Pāriet uz saturu

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 post describes a Chinese stock selection screen combining price amplitude, a measure labeled today’s control, and a recent large daily gain. Its written logic adds a free-float market capitalization floor. The article also provides example formulas for…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

This post proposes a technical screen for Chinese A-share stocks. It selects shares with RSI below 65, seven consecutive sessions in which the close is no higher than the open, and a KDJ stochastic K reading below 20. The accompanying code sketch describes…

Ķīnas tirgiAkcijasTehniskie indikatoriAtgriešanās pie vidējās vērtības
SuperMind

This Chinese stock selection proposal combines turnover between 3% and 12%, an opening price near the 10-day moving average, and a prior-day signal described as main-force control. The formula reference adds conditions requiring the close to exceed both the…

Ķīnas tirgiAkcijasCenas impulssTehniskie indikatori
SuperMind

This stock-selection proposal targets main-board shares associated with robotics. It filters for turnover between three and twelve percent, circulating market value below 10 billion yuan, and a weekly five-period moving average crossing above the ten-period…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This Chinese-language post proposes a stock screen combining three signals: today’s increase in holdings above five percent, an institutional buying indication, and shortening green MACD bars on a 15-minute chart. It interprets the first two as evidence of…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This note outlines a chart-based stock screen using at least five converging moving averages, a rounded or arc-shaped price pattern, and a focus on stocks associated with 2021. It illustrates possible moving-average periods of 5, 10, 20, 50, and 200 days,…

AkcijasTehniskie indikatoriSekošana tendenceiVēsturisko datu pārbaude
SuperMind

This note presents a Chinese equity screen combining turnover between 3% and 12%, market capitalization below 10 billion yuan, positive profitability, and a closing price above the previous day’s low. It includes example formula and Python references for…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

This note describes an A-share stock screen combining three conditions: membership in the metaverse industry, a positive return based on the latest close versus the prior close, and circulating market capitalization below 10 billion yuan. It also gives…

AkcijasĶīnas tirgiCenas impulssRiska pārvaldība
SuperMind

This note proposes screening metaverse-related stocks for elevated recent turnover and a low KDJ reading, then adds valuation and leverage constraints. Its final selection logic combines sector membership, a turnover condition, KDJ below a threshold,…

AkcijasTehniskie indikatoriCenas impulssRiska pārvaldība
SuperMind

This note outlines a stock screen for companies in the metaverse theme whose average price is above its five-day moving average and that show a rebound after a recent decline. It frames the setup as a way to find stocks recovering from weakness, but does not…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
SuperMind

This document outlines a stock screen requiring price amplitude above a threshold, a current-day control measure above a threshold, and a MACD value below zero from two days earlier. It frames the inputs as measures of price movement, trading control or…

AkcijasTehniskie indikatoriĶīnas tirgi
SuperMind

This document describes an equity screening rule combining a metaverse industry label, a close above the previous day’s low, and a KDJ %K reading at or below 20. Its stated rationale is that the industry label identifies potential growth exposure, the price…

AkcijasTehniskie indikatoriĶīnas tirgi
SuperMind

The document proposes screening stocks using amplitude above one, a shortening negative MACD histogram on a 15-minute chart, and MACD at or above zero. It explains the intended rationale as combining price movement with a momentum indicator to identify…

Ķīnas tirgiAkcijasCenas impulssTehniskie indikatori
SuperMind

The document outlines a Chinese-equity screening idea that combines daily turnover between 3% and 12%, a positive product of price change and net large-order volume, and a rounded or arc-like candlestick condition. It presents sample formula and Python…

Ķīnas tirgiAkcijasTehniskie indikatoriTirgus mikrostruktūra
SuperMind

The document describes an A-share screening rule for companies assigned to the metaverse industry. It combines a float-size ceiling with an ownership-concentration ceiling, then returns qualifying stock codes and names. The accompanying Python example…

AkcijasĶīnas tirgiFaktoru ieguldīšana
SuperMind

This stock-selection rule filters for a high–low range above 1% of the high, a closing price of exactly 18.5 yuan, and circulating market value of at least 10 billion yuan. The document includes example selection logic and sorts qualifying names by trading…

AkcijasĶīnas tirgiTehniskie indikatoriFaktoru ieguldīšana
SuperMind

This stock screen combines a daily range condition with sustained profitability and a momentum indicator. It selects shares whose high–low range exceeds an ATR-based threshold, whose return on equity stayed above 15% across five years, and whose 14-period…

AkcijasFaktoru ieguldīšanaTehniskie indikatoriRiska pārvaldība
SuperMind

This post proposes a Chinese equity screen combining three conditions: daily amplitude above a threshold, at least two limit-up events within a 500-trading-day window, and a reversal or engulfing-style candle condition. It gives formula references and an…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

The proposed screen selects main-board stocks with circulating market value between 5 billion and 10 billion yuan, turnover between 3% and 12%, and a stated daily maximum decline between 4% and 5%. The article presents the filters as a way to find stocks…

Ķīnas tirgiAkcijasTehniskie indikatoriRiska pārvaldība
SuperMind

This stock-selection post describes a screen using three short-term conditions: a positive ten-day return below 35%, daily amplitude above 1%, and a stated concentration measure below 20%. It frames these filters as a way to identify stocks with recent gains…

Ķīnas tirgiAkcijasTehniskie indikatoriRiska pārvaldība
SuperMind

This community post proposes a Chinese stock screen within the metaverse industry. It filters for prior-session actual turnover between 3% and 28%, an opening-auction price move ranked among the strongest in the market, and combined net buying by very large…

Ķīnas tirgiAkcijasTirgus mikrostruktūraRīkojumu izpilde
SuperMind

This article reviews global equity factor performance in the third quarter of 2021 and compares defensive factor positioning. It reports that momentum and low residual volatility led the pure factor results, while liquidity lagged. Regional index outcomes…

AkcijasFaktoru ieguldīšanaCenas impulssSvārstīgums
SuperMind

This Chinese-language article describes an equity screen for Shenzhen main-board stocks associated with the metaverse theme. It selects stocks with a positive return, positive price-to-earnings and price-to-book ratios, and upper bounds on both valuation…

AkcijasĶīnas tirgiFaktoru ieguldīšana
SuperMind

This document outlines a stock-selection screen that ranks securities by capital strength and requires weekly MACD to be above zero, alongside a year-based filter referring to 2021. It explains capital strength through trading volume and value, and describes…

Ķīnas tirgiAkcijasCenas impulssTehniskie indikatori