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
Quantpedia
86 dokumenttia
TqSdk
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 forum post asks how to choose among three paid equity strategies using platform rankings and reported performance figures. The examples contrast a highly ranked strategy with large cumulative gains but a substantial drawdown, a smoother strategy with a…

OsakkeetHistoriatestausRiskienhallinta
BigQuant

The document summarizes a study that derives a listed-company default probability factor, MPD, from the Black–Scholes–Merton option-pricing framework under stated assumptions. It evaluates MPD as a measure of corporate credit risk and examines how portfolios…

OsakkeetFaktoripohjainen sijoittaminenRiskienhallintaKiinan markkinat
BigQuant

This post introduces the Accumulation Swing Index (ASI), also called the oscillating swing index, and lists open, high, low, and close prices as its inputs. It presents pseudocode that compares current prices with prior close, low, and open values, builds…

Tekniset indikaattoritOsakkeet
BigQuant

The discussion explains that weights in a multi-strategy portfolio can represent each strategy’s share of the combined allocation and can also be used to calculate a weighted portfolio return. Its example divides capital evenly among three strategies,…

Salkun muodostaminenHistoriatestausPosition koon määrittäminen
BigQuant

The article explains stock price changes through macroeconomic forces such as growth, interest rates, inflation, policy, and global capital flows, alongside company performance, supply and demand, news, and investor sentiment. It describes how retail and…

OsakkeetTekniset indikaattoritHintamomentumVolatiliteetti
BigQuant

This study summary describes using recurrent neural networks, including long short-term memory cells, to forecast stock movements separately by industry. The rationale is that firms in one sector may share common influences, while industries can differ in…

OsakkeetKoneoppiminenFaktoripohjainen sijoittaminenTilastotiede
BigQuant

This report summary describes using gene expression programming, a heuristic inspired by biological evolution, to discover price-volume factors from short-horizon data. Because many candidate factors had weakly monotonic group returns or no excess return in…

OsakkeetFaktoripohjainen sijoittaminenKoneoppiminenTilastotiede
BigQuant

This research summary presents a framework for classifying industry and thematic funds and using the resulting categories to form candidate investment pools. Fund contracts’ investment requirements serve as a core eligibility basis. The framework begins with…

Kiinan markkinatOsakkeetSalkun muodostaminenFaktoripohjainen sijoittaminen
BigQuant

This analysis proposes six dimensions for assessing Chinese brokerage firms’ monthly stock recommendations: absolute returns, return relative to volatility, sector selection, stock-level excess returns, ability to identify emerging winners, and selection…

OsakkeetTilastotiedeRiskienhallintaFaktoripohjainen sijoittaminen
BigQuant

This review of Asness and Frazzini examines how price timing affects the book-to-price measure used to form the HML value factor. The standard Fama–French approach pairs accounting book value with a prior fiscal year-end price, then updates portfolios…

OsakkeetFaktoripohjainen sijoittaminenHintamomentumSalkun muodostaminen
BigQuant

This brief platform support exchange answers where to find the dividend_yield_ratio field after a user cannot locate it in the dividend information table. The response points to a precomputed stock factors table, explaining that the requested field is…

OsakkeetKiinan markkinatFaktoripohjainen sijoittaminen
BigQuant

This article explains how to turn future returns into labels for supervised trading models, arguing that the label should match the instrument, trading horizon, liquidity, and costs of the intended strategy. It first describes using an n-period return as a…

KoneoppiminenTilastotiedeHistoriatestausRiskienhallinta
BigQuant

This summary of a securities research report examines alternative data, focusing on company job postings. It describes a hiring-count factor tested over 2017–2021, reporting that preprocessing and filtering job listings improved its backtest performance. The…

OsakkeetFaktoripohjainen sijoittaminenTapahtumapohjainen kaupankäyntiHistoriatestaus
BigQuant

The article compares two Chinese equity approaches built around market capitalization. The traditional strategy selects the smallest-cap stocks for a portfolio. The AI version uses a StockRanker ranking model with market capitalization as its sole feature.…

OsakkeetFaktoripohjainen sijoittaminenKoneoppiminenHistoriatestaus
BigQuant

This report forecasts additions to and removals from the CSI 300, CSI 500, and SSE 50 indexes. It uses index construction rules together with stock trading and financial data to estimate which securities may enter or leave at a scheduled review. The document…

OsakkeetKiinan markkinatTapahtumapohjainen kaupankäyntiTilastotiede
BigQuant

This research note outlines a machine-learning framework for timing equity factors. It discusses candidate signals such as valuation spreads between factors and correlations within factor pairs, then examines how those measures relate to subsequent factor…

OsakkeetFaktoripohjainen sijoittaminenKoneoppiminenHistoriatestaus
BigQuant

This article describes three sources of perceived information advantages for quantitative funds: alternative data such as satellite imagery, online sales and shipping activity; automated monitoring of retail investor discussion and sentiment; and rapid…

OsakkeetKoneoppiminenMarkkinatunnelmaToimeksiantojen toteutus
BigQuant

This research review presents a model in which active fund managers trade more when they see greater profit opportunities, then examines whether turnover predicts later benchmark-adjusted performance. Evidence from a historical sample of US active equity…

OsakkeetTilastotiedeMarkkinoiden mikrorakenneMarkkinatunnelma
BigQuant

This support note explains why entering an index symbol in a visual strategy builder’s code-list module does not select the index constituents. The module expects individual stock codes, or can be left empty to include all A-share stocks. As a result, using…

Kiinan markkinatOsakkeetHistoriatestaus
BigQuant

This post outlines a beginner workflow for developing a rule-based stock selection strategy on a quantitative research platform. It describes combining five selection rules, mainly aimed at finding stocks that have fallen and begun to stabilize, with some…

OsakkeetPalautuminen keskiarvoonHistoriatestausPosition koon määrittäminen
BigQuant

The article explains the Deep Momentum Network (DMN) approach to multi-asset trend following. Rather than predict returns and determine portfolio weights separately, the model outputs each asset’s next-period position, bounded between long and short…

FutuuritTrendin seuraaminenKoneoppiminenSalkun muodostaminen
BigQuant

This research summary explains feature selection as a way to reduce a model’s input set before training. It outlines filter, wrapper, and embedded approaches, with filter criteria including variance or entropy, statistical measures such as F-scores and…

Kiinan markkinatOsakkeetKoneoppiminenFaktoripohjainen sijoittaminen
BigQuant

This research summary compares a basic reversal factor based on roughly one month of returns with volume-weighted high-frequency and structured reversal factors. The structured version incorporates both momentum and reversal behavior in high-frequency data.…

OsakkeetPalautuminen keskiarvoonHintamomentumFaktoripohjainen sijoittaminen
BigQuant

The article argues that two features of China’s A-share market may hinder a durable, gradual bull market: quantitative trading and what it calls excessive corporate financing. It attributes the impact of quantitative strategies to their technological and…

Kiinan markkinatOsakkeetMarkkinoiden mikrorakenneMarkkinatunnelma