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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
WonderTrader
Dokumentu skaits: 14
Alphalens
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 document shows a stock-ranking workflow that derives 30-day and 90-day returns, ranks securities cross-sectionally, filters the universe, and creates a forward-return label for model training. A StockRanker module trains on historical data and predicts…

AkcijasCenas impulssMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This discussion presents a daily Chinese A-share small-cap strategy that filters stocks by listing age, valuation, and turnover rank, sorts the candidates by market capitalization, and targets a basket of twenty names. Its rebalance handler identifies…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The article describes a stock selection experiment in which a machine learning model used only days since listing. Its reported backtest showed positive returns, though with high volatility. The authors attribute performance partly to a rising market during…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This Chinese-language article outlines a six-step process for developing stock selection rules: find an idea, define its logic, run an initial backtest, tune parameters, check for overfitting, and select strategies for combination. Its example setups look…

AkcijasĶīnas tirgiTehniskie indikatoriVēsturisko datu pārbaude
BigQuant

This study summary examines active-trading (ACT) factors in Chinese equities, which distinguish aggressive from passive executions and group activity by trade size. It argues that the undivided ACT factor has weak stock-selection power, then splits…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTirgus mikrostruktūra
BigQuant

The report studies how equity factors behave across groups of A-share stocks sorted by price amplitude, using ten groups from lower to higher amplitude. It reports that price and volume factor effectiveness rises as amplitude increases, while fundamental…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaSvārstīgums
BigQuant

This page summarizes a lecture on multi-step and off-policy algorithms in reinforcement learning. It identifies the subject as methods that learn from sequences of rewards across multiple steps while handling data generated by a policy different from the one…

MašīnmācīšanāsStatistika
BigQuant

This document outlines four indicators that combine price and trading volume: the Money Flow Index (MFI), On-Balance Volume (OBV), Price Volume Trend (PVT), and a simplified OBV variant called SOBV. MFI compares rolling positive and negative money flows,…

Tehniskie indikatoriStatistikaAkcijas
BigQuant

This short assignment response defines quantitative investing as turning a personally developed set of screening and trading rules into a computer-executable process. It presents rule design and implementation as the core idea: formulate conditions for…

StatistikaMašīnmācīšanās
BigQuant

This submitted assignment defines quantitative investing as making investment decisions with mathematical and statistical analysis, historical data, and computing. It describes several practical advantages: processing large datasets, applying rules…

StatistikaVēsturisko datu pārbaudeMašīnmācīšanās
BigQuant

The report describes an AI-based score for assessing intraday trading opportunity in Chinese stocks and broad indices. Using 20 trading days of one-minute returns, it compares observed returns with randomly constructed return sequences. A higher score…

Ķīnas tirgiAkcijasMašīnmācīšanāsStatistika
BigQuant

The document introduces covariance as a measure of how two variables vary together, defining it through the expected product of each variable’s deviation from its mean. Positive covariance indicates a tendency to move in the same direction, while negative…

StatistikaPortfeļa veidošanaRiska pārvaldība
BigQuant

This technical note presents the MEJT indicator as a price-action rule built from high, low, and close data. Its buy condition checks whether the current high exceeds the prior high, the close exceeds a high from two periods earlier, and the close lies…

Tehniskie indikatoriAkcijasCenas impulss
BigQuant

This research summary explores stock-selection signals built from Level 2, trade-by-trade data. It classifies trades by order size and direction, then constructs measures such as the share of trading represented by different order categories and net buying…

Ķīnas tirgiAkcijasTirgus mikrostruktūraFaktoru ieguldīšana
BigQuant

This report summary presents a factor model for ranking managers of Chinese public equity and equity-oriented hybrid funds. It builds a manager-level investment index by weighting the funds each manager oversees by fund size, then evaluates candidate…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The document summarizes research on whether company managers’ tone in financial communications predicts equity returns. It describes building a sentiment index by applying text analysis to corporate reports and earnings-call transcripts, then comparing its…

AkcijasTirgus noskaņojumsStatistika
BigQuant

This report summary outlines a fixed-income-plus-equity portfolio design in three stages: strategic allocation, tactical timing, and capital management. Strategic weights are set with a mean-variance model using broad equity and bond indices, with periodic…

Vairāku aktīvu tirdzniecībaFiksēta ienākuma instrumentiAkcijasPortfeļa veidošana
BigQuant

This Chinese Q&A explains why a genetic programming factor-mining workflow can report factors that pass a test yet return an empty factor collection. The answer says the run log shows that the global fitness test did not pass, so no factors were retained. A…

MašīnmācīšanāsFaktoru ieguldīšanaStatistika
BigQuant

This guide lists reasons a submitted quantitative factor may fail to produce an evaluation score. It recommends checking factor-analysis output on the designated dataset before submission and verifying that a machine-learning model completed training and…

AkcijasMašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

The document summarizes research that classifies corporate bond funds as liquidity demanders or suppliers according to how their trades align with dealer inventory cycles. Using TRACE transactions and fund holdings, the underlying study infers inventory…

Fiksēta ienākuma instrumentiTirgus mikrostruktūraRiska pārvaldībaStatistika
BigQuant

This introductory explanation describes how a convolutional neural network turns an image into a class prediction. A filter slides across local pixel regions to produce feature maps; filter count, stride, and zero padding affect the maps’ depth and…

MašīnmācīšanāsStatistika
BigQuant

A BigQuant user reports that a stock AI strategy template fails with an index error when its prediction or backtest date range includes January 1, 2016. The issue is described as intermittent and associated with using certain factors. The suggested…

Vēsturisko datu pārbaudeRīkojumu izpilde
BigQuant

This Chinese-language discussion addresses a backtest display that continues to list a stock after an order targets a zero portfolio weight. The listed position has a quantity of zero, which can look like an unsettled holding or a bookkeeping defect. The…

Vēsturisko datu pārbaudeAkcijasRīkojumu izpilde
BigQuant

This short note outlines a way to represent the cost basis of stock holdings across market participants. It proposes estimating average holding cost by weighting transaction prices by traded quantities, then using daily costs and trading volume to estimate…

AkcijasStatistikaFaktoru ieguldīšana