Band position. It targets stocks with turnover between 3% and 12%, an IPO year of 2021, and a closing price above the Bollinger middle band but below the upper…

AkcijasĶīnas tirgiTehniskie indikatoriSvārstīgums
SuperMind

This document describes a Chinese stock-selection screen combining turnover with year-over-year growth in net profit attributable to parent-company shareholders. It specifies turnover between 3% and 12%, profit growth above 20% and no more than 100%, and an…

AkcijasĶīnas tirgiFaktoru ieguldīšanaStatistika
SuperMind

This note describes a Chinese equity screen that combines amplitude above 1, a prior-day proxy for main-investor control, and return on equity above 15% in each of five consecutive years. The proposed logic aims to pair active price movement and recent…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTehniskie indikatori
SuperMind

This note presents a technical screen for Chinese stocks using daily amplitude above 1, a rounded or arc-like candle pattern, and a MACD reading below zero from two days earlier. It explains the amplitude condition as a way to find stocks with notable price…

AkcijasĶīnas tirgiTehniskie indikatoriSvārstīgums
SuperMind

This note describes a Chinese equity screen combining turnover, year-over-year net profit growth attributable to parent-company shareholders, and the ratio of external to internal trading volume. It selects stocks with turnover between 3% and 12%, profit…

AkcijasĶīnas tirgiTirgus mikrostruktūraFaktoru ieguldīšana
SuperMind

This post outlines an A-share stock screen using three conditions: a daily high-low range above a threshold, a nonempty convertible-bond name field, and a positive but capped return over ten days. It presents both an indicator-formula example and a Python…

AkcijasĶīnas tirgiSvārstīgumsCenas impulss
SuperMind

This post describes an A-share screening recipe combining a price-range condition, exclusion of stocks marked ST, a five-session closing-high condition, and a limit on the stock’s price increase at 9:25. It also says to select stocks before 10:00. The…

AkcijasĶīnas tirgiCenas izrāviensCenas impulss
SuperMind

The document describes a China-market stock screen that selects companies in the metaverse theme, with circulating market capitalization above 10 billion yuan and a 2019 dividend payout ratio above 25%. It defines payout ratio as dividends per share relative…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaRiska pārvaldība
SuperMind

The document presents a calculation for charting the annualized return of a fund over time. It retrieves daily closing prices for a specified fund and date range, then compares each later close with the first close to calculate cumulative return. It…

StatistikaVēsturisko datu pārbaude
SuperMind

The document proposes a China A-share screening rule focused on companies classified in the metaverse industry. Its initial filters include a limit on tradable share count and a positive MACD reading. A later version adds a market-cap ceiling and selects…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This brief community post proposes adding two filters to a small-cap stock strategy: rank companies from lower to higher debt ratio and require five-year average return on equity to exceed 10%. The title advertises an annualized return of 41% and a win rate…

AkcijasFaktoru ieguldīšanaĶīnas tirgi
SuperMind

This stock-selection proposal filters for main-board shares with turnover between 3% and 12%, a daily gain above 1%, and a newly formed KDJ golden cross. The accompanying Python example checks recent K and D values for a crossover and adds a J-value…

AkcijasTehniskie indikatoriCenas impulss
SuperMind

The document proposes a stock screen using three conditions: daily high-to-low amplitude above 1%, a K-line-related value below 20, and prior-day turnover above 8%. It frames amplitude as a volatility filter, the K-line threshold as a low-position condition,…

AkcijasTehniskie indikatoriSvārstīgums
SuperMind

The article explains that Ethereum’s mempool holds transactions awaiting inclusion in a block, and that fees and maximal extractable value can influence transaction priority. It distinguishes real-time pending-transaction data from historical records and…

KriptoaktīviBlokķēdes datiRīkojumu izpildeTirgus mikrostruktūra
SuperMind

This A-share stock screen combines a volatility condition, a weekly moving-average crossover, and positive daily price action. It seeks stocks with amplitude above 1, a weekly MA5 crossing above MA10, and a daily gain greater than 1%, while restricting the…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

The post describes a Chinese equity screening rule combining daily price movement, circulating market capitalization, and a rounded or arc-shaped chart pattern. Its stated conditions are amplitude above 1, circulating market value above 10 billion yuan, and…

AkcijasĶīnas tirgiTehniskie indikatoriVēsturisko datu pārbaude
SuperMind

This Chinese stock-screening proposal combines three characteristics: at least five moving averages converging near one another, a preferred enterprise type, and at least two limit-up sessions within a 500-day lookback. The article interprets moving-average…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

The document outlines a Chinese A-share screening idea focused on stocks classified in the metaverse theme. Its initial conditions are turnover above 8% on the previous day and an increase in the K value of the KDJ indicator. The final stated rule adds a…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
SuperMind

This note presents a proposed screen for Shenzhen main-board shares that excludes Beijing-listed A-shares. Its initial conditions limit price-to-earnings and price-to-book ratios to stated ranges and require daily high-low amplitude above a threshold. An…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTehniskie indikatori