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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
Lumibot strategies
Dokumentu skaits: 7
QuantRocket
Dokumentu skaits: 7
Awesome Quant
Dokumentu skaits: 1

Meklēt bibliotēkā

Dokumentu skaits: 3,481

BigQuant

This short platform discussion presents a manual construction of the KDJ oscillator alongside built-in KDJ functions. It first derives the raw stochastic value from the close’s position within the rolling ten-period high-low range. It then applies smoothing…

Tehniskie indikatoriAkcijasStatistika
BigQuant

This overview surveys practical considerations for AI and quantitative trading, from data quality and model choice to backtesting, risk controls, and portfolio allocation. It highlights that historical results can mislead when models are overfit or…

MašīnmācīšanāsStatistikaRiska pārvaldībaVēsturisko datu pārbaude
BigQuant

This course-lecture summary introduces the role of planning and learned models in reinforcement learning. It identifies Dyna and Monte Carlo tree search as examples of algorithms that use models, and notes that the lecture is presented by research engineer…

MašīnmācīšanāsStatistika
BigQuant

This research note describes an earnings-expectations approach to positioning in Sanhuan Group, a Chinese electronics company. It constructs an “earnings overpricing” index within a fixed price-to-earnings valuation framework and uses the measure to allocate…

AkcijasĶīnas tirgiAtgriešanās pie vidējās vērtībasVēsturisko datu pārbaude
BigQuant

This research summary examines whether mutual fund managers’ tendency to follow prior institutional trading can reveal investment skill. It describes a fund-level dynamic herding measure, FH, and tests whether funds with stronger herding subsequently…

AkcijasStatistikaFaktoru ieguldīšanaASV tirgi
BigQuant

This report studies whether changes in leading companies’ expected earnings can help select industries for rotation. Its premise is that revisions to a sector leader’s expected fundamentals may influence subsequent returns across related stocks. The analysis…

AkcijasFaktoru ieguldīšanaCenas impulssPortfeļa veidošana
BigQuant

This report examines whether selecting stocks within industries can improve factor comparisons by reducing structural differences between sectors. It groups stocks using a detailed industry classification, removes recent listings and very small firms whose…

AkcijasFaktoru ieguldīšanaVēsturisko datu pārbaudePortfeļa veidošana
BigQuant

This report examines stocks that mutual funds hold at a larger portfolio weight than their benchmark index. It describes these holdings as tending toward larger, higher-valued companies and certain sectors, and reports that many outperformed during fund…

AkcijasĶīnas tirgiFaktoru ieguldīšanaPortfeļa veidošana
BigQuant

The document explains how a backtest engine turns an investment strategy into simulated trades. It distinguishes strategy logic, such as selecting securities and generating signals, from trade logic, such as deciding position size, rebalance timing, order…

Vēsturisko datu pārbaudeRīkojumu izpildePozīcijas apjoma noteikšanaRiska pārvaldība
BigQuant

This document introduces the cyclically adjusted price-to-earnings ratio, or CAPE, as a way to compare equity prices with a decade of inflation-adjusted earnings. It summarizes historical analyses of the S&P 500 and Shanghai Composite, reporting that higher…

AkcijasAtgriešanās pie vidējās vērtībasStatistikaVēsturisko datu pārbaude
BigQuant

The document explains the cyclically adjusted price-to-earnings ratio (CAPE), calculated as an inflation-adjusted share price divided by the average inflation-adjusted earnings per share over the prior decade. It compares CAPE with future real returns for…

AkcijasĶīnas tirgiASV tirgiAtgriešanās pie vidējās vērtības
BigQuant

This article describes a stock-selection screen based on three conditions: a daily high-low range above a stated threshold, an opening price near the ten-day moving average, and at least five years since listing. The intended interpretation is that elevated…

Ķīnas tirgiAkcijasTehniskie indikatoriSvārstīgums
BigQuant

This page organizes a beginner-oriented quantitative trading curriculum and links to lessons and example strategy projects. The listed foundations include Python, pandas analysis, historical and financial data access, visualization, and DataFrame plotting.…

AkcijasPāru tirdzniecībaTehniskie indikatoriFaktoru ieguldīšana
BigQuant

This report examines whether commodity futures signals and trades should use the most liquid main contract or an actively traded near-month contract. It defines active near-month contracts using liquidity and price sensitivity, then compares contract choices…

IzejvielasNākotnes līgumiCenas impulssPārnese
BigQuant

This short response outlines basic risk controls at both the individual-stock and broad-market levels. For a stock position, it suggests monitoring profit and loss and using risk indicators to trigger an exit, with moving-average crossovers and a multi-day…

AkcijasRiska pārvaldībaCenas impulssTehniskie indikatori
BigQuant

The document examines how factor exposure limits in a multi-factor portfolio optimization model affect active returns and risk. Raising the caps can increase excess return, drawdown, and tracking error, so the change in return relative to the change in risk…

Faktoru ieguldīšanaPortfeļa veidošanaRiska pārvaldībaĶīnas tirgi
BigQuant

This post summarizes a 2017 J.P. Morgan report on using large and alternative datasets in investment research. It groups alternative data into information generated by individuals, business processes, and sensors, with examples such as social media,…

MašīnmācīšanāsStatistikaTirgus noskaņojumsAkcijas
BigQuant

This forum post reports that a strategy based on a platform template runs, but simulation fails at a dropna step because of NaN values after the user adds factors. The author’s attempted fix was to fetch 90 calendar days of earlier daily index data,…

Vēsturisko datu pārbaudeStatistikaAkcijas
BigQuant

The document describes a China equity screening rule for metaverse-related stocks. It combines a rounded or arc-like price pattern with prior auction turnover above 0.26, alongside formula conditions involving price movement around the open, recent up-day…

AkcijasĶīnas tirgiTehniskie indikatori
BigQuant

This brief Chinese-language forum post asks how to identify stocks that break above a price level and then do not fall more than 10%. It suggests describing the breakout as a new 100-day high: compare the current high with the maximum high over the preceding…

AkcijasCenas izrāviensTehniskie indikatori
BigQuant

The post reports a discrepancy between a backtesting platform’s monthly return for the CSI 300 benchmark and an external reference calculation. It attributes the difference to the platform using the first day of the month’s closing price as the starting…

Vēsturisko datu pārbaudeStatistikaPortfeļa veidošana
BigQuant

This note introduces the Awesome Oscillator (AO) and gives its basic input and calculation. It uses median price, defined as the average of the session high and low, then subtracts a longer-period moving average from a shorter-period moving average. The…

Tehniskie indikatori