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Kennisbibliotheek

Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.

Quant Q&A
20,364 documenten
SuperMind
12,226 documenten
OKX Learn
8,431 documenten
Strategy library
7,910 documenten
MQL5 code base
7,090 documenten
BigQuant
3,481 documenten
Bitget Academy
3,298 documenten
MQL5 articles
3,012 documenten
TradingView scripts
1,976 documenten
ProRealCode
1,507 documenten
Deribit Insights
1,232 documenten
Machine Learning for Trading
1,124 documenten
arXiv papers
1,033 documenten
Amberdata research
766 documenten
FMZ forum
682 documenten
FMZ digest
662 documenten
vn.py community
560 documenten
QuantInsti blog
511 documenten
Galaxy Research
340 documenten
QuantStart
246 documenten
Stratmill research code
219 documenten
Robot Wealth
195 documenten
NautilusTrader
191 documenten
Hummingbot docs
181 documenten
Paradigm research
175 documenten
Lumibot
164 documenten
Kraken Learn
163 documenten
Bibliotheek quantcursussen
157 documenten
OctoBot
152 documenten
Cryptohopper blog
144 documenten
Systematic trading blog (Rob Carver)
132 documenten
Qlib
116 documenten
TqSdk
86 documenten
Quantpedia
86 documenten
Hyperliquid docs
79 documenten
Freqtrade
68 documenten
Hudson & Thames
62 documenten
Awesome Systematic Trading
61 documenten
backtrader
54 documenten
vn.py
50 documenten
Binance API docs
45 documenten
Quantopian-colleges
45 documenten
FMZ guides
38 documenten
pysystemtrade
34 documenten
Freqtrade docs
32 documenten
quant-trading
31 documenten
FinRL
28 documenten
Zipline
22 documenten
FMZ live strategies
21 documenten
Jesse
17 documenten
pyfolio
16 documenten
Alphalens
14 documenten
WonderTrader
14 documenten
backtesting.py
11 documenten
Technical Analysis
9 documenten
QTPyLib
8 documenten
QuantRocket
7 documenten
Lumibot strategies
7 documenten
Awesome Quant
1 documenten

Doorzoek de bibliotheek

16 documenten

pyfolio

The example shows how to use Pyfolio to create a returns tear sheet for a single stock. It retrieves daily returns for Facebook through a Pyfolio utility, then passes that return series to a tear-sheet function with a live-start date. The stated output is a…

AandelenStatistiekBacktesten
pyfolio

The document explains a MetaTrader 5 indicator that marks hammer, inverted hammer, and color variants on price charts. It identifies patterns by measuring candle bodies and wick proportions, then places a colored arrow near the candle’s high or low to flag a…

Technische indicatorenVolatiliteit
pyfolio

This code provides several ways to assess how a backtested equity portfolio might interact with market liquidity. It aggregates executed shares by ticker and day, compares those totals with daily bar volume, and identifies each name’s largest observed share…

AandelenRisicobeheerOrderuitvoeringBacktesten
pyfolio

This tutorial explains how to assess strategy performance by examining completed round-trip trades: positions opened and later wholly or partly closed. It argues that trade-level frequency, duration, and profitability can reveal whether results came from…

BacktestenStatistiekPortefeuilleconstructie
pyfolio

These release notes describe additions to pyfolio, a toolkit for evaluating trading portfolios. New analyses include performance attribution to common factors, factor and sector risk exposures, rolling volatility, capacity, bootstrap uncertainty in…

PortefeuilleconstructieRisicobeheerStatistiekBacktesten
pyfolio

The document describes a reporting workflow for analyzing a trading strategy from return data and, when available, holdings, transactions, benchmark returns, market data, and factor information. Its full report brings together return and event analysis, then…

BacktestenRisicobeheerPortefeuilleconstructieOrderuitvoering
pyfolio

This utility module prepares trading results for performance analysis. It extracts returns, positions, and transactions from a backtest, normalizes dates, and converts positions into a format suitable for reporting. It also includes display helpers,…

BacktestenStatistiek
pyfolio

Pyfolio is presented as a Python library for analyzing the performance and risk of financial portfolios, with compatibility for the Zipline backtesting library. Its central reporting tool is a tear sheet: a collection of plots intended to give a broad view…

PortefeuilleconstructieRisicobeheerBacktestenStatistiek
pyfolio

This notebook demonstrates a pyfolio workflow for examining one stock’s returns against the canonical Fama–French factors. It first plots rolling factor betas directly from the stock return series, then calculates those betas for use as benchmark returns in…

AandelenFactorbeleggenStatistiekBacktesten
pyfolio

This Python utility collection summarizes portfolio positions over time. It converts position values into allocations, identifies the largest long, short, and absolute positions, and calculates maximum and median long and short concentrations. A separate…

PortefeuilleconstructieRisicobeheerBacktesten
pyfolio

The document describes a trade-analysis method that turns a stream of transactions into completed round trips. It first combines nearby transactions in the same direction, using volume-weighted average prices, then matches opposing quantities in FIFO order…

StatistiekBacktestenRisicobeheerPositiegrootte
pyfolio

This document describes a portfolio analysis workflow that attributes a return series to selected risk factors. It combines daily returns, holdings, factor returns, and security-level factor loadings, converting dollar positions to portfolio weights and…

FactorbeleggenPortefeuilleconstructieRisicobeheerStatistiek
pyfolio

This Python module documents time-series analytics for evaluating investment returns. It wraps metrics such as drawdown, annualized return and volatility, Calmar, Omega, Sortino, Sharpe, alpha, and beta, along with turnover-related utilities. Several risk…

RisicobeheerStatistiekBacktesten
pyfolio

This tutorial explains how to use Pyfolio’s transaction tear sheet to examine how strategy performance changes under different slippage assumptions. It describes the `slippage` argument to `create_full_tear_sheet`: a specified basis-point penalty is applied…

BacktestenOrderuitvoeringRisicobeheerStatistiek
pyfolio

This review summarizes three studies on stop-loss rules. The first applies a 10% loss threshold to broad U.S. equity exposure, shifting proceeds into long-term government bonds until the market recovers. The second compares fixed and trailing stops with…

AandelenMomentumRisicobeheerBacktesten
pyfolio

This document provides a predefined catalog of date ranges associated with notable market events and broader market regimes. The event windows include the dot-com period, the September 11 attacks, the global financial crisis, the Flash Crash, Fukushima, the…

BacktestenGebeurtenisgestuurdAmerikaanse markten