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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
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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
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QuantInsti blog
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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
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Jesse
Dokumentu skaits: 17
pyfolio
Dokumentu skaits: 16
Alphalens
Dokumentu skaits: 14
WonderTrader
Dokumentu skaits: 14
backtesting.py
Dokumentu skaits: 11
Technical Analysis
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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 document reviews research on whether crowded mutual fund ownership predicts subsequent stock returns. It defines stock-level crowding as active mutual fund ownership divided by average share turnover, using lagged holdings and turnover data. Stocks are…

AkcijasFaktoru ieguldīšanaStatistikaVēsturisko datu pārbaude
BigQuant

This document describes the first BigQuant strategy development interest group, scheduled for May through July 2024. Its purpose is to help learners build strategy templates and demonstrations, reproduce common methods, and improve their implementation…

AkcijasNākotnes līgumiOpcijasMašīnmācīšanās
BigQuant

This page presents a beginner-oriented quantitative trading curriculum for readers who may lack finance background. It points to introductory material on Python, Pandas, historical market data, financial data handling, and data visualization. It also lists…

AkcijasPāru tirdzniecībaFaktoru ieguldīšanaTehniskie indikatori
BigQuant

This research summary tests whether industry returns left after removing common style exposures can help rank sectors for rotation. It reports a strong difference between markets: an alpha based on the Fama–French three-factor model works for US industries,…

AkcijasĶīnas tirgiASV tirgiFaktoru ieguldīšana
BigQuant

This tutorial explains how convolutional neural networks can improve on a simpler model for classifying MNIST handwritten digits. It reports accuracy of about 99.2% for the CNN, compared with 91% for the earlier model, but gives no experimental setup or…

MašīnmācīšanāsStatistika
BigQuant

The article describes a staged approach for managing a losing stock position. It advises against repeatedly buying during a decline and suggests waiting for a pause in falling prices alongside an expansion in volume before adding. After a rebound, it…

AkcijasAtgriešanās pie vidējās vērtībasTehniskie indikatoriRiska pārvaldība
BigQuant

The document proposes a cross-sectional trading factor that combines price location, order book imbalance, trading activity, and volatility. Its price component is the percentage difference between VWAP and the midpoint, intended to reflect whether…

AkcijasFaktoru ieguldīšanaTirgus mikrostruktūraTehniskie indikatori
BigQuant

This semiautomatic Expert Advisor opens a trade when price crosses a chart line. It supports trendlines, angle-based trendlines, vertical lines, and horizontal lines, with options to follow the line’s stated direction or restrict entries to buys or sells.…

Tehniskie indikatoriRīkojumu izpildePozīcijas apjoma noteikšanaRiska pārvaldība
BigQuant

This research describes a systematic trend-following strategy for commodity futures. It first uses MACD crossings, filtered by cumulative distance relative to ATR, to classify rising and falling trends. It then revises those classifications when price…

Nākotnes līgumiIzejvielasSekošana tendenceiTehniskie indikatori
BigQuant

The article proposes a discretionary method for managing a losing stock position. It cautions against adding shares during an ongoing decline and instead waits for apparent stabilization near a prior low, including sideways action and increased volume. After…

Ķīnas tirgiAkcijasAtgriešanās pie vidējās vērtībasRiska pārvaldība
BigQuant

This overview introduces artificial intelligence as the design of systems that make decisions or predictions, including methods such as machine learning and probabilistic reasoning. It explains why these methods can help with complex tasks involving many…

MašīnmācīšanāsAkcijasFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This research summary reports tests of alternative and growth factors for selecting Hong Kong stocks. Of 17 alternative factors examined, most showed relatively weak selection effects; realized return kurtosis over 240 days and the ratio of recent to…

AkcijasFaktoru ieguldīšanaStatistikaĶīnas tirgi
BigQuant

This research summary adapts the Merrill investment clock to Chinese A-shares by grouping sectors according to their economic-cycle behavior and proposed roles across recovery, overheating, stagflation, and recession. It describes two index families:…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

This 2018 research summary assesses the outlook for Chinese equities in 2019 using earnings, credit growth, producer prices, retail sales, and valuation. It argues that slower growth and gradual valuation improvement limit broad market upside, and advises…

Ķīnas tirgiAkcijasStatistikaRiska pārvaldība
BigQuant

This article recounts episodes in China’s A-share market where stock prices were said to react to unrelated public events, celebrity news, wordplay, ticker numbers, entertainment, or product associations. Examples include stocks linked by similar names to…

AkcijasĶīnas tirgiTirgus noskaņojumsUz notikumiem balstīta tirdzniecība
BigQuant

The document defines a price-based momentum factor as the 44-day average closing price divided by the current close, minus one. It compares two ways to analyze the factor: using a platform expression engine and supplying custom data. The stated test covers…

AkcijasCenas impulssFaktoru ieguldīšanaVēsturisko datu pārbaude
BigQuant

The report summary describes a two-stage stock selection process intended to enhance a fundamental factor model. First, the fundamental model creates an initial stock universe; a deep learning model then screens that set again. The stated rationale is to…

AkcijasFaktoru ieguldīšanaMašīnmācīšanāsĶīnas tirgi
BigQuant

This post raises questions about the usefulness and timing of Chinese stock opening-auction data. It proposes deriving unmatched order volume by subtracting cancellation and matched-trade volume from total submitted volume, then estimating an unmatched…

AkcijasĶīnas tirgiTirgus mikrostruktūraStatistika
BigQuant

This support note explains an apparent mismatch between the number of shares bought and sold in a stock backtest. A user observed buying 300 shares of China Yangtze Power and later selling 310, and asked how to correct the discrepancy. The reply attributes…

AkcijasVēsturisko datu pārbaude
BigQuant

The discussion addresses a question about why a strategy cannot retrieve current factor values. The reply suggests the issue may be that some stocks or dates produce missing values, rather than that factor retrieval has failed altogether. It recommends…

Faktoru ieguldīšanaStatistika
BigQuant

This forum post investigates a simulated-trading run that fails while rendering performance charts with BigTrader v9. The author suspects that the newer module cannot draw results when the run covers only one day, unlike an earlier backtesting module.…

Vēsturisko datu pārbaudeStatistika
BigQuant

This page summarizes a research paper on using intraday price and volume data to build enhancement signals. Its outline moves from converting high-frequency observations into lower-frequency features, to using genetic programming to discover price-volume…

AkcijasTehniskie indikatoriMašīnmācīšanāsFaktoru ieguldīšana
BigQuant

This introductory guide describes a supervised machine-learning workflow for quantitative equity strategies. It begins by defining the stock universe and target, such as a future return, volatility, or ranking, then splitting historical observations by time…

AkcijasMašīnmācīšanāsStatistikaVēsturisko datu pārbaude
BigQuant

This forum question compares portfolio holding ratios under two execution schedules: buying with one TWAP setting and selling with another, versus buying at the open and selling at the close. It provides a trading function for a ranked stock strategy that…

AkcijasMašīnmācīšanāsPozīcijas apjoma noteikšanaRīkojumu izpilde