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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
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 explains how time-series momentum (TSMOM) and moving-average crossover (MACROSS) strategies can represent closely related ways to measure price trends. It describes expressing trend signals through past prices or returns, and using “signature”…

Sekošana tendenceiCenas impulssTehniskie indikatoriStatistika
BigQuant

This document describes a Chinese A-share selection screen combining robot-industry exposure, small free-float market capitalization, turnover, and the stock’s price change at 9:25. The stated selection logic calls for turnover between 3% and 12%, robot…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
BigQuant

A platform forum exchange addresses whether a daily backtest that predicts a five-day return using the current day’s closing price can access that day’s prediction before the close is available. The response explains that, in the described daily setup, the…

Vēsturisko datu pārbaudeMašīnmācīšanāsStatistika
BigQuant

This guide presents local checks for two common failures in factor submissions: missing values near the evaluation window’s start and look-ahead bias. For time-series factors, it recommends querying enough earlier data to cover the longest lag or rolling…

AkcijasMašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

The article summarizes research that uses active share, or portfolio deviation from a benchmark, as a proxy for a fund manager’s conviction. It reports that strong past performance is associated with subsequent increases in active share, particularly for…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This dated market note reports RSRS timing-model views for several Chinese equity benchmarks on April 8, 2020. The model remained bullish on the Shanghai Composite, CSI 500, and ChiNext, while taking a more cautious view of the SSE 50 and CSI 300. No…

AkcijasĶīnas tirgiSekošana tendenceiTehniskie indikatori
BigQuant

The article presents regular investing in broad market index funds as a low-effort, long-term approach, arguing that it can reduce the need for stock picking and market timing. It attributes active managers’ difficulty beating indexes to fees, short-term…

Vairāku aktīvu tirdzniecībaĶīnas tirgiASV tirgiPortfeļa veidošana
BigQuant

The document summarizes research on combining market anomalies and on refining multi-factor models. For anomaly strategies, it reports that returns to many well-known anomalies declined after academic publication, while diversified long-short portfolios…

AkcijasFaktoru ieguldīšanaCenas impulssStatistika
BigQuant

This page summarizes several quantitative investing topics: timing equity factors with cross-sectional and time-series indicators; adjusting portfolios to target volatility; modeling changing stock-bond correlations; assessing tactical asset-allocation…

Vairāku aktīvu tirdzniecībaFaktoru ieguldīšanaSvārstīgumsPortfeļa veidošana
BigQuant

This note summarizes when to use several portfolio allocation approaches. A naïve allocation is presented as a practical choice when the universe is broad and return forecasts are difficult. Risk parity and minimum variance are associated with settings where…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRiska pārvaldība
BigQuant

This Chinese-language summary presents a 2021 survey of reinforcement learning in financial decision-making. It introduces Markov decision processes as a foundation, then identifies value-based and policy-based learning approaches, including extensions that…

MašīnmācīšanāsPortfeļa veidošanaRīkojumu izpildeTirgus veidošana
BigQuant

This strategy combines model-based stock ranking with technical timing. It starts from a manually defined CSI 150-related universe, then ranks candidates using an AI prediction score and volume, turnover, and large-order money-flow factors. Timing filters…

AkcijasMašīnmācīšanāsSekošana tendenceiCenas impulss
BigQuant

This guide surveys how machine learning can support quantitative investing, from data preparation and model evaluation to portfolio selection. It explains supervised, unsupervised, and reinforcement learning, and reviews methods including regularized…

MašīnmācīšanāsStatistikaFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This Chinese strategy discussion combines hand-built stock selection rules with a CNN classifier. It describes converting screening conditions into Boolean features so a model can learn to identify stocks expected to show short-term strength. The feature…

Ķīnas tirgiAkcijasMašīnmācīšanāsCenas impulss
BigQuant

The document presents a short-term timing approach based on the Hilbert transform. It assumes price movements contain a long-term trend, shorter cyclical fluctuations, and noise. The proposed process first smooths the data with a 20-period moving average,…

AkcijasTehniskie indikatoriStatistikaCenas impulss
BigQuant

This weekly Chinese equity factor note compares value and growth styles and reports relative performance across six industry-neutral long-short factor groups. Value and growth were described as broadly balanced, while quality measures such as return on…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaAtgriešanās pie vidējās vērtības
BigQuant

The article describes combining two Chinese equity selection models designed for different market conditions: a rebound after an index decline and a mild index pullback. Each model includes market environment features so that it produces candidates only when…

AkcijasĶīnas tirgiVēsturisko datu pārbaudeCenas impulss
BigQuant

This research summary describes a dynamic rebalancing overlay for multi-factor equity portfolios. The portfolio keeps its regular monthly rebalance, while market-cap factor exposure is checked daily mid-month. If exposure breaches a set threshold, the…

AkcijasFaktoru ieguldīšanaRiska pārvaldībaPortfeļa veidošana
BigQuant

The post contains a proposed Chinese stock screen built from financial, valuation, price-action, and intraday-style indicators. Its filters include market-cap and asset thresholds, revenue growth and return on equity conditions, turnover and price-change…

Ķīnas tirgiAkcijasTehniskie indikatoriStatistika
BigQuant

The article explains genetic algorithms as population-based search methods inspired by biological evolution. It walks through encoding candidate solutions as chromosomes, evaluating them with a fitness function, selecting parents, combining them through…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

This study applies Lasso, adaptive Lasso, and Elastic Net to select equity factors and combine them into stock-selection signals. Its motivation is that correlated predictors can make ordinary multivariate regression unstable. The reported experiments show…

AkcijasĶīnas tirgiFaktoru ieguldīšanaMašīnmācīšanās
BigQuant

The post asks whether a factor-analysis module’s rebalance period measures returns before or after the factor observation. The author expected each factor-sorted group’s reported return to represent the return earned by holding its stocks for the stated…

Faktoru ieguldīšanaVēsturisko datu pārbaudeStatistika
BigQuant

This April 2020 brokerage-report summary argues that overseas pandemic concerns had weakened prices without materially lowering broad-market valuations or company earnings forecasts at that point. It frames semiconductor shares against a late-2018 price…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This document summarizes Ibbotson and Chen’s supply-side approach to estimating long-run equity returns and risk premiums. It decomposes US stock returns over 1926–2000 using inflation, earnings, dividends, valuation multiples, payout ratios, book value,…

AkcijasStatistikaFaktoru ieguldīšanaASV tirgi