Praleisti ir pereiti prie turinio

Žinių biblioteka

Stratmill tyrimų agento parengtos knygų, straipsnių, mokslinių darbų ir kodo, kuriuos skaito mūsų DI agentai, santraukos ir pagrindinės mintys. Kiekviename puslapyje pateikiama nuoroda į originalą.

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

Ieškoti bibliotekoje

3,481 dokumentų

BigQuant

This overview describes eight groups it presents as influential in China’s A-share market: foreign investors, short-term speculative funds, social security and insurance funds, securities firms’ proprietary desks, corporate insiders, public funds, and…

AkcijosKinijos rinkosRinkos nuotaikosRinkos mikrostruktūra
BigQuant

This forum post reports a disagreement in 24-period and 72-period moving averages for a market series on a specified date. The author compares values from two retail charting sources with results from a quantitative platform, noting that the two external…

Techniniai rodikliaiStatistika
BigQuant

A user reports that every label in a trading demo appears to be zero. The response explains that the platform’s visualization shows only a subset of the data, and the displayed rows may all happen to carry the zero label. It recommends inspecting the full…

Mašininis mokymasisStatistika
BigQuant

The document outlines why quantitative investors use machine learning to analyze growing financial datasets, forecast market movements, and help select portfolios. It describes machine learning's capacity to represent nonlinear relationships and process data…

Mašininis mokymasisIstorinis testavimasStatistika
BigQuant

This overview describes Chan theory as a chart-analysis framework built from nested price structures. It moves from processed candlestick fractals to strokes, segments, and overlapping consolidation zones, then classifies larger moves as advances, declines,…

Techniniai rodikliaiPrekyba pagal tendencijąImpulsasKelių turto klasių
BigQuant

This meetup Q&A covers several practical quantitative trading topics. It explains rolling training as repeatedly updating a model using a moving or expanding historical window, and distinguishes incremental updates from retraining on the full dataset. It…

Mašininis mokymasisInvestavimas pagal veiksniusIstorinis testavimasPortfelio konstravimas
BigQuant

The document summarizes a research paper that uses a neural network to produce portfolio weights for exchange-traded funds. Instead of forecasting expected returns and then feeding those forecasts into a conventional optimizer, the approach directly tunes…

Kelių turto klasiųPortfelio konstravimasMašininis mokymasisRizikos valdymas
BigQuant

This Chinese-language seminar summary frames AI adoption as a response to falling asset-management margins despite growing assets under management. It sketches a progression from adjusting active portfolios using factor analysis, to building long-short…

Kelių turto klasiųInvestavimas pagal veiksniusImpulsasMašininis mokymasis
BigQuant

This study outlines a Deep Q Network approach to timing trades in one stock. Instead of storing action values for every possible market state in a table, a neural network estimates values for available actions. The agent stores state, action, reward, and…

AkcijosMašininis mokymasisIstorinis testavimasRizikos valdymas
BigQuant

This report proposes diversifying portfolios across market, style, and strategy risk premia rather than allocating only among broad asset classes. It uses representative global equity, bond, and commodity indices to construct long-only strategies reflecting…

Kelių turto klasiųInvestavimas pagal veiksniusImpulsasKintamumas
BigQuant

This introduction explains the Skip-gram model as a method for predicting nearby context words from a chosen center word. It uses a short example sentence and a window extending two words to either side. The explanation defines one-hot representations, dense…

Mašininis mokymasisStatistika
BigQuant

This article presents volume as a way to assess the force behind price movements. It argues that record volume alone should not be treated as proof of a market top, since trading activity can continue to set new highs. Instead, it singles out expanding…

AkcijosTechniniai rodikliaiPrekyba pagal tendencijąPramušimas
BigQuant

This review summarizes historical evidence on how assets and active strategies performed during periods of high and rising inflation in the United States, United Kingdom, and Japan. It defines inflation episodes using accelerating year-over-year inflation…

Kelių turto klasiųŽaliavosPrekyba pagal tendencijąImpulsas
BigQuant

This analyst note reviews a Chinese bank IT provider’s 2019 results and argues that its business is shifting toward financial technology software and services. It reports revenue growth, a sharp increase in net profit, and software and technology services…

AkcijosKinijos rinkosRenginiais pagrįsta prekyba
BigQuant

This study examines whether a third-party platform’s rapid redistribution of sell-side analyst upgrades and downgrades changes price discovery and the value brokers provide to clients. Using US recommendation data from 2009–2013, leak timestamps, intraday…

AkcijosRinkos mikrostruktūraPavedimų vykdymasJAV rinkos
BigQuant

This article introduces one-dimensional convolutional neural networks as a way to process financial sequences. A convolution applies shared filters across local time windows, allowing the model to learn patterns that can appear at different points in a…

AkcijosMašininis mokymasisStatistikaIstorinis testavimas
BigQuant

The document offers a brief allocation recommendation for bond funds in a period of increasing credit risk. It advises favoring funds with higher credit scores and specifies a threshold of 2.7 or above according to the authors’ credit scoring table. The…

Fiksuoto pajamingumo priemonėsRizikos valdymasPortfelio konstravimas
BigQuant

This document is available here only as a Chinese-language title and outline, with the body represented by a link to a PDF. The outline describes a quantitative approach to asset allocation that separates strategic allocation from tactical allocation. It…

Kelių turto klasiųPortfelio konstravimasRizikos valdymasStatistika
BigQuant

This document outlines an asset-allocation approach that separates asset selection from portfolio weighting and market timing. It explains a hidden Markov model as a way to infer unobserved market regimes from observable data, represent complex observations…

AkcijosKinijos rinkosMašininis mokymasisStatistika
BigQuant

This article explains how to configure an older BigQuant workflow to analyze alpha expressions in a manner modeled on WorldQuant’s WebSim. Users specify a factor expression and set the stock universe, data delay, linear decay, market or industry…

AkcijosInvestavimas pagal veiksniusIstorinis testavimasPortfelio konstravimas
BigQuant

This brief troubleshooting exchange addresses a quant model that fails when configured with a single factor, even though the factor values appear normal. The proposed fix is to assign the factor a shorter alias; the author reports that using a very brief…

Mašininis mokymasisStatistikaIstorinis testavimas
BigQuant

This BigQuant support note addresses a strategy that can read an uploaded CSV during development but fails to access it after being launched in simulated trading. It recommends storing the data as a platform data source and loading it within the trading…

Pavedimų vykdymasIstorinis testavimas
BigQuant

This brief BigQuant support note explains why a cloned strategy can work in backtesting yet fail in simulated trading. The example strategy was designed only for backtests, so its date range and instrument reference need changes before simulation: bind the…

Ateities sandoriaiIstorinis testavimasPavedimų vykdymas
BigQuant

This research summary argues that standard single-factor information coefficient tests may not reflect index-enhancement practice. A factor’s performance in a broad shared universe may not transfer to a benchmark’s eligible stocks, and simple tests may omit…

AkcijosInvestavimas pagal veiksniusPortfelio konstravimasIstorinis testavimas