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
Quantopian-luennot
45 dokumenttia
Binance API docs
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
Alphalens
14 dokumenttia
WonderTrader
14 dokumenttia
backtesting.py
11 dokumenttia
Technical Analysis
9 dokumenttia
QTPyLib
8 dokumenttia
QuantRocket
7 dokumenttia
Lumibot strategies
7 dokumenttia
Awesome Quant
1 dokumenttia

Hae kirjastosta

3,481 dokumenttia

BigQuant

This introductory article presents ten practical points about machine learning for non-specialists. It emphasizes that machine learning finds patterns in data, so data quality, accurate labels, representative samples, and careful transformations often matter…

KoneoppiminenTilastotiedeRiskienhallinta
BigQuant

The article explains how a long-only smart beta portfolio can combine equity factors to seek steadier returns than relying on one factor. It highlights value, low beta, profitability, investment, momentum, and size, arguing that their different return…

OsakkeetFaktoripohjainen sijoittaminenSalkun muodostaminenHintamomentum
BigQuant

This report evaluates whether changes in listed companies’ employee compensation can help distinguish future stock returns. It compares growth in compensation payable, which represents amounts owed, with growth in compensation already paid, and also…

OsakkeetFaktoripohjainen sijoittaminenKiinan markkinat
BigQuant

The post asks how to define groups for a learning-to-rank model in a stock selection setting. In ranking algorithms, each group contains observations that should be ranked against one another; the author proposes grouping stock samples by trading date so…

OsakkeetKoneoppiminenTilastotiede
BigQuant

This convertible-bond research note describes a relative-value approach based on the embedded option. It first compares implied volatility with the underlying stock’s historical volatility: relatively low implied volatility is treated as evidence that the…

VolatiliteettiJohdannaisten hinnoitteluHistoriatestausKorkosijoitukset
BigQuant

FactorVAE is described as a probabilistic dynamic factor model that applies a variational autoencoder to noisy market data to forecast cross-sectional stock returns. In training, an encoder-decoder uses future returns to identify posterior factors that…

OsakkeetKoneoppiminenFaktoripohjainen sijoittaminenTilastotiede
BigQuant

This introductory article explains Matplotlib’s role in Python data visualization and outlines the Figure and Axes objects, along with its state-based pyplot and object-oriented interfaces. It notes that the library supports chart types such as lines,…

OsakkeetTilastotiede
BigQuant

The report develops stock selection factors from abnormal operating cash flow as proxies for real earnings management. Its premise is that firms can alter real business activity to make reported performance look stronger, potentially harming future…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenHistoriatestaus
BigQuant

This research summary discusses how asset prices can reflect economic expectations before official data is released. It proposes comparing year-over-year changes in a basket of asset prices with economic indicators, and comparing asset-price changes with…

Usean omaisuuslajin sijoittaminenTilastotiedeHintamomentum
BigQuant

This trading exercise translates a discretionary method into explicit stock-selection rules. A candidate must close above its five-day moving average for three consecutive days and remain within ten percent above its lowest price over the previous twenty…

OsakkeetTekniset indikaattoritHintamomentumRiskienhallinta
BigQuant

The report develops stock-selection factors from one-minute Chinese equity data, adding transaction counts to price and volume inputs. It examines average trade value and the direction of large-trade activity, then evaluates these signals both alone and…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenKorkean taajuuden kaupankäynti
BigQuant

This platform guide distinguishes raw financial statement records from processed point-in-time (PIT) data and explains why the distinction matters in quantitative investing. Raw records are organized by instrument, report date, publication date, and change…

OsakkeetKiinan markkinatHistoriatestausTilastotiede
BigQuant

This brief Chinese-language forum exchange concerns whether a user can retrieve feature importance from a random forest in a visual quantitative-strategy platform. The reply identifies the random forest module and suggests creating a visual strategy template…

KoneoppiminenFaktoripohjainen sijoittaminen
BigQuant

This monthly review compares equity factors using information coefficients (IC) and long-short returns. It reports that growth and turnover factors were relatively consistent over the preceding three months, while profitability and reversal results varied;…

OsakkeetFaktoripohjainen sijoittaminenTilastotiedeHistoriatestaus
BigQuant

This research summary studies whether lagged returns from related industries can help predict industry returns for sector rotation. It motivates the question by noting that such predictability would not be expected in an ideal frictionless market, while real…

OsakkeetKiinan markkinatKoneoppiminenTilastotiede
BigQuant

This research summary examines low-volatility investing in overseas markets and mainland Chinese equities. It presents the low-volatility anomaly as a source of long-run risk-adjusted performance, with a behavioral explanation: speculative demand and…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenVolatiliteetti
BigQuant

This report summary describes a Barra-style risk model for Hong Kong stocks, built to study factor returns and portfolio exposures. Its data foundation combines security details, prices, trading activity, Hang Seng industry classifications, valuation…

OsakkeetFaktoripohjainen sijoittaminenTilastotiedeKiinan markkinat
BigQuant

The article outlines common aims of algorithmic execution: splitting a parent order to follow market volume, reducing market impact, and concealing trading intent. It also presents automation as a way to improve order-entry efficiency and reduce manual…

Toimeksiantojen toteutusMarkkinoiden mikrorakenneKorkean taajuuden kaupankäyntiKoneoppiminen
BigQuant

This article presents a revised dividend-factor stock selection strategy for Chinese equities and a simpler equal-weight alternative using dividend-themed ETFs. The V2 stock strategy expands the portfolio from 8 to 20 holdings, rebalances monthly instead of…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenHintamomentum
BigQuant

This note proposes screening Chinese equities associated with the metaverse theme, requiring a positive institutional-flow measure and a weekly crossover involving a 30-week reference. The intended rationale is to combine thematic exposure, evidence of…

OsakkeetKiinan markkinatTrendin seuraaminenTekniset indikaattorit
BigQuant

This overview explains Chinese depositary receipts (CDRs) as securities traded domestically that represent rights in foreign shares through a depositary institution. It summarizes the historical pilot eligibility criteria described in the document, says…

OsakkeetKiinan markkinatMarkkinoiden mikrorakenneRiskienhallinta
BigQuant

This forum post describes a reproducibility question in a quantitative modeling workflow. The author sets a learning-rate parameter to the same value in the model and in a hyperparameter-search module, and configures the search grid to contain only that…

KoneoppiminenTilastotiedeHistoriatestaus
BigQuant

The document explains sliced inverse regression (SIR) as a supervised dimension-reduction method for financial prediction. Unlike principal component analysis, which summarizes variation in predictors without using the target, SIR uses the forecast variable…

OsakkeetTilastotiedeFaktoripohjainen sijoittaminenSalkun muodostaminen
BigQuant

This overview surveys models used to relate asset values or expected returns to risk and other inputs. It describes CAPM as linking expected return to the risk-free rate and market exposure, then introduces multifactor models such as Fama–French, which add…

Usean omaisuuslajin sijoittaminenFaktoripohjainen sijoittaminenJohdannaisten hinnoitteluKorkosijoitukset