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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
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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
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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 brief response defines quantitative investing as using tools to extract patterns from historical data. It frames time-series analysis as a way to improve the odds of making a successful decision and cross-sectional analysis as a way to seek better…

StatistikaMašīnmācīšanāsPortfeļa veidošana
BigQuant

The document explains a model-training error caused by a feature stored as an object or string rather than a numeric value. Its diagnostic approach is to inspect the training data and identify which input column has the incompatible type. The example points…

MašīnmācīšanāsStatistika
BigQuant

This forum post asks how to migrate several legacy factors from an earlier version of a quantitative research platform into a newer one. The expressions cover an industry-grouped beta adjustment, a rolling correlation between log volume and absolute returns,…

AkcijasFaktoru ieguldīšanaStatistika
BigQuant

This Chinese research note explores bull-bear indicators in three equity applications: timing broad indices, rotating among industries, and setting index constituent exposure. For direct index timing, it adapts the dual moving average idea by comparing…

AkcijasĶīnas tirgiSekošana tendenceiTehniskie indikatori
BigQuant

This research summary explains how alpha factors can be represented by factor-mimicking portfolios (FMPs) under a stock covariance model, and how linear combinations of factors correspond to combinations of their mimicking portfolios. It frames factor…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaStatistika
BigQuant

This description explains a limit-order market-making strategy based on the Avellaneda–Stoikov framework. It sets a reservation price and optimal bid-ask spread using estimated volatility, order-book liquidity, inventory relative to a target allocation, the…

Tirgus veidošanaKriptoaktīviTirgus mikrostruktūraRīkojumu izpilde
BigQuant

This report outlines a short-term reversal strategy for China’s CSI 800 stocks. It combines a moving-average system intended to classify trend state through cycle analysis with multi-timeframe MACD divergence. A bullish divergence occurs when price makes a…

AkcijasAtgriešanās pie vidējās vērtībasTehniskie indikatoriVēsturisko datu pārbaude
BigQuant

This research report studies portfolio construction around a weekly stock-selection model called AlphaNet, built from price and volume data. It examines several portfolio optimization approaches intended to accommodate different risk and return objectives.…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This research summary proposes a stock illiquidity factor based on the shortest path through intraday candlestick data. It aims to improve on a conventional proxy that relates price returns to trading volume, which the authors say can be unreliable during…

AkcijasFaktoru ieguldīšanaAugstas frekvences tirdzniecībaTirgus mikrostruktūra
BigQuant

The document explains two settings in a rolling-data module used to prepare features for securities. The window size controls how many consecutive observations are grouped into each sample. Its example uses five sequential values and a window of three to…

MašīnmācīšanāsStatistika
BigQuant

This research summary reviews the low-volatility equity factor across overseas markets and mainland Chinese shares. It describes the behavioral explanation that highly volatile stocks may be more vulnerable to speculation and rich valuations, while…

AkcijasĶīnas tirgiSvārstīgumsFaktoru ieguldīšana
BigQuant

The document outlines a single-asset strategy based on the average of 30-day and 72-day moving averages. It treats a close below 80% of that average as a deeply discounted condition, then waits for a 5% rebound from the triggering close before buying. For an…

AkcijasAtgriešanās pie vidējās vērtībasTehniskie indikatoriVēsturisko datu pārbaude
BigQuant

This essay describes quantitative trading as the automated execution of investment models designed and assessed by people, rather than as an independent mechanism for discovering profitable strategies. It emphasizes that rule-based systems can apply…

Rīkojumu izpildeRiska pārvaldībaVēsturisko datu pārbaude
BigQuant

This BigQuant support exchange explains a prediction error associated with missing or infinite values while constructing label bins. It suggests checking whether the target label is absent or contains many missing values. The further diagnosis is that the…

AkcijasMašīnmācīšanāsStatistikaĶīnas tirgi
BigQuant

This article explains the theoretical link between beta pricing models, including the Fama-French three-factor model, and stochastic discount factors (SDFs). It distinguishes absolute pricing, which connects asset values to macroeconomic risks, from relative…

Faktoru ieguldīšanaAkcijasStatistika
BigQuant

This review summarizes research testing whether small-cap stocks continued to outperform large caps across calendar effects in US equities. The underlying study forms portfolios from the smallest and largest size groups and examines the return spread around…

AkcijasFaktoru ieguldīšanaStatistikaASV tirgi
BigQuant

A BigQuant forum answer addresses why a stock selection strategy’s returns changed from positive to negative after neutralizing and standardizing its inputs. The reply explains that StockRanker uses a tree-based structure and, according to the respondent,…

AkcijasMašīnmācīšanāsVēsturisko datu pārbaudeStatistika
BigQuant

This study examines whether generative adversarial networks can produce realistic multi-asset financial time series. It combines the convolutional architecture of DCGAN with the Wasserstein-distance loss used by WGAN: convolutional layers replace some…

MašīnmācīšanāsStatistikaVairāku aktīvu tirdzniecībaAkcijas
BigQuant

This strategy rotates among a fixed pool of 11 ETFs spanning several asset classes and markets. Every five trading days, it ranks candidates using a 25-day linear-regression slope multiplied by R-squared, then adds a short-term signal based on the five-day…

Vairāku aktīvu tirdzniecībaSekošana tendenceiCenas impulssPortfeļa veidošana
BigQuant

This report summarizes stock-selection results for several machine-learning models across broad Chinese equity universes and constituent groups tied to the CSI 300, CSI 500, and CSI 800. It compares weekly, recent-month, and 2018-to-date excess returns, and…

Ķīnas tirgiAkcijasMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This brief forum entry addresses an SQL error encountered while trying to neutralize a rolling turnover measure against total market capitalization. It points readers toward a regression-residual function as an alternative to a direct neutralization call.…

AkcijasFaktoru ieguldīšanaStatistika