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
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: 3,481

BigQuant

This document summarizes a research approach that uses Google Trends search activity to inform equity portfolio weights. It treats search volume as a measure of how popular a stock is and assumes that popularity is related to risk. The portfolio therefore…

AkcijasPortfeļa veidošanaRiska pārvaldībaTirgus noskaņojums
BigQuant

The article argues that algorithmic trading has changed the experience of retail equity investors. It describes quant systems as data-driven and fast, and claims their trading can contribute to index moves that diverge from the performance of individual…

AkcijasTirgus mikrostruktūraRiska pārvaldībaĶīnas tirgi
BigQuant

The report outlines a framework for timing equity factors whose performance has become less stable. It first examines indicators such as valuation spreads and pairwise correlations, testing their relationship with future factor returns. It then uses a random…

AkcijasFaktoru ieguldīšanaMašīnmācīšanāsPortfeļa veidošana
BigQuant

This assignment response translates two discretionary stock approaches into rule-based proposals. One combines recent institutional fund inflows, positive company earnings, improving per-share profit, elevated trading volume, and a price ceiling relative to…

AkcijasCenas impulssTehniskie indikatoriVēsturisko datu pārbaude
BigQuant

This forum post describes an AttributeError in a BigQuant high-frequency backtest. The copied trade-module code treats each key in the portfolio positions mapping as an object with a symbol attribute. In the HFTrade interface, the key is already a string…

Vēsturisko datu pārbaudeRīkojumu izpilde
BigQuant

The document summarizes a 2020 study on whether investor attention measured through Baidu search activity can help forecast volatility in Chinese equities. The researchers compare a baseline GARCH model with an expanded version that includes search volumes…

AkcijasStatistikaTirgus noskaņojumsĶīnas tirgi
BigQuant

This presentation interprets findings from a 2021 survey of Chinese quantitative investment institutions and discusses how the sector was developing at that time. It covers strategy mixes, research organization, talent, artificial intelligence, alternative…

AkcijasNākotnes līgumiMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This meetup Q&A contrasts futures CTA strategies, often framed around trend following, with equity multi-factor strategies that combine signals such as value, momentum, quality, and size. It outlines a Bollinger Band example for futures: calculate a…

Nākotnes līgumiAkcijasSekošana tendenceiTehniskie indikatori
BigQuant

This research summary examines quantitative stock selection among Chinese technology companies. It highlights research and development spending as a candidate signal and also discusses profitability, earnings growth, valuation, company size, turnover, and…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This example builds a simple portfolio analysis workflow that generates a daily value series for several allocation weights and plots the paths together. A configuration object holds the tested weights, chart dimensions, and date range. The demonstration's…

Portfeļa veidošanaVēsturisko datu pārbaudeStatistika
BigQuant

This project explores combining strategies associated with different market styles. The author says market styles can persist over a period, so a strategy that fits a clearly expressed style may adapt better to prevailing conditions. They changed a provided…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRīkojumu izpildeVēsturisko datu pārbaude
BigQuant

This research summary examines analyst recoverage: the first new recommendation after an analyst or brokerage has stopped covering a stock for at least six months. It compares recoverage with initial coverage and ordinary rating changes, using U.S. analyst…

AkcijasUz notikumiem balstīta tirdzniecībaCenas impulssVēsturisko datu pārbaude
BigQuant

The document describes a beginner’s question about passing results from earlier BigQuant modules into a backtest. The proposed strategy uses a fixed universe of ten stocks, ranks them daily by five-day return in ascending order, buys the five lowest-ranked…

AkcijasCenas impulssVēsturisko datu pārbaudePortfeļa veidošana
BigQuant

This article proposes using a dashboard of the Hurst exponent, ADX, and a linear-regression score to contextualize Smart Money Concepts and ICT price-action setups. Hurst is calculated from log returns with rescaled range analysis: readings above 0.55 are…

Tehniskie indikatoriStatistikaAtgriešanās pie vidējās vērtībasSekošana tendencei
BigQuant

This Chinese-language research summary studies whether public equity fund stock exposure can inform market timing in the China A-share market. It uses a moving-average system to distinguish trending from range-bound regimes, analyzes how fund positioning…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaSekošana tendencei
BigQuant

This guide presents a relative strength index strategy using overbought and oversold thresholds. It describes calculating RSI from rolling average gains and losses, generating short signals above 70 and long signals below 30, and optionally filtering trades…

Tehniskie indikatoriAtgriešanās pie vidējās vērtībasVēsturisko datu pārbaudeRiska pārvaldība
BigQuant

This summary describes a method for constructing broad stock factor exposures and checking factor usefulness in a multifactor model. It presents returns as a linear combination of factor contributions plus an unexplained residual, and emphasizes examining…

AkcijasFaktoru ieguldīšanaStatistikaPortfeļa veidošana
BigQuant

This research report proposes improving a conventional stock reversal signal by splitting each stock’s recent daily returns according to average trade size. For each lookback window, it ranks days by daily turnover divided by trade count, compounds returns…

AkcijasAtgriešanās pie vidējās vērtībasFaktoru ieguldīšanaTirgus mikrostruktūra
BigQuant

This tutorial introduces Apache Arrow as a columnar format for in-memory computing and PyArrow as its Python interface, with integration for pandas, NumPy, and native Python objects. It demonstrates creating an Arrow scalar, converting a pandas DataFrame…

Statistika
BigQuant

This BigQuant platform report investigates Beijing Stock Exchange records in the Chinese stock factors table and how they interact with a basic stock-selection query. The author queries instruments with the Beijing suffix for a single date and reports 249…

Ķīnas tirgiAkcijasStatistikaVēsturisko datu pārbaude
BigQuant

This factor-monitoring summary compares Chinese equity signals over weekly, monthly, year-to-date, and one-year windows. It reports rankings for long-only absolute returns, long-short returns, information ratios, and relative strength. The factors discussed…

AkcijasĶīnas tirgiFaktoru ieguldīšanaStatistika
BigQuant

This factor note defines a volume-weighted measure of a stock’s intraday relative price range. For each instrument and date, it calculates the high-low range divided by the opening price, weights that value by volume, and divides the summed weighted values…

AkcijasSvārstīgumsFaktoru ieguldīšanaStatistika
BigQuant

This article summary presents a quantitative framework for combining conventional alpha factors with ESG-related signals in equity portfolios. It distinguishes exclusion screens, ESG integration, and impact investing, then focuses on integration: investors…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This tutorial explains how to combine daily stock-price observations with less frequent dividend records using an ASOF JOIN. The example pairs records by instrument and date, allowing each daily price row to be associated with a nearby dividend record even…

AkcijasĶīnas tirgiStatistikaPortfeļa veidošana