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

The article presents a short-term stock screening pattern built around three sequential signals. First, it looks for at least five generally rising sessions, allowing one modest down day while the overall movement stays upward. Next, it requires a session…

OsakkeetLäpimurtoTekniset indikaattorit
BigQuant

This tutorial outlines a TensorFlow LSTM workflow for forecasting stock or index returns. It recommends transforming closing prices into returns, then arranging recent observations into sequential input groups with the next observations as prediction labels.…

KoneoppiminenOsakkeetKiinan markkinatHistoriatestaus
BigQuant

This research summary compares ordinary cross-validation with time-series cross-validation for model tuning on sequential data. Randomly assigning observations to folds can train a model on future observations and validate it on the past, violating the…

KoneoppiminenTilastotiedeHistoriatestausOsakkeet
BigQuant

This competition update reports diagnostics for the ten highest-ranked anonymous factors and tracks a CSI 1000 enhancement portfolio. Factor profiles include average Barra style exposures and sector-level average Rank ICs, calculated from cross-sectional…

OsakkeetFaktoripohjainen sijoittaminenSalkun muodostaminenHistoriatestaus
BigQuant

The document introduces Moving Average Envelopes (MAE), a technical indicator built from a moving average of closing prices. It describes two bands: a lower envelope set below the average by a specified percentage and an upper envelope set above it by…

Tekniset indikaattorit
BigQuant

This study proposes adding daily risk monitoring to a traditional monthly rebalancing schedule for multi-factor equity portfolios. It tracks the portfolio’s exposure to the size factor during the month and rebalances on the next trading day when that…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenSalkun muodostaminen
BigQuant

The article summarizes research on whether forced mutual fund selling affects the prices and liquidity of related stocks. It describes a learning mechanism: investors may initially interpret a peer stock’s decline as evidence of negative information about…

OsakkeetMarkkinoiden mikrorakenneTilastotiedeRiskienhallinta
BigQuant

The report describes an expanded Black-Litterman asset allocation model that combines subjective views with quantitative portfolio construction. It uses behavioral finance and historical trend information, including momentum and reversal effects, to form…

Usean omaisuuslajin sijoittaminenSalkun muodostaminenHintamomentumPalautuminen keskiarvoon
BigQuant

This example tests whether market capitalization is associated with next-day stock returns using repeated cross-sectional ordinary least squares regressions. It joins Chinese stock factor data to daily bars, calculates forward returns from the next close,…

OsakkeetFaktoripohjainen sijoittaminenTilastotiedeHistoriatestaus
BigQuant

This research summary examines the validity of mainland China Stock Connect ownership data and describes portfolio strategies built from investor holdings, sector allocations, style exposures, leading trades, and net capital flows. It reports persistent…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenHintamomentum
BigQuant

This study builds a monthly Chinese equity factor from stock returns and volatility around intraday volume surges. It defines a surge as a minute when the increase in trading volume exceeds that stock’s daily average increase by more than one standard…

OsakkeetFaktoripohjainen sijoittaminenTekniset indikaattoritHistoriatestaus
BigQuant

This Chinese market note proposes that China’s credit impulse may be turning upward and argues that it tends to lead A-share earnings growth. On that assumption, it recommends overweighting Chinese equities, favoring growth over value, and ranking…

Usean omaisuuslajin sijoittaminenOsakkeetKorkosijoituksetKiinan markkinat
BigQuant

This reference catalogues TA-Lib functions and their input fields for calculating common trading indicators. It groups examples into overlapping price studies, volatility and volume-price measures, cycle functions, price transformations, momentum indicators,…

Tekniset indikaattoritTilastotiedeVolatiliteettiHintamomentum
BigQuant

This stock selection method turns a discretionary approach into a set of technical and liquidity filters, followed by periodic rebalancing. It looks for prices mostly above long-term moving averages, favorable short-term moving-average conditions, and moving…

Kiinan markkinatOsakkeetHintamomentumTrendin seuraaminen
BigQuant

This Chinese-language article discusses the proposed introduction of market making on China’s STAR Market. It explains that market makers continuously quote prices, sell inventory to buyers, and use their own capital to buy from sellers. They bear price risk…

OsakkeetMarkkinatakausMarkkinoiden mikrorakenneRiskienhallinta
BigQuant

This BigQuant forum post presents a daily-bar backtest example that submits several limit buy orders during a bar callback. Before each order, the callback reads available cash and frozen cash from the trading account. The reported output shows those values…

OsakkeetHistoriatestausToimeksiantojen toteutusRiskienhallinta
BigQuant

The article summarizes research on whether active mutual fund managers adjust trading to anticipated stock-picking opportunities. It defines stock-selection timing as trading more when opportunities to hold positive-alpha stocks are greater and less when…

OsakkeetFaktoripohjainen sijoittaminenTilastotiedeHistoriatestaus
BigQuant

This overview surveys unsupervised learning through two groups of methods: dimensionality reduction and clustering. For dimensionality reduction, it describes PCA as a way to project high-dimensional features into fewer dimensions using covariance-matrix…

KoneoppiminenTilastotiede
BigQuant

This study compares conventional return labels with alternative labels based on information ratio and Calmar ratio for AI stock selection models. It argues that labels incorporating returns, volatility, and drawdowns across a period may convey more…

Kiinan markkinatOsakkeetKoneoppiminenFaktoripohjainen sijoittaminen
BigQuant

This document outlines a simple KDJ-based trading strategy for the Chinese stock 605588, with the CSI 300 index named as a reference benchmark. It describes selling when K, D, and J are all in high territory, and buying when all three are in low territory.…

Kiinan markkinatOsakkeetTekniset indikaattorit
BigQuant

This article presents three MACD-based warnings for managing long equity positions: the indicator moving below its zero line, bearish divergence as price makes higher highs while MACD peaks decline, and a bearish crossover above zero. It interprets these…

OsakkeetTekniset indikaattoritHintamomentumRiskienhallinta
BigQuant

This article outlines a research approach for Chinese equities during sharp market-wide declines. It proposes identifying historical selloff days and studying two groups: stocks that hit their upper price limit despite the market drop, and stocks that hit…

OsakkeetTapahtumapohjainen kaupankäyntiHistoriatestausKiinan markkinat
BigQuant

This note summarizes the AlexNet deep convolutional neural network described in the ImageNet classification paper. It outlines the model’s use of multiple convolutional channels, five convolutional stages and three fully connected layers, and explains how…

KoneoppiminenTilastotiede
BigQuant

This strategy applies a stock-ranking model to a filtered pool of Chinese A-shares, aiming to find stocks that may rebound from depressed levels and hold up better when the broader market weakens. The target universe is the small and medium-sized board. Its…

OsakkeetKoneoppiminenPalautuminen keskiarvoonKiinan markkinat