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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: 3,481

BigQuant

This forum post raises a data-preparation problem in an LSTM trading strategy. The author reports that filtering rows before rolling sequence windows can remove observations that should remain part of the temporal context, causing later observations to shift…

MašīnmācīšanāsAkcijasStatistikaVēsturisko datu pārbaude
BigQuant

The article compares two ways to build equity strategies: train a predictive model on chosen factors and a defined stock universe, or specify screening conditions directly. The AI approach can be quicker to assemble, but its behavior may be hard to diagnose…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

The report reassesses valuation, growth, technical, and risk factors in Chinese A-shares after removing industry and style influences. Its core method is to adjust factor data for exposures such as industry, market capitalization, or beta, then test whether…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

Fundamental quantitative investing applies statistical methods and algorithms to company accounts, industry information, and macroeconomic data to rank stocks by estimated value or quality. The document describes analyzing profitability, financial strength,…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaStatistika
BigQuant

The paper examines how five emotions expressed on Sina Weibo—anger, disgust, fear, joy, and sadness—relate to Chinese stock returns. It uses machine learning to classify posts and wavelet analysis to study how the relationship changes over time and across…

Ķīnas tirgiAkcijasTirgus noskaņojumsMašīnmācīšanās
BigQuant

This competition entry modifies a Deep Alpha convolutional neural network by supplying five time slices for each of 98 factors, rather than using a single slice. The network structure and most parameters remain close to the original model because training…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This report examines changes in concentrated holdings among Chinese active equity funds using quarterly disclosures and a daily estimate of fund exposure to a core group of crowded stocks. It compares the core group’s holdings over time, separates changes in…

AkcijasĶīnas tirgiStatistikaFaktoru ieguldīšana
BigQuant

This support exchange concerns an IndexError that occurs when a Chinese convertible bond data table is read during a backtest. The same table can be read outside the backtest, and comparable reads of other tables within the backtest reportedly succeed. That…

Vēsturisko datu pārbaudeStatistikaĶīnas tirgi
BigQuant

This short BigQuant assignment describes a daily stock-ranking approach built from a combination of total equity and trailing-twelve-month average return on equity. Each day, the strategy holds stocks with comparatively high values of the combined factor. It…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This overview introduces three types of company data associated with FactSet: a six-level product and service based industry classification, company-to-company supply-chain relationships, and geographic revenue exposure. The classification is presented as a…

AkcijasFaktoru ieguldīšanaUz notikumiem balstīta tirdzniecībaPortfeļa veidošana
BigQuant

This forum exchange explains why a sell order can disappear in an event-driven backtest when the stock is suspended on the scheduled execution day. The order was generated on one day, but the attempted match on the next day failed because trading was halted;…

AkcijasVēsturisko datu pārbaudeRīkojumu izpilde
BigQuant

This weekly report describes two quantitative equity approaches. The first builds on holdings of successful active funds, then applies quantitative selection to create an enhanced portfolio benchmarked against active equity funds rather than broad market…

AkcijasFaktoru ieguldīšanaUz notikumiem balstīta tirdzniecībaTehniskie indikatori
BigQuant

This note explains beta as a measure of an asset’s market risk exposure and introduces the Fama–French three-factor model as a way to capture size and value style exposures. It describes how to quantify style performance with long-short portfolios: small-cap…

AkcijasFaktoru ieguldīšanaStatistikaĶīnas tirgi
BigQuant

This research proposes a stock selection factor that estimates similarity between companies using ten financial measures spanning profitability, solvency, operating efficiency, and growth. After cross-sectional normalization, it uses cosine similarity to…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaCenas impulss
BigQuant

This post sketches an experimental model for identifying leading stocks, using gradient boosted regression to score shares within a rolling window. The proposed label gives higher scores to stocks that rise more, experience smaller drawdowns, and break out…

AkcijasMašīnmācīšanāsCenas izrāviensRiska pārvaldība
BigQuant

This China equity strategy ranks stocks cross-sectionally using circulating market capitalization and earnings per share, combining the rankings into a total score and sorting from low to high. It excludes special-treatment and suspended stocks, requires…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

The article argues that retail traders often sell during fear and re-enter during excitement, while institutions may accumulate during declines and distribute into renewed demand. It recommends treating oneself as a follower, watching capital flows, and…

Tirgus noskaņojumsTirgus mikrostruktūraRiska pārvaldība
BigQuant

This review of research on equity factor investing explains why low unconditional correlations between factors may not provide protection when macroeconomic conditions deteriorate. It describes selecting fast-moving state variables that reflect expectations…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This report examines why investment portfolios turn over, how to manage that turnover, and how turnover control affects portfolio outcomes. It frames the drivers across the research and trading process: alpha changes influence target holdings, optimization…

Portfeļa veidošanaRīkojumu izpildeRiska pārvaldībaAkcijas
BigQuant

The document describes a hybrid approach that combines ARMA-GARCH models with several neural network types to identify market patterns and predict the direction of shocks in defense equities and foreign exchange. ARMA-GARCH models capture linear time-series…

MašīnmācīšanāsStatistikaSvārstīgumsAkcijas
BigQuant

This note describes a daily liquidity signal built from intraday order book snapshots. It sums bid and ask volumes across the first five levels, then measures their normalized imbalance as the difference divided by the total. Separately, it calculates…

Faktoru ieguldīšanaTirgus mikrostruktūraStatistika
BigQuant

The document briefly introduces a multi-factor investment platform, focusing on how to estimate stock and portfolio factor exposures. Stock exposures are derived from database values such as price-to-earnings and price-to-book ratios, then cleaned and…

Faktoru ieguldīšanaPortfeļa veidošanaStatistika