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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

This document describes a cross-sectional equity reversal factor that separates recent returns according to trading activity. For each stock, it looks back over 20 sessions, calculates average transaction value per trade each day, ranks the sessions by that…

AkcijasAtgriešanās pie vidējās vērtībasFaktoru ieguldīšanaTirgus mikrostruktūra
BigQuant

The report examines Chinese analyst earnings forecasts, ratings, and target prices as inputs to equity selection. It compares six ways to combine analyst estimates and finds that weighting forecasts by analysts’ past accuracy performs better than several…

AkcijasFaktoru ieguldīšanaStatistikaĶīnas tirgi
BigQuant

The document reports a pandas error raised when a factor-analysis workflow formats the minimum date with strftime. The shown failing expression takes the minimum of a date column and immediately formats it; pandas raises the error when that value is NaT, its…

StatistikaAkcijas
BigQuant

The article introduces XGBoost as a boosting ensemble and contrasts its sequential fitting of weak learners with the parallel training used in bagging. It explains residual-based improvement, complexity penalties, sample and feature subsampling, and…

MašīnmācīšanāsFaktoru ieguldīšanaAkcijasVēsturisko datu pārbaude
BigQuant

This summary of a Changjiang Securities study examines whether the weak side of equity style factors can help improve an index-enhancement portfolio. It reports historical measurements from 2007 through 2019, identifying negative excess returns for…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This report summary outlines a method for inferring informed trading from minute-level data. It first applies a “smartness” measure to identify trades attributed to smart money, then aggregates their relative execution prices into sentiment factor Q. Higher…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTirgus noskaņojums
BigQuant

This short assignment response describes quantitative investing as using data to check market observations and identify patterns. It frames the goal as building strategies that can be backtested and evaluated for stability over time, then using software to…

StatistikaVēsturisko datu pārbaudeRīkojumu izpilde
BigQuant

The example constructs price-based stock factors from recent daily observations. For each half-month sampling date, it retrieves a recent window of daily high, open, low, close, and average price data for the stock universe. It calculates three mean…

AkcijasFaktoru ieguldīšanaTehniskie indikatoriĶīnas tirgi
BigQuant

This research note proposes that stock price changes reflect market forces, firm-specific short-term information, longer-term fundamentals, and noise. It uses daily minute data and cross-lag regressions of returns on changes in trading volume to estimate…

AkcijasĶīnas tirgiFaktoru ieguldīšanaStatistika
BigQuant

This brief report summary reviews changes in Chinese public mutual fund holdings during the fourth quarter of 2017. It says fund exposure rose in several sectors, led by food and beverage and home appliances, with increases also reported for electrical…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The document explains a portfolio approach built around StockRanker, a model that ranks stocks using historical data. Each day, the strategy buys the highest-ranked candidates and sells or replaces holdings that fall toward the bottom of the ranking. The…

AkcijasMašīnmācīšanāsPortfeļa veidošanaPozīcijas apjoma noteikšana
BigQuant

The post outlines a low-frequency model for timing Chinese A-share exposure and selecting trading targets. It describes using daily Wind data and a large set of predetermined spreadsheet formulas to generate bullish or bearish signals and identify an…

AkcijasTehniskie indikatoriVēsturisko datu pārbaudeĶīnas tirgi
BigQuant

This brief BigQuant support exchange addresses a question about a backtest whose logs and apparent trade signals stop before the requested end date. The user says the run was set through the 23rd, exchange data stopped at the 17th, and the output log stopped…

Vēsturisko datu pārbaudeRiska pārvaldībaStatistika
BigQuant

This forum post raises a practical backtesting question: whether strategy data indexed by the current date can be used to trade at that same day’s open without introducing lookahead bias. The example ranks stocks using market capitalization, tradable market…

Vēsturisko datu pārbaudeRīkojumu izpildeAkcijasRiska pārvaldība
BigQuant

This dated Chinese equity market report combines market activity, sentiment, sector flows, concentration measures, and style-factor performance. It describes subdued overall valuations, rising consumer-sector turnover, a recovery in TMT activity, and strong…

AkcijasĶīnas tirgiFaktoru ieguldīšanaCenas impulss
BigQuant

This review examines whether controlling for quality changes the apparent size premium in equity markets and whether small-cap exposure can strengthen other factors. It compares size-factor returns with quality and related factor exposures using time-series…

AkcijasFaktoru ieguldīšanaStatistikaASV tirgi
BigQuant

The article reviews empirical research comparing commodity futures momentum based on total returns with momentum based on residual, or idiosyncratic, returns after accounting for selected systematic factors. The study forms ranked portfolios across…

IzejvielasNākotnes līgumiCenas impulssFaktoru ieguldīšana
BigQuant

This short exchange explains how to assign groups when training a learning-to-rank model on stock observations. The questioner expects each trading date to form a group, so the model ranks stocks against other stocks observed on that date, and asks where…

AkcijasMašīnmācīšanāsStatistika
BigQuant

This brief assignment reflects on translating discretionary investment ideas into a quantitative process. The author cites assessing future demand for a company’s product, with examples involving a stablecoin business in the context of US Treasury pressures…

AkcijasVēsturisko datu pārbaudeRiska pārvaldībaFaktoru ieguldīšana
BigQuant

This Chinese-language research digest summarizes two separate studies. The first considers adding trend-following strategies to target-date funds in defined-contribution retirement plans. It argues that these funds may concentrate too much risk in equities…

Sekošana tendenceiPortfeļa veidošanaAkcijasStatistika
BigQuant

This Chinese-language tutorial describes implementing a stock-ranking strategy in BigTrader by passing StockRanker predictions directly to the trading engine and coding the trade logic there. It allocates purchases among the top-ranked stocks using weights…

AkcijasMašīnmācīšanāsPozīcijas apjoma noteikšanaPortfeļa veidošana
BigQuant

This study mines short-horizon price and volume factors for Chinese equities, then examines how transaction costs and rebalance frequency affect portfolio results. It uses genetic programming to generate candidate daily factors from conventional price and…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This BigQuant note explains how to change a strategy template’s initialization settings from a declining weight schedule to equal weights. The existing example assigns larger allocations to earlier stocks using weights proportional to the inverse logarithm…

AkcijasPortfeļa veidošanaPozīcijas apjoma noteikšana