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
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

The document reviews research on whether investor sentiment helps explain stock return anomalies or appears predictive because of spurious regression. Persistent predictors can seem to forecast returns in finite samples when expected returns also vary over…

MarkkinatunnelmaTilastotiedeFaktoripohjainen sijoittaminenOsakkeet
BigQuant

This research summary examines high-frequency equity factors built from minute bars, tick data, and transaction-level records. The factors capture intraday skewness, downside volatility, reversals, late-session trading, price-volume relationships,…

OsakkeetKiinan markkinatKorkean taajuuden kaupankäyntiFaktoripohjainen sijoittaminen
BigQuant

This brief exchange explains how to exclude Beijing Stock Exchange listings when labeling Chinese A-share stocks with a default template. It recommends using an A-share filtering module or adding a custom Python filter to the workflow. The suggested…

OsakkeetKiinan markkinat
BigQuant

The report treats corporate events as equity style factors and evaluates them within a multi-factor research framework. It describes differences in event prevalence across industries and relationships between event factors: private placements and dividends…

OsakkeetFaktoripohjainen sijoittaminenTapahtumapohjainen kaupankäyntiTilastotiede
BigQuant

This Chinese-language article describes a customizable stock-selection strategy built around five rules, mainly seeking shares that appear oversold and stable, with some rules intended to identify possible rallies. It distinguishes rule-based coding from an…

OsakkeetPalautuminen keskiarvoonHistoriatestausFaktoripohjainen sijoittaminen
BigQuant

This A-share stock-selection approach combines an earnings-surprise proxy with the price response to earnings news. Instead of comparing reported profit with a numerical consensus estimate, it analyzes analyst research-report titles for wording that signals…

OsakkeetKiinan markkinatTapahtumapohjainen kaupankäyntiHintamomentum
BigQuant

The article describes a long-only Chinese equity strategy that uses a ResNet101 image classifier to identify strong stocks during pullbacks in an established uptrend. Its rationale combines buying a retracement in a strong trend, favoring stocks that…

OsakkeetKoneoppiminenTrendin seuraaminenHintamomentum
BigQuant

This research summary links debt-cycle phases across government, financial, and household sectors to movements in bonds, equities, and commodities in China. It describes a transmission mechanism: rising leverage and easier credit may support bond prices,…

Usean omaisuuslajin sijoittaminenOsakkeetRaaka-aineetKorkosijoitukset
BigQuant

This brief platform discussion answers how to access factor values from code in a BigQuant backtest. Its suggested workflow is to read the data frame attached to the backtest context’s data options during the initialization function, making the factor data…

Faktoripohjainen sijoittaminenHistoriatestaus
BigQuant

This tutorial introduces feedforward neural networks, convolutional neural networks, recurrent neural networks, and LSTMs. It explains how perceptrons combine features with learned weights and an activation function, how layers build representations, and how…

KoneoppiminenTilastotiede
BigQuant

The study estimates duration for medium- and long-term pure bond funds using rolling regressions of fund returns on bond-index returns across maturity segments. It compares constrained linear regression, stepwise selection, and LASSO, selecting LASSO to…

KorkosijoituksetTilastotiedeFaktoripohjainen sijoittaminenTapahtumapohjainen kaupankäynti
BigQuant

The response explains how to reference the current close alongside earlier close values in a BigQuant factor expression. It constructs a series of conditional comparisons: for each of several prior indexed closes, it checks whether the current close is lower…

OsakkeetTekniset indikaattoritKoneoppiminen
BigQuant

This report examines the small-cap premium in China’s A-share market and a model for timing shifts between small- and large-cap stocks. It measures the premium through small-cap stocks’ monthly excess return over large caps, and reports that the historical…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenSalkun muodostaminen
BigQuant

The article explains Bollinger Bands as a moving average surrounded by upper and lower bands set two standard deviations away. It also introduces band width as a way to identify volatility contraction, or a squeeze, which may precede a breakout but does not…

Tekniset indikaattoritVolatiliteettiPalautuminen keskiarvoonLäpimurto
BigQuant

The report combines cross-sectional equity factors with a set of three macro cycle indicators in a random forest model. The cycle features act as market state signals, allowing the model to apply different stock-selection logic across conditions. The authors…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenKoneoppiminen
BigQuant

This research summary presents a framework for replicating the returns of an index, fund, or individual stock with a selected set of holdings. It first applies LASSO regression to choose securities that capture the target portfolio’s characteristics, then…

OsakkeetKoneoppiminenSalkun muodostaminenHistoriatestaus
BigQuant

The article proposes a recovery approach for a losing equity position that avoids adding shares while price is still falling. It advises waiting for a low-price consolidation and a volume increase as signs that selling pressure may have eased, then using…

OsakkeetPalautuminen keskiarvoonTekniset indikaattoritRiskienhallinta
BigQuant

The document introduces Smart Beta as index investing that uses rules other than market-cap weighting to increase exposure to selected risk factors. It presents the approach as a middle ground between active and passive investing, with the potential to build…

Faktoripohjainen sijoittaminenSalkun muodostaminenOsakkeetYhdysvaltain markkinat
BigQuant

This guide describes participation in a Chinese A-share quantitative challenge with factor discovery and end-to-end modeling tracks. Both target future 30-minute VWAP returns using minute bars and order book snapshots. For the factor track, submissions…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenHistoriatestaus
BigQuant

This study note explains how to build a directed stock network from news coverage: a stock named in a headline is treated as a lead, while stocks mentioned in the article body are followers. Edge weights count their co-occurrences over a rolling window. The…

OsakkeetMarkkinatunnelmaTilastotiedeFaktoripohjainen sijoittaminen
BigQuant

This brief troubleshooting exchange addresses why two backtests can report different returns even when their factors and backtest dates match. The response attributes the mismatch primarily to a difference in training-set data volume. It adds that using the…

HistoriatestausKoneoppiminen
BigQuant

This brief forum exchange addresses a feature calculation that displayed missing values and offers a warning relevant to strategy construction: using a shift with a negative offset can incorporate future observations into a feature. Such a feature may appear…

HistoriatestausTilastotiedeKoneoppiminen
BigQuant

The note distinguishes selecting a stock universe from testing whether a factor can rank future returns within that universe. It describes factor analysis as sorting stocks by a factor, dividing them into equal-sized groups, and comparing group returns.…

OsakkeetFaktoripohjainen sijoittaminenTilastotiedeHistoriatestaus
BigQuant

This platform support discussion explains two apparent backtest anomalies in a single-stock strategy: why a position did not begin on the requested date at full allocation, and why the stock appeared to be sold later. The response attributes the delayed…

OsakkeetHistoriatestausTilastotiede