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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
Quantopian lekcijas
Dokumentu skaits: 45
Binance API docs
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
WonderTrader
Dokumentu skaits: 14
Alphalens
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

The submission contrasts discretionary and quantitative investing. It describes quantitative work as gathering and cleaning high-dimensional data, calculating factors, and using rules to make trades programmatically. It also suggests that machine learning…

MašīnmācīšanāsFaktoru ieguldīšanaRīkojumu izpildeRiska pārvaldība
BigQuant

The article challenges three barriers often thought to exclude individual investors from quantitative trading: advanced mathematics, coding skills, and large capital. It presents quant methods primarily as statistical tools for understanding markets, and…

StatistikaFaktoru ieguldīšanaAkcijasMašīnmācīšanās
BigQuant

The article summarizes research using Forcerank, a platform where participants rank stocks by expected performance over roughly a week. Regressions of consensus ranks on past returns show that participants extrapolate recent performance, with more weight on…

AkcijasTirgus noskaņojumsCenas impulssVēsturisko datu pārbaude
BigQuant

This sector-rotation study uses the absorption ratio to measure how concentrated the risks embedded in a group of asset returns are. The ratio reflects the share of total return variation explained by a small set of orthogonal components: higher readings are…

AkcijasFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This research summary examines whether hedge funds’ exposure to changes in investor sentiment predicts subsequent performance. It estimates each fund’s sentiment beta with a rolling 36-month window, forms equal-weighted portfolios by beta, and uses…

Tirgus noskaņojums
BigQuant

This 2018 report reviews managed futures, including how CTA strategies trade futures and options and how they differ by analysis method, trading style, holding period, and markets covered. It describes systematic and discretionary approaches alongside trend…

Nākotnes līgumiOpcijasSekošana tendenceiArbitrāža
BigQuant

The document describes a high-risk, high-turnover strategy for Chinese stocks under the T+1 trading convention. It looks for strong stocks that pull back and rebound, possible second moves in leading stocks, and sentiment-driven stocks that may reverse after…

AkcijasĶīnas tirgiMašīnmācīšanāsCenas impulss
BigQuant

This article outlines an asset allocation method that combines risk parity with the Black-Litterman framework. Risk parity portfolio weights serve as prior weights, which are used to infer prior expected returns. Short-term momentum views are then…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaCenas impulssRiska pārvaldība
BigQuant

This article explains how to model multi-stock trading as a Markov decision process and train a deep deterministic policy gradient (DDPG) agent with FinRL. The state includes prices, holdings, and cash; actions change holdings through buying, selling, or…

AkcijasĶīnas tirgiMašīnmācīšanāsVēsturisko datu pārbaude
BigQuant

This weekly review summarizes Chinese equity market conditions for the week ending April 3, 2020, then presents Haitong Securities’ market timing, factor, industry rotation, and strategy-performance views. Its evidence includes index returns, valuation and…

AkcijasĶīnas tirgiFaktoru ieguldīšanaSekošana tendencei
BigQuant

This brief BigQuant forum exchange concerns errors in a factor-analysis template when a factor’s coverage falls below the platform’s threshold. One concrete cause is division by zero: the expression using the difference between the high and close prices can…

AkcijasStatistikaFaktoru ieguldīšana
BigQuant

This essay groups purportedly successful stock market participants into six styles: patient traders who wait for rare setups, long-term value investors, specialists focused on one stock, short-term leaders-of-the-hot-sector traders, traders who repeat a…

AkcijasCenas impulssRiska pārvaldībaPozīcijas apjoma noteikšana
BigQuant

The document summarizes a portfolio construction method that translates target macroeconomic factor exposures into asset weights. It considers six factors: equity, real interest rates, credit, inflation, emerging markets, and commodities. A standard linear…

Vairāku aktīvu tirdzniecībaFaktoru ieguldīšanaPortfeļa veidošanaStatistika
BigQuant

This support note explains how to access trade details from a BigQuant backtest. It says the trade detail page does not offer a direct export, and recommends downloading the results for separate analysis. It also points users to the backtest module’s raw…

Vēsturisko datu pārbaudeRīkojumu izpilde
BigQuant

The post compares a hand-built 120-period Commodity Channel Index calculation with the platform’s built-in CCI function and asks why their results differ. It identifies the typical price as the average of the high, low, and close, then describes a formula…

Tehniskie indikatoriStatistika
BigQuant

This BigQuant forum post describes a custom Python module that runs successfully in AIStudio 3.0 but fails when submitted as a scheduled data task. The example reads the latest stored date, loads a local CSV of Bitfinex symbols, requests newer daily market…

KriptoaktīviRīkojumu izpilde
BigQuant

The post reports a BigQuant backtest failure in which the data extraction stage returned zero rows, and the backtest then raised an IndexError while constructing its rebalance schedule. The logs show the sequence from feature processing to an empty extracted…

Vēsturisko datu pārbaudeAkcijas
BigQuant

This brief BigQuant community post reflects on the challenge of improving a strategy’s Sharpe ratio while also controlling drawdowns. The author’s remarks suggest that judging a strategy by Sharpe alone can overlook the difficulty of managing losses. They…

StatistikaRiska pārvaldībaVēsturisko datu pārbaudeAkcijas
BigQuant

This Chinese research summary describes an equity index-enhancement approach using listed companies’ reported revenue by business line. It uses annual and interim report data, then applies Chinese-language keyword extraction and similarity analysis to…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The article presents quantitative investing as a way to replace discretionary buy and sell decisions with signals from systematic models. It attributes common retail timing mistakes to fear, greed, and reactions to market sentiment, and says rules-based…

Vairāku aktīvu tirdzniecībaStatistikaMašīnmācīšanāsRiska pārvaldība
BigQuant

This research summary presents TS-Boost, an equity factor-selection framework designed for financial data with shifting cross-sectional patterns and low signal-to-noise ratios. Instead of pooling observations from different dates into one training set, it…

AkcijasMašīnmācīšanāsFaktoru ieguldīšanaStatistika
BigQuant

This discussion describes a model-stacking problem in a Chinese quantitative research platform. A user has trained and persisted twenty StockRanker models using the same factor set but differing in date ranges, data filters, or prediction horizons. The goal…

AkcijasMašīnmācīšanāsPortfeļa veidošanaĶīnas tirgi
BigQuant

The document describes a daily classification approach for detecting positive momentum, negative momentum, or normal conditions in Bitcoin, Ethereum, and Litecoin. It uses historical prices and technical indicators as features, then compares classifiers…

KriptoaktīviCenas impulssMašīnmācīšanāsTehniskie indikatori