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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: 79,386

SuperMind

This Chinese stock selection strategy combines four filters: daily high-to-low amplitude above 1%, a robot-related industry concept, circulating market capitalization below 10 billion yuan, and appearance on the previous day’s trading activity list. It also…

AkcijasĶīnas tirgiSvārstīgumsUz notikumiem balstīta tirdzniecība
MQL5 code base

This document describes an expert advisor that trades signals from the MAOscillatorHist indicator. It enters a trade when the indicator histogram changes direction, with the signal evaluated at the close of a bar. The advisor depends on a compiled indicator…

Valūtu tirgusTehniskie indikatoriVēsturisko datu pārbaude
WonderTrader

This example implements a tick-driven futures or securities strategy using the wtpy framework. It estimates a theoretical price from the best bid and ask, weighted by the quantities resting on the opposite sides of the book, then compares that estimate with…

Augstas frekvences tirdzniecībaTirgus mikrostruktūraRīkojumu izpildeRiska pārvaldība
SuperMind

This Chinese-language post describes a short-term stock selection rule combining three filters: RSI below 65, share price below 12 yuan, and more than two limit-up sessions within the prior ten days. It frames the screen as a way to find relatively…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
Kraken Learn

The document introduces tokenized equities as blockchain-based tokens representing traditional shares, exchange-traded funds, or other investments. It describes fractional ownership and broader geographic access as potential benefits, and says smart…

AkcijasKriptoaktīviVairāku aktīvu tirdzniecība
BigQuant

The article argues that trading researchers should judge machine learning papers by simplicity, reproducibility, and generality, alongside reported empirical results. A method that performs well on a benchmark may not transfer to financial prediction, and a…

MašīnmācīšanāsStatistikaVēsturisko datu pārbaudeFaktoru ieguldīšana
SuperMind

This overview argues that no programming language is best for every algorithmic trading system. The choice depends on strategy frequency and volume, data needs, performance targets, development and maintenance costs, and the functions the system must…

Rīkojumu izpildeAugstas frekvences tirdzniecībaVēsturisko datu pārbaudePortfeļa veidošana
MQL5 code base

This indicator marks a candle’s midpoint and colors the marker according to whether the candle is bullish or bearish. It offers two midpoint definitions: the center of the full candle range, calculated from the high and low, or the center of the candle body,…

Tehniskie indikatoriAkcijas
BigQuant

This study compares ARIMA, a multilayer perceptron, an LSTM, and an ARIMA-GARCH combination for next-day NVIDIA share-price forecasts. It describes ARIMA as a model for linear time-series structure, neural networks as ways to capture nonlinear patterns, and…

AkcijasMašīnmācīšanāsStatistikaSvārstīgums
ProRealCode

The N Reversal Detector is a configurable chart indicator that combines reversal patterns with an optional trend filter. Users can define the prevailing trend with a fast and slow moving-average comparison, an ATR-based Supertrend, or Donchian Channel highs…

Tehniskie indikatoriSekošana tendenceiRiska pārvaldība
SuperMind

This A-share stock screen combines a 14-period RSI below 65 with an external-to-internal trading volume ratio above 1.3 and positive net buying by major participants during the opening auction. The page also gives example implementations and adds filters in…

AkcijasĶīnas tirgiTehniskie indikatoriTirgus noskaņojums
MQL5 code base

Corrected JMA is described as a variation on the Jurik Moving Average. It uses the JMA as a reference for measuring price deviations, then flattens the indicator’s value when those deviations are judged insignificant. This is intended to make directional…

Tehniskie indikatoriSekošana tendencei
BigQuant

This interview discusses how artificial intelligence, large datasets, and cloud computing support one another: AI methods analyze data, while cloud infrastructure supplies scalable storage and computing. It maps the data industry across collection, storage,…

AkcijasĶīnas tirgiMašīnmācīšanāsPortfeļa veidošana
MQL5 code base

EMAPredictive3 is described as a moving-average indicator built from the exponential moving average algorithm. Its defining construction is a linear interpolation between two EMAs calculated over different periods. The document attributes the indicator to…

Tehniskie indikatoriAkcijas
SuperMind

This Chinese equities screening idea combines several filters: exclude stocks marked ST, require an amplitude above one, apply a five-session closing-price high condition, and select stocks whose intraday low is down within a narrow four-to-five percent band…

AkcijasĶīnas tirgiAtgriešanās pie vidējās vērtībasVēsturisko datu pārbaude
SuperMind

This post describes a Chinese equity screen for stocks associated with the metaverse theme, with a close above the previous day’s low and a code beginning with 60. It combines a thematic classification, a simple price condition, and an exchange-listing code…

Ķīnas tirgiAkcijasTehniskie indikatoriPozīcijas apjoma noteikšana
SuperMind

This post outlines an equity screen requiring MACD above zero, upward divergence among current moving averages, and a positive price-to-earnings ratio. The stated rationale is to combine a bullish technical condition with rising price trends and a basic…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
SuperMind

This post describes a Chinese equity screen combining a daily high-to-low amplitude threshold, a minimum circulating market capitalization, and a positive weekly MACD histogram. It presents the conditions as a way to focus on larger listed companies with…

Ķīnas tirgiAkcijasTehniskie indikatoriCenas impulss
BigQuant

The report proposes improving traditional liquidity factors for Chinese A-share stock selection by incorporating information about intraday trading structure. It frames the approach as a reusable factor research method: identify market information omitted by…

Ķīnas tirgiAkcijasFaktoru ieguldīšanaTirgus mikrostruktūra
Lumibot

This report presents a short backtest of a strategy labeled “momentum-news-generic” against SPY, using Yahoo data. Over the stated period, the strategy had a slightly negative total return, negative annualized return, negative Sharpe and Sortino ratios, and…

AkcijasCenas impulssTirgus noskaņojumsVēsturisko datu pārbaude
vn.py

This guide explains how to construct, monitor, and trade synthetic spreads in the VeighNa SpreadTrading module. A spread can combine several contract legs using a formula, including pricing legs that are not traded, which supports relationships involving…

Nākotnes līgumiIzejvielasArbitrāžaPāru tirdzniecība
SuperMind

This Chinese-language strategy post outlines a mainland China equity screen combining capital strength, upward divergence of moving averages, and a profitability filter. The initial title specifies companies with market capitalization below 10 billion yuan…

Ķīnas tirgiAkcijasCenas impulssTehniskie indikatori
MQL5 code base

The Inverse Distance Weighted Moving Average (IDWMA) is presented as a price-smoothing indicator. Its calculation is described qualitatively: the line initially moves away from the average price, then approaches the price. The document says it is somewhat…

Tehniskie indikatori