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 document describes a macro allocation framework built from six risk factors covering growth, inflation, rates, credit, foreign exchange, and term spreads. It combines momentum signals with cycle-phase estimates, maps the resulting states into…

Usean omaisuuslajin sijoittaminenOsakkeetRaaka-aineetHintamomentum
BigQuant

This research summary compares monthly factors built from price and volume data with quarterly factors built from financial statements. It reports that market data factors were more often effective in long-short portfolios, and examines their relationship…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenHistoriatestaus
BigQuant

This post describes a Chinese equity screen combining a daily amplitude threshold, exclusion of stocks that closed at a limit-up the prior day, and a positive MACD reading. The stated rationale is to favor stocks with room to move that also show positive…

OsakkeetKiinan markkinatHintamomentumTekniset indikaattorit
BigQuant

The report outlines a multi-asset allocation framework that links macroeconomic risks to broad asset classes, styles, and industries. It identifies economic growth, interest rates, exchange rates, inflation, and credit as five central risk factors. A…

Usean omaisuuslajin sijoittaminenSalkun muodostaminenRiskienhallintaTilastotiede
BigQuant

The research note studies tail dependence between individual stocks and the broader market. Using copulas, it estimates upper-tail and lower-tail correlation, which describe whether assets tend to move together during extreme outcomes. The reported…

OsakkeetFaktoripohjainen sijoittaminenTilastotiede
BigQuant

The discussion explains a rolling sequence window as a way to assemble recent observations into a single training example. For instance, a model can receive a window of past factor values spanning several days, with the window advancing through time to…

KoneoppiminenTilastotiedeOsakkeet
BigQuant

This tutorial introduces recurrent neural networks as models for sequential data, then explains long short-term memory networks as a design intended to address the difficulty ordinary RNNs have retaining information across long sequences. It describes the…

KoneoppiminenOsakkeetTilastotiedeTekniset indikaattorit
BigQuant

This strategy description applies a simple price rule to one Chinese stock. It defines a reference high as the highest closing price over the past 20 days and a reference low as the lowest closing price over the past 10 days. The stated entry condition is…

OsakkeetKiinan markkinatTrendin seuraaminenTekniset indikaattorit
BigQuant

Average True Range (ATR), developed by J. Welles Wilder, measures price volatility rather than direction. The document defines true range as the greatest of the current high–low range and the distances from the previous close to the current high or low. ATR…

Tekniset indikaattoritVolatiliteettiRiskienhallinta
BigQuant

This Chinese equity screen selects stocks with amplitude above 1 and previous-day turnover above 8%, while excluding Beijing-listed shares. The article presents amplitude as a way to locate more volatile stocks and turnover as a sign of recent trading…

OsakkeetKiinan markkinatVolatiliteettiToimeksiantojen toteutus
BigQuant

This Chinese-language strategy note tests a simple stock screen for mainland China A-shares. It selects companies using three annual-report criteria: net profit growth above a threshold, return on equity above a threshold for three consecutive years, and a…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenHistoriatestaus
BigQuant

This introductory tutorial explains machine learning concepts and linear regression with finance examples. It distinguishes features from labels, outlines training, validation, and test data, and contrasts supervised with unsupervised learning,…

KoneoppiminenTilastotiedeFaktoripohjainen sijoittaminenHistoriatestaus
BigQuant

This research review applies conditional generative adversarial networks (cGANs) to multi-asset allocation. It conditions the model on recent asset-return sequences and random inputs to generate possible future paths, then estimates expected returns and…

Usean omaisuuslajin sijoittaminenKoneoppiminenSalkun muodostaminenRiskienhallinta
BigQuant

The document raises a position-management idea for an equity holding: after a stock gains 10%, sell half the shares to secure some of the unrealized profit while leaving the rest invested for further gains. Its example starts with 1,000 shares and proposes…

OsakkeetPosition koon määrittäminenRiskienhallintaHistoriatestaus
BigQuant

This research summary describes two strategies using constituents of the Shanghai 50. The momentum version ranks stocks by their return over the previous three trading days, selects the three strongest, weights them equally, and holds each cohort for nine…

OsakkeetKiinan markkinatHintamomentumPalautuminen keskiarvoon
BigQuant

This weekly report compares stock selection portfolios built with several artificial intelligence methods across broad Chinese A-share and index-constituent universes. It separates results for unconstrained selection, industry-neutral portfolios, and…

OsakkeetKoneoppiminenHistoriatestausKiinan markkinat
BigQuant

This report evaluates momentum based sector allocation in Chinese equities. It compares ordinary lookback return ranking with risk-adjusted alternatives, including Sharpe ratio, information ratio, and a path-adjusted measure that divides return by the sum of…

OsakkeetHintamomentumFaktoripohjainen sijoittaminenHistoriatestaus
BigQuant

This report summarizes quantitative approaches to enhancing returns over Chinese equity benchmarks while managing tracking error. It frames enhancement as a blend of active and passive investing and describes a top-down range of choices: market exposure,…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenSalkun muodostaminen
BigQuant

This document outlines a monthly long-only stock selection strategy for the Chinese A-share market. It ranks the universe using a market-cap rank and a 20-day average of price changes, combines the two into a score, then holds 50 equally weighted stocks with…

Kiinan markkinatOsakkeetFaktoripohjainen sijoittaminenHintamomentum
BigQuant

This research note explores additional technical factors for stock selection in the CSI 500 universe. It groups candidate measures into turnover behavior, illiquidity, and combined volume-price information. Turnover candidates include stability, coefficient…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminenTekniset indikaattorit
BigQuant

This report describes an initial step in building a multi-factor equity model: choosing which factors to combine. It proposes reducing the factor search space by including exposure relative to a benchmark as one screening criterion, alongside the strength of…

OsakkeetFaktoripohjainen sijoittaminenSalkun muodostaminenTilastotiede
BigQuant

This example strategy searches Chinese A-shares for stocks that recently had a long run of consecutive limit-up sessions and then broke the streak. It further requires positive average returns over the three days after the break, excludes several board…

Kiinan markkinatOsakkeetHintamomentumHistoriatestaus
BigQuant

The page explains a practical issue in BigTrader’s simulated-trading workflow: strategy calculations run in a container on a daily schedule, so in-memory values may not carry over between runs. If a strategy needs to retain information such as its net asset…

Toimeksiantojen toteutusRiskienhallinta