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Zināšanu bibliotēka

Stratmill pētniecības aģenta sagatavoti kopsavilkumi un galvenās atziņas par grāmatām, pētījumiem, rakstiem un kodu, ko lasa mūsu MI aģenti. Katrā lapā ir saite uz oriģinālu.

Quant Q&A
Dokumentu skaits: 20,364
SuperMind
Dokumentu skaits: 12,226
OKX Learn
Dokumentu skaits: 8,431
Strategy library
Dokumentu skaits: 7,910
MQL5 code base
Dokumentu skaits: 7,090
BigQuant
Dokumentu skaits: 3,481
Bitget Academy
Dokumentu skaits: 3,298
MQL5 articles
Dokumentu skaits: 3,012
TradingView scripts
Dokumentu skaits: 1,976
ProRealCode
Dokumentu skaits: 1,507
Deribit Insights
Dokumentu skaits: 1,232
Machine Learning for Trading
Dokumentu skaits: 1,124
arXiv papers
Dokumentu skaits: 1,033
Amberdata research
Dokumentu skaits: 766
FMZ forum
Dokumentu skaits: 682
FMZ digest
Dokumentu skaits: 662
vn.py community
Dokumentu skaits: 560
QuantInsti blog
Dokumentu skaits: 511
Galaxy Research
Dokumentu skaits: 340
QuantStart
Dokumentu skaits: 246
Stratmill research code
Dokumentu skaits: 219
Robot Wealth
Dokumentu skaits: 195
NautilusTrader
Dokumentu skaits: 191
Hummingbot docs
Dokumentu skaits: 181
Paradigm research
Dokumentu skaits: 175
Lumibot
Dokumentu skaits: 164
Kraken Learn
Dokumentu skaits: 163
Kvantitatīvās tirdzniecības kursu bibliotēka
Dokumentu skaits: 157
OctoBot
Dokumentu skaits: 152
Cryptohopper blog
Dokumentu skaits: 144
Systematic trading blog (Rob Carver)
Dokumentu skaits: 132
Qlib
Dokumentu skaits: 116
TqSdk
Dokumentu skaits: 86
Quantpedia
Dokumentu skaits: 86
Hyperliquid docs
Dokumentu skaits: 79
Freqtrade
Dokumentu skaits: 68
Hudson & Thames
Dokumentu skaits: 62
Awesome Systematic Trading
Dokumentu skaits: 61
backtrader
Dokumentu skaits: 54
vn.py
Dokumentu skaits: 50
Binance API docs
Dokumentu skaits: 45
Quantopian lekcijas
Dokumentu skaits: 45
FMZ guides
Dokumentu skaits: 38
pysystemtrade
Dokumentu skaits: 34
Freqtrade docs
Dokumentu skaits: 32
quant-trading
Dokumentu skaits: 31
FinRL
Dokumentu skaits: 28
Zipline
Dokumentu skaits: 22
FMZ live strategies
Dokumentu skaits: 21
Jesse
Dokumentu skaits: 17
pyfolio
Dokumentu skaits: 16
Alphalens
Dokumentu skaits: 14
WonderTrader
Dokumentu skaits: 14
backtesting.py
Dokumentu skaits: 11
Technical Analysis
Dokumentu skaits: 9
QTPyLib
Dokumentu skaits: 8
QuantRocket
Dokumentu skaits: 7
Lumibot strategies
Dokumentu skaits: 7
Awesome Quant
Dokumentu skaits: 1

Meklēt bibliotēkā

Dokumentu skaits: 3,481

BigQuant

This BigQuant forum entry addresses an error caused by referencing a locally created stock list as though it were an attribute of the backtest context. Its central correction is to use the variable where it was defined, without the context prefix, unless the…

AkcijasĶīnas tirgiTehniskie indikatoriVēsturisko datu pārbaude
BigQuant

This study outlines an experiment applying a Transformer model to short-horizon stock selection in China’s A-share market. Its target is each stock’s return over the next five trading days, using daily market data from 2015 through 2021. The inputs begin…

AkcijasĶīnas tirgiMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This Chinese-language excerpt summarizes a report about automatically generating trading strategies from time-series data. It frames conventional factor research, factor combinations, and rule combinations as labor-intensive manual work, then proposes using…

StatistikaFaktoru ieguldīšanaRiska pārvaldība
BigQuant

This document summarizes a research paper on factor timing, connecting it to market timing and factor investing. Its theoretical starting point is that an optimal portfolio corresponds to the stochastic discount factor (SDF). Under a conditional factor model…

Faktoru ieguldīšanaPortfeļa veidošanaStatistika
BigQuant

The report summary explains a method for finding monthly indicators that may signal earnings changes in cyclical industries before standard financial statements are published. It covers real estate, autos, transport, coal, nonferrous metals, steel, building…

AkcijasĶīnas tirgiStatistikaFaktoru ieguldīšana
BigQuant

This opinion article argues that traders should give more weight to market direction, entry timing, and risk discipline than to technical indicators alone. It warns against treating a falling price as automatically cheap, and favors aligning trades with…

AkcijasSekošana tendenceiRiska pārvaldībaCenas impulss
BigQuant

This post documents an attempted BigQuant workflow for combining daily Chinese stock bars with prefactor and money-flow tables. The query joins records by date and instrument, selects price and valuation fields, calculates returns, and applies a non-null…

Ķīnas tirgiAkcijasStatistika
BigQuant

This research summary treats intraday stock prices as time series rather than isolated observations. It applies empirical mode decomposition (EMD) to separate trend-like price signals from noise, then uses their relative strength to form a signal-to-noise…

AkcijasFaktoru ieguldīšanaAtgriešanās pie vidējās vērtībasStatistika
BigQuant

This weekly report reviews style-factor signals across several Chinese equity universes and summarizes recent quantitative fund performance and market conditions. It reports that price-and-volume factors, especially beta, were relatively strong in the week…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaCenas impulss
BigQuant

The discussion describes a proposed rolling evaluation in which training and testing periods are kept separate. Its example uses successive annual training windows and test windows offset into later years, with each test period’s results concatenated to form…

Vēsturisko datu pārbaudeStatistikaMašīnmācīšanās
BigQuant

The question asks how to limit the capital assigned to any one stock to 20% of the portfolio. The response refers to a calculated weight for a stock and places an order targeting a percentage of portfolio value. This points to a useful portfolio-construction…

AkcijasPozīcijas apjoma noteikšanaPortfeļa veidošana
BigQuant

This brief Chinese-language support note addresses why a filter applied directly in a DataSource may not work as expected. It recommends using a dedicated data-filter module, filtering the resulting dataframe with boolean conditions, or passing a query…

StatistikaAkcijas
BigQuant

The article questions whether asset pricing and investment can be understood only by predicting returns from company characteristics. It uses the example of rose prices on Valentine’s Day: prices rise when demand is concentrated and sellers are scarce, then…

Vairāku aktīvu tirdzniecībaTirgus mikrostruktūraStatistika
BigQuant

The example builds a single-stock daily strategy from trend and momentum conditions. Entry requires the 21-period simple moving average to exceed the 60-period average, price to be above both averages, and daily and weekly MACD histograms to be positive. It…

AkcijasSekošana tendenceiCenas impulssTehniskie indikatori
BigQuant

This article offers a checklist for choosing strategies from a trading community. It first asks investors to set their capital, acceptable drawdown, and return expectations, then distinguishes conservative from aggressive choices. The conservative profile…

AkcijasRiska pārvaldībaVēsturisko datu pārbaudePortfeļa veidošana
BigQuant

This report studies whether stocks that suffer sustained, sharp declines later rebound, and how to identify such episodes. It uses rolling-window local extrema with a 15-period parameter to mark turning points, then applies pattern conditions to select 2,048…

Ķīnas tirgiAkcijasAtgriešanās pie vidējās vērtībasVēsturisko datu pārbaude
BigQuant

This literature summary describes applying classification and regression trees (CART) to cross-sectional stock selection. The motivating advantage is that a tree can represent nonlinear relationships and interactions among variables, which linear models or…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaStatistika
BigQuant

This weekly Chinese equity research note tracks index-enhancement portfolios benchmarked to the CSI 300, CSI 500, and CSI 1000, and monitors single-factor portfolios within those indices and a public-fund-heavy-stock universe. Its portfolio construction is…

AkcijasFaktoru ieguldīšanaCenas impulssPortfeļa veidošana
BigQuant

This article outlines an approach to improving a random forest stock-selection model through factor screening and parameter experimentation. It first limits the stock universe by excluding firms under special treatment and delisted stocks. Candidate…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaStatistika
BigQuant

This document summarizes experiments with a convolutional neural network (CNN) trading model called Deep Alpha. It reports that a seven-layer version performed better than a two-layer version in a same-period comparison, which the authors attribute to…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

This brief response addresses preprocessing for deep-learning models applied to stock data, including Boolean values and infinite observations. Its direct recommendation for infinities is to remove them. For broader feature preparation, it lists outlier…

MašīnmācīšanāsAkcijasStatistika
BigQuant

This report compares AdaBoost, gradient boosting decision trees, and XGBoost as tools for selecting Chinese equities from factor data. Its workflow covers feature and label preparation, preprocessing, in-sample fitting, cross-validation, and out-of-sample…

MašīnmācīšanāsAkcijasFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

This note shows how to construct a simple stock relative-return factor using a precomputed Chinese equity dataset. It identifies one field as each stock’s daily return and another as the CSI 300 index return, then defines relative return as the stock return…

AkcijasĶīnas tirgiFaktoru ieguldīšanaStatistika
BigQuant

This troubleshooting note addresses errors that arise after adding a rolling-training module to an AI strategy for convertible bonds. It points to a revised notebook and identifies two implementation changes associated with the fix. First, the rolling…

MašīnmācīšanāsVēsturisko datu pārbaudeFiksēta ienākuma instrumentiĶīnas tirgi