নলেজ লাইব্রেরি

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,…

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…

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…

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…

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…

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…

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…

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…

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…

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…

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…

This report tracks three China-focused active equity quant portfolios against an active mutual fund benchmark: an enhancement portfolio built from selected fund holdings, a portfolio that filters earnings-surprise stocks, and an enhancement portfolio based…

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…

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…

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…

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…

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…

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…

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,…

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…

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…

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…

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…

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…