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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
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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
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Amberdata research
Dokumentu skaits: 766
FMZ forum
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FMZ digest
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vn.py community
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QuantInsti blog
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Galaxy Research
Dokumentu skaits: 340
QuantStart
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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
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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 Chinese equity screen selects stocks with turnover between 3% and 12%, first-level bid volume greater than first-level ask volume, and a ratio involving the prior day’s turnover and the current opening-auction volume relative to prior volume that falls…

Ķīnas tirgiAkcijasRīkojumu izpildeTirgus mikrostruktūra
SuperMind

This Chinese equity screen combines three filters: membership in the metaverse sector, a positive institutional-flow measure, and a rising-bottom price condition. The note gives an indicator formula that compares recent and longer-window lows and checks…

AkcijasTehniskie indikatoriĶīnas tirgiCenas impulss
SuperMind

This note describes a simple Chinese equity screen combining daily turnover between 3% and 12%, an opening price within roughly 5% of the ten-day moving average, and a rising-bottom condition. It frames turnover as a liquidity filter, proximity to the moving…

AkcijasTehniskie indikatoriĶīnas tirgiSekošana tendencei
SuperMind

This short-term stock screen selects shares with turnover between 3% and 12%, an appearance on the previous day’s trading leaderboard, and a 14-period RSI below 65. The document frames turnover and leaderboard appearance as signs of trading activity, while…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This screen combines daily price range, a share-price ceiling, and a moving-average crossover. It seeks stocks with amplitude above 1%, a closing price below 20, and a 5-period moving average crossing above a 10-period average. The text describes the…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This stock screen combines three conditions: daily amplitude above 1%, circulating market value above 10 billion yuan, and a decline between 4% and 5% under the document’s stated calculation. It presents the setup as a way to find sizable, active stocks that…

AkcijasĶīnas tirgiSvārstīgumsTehniskie indikatori
SuperMind

This note outlines a screen for metaverse-related stocks using a positive recent return and a moving-average condition. The initial description refers to the average price being above the five-day moving average, while the final selection rule and formulas…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This equity screen selects stocks whose codes begin with 60, whose turnover rate is between 3% and 12%, and whose prior-day main fund flow is positive. The rationale is to combine a trading-activity range with a signal that large investor flows were…

AkcijasĶīnas tirgiTirgus mikrostruktūraTehniskie indikatori
SuperMind

This note describes an equity screen for stocks with at least five overlapping moving averages and a 20-day moving average above the 120-day average. The proposed interpretation is that clustered averages indicate short- and long-term trend agreement, while…

AkcijasTehniskie indikatoriSekošana tendencei
SuperMind

This proposed Chinese A-share screen selects stocks associated with the metaverse theme whose circulating share count is at most 5.5 billion and whose weekly closing price crosses above its 30-week moving average. The article presents the crossover as a way…

AkcijasĶīnas tirgiTehniskie indikatoriSekošana tendencei
SuperMind

This proposed A-share screen combines three short-term signals: a shorter 15-minute MACD histogram, a report that major investors controlled trading the previous day, and a daily increase in holdings above 5%. The document treats the capital-flow and control…

AkcijasTehniskie indikatoriCenas impulssRiska pārvaldība
SuperMind

The proposed stock screen starts with Chinese A-shares, excludes Beijing-listed stocks, and selects shares whose turnover rate falls between 3% and 12%. It then requires first-level bid volume to exceed first-level ask volume, using displayed order-book…

AkcijasĶīnas tirgiTirgus mikrostruktūraRīkojumu izpilde
SuperMind

This Chinese stock-screening example selects companies classified in the metaverse industry whose opening price is near the ten-day moving average, with relative volume between 1.5 and 6. The accompanying explanation frames the industry classification and…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
SuperMind

This stock-selection note combines two filters: rank stocks by the strength of fund inflows and retain those that reached the daily price limit at least once during the prior 25 days. It describes stronger inflows and recent limit-up moves as signs of…

Ķīnas tirgiAkcijasCenas impulssTehniskie indikatori
SuperMind

This article proposes a stock screen combining a technical condition with valuation and size filters. It selects equities with an RSI below a stated threshold, positive trailing price-to-earnings ratio, and free-float market capitalization within a specified…

AkcijasĶīnas tirgiTehniskie indikatoriFaktoru ieguldīšana
SuperMind

This article describes a Chinese-equity screening rule that restricts candidates to the metaverse industry, looks for a newly formed KDJ bullish crossover, and requires reported outside trading volume to exceed inside volume by a stated ratio. It frames the…

AkcijasĶīnas tirgiTehniskie indikatoriTirgus mikrostruktūra
SuperMind

This Chinese-language strategy note proposes adding stocks to a candidate pool when they have a price range above a stated threshold, at least one limit-up event in the preceding 25 days, and a current close above the previous session’s low. It interprets…

AkcijasCenas impulssSvārstīgumsTehniskie indikatori
SuperMind

This post proposes screening Chinese A-share stocks using a daily price-range threshold above one percent and a requirement related to outstanding convertible-bond names, then ranking candidates by average traded value. Its stated rationale combines trading…

AkcijasĶīnas tirgiSvārstīgumsFaktoru ieguldīšana
SuperMind

This Chinese-language note proposes screening stocks for a price range greater than one, a shortening negative MACD histogram on a 15-minute chart, and a selected company type. It frames the range as a volatility filter and the histogram condition as a…

AkcijasTehniskie indikatoriSvārstīgumsĶīnas tirgi
SuperMind

The article proposes a short-term stock screen combining three conditions: daily high-low amplitude above one percent, a large-order net-flow ranking threshold, and exclusion of stocks listed on China's STAR Market. Candidates are then ranked by trading…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
SuperMind

This Chinese-language post outlines an equity screen using three main conditions: amplitude above 1, rising bottoms, and exclusion of stocks that hit the daily limit the previous day. It provides sample indicator formulas and Python-like logic, and proposes…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This Chinese-language post describes a stock screen that requires amplitude above 1, ranks candidates by today’s auction amount, and selects the top five, while also requiring external volume to exceed internal volume by a stated ratio of 1.3. The post…

AkcijasĶīnas tirgiTehniskie indikatoriTirgus mikrostruktūra
SuperMind

This BigQuant example describes a stock-selection workflow that calculates each stock’s 15-day price change and 20-day average turnover from local CSV files. It ranks stocks by 15-day change, keeps the lower half, joins those names to a current stock list,…

Ķīnas tirgiAkcijasAtgriešanās pie vidējās vērtībasTehniskie indikatori
SuperMind

This note describes a Chinese equity screen using daily amplitude above 1, a controlling-shareholder measure above 21, and turnover between 3% and 12%. It presents these filters as a way to find active, tradable stocks while avoiding names that are either…

Ķīnas tirgiAkcijasTehniskie indikatoriTirgus mikrostruktūra