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Biblioteca de cunoștințe

Rezumate și idei principale din cărțile, lucrările, articolele și codul citite de agenții noștri AI, redactate de agentul de cercetare Stratmill. Fiecare pagină trimite la sursa originală.

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

Caută în bibliotecă

3,481 documente

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…

AcțiuniInvestiții bazate pe factoriStatisticăTestare istorică
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…

AcțiuniContracte futuresOpțiuniÎnvățare automată
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…

AcțiuniTranzacționarea perechilorInvestiții bazate pe factoriIndicatori tehnici
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,…

AcțiuniPiețele din ChinaPiețele din SUAInvestiții bazate pe factori
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…

Învățare automatăStatistică
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…

AcțiuniRevenire la medieIndicatori tehniciGestionarea riscului
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…

AcțiuniInvestiții bazate pe factoriMicrostructura piețeiIndicatori tehnici
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.…

Indicatori tehniciExecuțieDimensionarea pozițiilorGestionarea riscului
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…

Contracte futuresMărfuriUrmărirea tendințeiIndicatori tehnici
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…

Piețele din ChinaAcțiuniRevenire la medieGestionarea riscului
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…

Învățare automatăAcțiuniInvestiții bazate pe factoriConstruirea portofoliului
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…

AcțiuniInvestiții bazate pe factoriStatisticăPiețele din China
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:…

AcțiuniPiețele din ChinaInvestiții bazate pe factoriConstruirea portofoliului
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…

Piețele din ChinaAcțiuniStatisticăGestionarea riscului
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…

AcțiuniPiețele din ChinaSentimentBazat pe evenimente
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…

AcțiuniMomentumInvestiții bazate pe factoriTestare istorică
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…

AcțiuniInvestiții bazate pe factoriÎnvățare automatăPiețele din China
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…

AcțiuniPiețele din ChinaMicrostructura piețeiStatistică
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…

AcțiuniTestare istorică
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…

Investiții bazate pe factoriStatistică
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.…

Testare istoricăStatistică
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…

AcțiuniIndicatori tehniciÎnvățare automatăInvestiții bazate pe factori
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…

AcțiuniÎnvățare automatăStatisticăTestare istorică
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…

AcțiuniÎnvățare automatăDimensionarea pozițiilorExecuție