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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 event announcement describes a strategy framework that uses macroeconomic timing to adjust a multi-asset portfolio as markets and economic conditions change. The stated approach is to monitor macro indicators for turning points in the economic cycle,…

Vairāku aktīvu tirdzniecībaPortfeļa veidošanaStatistikaRiska pārvaldība
BigQuant

This introductory article explains Python’s built-in data types, distinguishing ordered sequences such as lists, tuples, and strings from sets and dictionaries. Its main focus is the list: an ordered, resizable collection that can contain objects of…

Statistika
BigQuant

This report summary presents a strategic allocation framework that shifts the budgeting unit from asset classes to macroeconomic drivers, aiming to reduce exposure to changing correlations among assets. Principal component analysis is used to derive five…

Vairāku aktīvu tirdzniecībaFaktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldība
BigQuant

This brief strategy example uses two simple moving averages of closing prices to generate entry and exit signals. It buys when the five-day average rises above the ten-day average, placing the order at the following session’s open. After entering, it sells…

AkcijasSekošana tendenceiTehniskie indikatoriRīkojumu izpilde
BigQuant

The discussion considers how to evaluate a Chinese stock-market tactic that buys during the session as a stock approaches its first daily price-limit rise, then sells on the following day. Because the entry decision happens intraday, it suggests using…

AkcijasĶīnas tirgiVēsturisko datu pārbaudeCenas izrāviens
BigQuant

The document reports a factor built from the standardized ratio of closing price to its 20-period average, divided by price-to-book, then normalized by its 60-period standard deviation. It gives an information coefficient of -0.02 and reports cumulative…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This research replication constructs a Chinese equity factor called “Flowers in the Forest” by calculating three component factors and combining them with equal weights. The source describes deriving the daily factor from high-frequency data, a…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaStatistika
BigQuant

The document introduces volume-synchronized probability of informed trading (VPIN) as a practical way to assess order flow toxicity. It frames toxicity as informed traders’ ability to trade against liquidity providers, potentially causing market makers to…

Augstas frekvences tirdzniecībaTirgus mikrostruktūraRiska pārvaldībaStatistika
BigQuant

This short description covers an indicator that monitors candlesticks for breaks through a dot-dash channel. When a break occurs, it can issue an alert and send email or push notifications. The accompanying captions refer to a channel break on the first bar…

Tehniskie indikatoriCenas izrāviensRīkojumu izpilde
BigQuant

The document lists fitness measures for a genetic programming module that evolves stock factors. It describes ICIR as the mean information coefficient divided by its standard deviation, with the information coefficient measuring the relationship between…

AkcijasFaktoru ieguldīšanaMašīnmācīšanāsStatistika
BigQuant

This tutorial describes generating synthetic multivariate financial time series with SDV’s PAR synthesizer, a conditional autoregressive GAN approach. It fits sequences using asset identifiers and stable context fields, with returns and volume as modeled…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaudeAkcijas
BigQuant

This report tracks three China-focused active equity quant portfolios against an active mutual fund benchmark: an enhancement portfolio built from selected fund holdings, a portfolio that filters earnings-surprise stocks, and an enhancement portfolio based…

AkcijasĶīnas tirgiFaktoru ieguldīšanaUz notikumiem balstīta tirdzniecība
BigQuant

This study examines whether unusual changes in industry returns can help anticipate moves in the CSI 300 and CSI 500. It treats extreme long-short industry returns and subsequent trend breakouts as signals, comparing monthly and weekly observations across…

AkcijasĶīnas tirgiCenas izrāviensCenas impulss
BigQuant

This brief Chinese-language support thread concerns a rolling-training error reported as “NaTType does not support.” A response attributes the failure to a module whose output lacks a date column, and says the rolling-training process needs that field. The…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

This study summary describes a low-risk portfolio approach for South Korean equities. It forecasts KOSPI 200 constituent volatility with Gaussian process regression, support vector regression, an artificial neural network, and GARCH, then ranks stocks into…

AkcijasMašīnmācīšanāsSvārstīgumsPortfeļa veidošana
BigQuant

This brief summary introduces Markowitz modern portfolio theory as a framework for choosing asset weights by balancing expected return and risk. It describes mean and variance as ways to represent asset return characteristics and investor preferences, then…

Portfeļa veidošanaStatistikaRiska pārvaldība
BigQuant

This brief platform support exchange answers whether BigQuant backtest performance reflects commissions and stamp tax. The response says the displayed results include these trading costs, and that their settings can be adjusted during strategy…

Vēsturisko datu pārbaudeRīkojumu izpildeĶīnas tirgi
BigQuant

This competition update reports diagnostics for the highest-ranked factors and tracks a CSI 1000 index enhancement portfolio. Factor descriptions are anonymized: the report gives average style exposures estimated through daily multivariate regressions and…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This note describes a stock-selection screen combining daily price amplitude above one percent, current volume above ten thousand lots, a higher open, and prior-day turnover above eight percent. It interprets amplitude and volume as signs of active trading,…

Ķīnas tirgiAkcijasSvārstīgumsTehniskie indikatori
BigQuant

This discussion post argues that a quantitative trading operation may need to work across high, medium, and low frequencies. It characterizes high-frequency trading as a field with relatively clear technical aims, including reducing latency and improving…

Augstas frekvences tirdzniecībaPortfeļa veidošanaRīkojumu izpilde
BigQuant

This Chinese-language overview argues that entry and exit timing within a short holding period can affect results, even when a broad market is flat or rising. It contrasts a common schedule of buying at the open and selling at the following close with the…

AkcijasRīkojumu izpildeStatistikaVēsturisko datu pārbaude
BigQuant

The document describes a stock-price forecasting study that compares a Kalman filter, XGBoost, and a hybrid that uses XGBoost to select features before passing them to a Kalman filter. Inputs include price and volume data, accounting measures, and quarterly…

AkcijasMašīnmācīšanāsStatistikaTirgus noskaņojums
BigQuant

This brief answer addresses how to keep multiple StockRanker models available for reuse. It recommends following a separate guide’s approach to load the models, then placing them together in a dictionary for storage. The intended benefit is faster future…

Mašīnmācīšanās
BigQuant

This article introduces dense neural networks and backpropagation, then outlines an A-share stock selection experiment. A DNN uses connected layers of neurons; training sends predictions forward and propagates errors backward to update weights. The example…

Ķīnas tirgiAkcijasMašīnmācīšanāsFaktoru ieguldīšana