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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
Lumibot strategies
Dokumentu skaits: 7
QuantRocket
Dokumentu skaits: 7
Awesome Quant
Dokumentu skaits: 1

Meklēt bibliotēkā

Dokumentu skaits: 3,481

BigQuant

This article collects practical lessons from building supervised machine learning strategies for stock selection. It recommends keeping training and test data separate and splitting financial time series chronologically, since random splits can mix market…

AkcijasMašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

This Chinese equity screening proposal combines three conditions: a price amplitude above 1%, free float no greater than 5.5 billion shares, and short-, medium-, and longer-term moving averages aligned upward. The article interprets the amplitude filter as…

AkcijasCenas impulssTehniskie indikatoriĶīnas tirgi
BigQuant

This report examines whether updating fundamental signals more frequently can improve stock selection during China’s annual report season. It argues that factor signals decay over time, but more frequent rebalancing offers limited benefit for fundamental…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaUz notikumiem balstīta tirdzniecība
BigQuant

This Chinese-language research report outlines an equity index evaluation framework based on Barra-style risk factors. It proposes decomposing index returns into market, industry, and style contributions, including size, valuation, and volatility, then using…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This post presents a rules-based foreign exchange strategy intended to trade rebounds within a trend. It uses a short moving average channel derived from prior highs and lows, together with MACD sign as a directional filter. Long entries are placed as limit…

Valūtu tirgusTehniskie indikatoriAtgriešanās pie vidējās vērtībasVēsturisko datu pārbaude
BigQuant

This study compares three futures signals: cumulative return over a lookback window, an average based on cumulative daily steps, and a trend measure incorporating Garman–Klass volatility. It reports information-coefficient statistics, groups contracts into…

Nākotnes līgumiCenas impulssSekošana tendenceiSvārstīgums
BigQuant

This research summary describes a multi-factor tracking framework built around the pricing logic of factors. It examines historical returns for technical, expectation-based, and financial factors using a modified cross-sectional regression approach. To…

AkcijasFaktoru ieguldīšanaStatistikaPortfeļa veidošana
BigQuant

This excerpt summarizes a dated market note about the China 50 ETF and volatility conditions. It interprets a rise in the trading-volume put-call ratio as a shift toward cautious sentiment and notes a premium in at-the-money call implied volatility relative…

OpcijasSvārstīgumsTirgus noskaņojumsĶīnas tirgi
BigQuant

This article explains the All Weather approach as a way to build a portfolio without relying on precise economic-cycle forecasts. It frames the macro environment along growth and inflation dimensions, then describes matching stocks, bonds, commodities, gold,…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRiska pārvaldībaVēsturisko datu pārbaude
BigQuant

This BigQuant example addresses a limitation in which its high-frequency feature extraction module calculates within a single day, while a researcher may need features from minute data spanning multiple days. It shows a distributed workflow using FAI: read…

AkcijasAugstas frekvences tirdzniecībaStatistikaCenas impulss
BigQuant

This overview explains how high-frequency trading can profit from price movements and fragmented U.S. securities markets. Because a listed security may trade on multiple exchanges, differences in liquidity, participants, or information timing can create…

Augstas frekvences tirdzniecībaArbitrāžaTirgus veidošanaTirgus mikrostruktūra
BigQuant

The document surveys a platform’s patent database and describes how patent characteristics are turned into equity-selection factors. It groups patent information by patent type, application or grant status, and measures such as counts, claims, citations,…

AkcijasFaktoru ieguldīšanaVēsturisko datu pārbaudeĶīnas tirgi
BigQuant

The document explains how to set custom trading intervals in a trading engine by using a trading calendar. It points to two examples: rebalancing on Mondays and rebalancing at the start of each month. These schedules let a strategy follow calendar-based…

Rīkojumu izpildePortfeļa veidošana
BigQuant

This post presents a China equity screening rule combining a turnover-rate band of 3% to 12%, outside volume greater than 1.3 times inside volume, and a rise of more than 5% in the stated close-position ratio. Its example formulas add a minimum volume…

Ķīnas tirgiAkcijasTehniskie indikatoriTirgus mikrostruktūra
BigQuant

The article compares decade-scale changes in six major equity indices and argues that long-run wealth accumulation depends on the balance between advances and declines, rather than on the count of up and down sessions. It describes a pattern of relatively…

AkcijasCenas impulssStatistikaĶīnas tirgi
BigQuant

This BigQuant strategy tutorial outlines a framework for combining a broad-market risk signal with individual-stock timing and position management. The proposed workflow builds separate feature expressions for index-level risk and stock-level conditions,…

Ķīnas tirgiAkcijasRiska pārvaldībaPozīcijas apjoma noteikšana
BigQuant

The article compares a StockRanker stock-selection strategy with a Deepalpha deep neural network using the same 17 short-term factors, training data, and backtest setup. The strategy selects the top-ranked A-share stock each trading day and sells it the…

Ķīnas tirgiAkcijasMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This weekly Chinese equity-market note describes a market-regime framework using the distance between 20-day and 120-day moving averages on a broad market index, alongside a measure of market-wide profit-making activity. In the reported week, the short…

AkcijasĶīnas tirgiTehniskie indikatoriSekošana tendencei
BigQuant

The document introduces generative adversarial networks (GANs), a framework that trains two models against each other. A generator creates samples intended to resemble the training data, while a discriminator estimates whether a sample is real or generated.…

MašīnmācīšanāsStatistika
BigQuant

This summary of a research report examines the Chinese New Year calendar effect in mainland Chinese equities and how it relates to common quantitative factors. It reports that the broad market historically had an unusually favorable risk-adjusted profile…

AkcijasĶīnas tirgiFaktoru ieguldīšanaAtgriešanās pie vidējās vērtības
BigQuant

The document explains how Level 2 equity market data extends a conventional five-level view to ten bid and ask levels, with aggregate depth, order queue details, and individual order records. It describes possible intraday interpretations of visible…

AkcijasTirgus mikrostruktūraRīkojumu izpilde
BigQuant

This study examines whether the number of executions recorded in tick data can improve a conventional reversal factor for Chinese A-share equities. It divides the traditional factor using trade-count information and proposes a refined reversal signal. The…

Ķīnas tirgiAkcijasAtgriešanās pie vidējās vērtībasFaktoru ieguldīšana
BigQuant

The post asks how to reproduce a Chinese stock industry return series from constituent-level data. It identifies three aggregation methods: equal weighting, weighting by total shares, and weighting by free-float shares. The example query retrieves…

AkcijasStatistikaĶīnas tirgi
BigQuant

The paper proposes a decision-focused learning framework for prediction followed by optimization when the downstream objective includes linear soft constraints. Such constraints penalize violations with a maximum function, which is nondifferentiable at its…

MašīnmācīšanāsPortfeļa veidošanaStatistika