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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
Quantpedia
Dokumentu skaits: 86
TqSdk
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

This report describes a Chinese equity-selection process built from daily price and volume data. Genetic programming generates candidate factors: Rank IC is used to find factors with linear predictive relationships, while mutual information targets nonlinear…

AkcijasĶīnas tirgiMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This practitioner report reviews DeepAlpha, a neural model used to rank stocks from factor inputs, and compares it with other approaches. The author says a simple fully connected network appeared competitive with decision-tree and linear models, and…

MašīnmācīšanāsAkcijasFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This overview catalogs machine learning approaches that can be applied to quantitative investing, grouping them into tree models, linear models, unsupervised methods, deep learning, ensembles, and online learning. It links model families to tasks such as…

MašīnmācīšanāsFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

The document summarizes research on converting stock factors with strong negative-return associations into a long-only selection process. Because short selling is limited in the A-share market, it describes reversing the use of such signals: remove stocks…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This report describes a monthly stock selection model that uses a random forest, an ensemble of decision trees trained with bagging, to estimate each stock’s probability of rising in the next period. Its workflow covers feature and label preparation,…

Ķīnas tirgiAkcijasMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This research review explains how earnings beta can measure a company’s exposure to systematic risk through the co-movement of its earnings with aggregate market earnings. It compares eleven earnings series built from realized or analyst-forecast earnings,…

AkcijasFaktoru ieguldīšanaStatistikaRiska pārvaldība
BigQuant

This literature summary covers two separate topics. The first describes a framework for studying how mutual fund managers’ personal characteristics relate to performance, decomposing outcomes into overall results, return and risk, and timing and security…

Vairāku aktīvu tirdzniecībaOpcijasPortfeļa veidošanaSvārstīgums
BigQuant

This research summary describes an adaptation of Robert Whitelaw’s time-varying Sharpe ratio, or Tsharp, for timing the Shanghai Composite. Unlike a conventional Sharpe ratio, the measure estimates returns and variance through regression, making it…

Ķīnas tirgiAkcijasStatistikaVēsturisko datu pārbaude
BigQuant

This opinion article argues that algorithmic trading can harm investors who rely on widely known indicator signals or chase sharp price moves, while creating opportunities through the liquidity and volatility it adds. It describes three potential…

AkcijasRīkojumu izpildeRiska pārvaldībaTehniskie indikatori
BigQuant

This study organizes fifty Wind order-flow measures into eight groups, including inflows, outflows, net active buying, and large-order net flows. It assesses their stock-selection value with industry-neutral portfolio sorts, cross-sectional regressions, and…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaTirgus mikrostruktūra
BigQuant

This document summarizes an ETF tracking workbook and describes the market landscape it was designed to monitor. It reports that broad-market and sector or theme ETFs formed the largest non-money-market categories, with examples of major index exposures and…

Ķīnas tirgiAkcijasPortfeļa veidošana
BigQuant

This market-monitoring report reviews Chinese equities during the early 2020 pandemic shock. It combines trading activity, valuation, turnover, sentiment, sector relative performance, fund concentration, and style-factor signals. Its readings describe…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaStatistika
BigQuant

The document reports a platform-specific issue in which an XGBoost classifier’s probability prediction function returns only endpoint values of zero and one. The user expected continuous class probabilities that could be used to rank securities, based on…

MašīnmācīšanāsAkcijas
BigQuant

This introductory guide explains how machine learning can be applied to quantitative investing through a staged research workflow. Using a fruit-selection analogy, it describes gathering historical data, defining the prediction target, labeling observations,…

MašīnmācīšanāsAkcijasVēsturisko datu pārbaudeStatistika
BigQuant

This discussion distinguishes machine learning for execution optimization from machine learning for directional trading. Execution begins with a known trade and seeks to reduce its cost; alpha generation must identify price direction and capture enough of…

MašīnmācīšanāsAugstas frekvences tirdzniecībaTirgus mikrostruktūraRīkojumu izpilde
BigQuant

This article describes combining two stock selection models designed for different Chinese equity market conditions: a rebound after an index decline, and a mild market pullback after stocks hit their daily price limit. Each model includes market environment…

Ķīnas tirgiAkcijasVēsturisko datu pārbaudeCenas impulss
BigQuant

This Chinese-language support post describes a feature-engineering question: how to count, over a recent window, the days when a five-day moving average exceeds a ten-day average. The proposed first feature assigns a positive or negative value according to…

AkcijasTehniskie indikatoriMašīnmācīšanās
BigQuant

This forum post raises a data-timing problem in a trading workflow. In the described backtest, a signal generated on one day is placed the next day, and pre-market processing can apparently retrieve that day’s opening and closing values through historical…

Vēsturisko datu pārbaudeRīkojumu izpildeStatistika
BigQuant

This article describes 25 reasons retail investors may lose money in equities. It groups them around information and research gaps, overreliance on one strategy, emotional and herd-driven decisions, limited planning and experience, concentrated portfolios,…

AkcijasRiska pārvaldībaPortfeļa veidošanaTirgus noskaņojums
BigQuant

This review explains why diversification may help more during declines than during gains, and why average correlations can hide that difference. It recommends estimating an asset pair’s downside relationship by conditioning on the portfolio’s main growth…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRiska pārvaldībaStatistika
BigQuant

This review describes a method for identifying crowded markets and distinguishing potential bubble build-up from unwinding. It measures sector or factor clustering from return covariances and principal components, then pairs that measure with relative…

AkcijasFaktoru ieguldīšanaCenas impulssPortfeļa veidošana
BigQuant

The document raises a question about possible look-ahead bias in a stock-selection workflow. It points to a five-day return calculated from prices shifted forward in time and asks whether using that value as a feature in a test set leaks future information.…

AkcijasMašīnmācīšanāsVēsturisko datu pārbaudeStatistika
BigQuant

The document presents a modified MACD indicator built by replacing standard exponential moving averages with a three-stage smoothing expression. It applies that transformation separately to closing prices over 12- and 26-period spans to form DIF, then…

Tehniskie indikatoriMašīnmācīšanāsAkcijas
BigQuant

This report summary examines a 2018 forecast of CSI 300 constituent changes and the potential trading flows associated with index funds adjusting their holdings. The forecast uses the index's periodic review rules, including measures of past-year trading…

AkcijasUz notikumiem balstīta tirdzniecībaRīkojumu izpildeĶīnas tirgi