Siirry sisältöön

Tietokirjasto

Stratmillin tutkimusagentin kirjoittamia tiivistelmiä ja keskeisiä ajatuksia kirjoista, tutkimuksista, artikkeleista ja koodista, joita tekoälyagenttimme lukevat. Jokaisella sivulla on linkki alkuperäislähteeseen.

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

Hae kirjastosta

3,481 dokumenttia

BigQuant

This document explains how time-series momentum (TSMOM) and moving-average crossover (MACROSS) strategies can represent closely related ways to measure price trends. It describes expressing trend signals through past prices or returns, and using “signature”…

Trendin seuraaminenHintamomentumTekniset indikaattoritTilastotiede
BigQuant

This document describes a Chinese A-share selection screen combining robot-industry exposure, small free-float market capitalization, turnover, and the stock’s price change at 9:25. The stated selection logic calls for turnover between 3% and 12%, robot…

OsakkeetKiinan markkinatHintamomentumTekniset indikaattorit
BigQuant

A platform forum exchange addresses whether a daily backtest that predicts a five-day return using the current day’s closing price can access that day’s prediction before the close is available. The response explains that, in the described daily setup, the…

HistoriatestausKoneoppiminenTilastotiede
BigQuant

This guide presents local checks for two common failures in factor submissions: missing values near the evaluation window’s start and look-ahead bias. For time-series factors, it recommends querying enough earlier data to cover the longest lag or rolling…

OsakkeetKoneoppiminenTilastotiedeHistoriatestaus
BigQuant

The article summarizes research that uses active share, or portfolio deviation from a benchmark, as a proxy for a fund manager’s conviction. It reports that strong past performance is associated with subsequent increases in active share, particularly for…

OsakkeetFaktoripohjainen sijoittaminenSalkun muodostaminenRiskienhallinta
BigQuant

This dated market note reports RSRS timing-model views for several Chinese equity benchmarks on April 8, 2020. The model remained bullish on the Shanghai Composite, CSI 500, and ChiNext, while taking a more cautious view of the SSE 50 and CSI 300. No…

OsakkeetKiinan markkinatTrendin seuraaminenTekniset indikaattorit
BigQuant

The article presents regular investing in broad market index funds as a low-effort, long-term approach, arguing that it can reduce the need for stock picking and market timing. It attributes active managers’ difficulty beating indexes to fees, short-term…

Usean omaisuuslajin sijoittaminenKiinan markkinatYhdysvaltain markkinatSalkun muodostaminen
BigQuant

The document summarizes research on combining market anomalies and on refining multi-factor models. For anomaly strategies, it reports that returns to many well-known anomalies declined after academic publication, while diversified long-short portfolios…

OsakkeetFaktoripohjainen sijoittaminenHintamomentumTilastotiede
BigQuant

This page summarizes several quantitative investing topics: timing equity factors with cross-sectional and time-series indicators; adjusting portfolios to target volatility; modeling changing stock-bond correlations; assessing tactical asset-allocation…

Usean omaisuuslajin sijoittaminenFaktoripohjainen sijoittaminenVolatiliteettiSalkun muodostaminen
BigQuant

This note summarizes when to use several portfolio allocation approaches. A naïve allocation is presented as a practical choice when the universe is broad and return forecasts are difficult. Risk parity and minimum variance are associated with settings where…

Usean omaisuuslajin sijoittaminenSalkun muodostaminenRiskienhallinta
BigQuant

This Chinese-language summary presents a 2021 survey of reinforcement learning in financial decision-making. It introduces Markov decision processes as a foundation, then identifies value-based and policy-based learning approaches, including extensions that…

KoneoppiminenSalkun muodostaminenToimeksiantojen toteutusMarkkinatakaus
BigQuant

This strategy combines model-based stock ranking with technical timing. It starts from a manually defined CSI 150-related universe, then ranks candidates using an AI prediction score and volume, turnover, and large-order money-flow factors. Timing filters…

OsakkeetKoneoppiminenTrendin seuraaminenHintamomentum
BigQuant

This guide surveys how machine learning can support quantitative investing, from data preparation and model evaluation to portfolio selection. It explains supervised, unsupervised, and reinforcement learning, and reviews methods including regularized…

KoneoppiminenTilastotiedeFaktoripohjainen sijoittaminenSalkun muodostaminen
BigQuant

This Chinese strategy discussion combines hand-built stock selection rules with a CNN classifier. It describes converting screening conditions into Boolean features so a model can learn to identify stocks expected to show short-term strength. The feature…

Kiinan markkinatOsakkeetKoneoppiminenHintamomentum
BigQuant

The document presents a short-term timing approach based on the Hilbert transform. It assumes price movements contain a long-term trend, shorter cyclical fluctuations, and noise. The proposed process first smooths the data with a 20-period moving average,…

OsakkeetTekniset indikaattoritTilastotiedeHintamomentum
BigQuant

This weekly Chinese equity factor note compares value and growth styles and reports relative performance across six industry-neutral long-short factor groups. Value and growth were described as broadly balanced, while quality measures such as return on…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenPalautuminen keskiarvoon
BigQuant

The article describes combining two Chinese equity selection models designed for different market conditions: a rebound after an index decline and a mild index pullback. Each model includes market environment features so that it produces candidates only when…

OsakkeetKiinan markkinatHistoriatestausHintamomentum
BigQuant

This research summary describes a dynamic rebalancing overlay for multi-factor equity portfolios. The portfolio keeps its regular monthly rebalance, while market-cap factor exposure is checked daily mid-month. If exposure breaches a set threshold, the…

OsakkeetFaktoripohjainen sijoittaminenRiskienhallintaSalkun muodostaminen
BigQuant

The post contains a proposed Chinese stock screen built from financial, valuation, price-action, and intraday-style indicators. Its filters include market-cap and asset thresholds, revenue growth and return on equity conditions, turnover and price-change…

Kiinan markkinatOsakkeetTekniset indikaattoritTilastotiede
BigQuant

The article explains genetic algorithms as population-based search methods inspired by biological evolution. It walks through encoding candidate solutions as chromosomes, evaluating them with a fitness function, selecting parents, combining them through…

KoneoppiminenTilastotiedeHistoriatestaus
BigQuant

This study applies Lasso, adaptive Lasso, and Elastic Net to select equity factors and combine them into stock-selection signals. Its motivation is that correlated predictors can make ordinary multivariate regression unstable. The reported experiments show…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenKoneoppiminen
BigQuant

The post asks whether a factor-analysis module’s rebalance period measures returns before or after the factor observation. The author expected each factor-sorted group’s reported return to represent the return earned by holding its stocks for the stated…

Faktoripohjainen sijoittaminenHistoriatestausTilastotiede
BigQuant

This April 2020 brokerage-report summary argues that overseas pandemic concerns had weakened prices without materially lowering broad-market valuations or company earnings forecasts at that point. It frames semiconductor shares against a late-2018 price…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenSalkun muodostaminen
BigQuant

This document summarizes Ibbotson and Chen’s supply-side approach to estimating long-run equity returns and risk premiums. It decomposes US stock returns over 1926–2000 using inflation, earnings, dividends, valuation multiples, payout ratios, book value,…

OsakkeetTilastotiedeFaktoripohjainen sijoittaminenYhdysvaltain markkinat