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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

The article presents a short-term stock screening pattern built around three sequential signals. First, it looks for at least five generally rising sessions, allowing one modest down day while the overall movement stays upward. Next, it requires a session…

AkcijasCenas izrāviensTehniskie indikatori
BigQuant

This tutorial outlines a TensorFlow LSTM workflow for forecasting stock or index returns. It recommends transforming closing prices into returns, then arranging recent observations into sequential input groups with the next observations as prediction labels.…

MašīnmācīšanāsAkcijasĶīnas tirgiVēsturisko datu pārbaude
BigQuant

This research summary compares ordinary cross-validation with time-series cross-validation for model tuning on sequential data. Randomly assigning observations to folds can train a model on future observations and validate it on the past, violating the…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaudeAkcijas
BigQuant

This competition update reports diagnostics for the ten highest-ranked anonymous factors and tracks a CSI 1000 enhancement portfolio. Factor profiles include average Barra style exposures and sector-level average Rank ICs, calculated from cross-sectional…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaVēsturisko datu pārbaude
BigQuant

The document introduces Moving Average Envelopes (MAE), a technical indicator built from a moving average of closing prices. It describes two bands: a lower envelope set below the average by a specified percentage and an upper envelope set above it by…

Tehniskie indikatori
BigQuant

This study proposes adding daily risk monitoring to a traditional monthly rebalancing schedule for multi-factor equity portfolios. It tracks the portfolio’s exposure to the size factor during the month and rebalances on the next trading day when that…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The article summarizes research on whether forced mutual fund selling affects the prices and liquidity of related stocks. It describes a learning mechanism: investors may initially interpret a peer stock’s decline as evidence of negative information about…

AkcijasTirgus mikrostruktūraStatistikaRiska pārvaldība
BigQuant

The report describes an expanded Black-Litterman asset allocation model that combines subjective views with quantitative portfolio construction. It uses behavioral finance and historical trend information, including momentum and reversal effects, to form…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaCenas impulssAtgriešanās pie vidējās vērtības
BigQuant

This example tests whether market capitalization is associated with next-day stock returns using repeated cross-sectional ordinary least squares regressions. It joins Chinese stock factor data to daily bars, calculates forward returns from the next close,…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This research summary examines the validity of mainland China Stock Connect ownership data and describes portfolio strategies built from investor holdings, sector allocations, style exposures, leading trades, and net capital flows. It reports persistent…

AkcijasĶīnas tirgiFaktoru ieguldīšanaCenas impulss
BigQuant

This study builds a monthly Chinese equity factor from stock returns and volatility around intraday volume surges. It defines a surge as a minute when the increase in trading volume exceeds that stock’s daily average increase by more than one standard…

AkcijasFaktoru ieguldīšanaTehniskie indikatoriVēsturisko datu pārbaude
BigQuant

This Chinese market note proposes that China’s credit impulse may be turning upward and argues that it tends to lead A-share earnings growth. On that assumption, it recommends overweighting Chinese equities, favoring growth over value, and ranking…

Vairāku aktīvu tirdzniecībaAkcijasFiksēta ienākuma instrumentiĶīnas tirgi
BigQuant

This reference catalogues TA-Lib functions and their input fields for calculating common trading indicators. It groups examples into overlapping price studies, volatility and volume-price measures, cycle functions, price transformations, momentum indicators,…

Tehniskie indikatoriStatistikaSvārstīgumsCenas impulss
BigQuant

This stock selection method turns a discretionary approach into a set of technical and liquidity filters, followed by periodic rebalancing. It looks for prices mostly above long-term moving averages, favorable short-term moving-average conditions, and moving…

Ķīnas tirgiAkcijasCenas impulssSekošana tendencei
BigQuant

This Chinese-language article discusses the proposed introduction of market making on China’s STAR Market. It explains that market makers continuously quote prices, sell inventory to buyers, and use their own capital to buy from sellers. They bear price risk…

AkcijasTirgus veidošanaTirgus mikrostruktūraRiska pārvaldība
BigQuant

This BigQuant forum post presents a daily-bar backtest example that submits several limit buy orders during a bar callback. Before each order, the callback reads available cash and frozen cash from the trading account. The reported output shows those values…

AkcijasVēsturisko datu pārbaudeRīkojumu izpildeRiska pārvaldība
BigQuant

The article summarizes research on whether active mutual fund managers adjust trading to anticipated stock-picking opportunities. It defines stock-selection timing as trading more when opportunities to hold positive-alpha stocks are greater and less when…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This overview surveys unsupervised learning through two groups of methods: dimensionality reduction and clustering. For dimensionality reduction, it describes PCA as a way to project high-dimensional features into fewer dimensions using covariance-matrix…

MašīnmācīšanāsStatistika
BigQuant

This study compares conventional return labels with alternative labels based on information ratio and Calmar ratio for AI stock selection models. It argues that labels incorporating returns, volatility, and drawdowns across a period may convey more…

Ķīnas tirgiAkcijasMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This document outlines a simple KDJ-based trading strategy for the Chinese stock 605588, with the CSI 300 index named as a reference benchmark. It describes selling when K, D, and J are all in high territory, and buying when all three are in low territory.…

Ķīnas tirgiAkcijasTehniskie indikatori
BigQuant

This article presents three MACD-based warnings for managing long equity positions: the indicator moving below its zero line, bearish divergence as price makes higher highs while MACD peaks decline, and a bearish crossover above zero. It interprets these…

AkcijasTehniskie indikatoriCenas impulssRiska pārvaldība
BigQuant

This article outlines a research approach for Chinese equities during sharp market-wide declines. It proposes identifying historical selloff days and studying two groups: stocks that hit their upper price limit despite the market drop, and stocks that hit…

AkcijasUz notikumiem balstīta tirdzniecībaVēsturisko datu pārbaudeĶīnas tirgi
BigQuant

This note summarizes the AlexNet deep convolutional neural network described in the ImageNet classification paper. It outlines the model’s use of multiple convolutional channels, five convolutional stages and three fully connected layers, and explains how…

MašīnmācīšanāsStatistika
BigQuant

This strategy applies a stock-ranking model to a filtered pool of Chinese A-shares, aiming to find stocks that may rebound from depressed levels and hold up better when the broader market weakens. The target universe is the small and medium-sized board. Its…

AkcijasMašīnmācīšanāsAtgriešanās pie vidējās vērtībasĶīnas tirgi