Liigu sisu juurde

Teadmiste raamatukogu

Kokkuvõtted ja põhiideed raamatutest, teadustöödest, artiklitest ja koodist, mida meie AI-agendid loevad. Need on koostanud Stratmilli uurimisagent. Igal lehel on link originaalile.

Quant Q&A
20,364 dokumenti
SuperMind
12,226 dokumenti
OKX Learn
8,431 dokumenti
Strategy library
7,910 dokumenti
MQL5 code base
7,090 dokumenti
BigQuant
3,481 dokumenti
Bitget Academy
3,298 dokumenti
MQL5 articles
3,012 dokumenti
TradingView scripts
1,976 dokumenti
ProRealCode
1,507 dokumenti
Deribit Insights
1,232 dokumenti
Machine Learning for Trading
1,124 dokumenti
arXiv papers
1,033 dokumenti
Amberdata research
766 dokumenti
FMZ forum
682 dokumenti
FMZ digest
662 dokumenti
vn.py community
560 dokumenti
QuantInsti blog
511 dokumenti
Galaxy Research
340 dokumenti
QuantStart
246 dokumenti
Stratmill research code
219 dokumenti
Robot Wealth
195 dokumenti
NautilusTrader
191 dokumenti
Hummingbot docs
181 dokumenti
Paradigm research
175 dokumenti
Lumibot
164 dokumenti
Kraken Learn
163 dokumenti
Kvantkursuste raamatukogu
157 dokumenti
OctoBot
152 dokumenti
Cryptohopper blog
144 dokumenti
Systematic trading blog (Rob Carver)
132 dokumenti
Qlib
116 dokumenti
TqSdk
86 dokumenti
Quantpedia
86 dokumenti
Hyperliquid docs
79 dokumenti
Freqtrade
68 dokumenti
Hudson & Thames
62 dokumenti
Awesome Systematic Trading
61 dokumenti
backtrader
54 dokumenti
vn.py
50 dokumenti
Binance API docs
45 dokumenti
Quantopiani loengud
45 dokumenti
FMZ guides
38 dokumenti
pysystemtrade
34 dokumenti
Freqtrade docs
32 dokumenti
quant-trading
31 dokumenti
FinRL
28 dokumenti
Zipline
22 dokumenti
FMZ live strategies
21 dokumenti
Jesse
17 dokumenti
pyfolio
16 dokumenti
WonderTrader
14 dokumenti
Alphalens
14 dokumenti
backtesting.py
11 dokumenti
Technical Analysis
9 dokumenti
QTPyLib
8 dokumenti
QuantRocket
7 dokumenti
Lumibot strategies
7 dokumenti
Awesome Quant
1 dokumenti

Otsi raamatukogust

3,481 dokumenti

BigQuant

This report studies trend-based allocation across CITIC Securities’ first-level industry indexes. It frames the process as choosing assets, measuring momentum, selecting industries, assigning weights, and managing portfolio tracking. The analysis describes…

AktsiadTrendijärgimineMomentumPortfelli koostamine
BigQuant

This brief exchange explains an apparent one-day mismatch between backtest output logs and the trade details table. The stated cause is the difference between a signal and its execution: the log records when a price bar arrives and triggers the signal, while…

Tagantjärele testimineTehingute täitmine
BigQuant

The document asks why a historical price-to-earnings series for China’s pharmaceutical industry differs from a series calculated in code. The example selects constituents classified under the 2021 Shenwan industry scheme, joins them to daily stock valuation…

AktsiadHiina turudStatistika
BigQuant

The document summarizes a strategy that combines the relative strength index, exponential moving average, volume-weighted average price, and moving average convergence divergence to generate trading signals. It describes this as a “Weapon Candle” approach…

AktsiadTehnilised indikaatoridMomentumTagantjärele testimine
BigQuant

The article describes a discretionary review process for short-term stock theme trading. It begins by scanning the ten highest-turnover names, then compares sectors by the number of stocks advancing from a first limit-up day to a second. Next, it checks the…

AktsiadMomentumLäbimurreTuru mikrostruktuur
BigQuant

The article argues that widely used price-based stop rules can cluster orders around obvious levels, where sudden moves may trigger many exits. It recommends shifting attention from a fixed loss percentage on each position to a preset total loss budget, with…

RiskijuhtiminePositsiooni suuruse määramineAktsiad
BigQuant

This short forum exchange reports a TypeError encountered when opening a backtesting module. The response attributes it to a strategy parameter-name list containing an item that is not a string, which prevents the framework from using that item as an…

Tagantjärele testimine
BigQuant

This review summarizes research on U.S. actively managed equity mutual funds during the acute COVID-19 market crisis. The study compares net fund returns with broad-market, style-matched, prospectus, and factor-model benchmarks, and examines investor flows…

AktsiadUSA turudFaktorinvesteerimineStatistika
BigQuant

A user reports that a price-ratio buy condition never triggers during a year-long backtest, although the sell condition does. The response attributes the training error to StockRanker receiving only two factors, with a Boolean factor causing the model to…

AktsiadMasinõpeTagantjärele testimine
BigQuant

The document explains abnormal idiosyncratic volatility (AIV), a price based measure of information risk around earnings announcements. It compares each stock’s residual volatility during the five trading days before earnings announcements with its residual…

AktsiadVolatiilsusFaktorinvesteerimineSündmustepõhine
BigQuant

The document describes pure factor portfolios as a way to test whether a given equity factor has explanatory or investment value while controlling exposure to other factors. A pure portfolio is constructed to have unit exposure to the factor under…

FaktorinvesteeriminePortfelli koostamineStatistikaAktsiad
BigQuant

The document introduces the Commodity Selection Index (CSI), specifying high, low, close, and a lookback length as inputs. Its pseudocode builds an average true range measure from intraday range and gaps relative to the previous close. It also estimates…

ToorainedFutuuridTehnilised indikaatoridTrendijärgimine
BigQuant

This short project note outlines a workflow for preparing data and fitting a predictive model. It divides SQL data construction into a base dataset, a normalized dataset, and a final feature-and-label stage. The note mentions cross-sectional factor ranking…

MasinõpeStatistikaTagantjärele testimine
BigQuant

This weekly report compares three Huatai fundamental stock-selection portfolios during a market pullback: a high-dividend-yield model, a low price-to-sales model, and a relative price-to-earnings model. It reports absolute returns, benchmark-relative…

AktsiadFaktorinvesteeriminePortfelli koostamineTagantjärele testimine
BigQuant

The article offers four proposed reasons Chinese quantitative funds may find A-shares attractive relative to US equities: a large listed universe and volatile small-cap stocks, lower trading costs, a higher retail share of trading that may create mispricing,…

Hiina turudUSA turudAktsiadTuru mikrostruktuur
BigQuant

This excerpt argues that risk control and position sizing deserve priority alongside stock selection and timing. Its proposed framework smooths risk decisions with a moving-average tracking system and calculates a daily score from market or stock…

RiskijuhtiminePositsiooni suuruse määramineTrendijärgimineTehnilised indikaatorid
BigQuant

This report applies the Fama-French three-factor framework to China’s A-share market, examining monthly evidence from April 2010 through October 2022. It describes the market excess return factor, SMB for company size, and HML for book-to-market, with SMB…

AktsiadFaktorinvesteerimineStatistikaHiina turud
BigQuant

This monthly allocation report describes a strategic portfolio framework that maps broad asset holdings to underlying macroeconomic risks. It uses principal component analysis to define five factors—economic growth, interest rates, credit, term spread, and…

Mitme varaklassigaAktsiadFikseeritud tuluga väärtpaberidFaktorinvesteerimine
BigQuant

This Chinese-language strategy note combines two China A-share stock-selection approaches: a small-cap, low-turnover portfolio and a dividend-focused portfolio that excludes the smallest stocks. The first ranks eligible stocks using market capitalization and…

AktsiadHiina turudFaktorinvesteeriminePortfelli koostamine
BigQuant

This report summary describes a growth-stock selection approach for Chinese equities that pairs an earnings-surprise signal with the next-day price gap after an earnings announcement. Rather than comparing reported earnings with a numeric consensus estimate,…

Hiina turudAktsiadSündmustepõhineMomentum
BigQuant

The document explains how to calculate a 14-period Relative Strength Index from closing prices. It separates price changes into gains and losses, averages the first period simply, then smooths later averages by carrying forward the prior value. The ratio of…

AktsiadTehnilised indikaatoridMomentumTrendijärgimine
BigQuant

This document presents the China Securities Innovative Drug Industry Index as a way to track listed companies whose main businesses involve innovative drug research and development. It describes the index’s exposure to pharmaceutical subsectors, company…

AktsiadHiina turudPortfelli koostamine
BigQuant

This report introduces gcForest, or multi-grained cascade forest, as a tree-based deep learning model proposed as an alternative to deep neural networks. It argues that the method may suit financial data because it is described as more robust to…

MasinõpeTagantjärele testimineStatistikaPortfelli koostamine
BigQuant

This report evaluates risk parity, an asset allocation approach that aims to equalize the risk contributions of assets in a portfolio. It tests the approach using two historical data window designs: rolling windows and expanding recursive windows. For each…

Mitme varaklassigaPortfelli koostamineRiskijuhtimineTagantjärele testimine