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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
Quantopian lekcijas
Dokumentu skaits: 45
Binance API docs
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: 79,386

SuperMind

This A-share selection rule combines a daily high-low range above one percent, a nonempty unsettled convertible-bond name, and a newly formed upward KDJ crossover. The provided formula further requires the K value to meet or exceed D after being at or below…

AkcijasĶīnas tirgiTehniskie indikatoriSvārstīgums
SuperMind

This note presents an A-share stock screen that ranks names by capital strength, keeps the five with the largest auction value for the day, and requires the 30-day moving average to be rising. It interprets fund flow and auction activity as signs of…

AkcijasĶīnas tirgiSekošana tendenceiCenas impulss
SuperMind

The document proposes screening Chinese metaverse-related stocks for a positive daily close-to-close return while the session low falls between 4% and 5% below the previous close. This combines a positive closing result with a substantial intraday decline,…

Ķīnas tirgiAkcijasAtgriešanās pie vidējās vērtībasTehniskie indikatori
Kraken Learn

The document defines crypto trading signals as alerts, supplied manually or algorithmically, that can identify potential entries, exits, and risk levels. Their stated purpose is to help traders act on trade ideas without having to develop every idea…

KriptoaktīviRiska pārvaldībaRīkojumu izpilde
SuperMind

This post describes a Chinese equity screening rule combining daily price range, a historical dividend measure, and a fixed share price. Its stated logic selects stocks whose high-to-low amplitude exceeds a threshold, whose 2019 dividend ratio is above a…

AkcijasĶīnas tirgiSvārstīgumsFaktoru ieguldīšana
BigQuant

This opinion piece argues that quantitative trading’s advantages extend beyond execution speed. It emphasizes systematic research and iteration: models can combine many fundamental and price-based signals, test relationships across historical data, and…

Vairāku aktīvu tirdzniecībaMašīnmācīšanāsStatistikaRiska pārvaldība
Amberdata research

This market snapshot links a sharp crypto price swing with changes in centralized exchange activity, DeFi lending flows, and miner behavior. It reports that spot trading volumes recovered after a brief decline, with Binance gaining share and Bybit trading…

KriptoaktīviTūlītējo darījumu tirgiSvārstīgumsBlokķēdes dati
SuperMind

This stock screen combines a price-range filter, a five-day moving-average condition, and a metaverse-related classification. It selects shares whose high-to-low range, measured against the previous close, exceeds a threshold; whose closing price is above…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
BigQuant

This overview compares machine learning and deep learning methods for predicting stock prices and trends. It describes LSTM and GRU recurrent networks for sequential data, CNNs for extracting patterns, bidirectional LSTMs, and deep neural networks. It also…

AkcijasMašīnmācīšanāsStatistikaVēsturisko datu pārbaude
Lumibot

This example outlines a disclosure-following workflow based on public House periodic transaction reports. It distinguishes the transaction date from the date a filing becomes public, and says a strategy should only make a record available from publication…

Uz notikumiem balstīta tirdzniecībaAkcijasOpcijasRiska pārvaldība
SuperMind

This stock screen combines a turnover range of 3% to 12%, a K value below 20, and year-over-year net profit growth above 20% and no more than 100%. The article frames the turnover condition as a liquidity filter, the K threshold as a technical selection…

AkcijasTehniskie indikatoriFaktoru ieguldīšanaĶīnas tirgi
MQL5 code base

This document describes a MetaTrader 5 SuperTrend indicator that adapts its volatility bands and selected trade parameters using prior signal outcomes. It offers reversal and failed-breakout signal modes, with optional confirmation from RSI, tick-volume…

Tehniskie indikatoriSekošana tendenceiMašīnmācīšanāsRiska pārvaldība
SuperMind

This Chinese stock-screening example combines a technical threshold with price movement, large-order net volume, and a popularity ranking. It proposes selecting shares with RSI below 65, using the product of percentage change and large-order net volume as a…

AkcijasTehniskie indikatoriTirgus noskaņojums
SuperMind

This stock-selection rule combines three short-term conditions: daily amplitude above 1%, an opening price within about 5% of the 10-day moving average, and buying-side volume more than 1.3 times selling-side volume. The accompanying explanation interprets…

AkcijasTehniskie indikatoriTirgus noskaņojums
SuperMind

This document outlines a Chinese stock selection rule using turnover of 3% to 12%, circulating market capitalization from 1 to 55 billion yuan, and a control activity indicator above 21. The page gives the criteria in prose and in formula-like and Python…

Ķīnas tirgiAkcijasTehniskie indikatoriRiska pārvaldība
SuperMind

The document describes a Chinese equity screen that selects beverage manufacturing stocks with daily turnover between 3% and 12% and a reversal or engulfing-style pattern. Its rationale is that turnover may indicate trading activity while the pattern may…

Ķīnas tirgiAkcijasTehniskie indikatoriAtgriešanās pie vidējās vērtības
SuperMind

This post proposes screening Chinese stocks by daily price amplitude, a measure described as current-day shareholder control, and prior-day major-investor net buying. It presents the combination as a way to capture market sentiment and capital flows. The…

AkcijasĶīnas tirgiTehniskie indikatoriTirgus noskaņojums
SuperMind

This post describes a Chinese equity screening idea that combines three criteria: a dividend payout ratio above 25% in 2019, strong capital activity ranked from high to low, and evidence of major-investor control on the prior day. It suggests turnover and…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTirgus noskaņojums
MQL5 code base

This product description outlines an automated trading system that uses an “Accelerator” indicator and trades from the open price of a candle. It says the EA is intended for major forex pairs and NASDAQ stocks, across timeframes, and recommends trying it on…

Valūtu tirgusAkcijasTehniskie indikatoriRiska pārvaldība
SuperMind

This ProRealTime indicator estimates average forward price movement conditional on the current reading of RSI, Stochastic, Williams %R, or ADX. It divides indicator readings into 20 five-point ranges, then separates observations according to whether the…

Tehniskie indikatoriStatistikaVēsturisko datu pārbaude
SuperMind

This Chinese equity screening rule combines a turnover filter with a recent price pattern and listing-year condition. It selects stocks with turnover between 3% and 12%, listed in 2021, whose closing prices have fallen for three consecutive days. The post…

AkcijasTehniskie indikatoriAtgriešanās pie vidējās vērtībasĶīnas tirgi
SuperMind

This Chinese-market stock screen combines three historical activity filters: daily amplitude above 1%, turnover between 2% and 9%, and at least two limit-up events within a 500-day lookback. The document frames amplitude as a volatility measure, turnover as…

Ķīnas tirgiAkcijasSvārstīgumsTehniskie indikatori
MQL5 code base

This indicator modification takes a standard volume measure and divides it by the spread. The author removed the original color-changing behavior because it was considered unhelpful after the transformation, then added an option to turn spread adjustment on…

Tehniskie indikatoriTirgus mikrostruktūraVēsturisko datu pārbaude