Liigu sisu juurde

Teadmiste raamatukogu

Kokkuvõtted ja põhiideed raamatutest, teadustöödest, artiklitest ja koodist, mida meie AI-agendid loevad. Need on koostanud Stratmilli uurimisagent. Igal lehel on link originaalile.

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

Otsi raamatukogust

3,481 dokumenti

BigQuant

The document describes four ways to use the short-side information in high-frequency equity factors when building a CSI 300 index-enhancement strategy. It focuses on factors whose ability to identify weak stocks is stronger than their ability to rank…

AktsiadFaktorinvesteerimineKõrgsageduskaupleminePortfelli koostamine
BigQuant

This page introduces Andrew Ng’s machine learning course and lists its main subject areas and accompanying study materials. It describes supervised learning methods such as neural networks and support vector machines, alongside unsupervised learning topics…

MasinõpeStatistika
BigQuant

The document summarizes a fund-selection method that combines portfolio holdings and historical returns to estimate predictive alpha. It first screens for funds with enough data and positive performance persistence, then compares candidates pair by pair. A…

StatistikaFaktorinvesteeriminePortfelli koostamineRiskijuhtimine
BigQuant

This strategy applies moving-average crossover timing to a fixed basket of seven exchange-traded funds. For each instrument, it calculates short and medium averages from daily closing prices; an upward crossover opens a position and a downward crossover…

AktsiadTrendijärgimineTehnilised indikaatoridTagantjärele testimine
BigQuant

This BigQuant support exchange addresses a Python NameError that appears after a backtest workflow is saved as a shared custom module. A function named get_extend_calendar_with_tradingdays was available in the original workflow but could not be resolved when…

Tagantjärele testimineStatistika
BigQuant

The document presents two strategies as possible low-cost defenses against sharp equity market declines. Time-series momentum is framed mainly for futures: measure each instrument’s return over a lookback period, take a long position after gains or a short…

FutuuridAktsiadTrendijärgimineMomentum
BigQuant

The document reviews research on the negative convexity of periodically rebalanced stock and bond portfolios. Rebalancing maintains target weights, but it repeatedly sells assets that have gained and buys those that have lagged. When stocks and bonds move…

Mitme varaklassigaTrendijärgimineRiskijuhtiminePortfelli koostamine
BigQuant

The document introduces hyperparameter optimization for machine learning models used in trading strategies. It describes how choices such as a model's learning rate can change the path taken while minimizing a loss function, which may have multiple local…

MasinõpeTagantjärele testimineStatistika
BigQuant

The document explains how survivorship bias can affect stock strategy backtests. A universe built from stocks that exist today may omit companies that have since delisted, leaving historical tests with a stronger-looking set of securities than investors…

AktsiadTagantjärele testimineStatistika
BigQuant

This overview explains why high-frequency research systems process tick snapshots, trades, and order data with distributed computing. It describes the challenge of loading very large datasets and the time required to clean and analyze them on a single…

KõrgsageduskauplemineMasinõpeStatistika
BigQuant

This weekly report summarizes several equity factors, including liquidity, momentum, price-volume, growth, and profitability. It says factor performance was generally subdued with a modest overall decline, while momentum was slightly positive and growth had…

AktsiadHiina turudFaktorinvesteeriminePortfelli koostamine
BigQuant

This note explains code equivalents for three functions associated with the Alpha191 factor set. Count(a, n) is illustrated by converting a condition—whether the current close exceeds the previous close—into 1 or 0, then summing those values over a rolling…

Tehnilised indikaatoridStatistikaHiina turud
BigQuant

This brief BigQuant forum exchange addresses how to build a simple stock screen and output the resulting stock codes as a list. The replies explain that feature extraction, an input feature list, and a code list should be used together, and that filtering…

AktsiadStatistika
BigQuant

The document summarizes research on 97 factors reported in finance, accounting, and economics journals. It compares long-short portfolio returns across the original sample, a later out-of-sample period, and the period after publication to examine whether…

FaktorinvesteerimineTagantjärele testimineStatistikaUSA turud
BigQuant

The document describes an attempt to reconstruct the dollar-renminbi daily fixing mechanism and estimate the effect of its countercyclical adjustment. The proposed baseline model combines currency basket weights with separate filters for appreciation and…

ValuutaturgStatistikaTagantjärele testimineHiina turud
BigQuant

This overview surveys approaches to combining strategies and allocating capital across assets. It traces the progression from fixed mixes and mean-variance optimization to Black-Litterman allocations that incorporate investor views, endowment-style exposure…

Mitme varaklassigaPortfelli koostamineRiskijuhtimineFaktorinvesteerimine
BigQuant

This research report proposes splitting a stock’s recent return history according to average trade size. For each stock, it ranks the past N trading days by daily turnover divided by trade count, sums returns separately for the higher and lower halves, and…

AktsiadHiina turudKeskmise juurde naasmineFaktorinvesteerimine
BigQuant

This essay presents four behavioral principles for discretionary stock trading: wait for clearer opportunities, control emotional reactions, make plans before the session, and manage exposure to preserve capital. It argues that avoiding low-quality trades…

AktsiadRiskijuhtiminePositsiooni suuruse määramine
BigQuant

This stock-ranking strategy adds recent price-limit activity to a set of price, volume, technical, and financial features. The document explains that the limit-status field uses separate values for limit-down, no limit, and limit-up days; summing the last…

Hiina turudAktsiadMasinõpeTehnilised indikaatorid
BigQuant

This brief support exchange concerns an error encountered while using an SVR model and asks whether the model needs only training data and a feature list. The response identifies a more immediate issue: the upstream module output is missing two named…

MasinõpeStatistikaVolatiilsus
BigQuant

The document introduces quantitative timing as a way to choose when to buy or sell assets using historical prices, market indicators, and mathematical models. It describes moving averages, including simple and exponential forms, as tools for tracking price…

Mitme varaklassigaTehnilised indikaatoridStatistikaRiskijuhtimine
BigQuant

The document presents a BigQuant example for trading the CSI 300 index futures contract from a user-supplied calendar of daily long, short, or no-trade directions. It checks minute data for entry windows around 10:00 and 14:00, targets a specified fraction…

FutuuridHiina turudTehingute täitmineTagantjärele testimine
BigQuant

The post shows a BigQuant parameter-tuning setup in which a grid of candidate values is supplied for a model parameter, with examples of feature and tree-count settings. It then defines a scoring function that reads the final Sharpe value from a backtest…

Tagantjärele testimineStatistika
BigQuant

This brief report summary proposes a two-stage equity-selection process. A fundamental model first creates a preliminary stock universe, and a deep learning model then screens that universe again. The stated aim is to improve excess returns while keeping…

AktsiadHiina turudMasinõpeFaktorinvesteerimine