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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

The document describes a macro allocation framework built from six risk factors covering growth, inflation, rates, credit, foreign exchange, and term spreads. It combines momentum signals with cycle-phase estimates, maps the resulting states into…

Vairāku aktīvu tirdzniecībaAkcijasIzejvielasCenas impulss
BigQuant

This research summary compares monthly factors built from price and volume data with quarterly factors built from financial statements. It reports that market data factors were more often effective in long-short portfolios, and examines their relationship…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This post describes a Chinese equity screen combining a daily amplitude threshold, exclusion of stocks that closed at a limit-up the prior day, and a positive MACD reading. The stated rationale is to favor stocks with room to move that also show positive…

AkcijasĶīnas tirgiCenas impulssTehniskie indikatori
BigQuant

The report outlines a multi-asset allocation framework that links macroeconomic risks to broad asset classes, styles, and industries. It identifies economic growth, interest rates, exchange rates, inflation, and credit as five central risk factors. A…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaRiska pārvaldībaStatistika
BigQuant

The research note studies tail dependence between individual stocks and the broader market. Using copulas, it estimates upper-tail and lower-tail correlation, which describe whether assets tend to move together during extreme outcomes. The reported…

AkcijasFaktoru ieguldīšanaStatistika
BigQuant

The discussion explains a rolling sequence window as a way to assemble recent observations into a single training example. For instance, a model can receive a window of past factor values spanning several days, with the window advancing through time to…

MašīnmācīšanāsStatistikaAkcijas
BigQuant

This tutorial introduces recurrent neural networks as models for sequential data, then explains long short-term memory networks as a design intended to address the difficulty ordinary RNNs have retaining information across long sequences. It describes the…

MašīnmācīšanāsAkcijasStatistikaTehniskie indikatori
BigQuant

This strategy description applies a simple price rule to one Chinese stock. It defines a reference high as the highest closing price over the past 20 days and a reference low as the lowest closing price over the past 10 days. The stated entry condition is…

AkcijasĶīnas tirgiSekošana tendenceiTehniskie indikatori
BigQuant

Average True Range (ATR), developed by J. Welles Wilder, measures price volatility rather than direction. The document defines true range as the greatest of the current high–low range and the distances from the previous close to the current high or low. ATR…

Tehniskie indikatoriSvārstīgumsRiska pārvaldība
BigQuant

This Chinese equity screen selects stocks with amplitude above 1 and previous-day turnover above 8%, while excluding Beijing-listed shares. The article presents amplitude as a way to locate more volatile stocks and turnover as a sign of recent trading…

AkcijasĶīnas tirgiSvārstīgumsRīkojumu izpilde
BigQuant

This Chinese-language strategy note tests a simple stock screen for mainland China A-shares. It selects companies using three annual-report criteria: net profit growth above a threshold, return on equity above a threshold for three consecutive years, and a…

AkcijasĶīnas tirgiFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This introductory tutorial explains machine learning concepts and linear regression with finance examples. It distinguishes features from labels, outlines training, validation, and test data, and contrasts supervised with unsupervised learning,…

MašīnmācīšanāsStatistikaFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This research review applies conditional generative adversarial networks (cGANs) to multi-asset allocation. It conditions the model on recent asset-return sequences and random inputs to generate possible future paths, then estimates expected returns and…

Vairāku aktīvu tirdzniecībaMašīnmācīšanāsPortfeļa veidošanaRiska pārvaldība
BigQuant

The document raises a position-management idea for an equity holding: after a stock gains 10%, sell half the shares to secure some of the unrealized profit while leaving the rest invested for further gains. Its example starts with 1,000 shares and proposes…

AkcijasPozīcijas apjoma noteikšanaRiska pārvaldībaVēsturisko datu pārbaude
BigQuant

This research summary describes two strategies using constituents of the Shanghai 50. The momentum version ranks stocks by their return over the previous three trading days, selects the three strongest, weights them equally, and holds each cohort for nine…

AkcijasĶīnas tirgiCenas impulssAtgriešanās pie vidējās vērtības
BigQuant

This weekly report compares stock selection portfolios built with several artificial intelligence methods across broad Chinese A-share and index-constituent universes. It separates results for unconstrained selection, industry-neutral portfolios, and…

AkcijasMašīnmācīšanāsVēsturisko datu pārbaudeĶīnas tirgi
BigQuant

This report evaluates momentum based sector allocation in Chinese equities. It compares ordinary lookback return ranking with risk-adjusted alternatives, including Sharpe ratio, information ratio, and a path-adjusted measure that divides return by the sum of…

AkcijasCenas impulssFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This report summarizes quantitative approaches to enhancing returns over Chinese equity benchmarks while managing tracking error. It frames enhancement as a blend of active and passive investing and describes a top-down range of choices: market exposure,…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This document outlines a monthly long-only stock selection strategy for the Chinese A-share market. It ranks the universe using a market-cap rank and a 20-day average of price changes, combines the two into a score, then holds 50 equally weighted stocks with…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaCenas impulss
BigQuant

This research note explores additional technical factors for stock selection in the CSI 500 universe. It groups candidate measures into turnover behavior, illiquidity, and combined volume-price information. Turnover candidates include stability, coefficient…

AkcijasĶīnas tirgiFaktoru ieguldīšanaTehniskie indikatori
BigQuant

This report describes an initial step in building a multi-factor equity model: choosing which factors to combine. It proposes reducing the factor search space by including exposure relative to a benchmark as one screening criterion, alongside the strength of…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaStatistika
BigQuant

This example strategy searches Chinese A-shares for stocks that recently had a long run of consecutive limit-up sessions and then broke the streak. It further requires positive average returns over the three days after the break, excludes several board…

Ķīnas tirgiAkcijasCenas impulssVēsturisko datu pārbaude
BigQuant

The page explains a practical issue in BigTrader’s simulated-trading workflow: strategy calculations run in a container on a daily schedule, so in-memory values may not carry over between runs. If a strategy needs to retain information such as its net asset…

Rīkojumu izpildeRiska pārvaldība