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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

3,481 documenten

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,…

Multi-assetPortefeuilleconstructieStatistiekRisicobeheer
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…

Statistiek
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…

Multi-assetFactorbeleggenPortefeuilleconstructieRisicobeheer
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…

AandelenTrendvolgendTechnische indicatorenOrderuitvoering
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…

AandelenChinese marktenBacktestenUitbraak
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…

AandelenFactorbeleggenStatistiekBacktesten
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…

Chinese marktenAandelenFactorbeleggenStatistiek
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…

Hoogfrequente handelMarktmicrostructuurRisicobeheerStatistiek
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…

Technische indicatorenUitbraakOrderuitvoering
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…

AandelenFactorbeleggenMachine learningStatistiek
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…

Machine learningStatistiekBacktestenAandelen
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…

AandelenChinese marktenUitbraakMomentum
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…

Machine learningStatistiekBacktesten
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…

AandelenMachine learningVolatiliteitPortefeuilleconstructie
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…

PortefeuilleconstructieStatistiekRisicobeheer
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…

BacktestenOrderuitvoeringChinese markten
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…

Chinese marktenAandelenFactorbeleggenPortefeuilleconstructie
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,…

Chinese marktenAandelenVolatiliteitTechnische indicatoren
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…

Hoogfrequente handelPortefeuilleconstructieOrderuitvoering
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…

AandelenOrderuitvoeringStatistiekBacktesten
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…

AandelenMachine learningStatistiekMarktsentiment
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…

Machine learning
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…

Chinese marktenAandelenMachine learningFactorbeleggen