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Kunnskapsbibliotek

Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.

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

Søk i biblioteket

3,481 dokumenter

BigQuant

The article presents five principles for short-term stock trading: prominent stocks may attract liquidity despite looking expensive; near-term prices reflect the balance of buying and selling shaped by expectations and sentiment; traders should seek gaps…

AksjerMarkedssentimentMomentumMarkedsmikrostruktur
BigQuant

The study measures a fund’s risk shifting by comparing the volatility implied by its latest disclosed holdings with the fund’s realized volatility over the same rolling period. Using quarterly holdings and return data for actively managed US domestic equity…

AksjerRisikostyringFaktorinvestering
BigQuant

This brief Chinese-language support note addresses how to use factors produced by a genetic factor-mining process. It says the discovered factor has an expression, but that a user must convert the expression manually before sending it to a factor analysis…

FaktorinvesteringMaskinlæring
BigQuant

This research outline proposes allocating among equity industries by tracking the behavior of different market participants. It motivates industry rotation with the observation that returns can diverge substantially across sectors and styles, so broad asset…

AksjerKinesiske markederMarkedssentimentPorteføljekonstruksjon
BigQuant

The document describes a commodity futures strategy that ranks 28 markets by changes in Twitter-derived sentiment. It calculates daily sentiment from keyword-matched posts using a financial sentiment dictionary, then forms equal-weighted long and short…

FuturesRåvarerMarkedssentimentFaktorinvestering
BigQuant

This document outlines an event-driven study of how MSCI inclusion announcements affected the prices of Chinese A-shares. It describes estimating CAPM parameters from a historical period, using those parameters and subsequent market index returns to…

Kinesiske markederAksjerHendelsesdrevet handelStatistikk
BigQuant

The document summarizes research on forecasting multiple future steps from limit order book data. Rather than predicting only one future point, the proposed approach uses sequence-to-sequence encoder-decoder networks with attention to generate a path of…

MarkedsmikrostrukturMaskinlæringHøyfrekvenshandelOrdreutførelse
BigQuant

This brief support note addresses a BigQuant workflow where a ranking strategy appears to backtest normally but produces no rebalance signals in simulated trading. It points to configuration and data-window checks: bind the code-list module’s end date to…

Historisk testingAksjer
BigQuant

The document introduces a moving-average arrangement scoring model, or MASS, that assesses market direction and trend strength from the relative ordering of multiple moving averages. It aims to combine the smoothness of longer averages with the quicker…

AksjerTrendfølgende handelMomentumTekniske indikatorer
BigQuant

This forum post presents a workflow for combining predictions from three model outputs. It merges the datasets on instrument and date, preserves columns that are not already present, renames each model’s prediction column, and computes their arithmetic mean…

MaskinlæringPorteføljekonstruksjonHistorisk testing
BigQuant

This tutorial explains how to use Seaborn to explore financial data through matrix plots, plot grids, regression plots, and style settings. It uses stock financial statement data to demonstrate correlation heatmaps, including annotations and color maps, and…

AksjerStatistikkTekniske indikatorer
BigQuant

This Chinese-language question and answer explains why a strategy’s apparently strong later years in a long backtest may not reproduce the same pattern when tested over those years alone. It identifies several possible causes rather than prescribing a single…

Historisk testingStatistikkAksjer
BigQuant

This reading list summarizes three studies on portfolio construction. One develops a finite-horizon allocation framework using nominal assets, with closed-form optimal strategies and utility. It describes how hedging demand depends on the investor’s horizon,…

Flere aktivaklasserPorteføljekonstruksjonRisikostyringRentepapirer
BigQuant

The page reports a user’s concern that the Chinese stock 600256 had incorrect values for the total-liabilities factor fs_total_liability_0 over a historical interval in 2021. The user says values for other periods agreed with Eastmoney data, while the…

AksjerKinesiske markederStatistikk
BigQuant

This BigQuant user question concerns a feature expression that calculates how many days have elapsed since a limit-up event within a recent window. The example marks sessions where return exceeds a threshold and the close equals the high, then uses a rolling…

AksjerKinesiske markederTekniske indikatorer
BigQuant

This report introduces a quantitative research approach that combines behavioral finance with trading indicators. It centers on George Soros’s theory of reflexivity and the author’s use of volume measures, with the stated aim of developing an indicator…

AksjerTekniske indikatorerStatistikk
BigQuant

The article tests whether a convolutional neural network can classify stock direction from chart-like images generated from OHLC data. Each sample uses 32 time steps, normalized to a 128-by-128 binary image: groups of columns mark open, high-low range, and…

AksjerMaskinlæringHistorisk testingStatistikk
BigQuant

This forum post raises an implementation question about deploying BigQuant StockRanker models for live trading through a brokerage server. The author believes StockRanker includes a gradient boosting decision tree model and asks whether deployment transfers…

MaskinlæringHistorisk testingOrdreutførelseStatistikk
BigQuant

The report describes a CTA approach for Chinese stock index futures that combines weekday return patterns with intraday effects. Its analysis notes higher return probabilities overnight and during the first half hour after the open, and different weekday…

FuturesKinesiske markederMomentumStatistikk
BigQuant

This article outlines a machine-learning stock selection strategy intended to find shares that may rebound after declines while limiting drawdowns during weak market conditions. It targets China’s small and medium-sized board, chosen for its activity and…

Kinesiske markederAksjerMaskinlæringTilbakevending mot gjennomsnittet
BigQuant

This guide explains how to participate in a BigQuant quantitative challenge using A-share minute bars and order-book snapshots to predict future 30-minute VWAP returns. It covers the factor-mining and end-to-end modeling tracks, available templates and data…

AksjerKinesiske markederMaskinlæringHistorisk testing
BigQuant

This discussion examines whether the length of a model’s training window changes an AI strategy’s results. It describes manually rolling training for a visual template strategy, comparing longer histories of five to ten years with shorter windows ranging…

MaskinlæringHistorisk testingStatistikk
BigQuant

The document describes Temporal Routing Adaptor (TRA), a way to extend a stock prediction model so it can learn from different patterns in market data. It notes that momentum and reversal behavior may coexist, which challenges the assumption that…

AksjerMaskinlæringStatistikkPorteføljekonstruksjon