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

Meklēt bibliotēkā

Dokumentu skaits: 12,226

SuperMind

This document presents an equity selection rule that combines a positive MACD signal, a 20-day moving average above the 120-day moving average, and year-over-year parent-company net profit growth above 20% and up to 100%. Its example implementation scans…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
SuperMind

This document describes a Chinese equity screening rule for beverage and alcohol import-export companies. It selects stocks with turnover between 3% and 12% and year-over-year parent-company net profit growth above 20% and up to 100%. The article also gives…

AkcijasĶīnas tirgiFaktoru ieguldīšanaRiska pārvaldība
SuperMind

This note outlines a daily stock screen combining a positive MACD reading, a share price below a stated ceiling, and a measure of trading activity based on the prior day’s turnover rate and the ratio of current auction volume to the previous day’s volume. It…

AkcijasTehniskie indikatoriCenas impulss
SuperMind

This note describes a stock screen based on three chart conditions: amplitude above a threshold, a rising price base, and a rounded-bottom pattern. It presents the pattern as a way to find stocks whose lows are gradually moving higher, while filtering out…

AkcijasTehniskie indikatoriVēsturisko datu pārbaude
SuperMind

This brief template note describes a stock-screening strategy that selects companies using natural-language queries. Its example combines a small-market-cap screen with recent net inflows from large investors, a prior period of contracting volume, and a…

AkcijasCenas impulssTehniskie indikatoriRiska pārvaldība
SuperMind

The document outlines a stock screen that selects companies in a designated metaverse category, requires a positive recent return, and excludes firms listed for less than a chosen period. Its rationale is to combine thematic exposure and recent performance…

AkcijasCenas impulssTehniskie indikatori
SuperMind

This post describes a rule-based screen for Chinese main-board stocks. It selects shares with turnover between 3% and 12%, a daily gain above 1%, and a circulating share count no greater than 5.5 billion. The stated intent is to combine trading activity,…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This Chinese-language stock screen combines an amplitude threshold above 1, a ranking among the five highest stocks by the day’s auction amount, and a rising K value from the KDJ indicator. Its rationale is to find stocks with larger price movement and…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This Chinese-language screening note describes selecting stocks with turnover between 3% and 12%, an external-to-internal trading volume ratio above 1.3, and a reversal pattern called “fan bao.” The stated rationale is to find active stocks where buying-side…

AkcijasĶīnas tirgiTehniskie indikatoriAtgriešanās pie vidējās vērtības
SuperMind

This stock-selection post proposes combining three filters: an increase in holdings share above a stated threshold, weekly MACD above its zero line, and at least two limit-up sessions within a stated lookback period. The accompanying explanation treats these…

AkcijasCenas impulssTehniskie indikatoriFaktoru ieguldīšana
SuperMind

This post presents a Chinese stock-selection screen using three main conditions: RSI below 65, float market capitalization between 5 billion and 10 billion yuan, and positive net buying attributed to major participants during the opening auction. Its Python…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This post describes a Chinese A-share stock screen combining daily price range, company size, profitability, turnover activity, and a weekday filter. It selects stocks with a daily high-to-low range of at least 1%, market capitalization up to 10 billion…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

The proposed Chinese equity screen focuses on stocks classified in the metaverse sector. It ranks them by the day’s auction amount, keeps the top five, and requires the prior day’s main-fund-flow measure to be positive. The post presents this as a way to…

AkcijasĶīnas tirgiTirgus mikrostruktūraRīkojumu izpilde
SuperMind

This stock-selection method screens Chinese equities using three filters: a 14-period RSI below 65, a ten-day price gain above zero but below 35%, and a ranking by individual-stock popularity. The accompanying Python example additionally considers stocks…

AkcijasTehniskie indikatoriCenas impulssĶīnas tirgi
SuperMind

A forum post asks how to retrieve popular industry indices through iWencai and pass them into a variable. The author describes a query combining industry indices with weekly KDJ conditions and recent main-fund-flow filters, then reports that a Python query…

Ķīnas tirgiAkcijasVēsturisko datu pārbaude
SuperMind

This tutorial explains how support vector machines classify data by finding a boundary with a wide margin, and how slack variables allow some classification errors in noisy data. It introduces kernel methods as a way to handle nonlinear boundaries by…

AkcijasMašīnmācīšanāsStatistikaVēsturisko datu pārbaude
SuperMind

This note describes a daily stock screen combining MACD above zero, a share price below 12 yuan, and three consecutive declines in closing price. It interprets the MACD condition as a positive trend context and the recent losses as a possible short-term…

AkcijasTehniskie indikatoriAtgriešanās pie vidējās vērtības
SuperMind

This stock-selection rule combines three filters: daily price range greater than 1%, closing price below 20, and prior-day trading value above 60 million. The article interprets the range as a sign of price movement and the trading-value threshold as a…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

This Chinese equity screen looks for stocks in the metaverse sector whose prior-day price is above the 250-day moving average while MACD was below zero two days earlier. The intended combination is sector exposure, a long-term price trend filter, and a…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

This stock screen combines a turnover range of 3% to 12%, appearance on the prior day’s market leaderboard, and a stated intraday drawdown between 4% and 5%. The article frames turnover as a liquidity filter, leaderboard presence as a sign of market…

AkcijasĶīnas tirgiTehniskie indikatoriRiska pārvaldība
SuperMind

This note proposes a short-term Chinese equity screen using three conditions: a price-range measure above 1, a 9:25 price rise below 6%, and a previous-day 9:15 matched price associated with a limit-down event. It frames the filters as a combination of…

AkcijasTehniskie indikatoriTirgus noskaņojumsĶīnas tirgi
SuperMind

This note describes a Chinese stock selection screen that combines a daily price-range condition, five consecutive years of return on equity above 15%, and a limit on tradable share count. It presents the range condition as a measure of volatility, the ROE…

AkcijasFaktoru ieguldīšanaSvārstīgumsĶīnas tirgi
SuperMind

This Chinese equity screen combines three conditions: daily high-low range relative to the opening price above 1%, a ten-day return above zero and below 35%, and bullish technical filters. The accompanying Python example calculates moving averages of the…

AkcijasĶīnas tirgiTehniskie indikatoriCenas impulss
SuperMind

The document introduces a 2017 Guotai Junan research report that constructed 191 short-cycle alpha factors from price and volume characteristics and combined them in a stock-selection system. It notes that the factor expressions resemble those in…

AkcijasĶīnas tirgiFaktoru ieguldīšanaVēsturisko datu pārbaude