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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
Lumibot strategies
Dokumentu skaits: 7
QuantRocket
Dokumentu skaits: 7
Awesome Quant
Dokumentu skaits: 1

Meklēt bibliotēkā

Dokumentu skaits: 12,226

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…

AkcijasĶīnas tirgiCenas impulssTirgus mikrostruktūra
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,…

AkcijasĶīnas tirgiTehniskie indikatoriSekošana tendencei
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…

AkcijasĶīnas tirgiTehniskie indikatoriSvārstīgums
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…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
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…

AkcijasĶīnas tirgiTehniskie indikatoriStatistika
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…

AkcijasĶīnas tirgiCenas impulss
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…

MašīnmācīšanāsVēsturisko datu pārbaudeStatistika
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…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
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…

AkcijasTehniskie indikatoriRiska pārvaldība
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,…

AkcijasTehniskie indikatoriCenas impulss
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…

AkcijasTehniskie indikatoriCenas impulss
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,…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
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,…

AkcijasĶīnas tirgiTirgus mikrostruktūraTehniskie indikatori
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…

AkcijasĶīnas tirgiSvārstīgumsTehniskie indikatori
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…

AkcijasĶīnas tirgiSvārstīgumsTehniskie indikatori
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…

AkcijasĶīnas tirgiCenas impulssFaktoru ieguldīšana
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…

AkcijasTehniskie indikatoriSekošana tendenceiĶīnas tirgi
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…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTehniskie indikatori
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…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
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…

Riska pārvaldībaVēsturisko datu pārbaudeRīkojumu izpildeMašīnmācīšanās
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…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
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…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
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…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
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…

AkcijasĶīnas tirgiTirgus mikrostruktūraTehniskie indikatori